Shift

Copyright © 2015 by Haydn Shaughnessy

Haydn Shaughnessy has asserted his moral rights to be recognized as the author of this work. All Rights Reserved.

Published January 2015 by The Disruption House, London.

No part of this book may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without written permission from the publisher.

For all inquiries please use the contact form at

www.HaydnShaughnessy.com.

ISBN-13 (paperback): 978-1-941420-03-4

ISBN-13 (eBook): 978-1-941420-02-7

19 18 17 16 15 1 2 3 4 5

1st edition, January 2015

Printed in the United States of America

Printed in the United Kingdom

Cover Design: DesignWorks Creative, Inc., www.designworkscreative.com

Interior Design: Fusion Creative Works, www.fusioncw.com

Editing: Robin Bethel, www.prosestudio.com

PRAISE FOR

Shift

“It’s impossible to do justice to a book of such breadth and depth in two or three lines. Shift tells us everything we wanted to know about the emerging digital economy but were afraid to ask. It gives a uniquely comprehensive and deep explanation of emerging leadership and management practices. Among many other important contributions to our understanding of economic change, it shows how the practice of strategic options building can help position companies in a time of uncertainty and provides insights into the new tools of managing complexity.”

—Stephen Denning, author, The Leader’s Guide to Radical Management

“Shift shines a light through all of the hype around driving and surviving disruptive change in business today. It is an engaging and thought-provoking read.”

—Amar Hanspal, senior vice president, IPG Product Group, Autodesk

“Shift is mandatory reading for anybody who wants to move beyond the tactics of enterprise innovation. Haydn has done a tremendous job in contextualizing innovation by elastic and fluid enterprises that have the capabilities to move at rocket speed in today’s fast moving disruption environment. The chapters on innovation capabilities, platform thinking and externalisation represent some of the most refreshing reflections and insights of the last decade. With Shift, Haydn has positioned himself in the same league as Clayton Christensen.”

—Peter Vander Auwera, co-founder, Innotribe

“To transform anything, start by changing your lens. Shift by Haydn Shaughnessy is a must-read lens-changer for innovation junkies everywhere.”

—Saul Kaplan, founder and chief catalyst, Business Innovation Factory

“Haydn Shaughnessy nails a key issue in modern business process evolution: THERE IS EVOLUTION, and it´s fast and game changing. The new wave and spirit of private entrepreneurship, often around enabling platforms, is changing the way business is being conducted everywhere. This book offers eye-opening insights into what is going on and what may be the implications of the new paradigmatic shifts taking place in business today.”

—Mikael Collan, professor of strategic finance, Lappeenranta University of Technology; author, The Pay-Off Method

“Shift is an excellent read with the academic rigour expected of Haydn Shaughnessy. The surprise was—I found this text uplifting. Outdated institutions that fail to meet the needs of the world’s peoples are unsustainable. The future holds a seismic shift and might, just might, be a much better place.

—Lynne Bowers, former NHS Director; National Third Sector Ambassador

“Haydn Shaughnessy’s new book, Shift, is a much-needed examination of how economic models are evolving in an increasingly connected world. By going beyond the current tech industry hype to detail how these new business and financial models impact work style, government participation and the decentralization of value creation, Shift delivers the level of depth that helps the readers understand the changing, global economic landscape—and what it means to them. Shift provides clear examples that serve as indicators of how the future will likely unfold, while detailing prescriptive guidance on what tomorrow’s successful companies need to do today to embrace thelong-term shifts ahead.”

—Matt Flanagan, founding partner, Fama PR, Inc.

“This book will prove particularly useful if you’re a senior IT or telecommunications professional, app developer or content producer. It shows step-by-step how to view investment priorities and return metrics through the lens of change management, as well as how to create new options for your own career trajectory or business model. Highly recommended to gain a deeper understanding of the current landscape and its balance of competing influences.”

—Erica Levy Klein, senior partner, LinkedInBusinessCoach.com

“Shift is a powerful work full of insight stemming from Haydn Shaughnessy’s formidable powers of observation and research. He paints a very vivid, very detailed picture about a global society, led by its ‘young’ people who are fed-up and no longer asking their ‘betters,’ government, or elders for relief—or permission. They’re taking matters into their own hands and disrupting convention in ways that work better for them—from Uber to Spotify to VICE to Tinder to Airbnb. Studied, cutting and instructive, this is a must-read for anyone interested in how and why things change—sometimes even in the blink of an eye.”

—Patrick Reynolds, chief strategy officer, Triton Digital

“‘Innovation’ is getting a bad name these days due to buzzword fatigue and, frankly, bad “best practices.” Haydn Shaughnessy has been working with companies in banking, telecommunications and tech to properly frame global forces of change, and map out strategies to react to or drive them. Shift should be on every senior executive’s e-book reader of choice.

—David Card, VP Research, Gigaom Research

“Haydn has done it again—he has seen the future and he is bringing it to us. And the future is already here; it is just not yet evenly distributed. Shift is about the rise of the commons—about individuals coming together in unprecedented scale to change the world in massively distributed open teams … and, in the process, rewriting the rules of economics, innovation, entrepreneurship and the nature of work. Haydn sees powerful shifts where other people see siloed phenomena. He has captured the spirit of how value gets created in the age of mobile, social and digital technologies. The only challenge I now face is not being able to put Shift down.”

—Lora Kratchounova, vice chair, MIT Enterprise Forum; principal, ScratchMM

“Reading Shift will not only teach you how to identify trends, but more importantly how to adapt, respond and profit from disruption.”

—Gene Marks, president, The Marks Group PC; columnist for The New York Times, Forbes.com and Entrepreneur.com

For Angharad

Best friend ever

And Milo

Big walking buddy

Contents

Preface

Shift One: Context

Introduction

Chapter 1: Shift Dynamics

Chapter 2: Shifting Out Dying Models of Business

Chapter 3: New Ways to Create Wealth

Shift II: The Enterprise

Introduction

Chapter 4: The Platform for Disruption

Chapter 5: Disruption and Renewal Strategies

Chapter 6: The Ecosystem Metaphor

Chapter 7: Friction Free Business

Chapter 8: Recalibrating Profits

Shift III: You

Introduction

Chapter 9: The Shift Away from Price

Chapter 10: The Shift to the Personal Economy

Chapter 11: Options

Chapter 12: Thinking and Acting Differently

Chapter 13: Future Shift

Afterword

Endnotes

Acknowledgments

About the Author

Preface

Shift stems from work that I did with Nick Vitalari in preparation for The Elastic Enterprise, our jointly authored book published in 2012. We began compiling the results of our research on the topic of next-generation enterprises in 2010. At the time, we were busy working at an economic think tank looking at companies that became stellar performers during the worst recession on record.

Through our research, it became obvious that even at the peak of the Great Recession, some companies were not remotely constrained by the economic crisis that engulfed the rest of the world. They grew. More than that, they embarked on journeys that would quickly establish them as the best-performing companies in history.

Partly by accident and partly by design, they changed the way their businesses functioned and influenced hundreds of businesses across the globe. They changed the basic operating model of the firm and the structure of the industries in which they operated. As a result, policy makers, executives, employees, and small businesses today are asking, have we created a different kind of engine for growth? And if so, what do we do next?

Ironically the “what’s next” question began to generate answers almost immediately.

What’s next is more disruption, but from new players and new perspectives that will reinforce the trends we documented in The Elastic Enterprise and bring even more profound change.

The best elastic enterprises have indeed shown a new way to create wealth that we all need to learn about, but they are also concentrating power and cash on a scale not seen before in commercial companies. These are the achievements of the Google Generation of companies, those that witnessed the dot-com boom and bust and went on to become world leaders in their fields, companies like Google itself, Facebook, Amazon, the revitalized Apple, and hardware companies such as CISCO. Their cash hordes reflect huge success and yet also a larger dysfunction in the capitalist economy. At the end of 2013, in the United States alone, approximately $2 trillion of cash was sitting in corporate bank accounts or on its way to shareholders, unspent. It had not been allocated to productive uses. Meanwhile banks have shied away from investment as they rebuild their capital reserves. In a sense this marks the end of the Google Generation’s dominance of innovation. We are reaching a point at which change will veer in new directions if for no better reason than this generation of entrepreneurs has run out of investment ideas.

The consequences can be seen in new thinking about value metrics and value distribution. Academic analysts and business writers are bemoaning the lack of societal value created by today’s commercial leaders. Meanwhile the disruption mindset has set off in new directions. Crypto-currencies, the best known of which is Bitcoin, are an entirely left-field or “wild card” development that will bring more widespread economic shifts. They are Black Swans created for ideological reasons.

Bitcoin is a new currency. But rather than being issued by a central bank or an international institution, it is an initiative of the open-source software movement. It is, basically, computer code.

How can a few programmers decide to launch a new currency and in the process strike fear into the heart of the banking community?1 What is it about ideas when connected to code that can suddenly become so disruptive? What, for that matter, is the legacy of the Google Generation of entrepreneurs who invented the modern software driven enterprise? What comes next?

Shift is an analysis of these questions. It provides a model for how to understand the ways that major disruptions take place.

The elastic enterprise based on a platform and ecosystem model is a powerful form of organization, a milestone as important as the development of factory production, but more disruption is on the way. Wild card or black swan disruption like crypto-currencies and a more decentralized form of business will further mutate the enterprise and the working lives of people like us.

The purpose of Shift is therefore threefold:

1. To help readers develop a better understanding of how structural shifts in the economy are forcing new personal journeys onto all of us and our children and to understand what these shifts mean for how we need to organize our own economic lives.

2. To explain the link between those economic changes and the new limits on how governments function. These changes are rendering governments impotent in important areas like job creation and, if governments can’t create jobs, then what will be their future value?

3. To bring some of the new concepts behind disruption into public debate.

Since the publication of The Elastic Enterprise, I’ve had the opportunity to talk to many more companies, investigate more cases, and engage with other writers, analysts, and audiences on the issue of disruption and how the economy will mutate further over the next five to ten years. As a writer on the Forbes.com platform, I had access to senior people in the world of finance, IT, telecoms, design, and other horizontal industries. I’ve also benefited from the generous donation of data from companies like StatSocial (who provided data from over 100 million Twitter accounts and 5,000 brands) and MarketPsych Data (who provided data on over 5,000 companies across eight stock markets). Funding from organizations such as SWIFT (the Society for Worldwide Interbank Financial Telecommunications), DIGILE (responsible for economic transformation in Finland), Apigee, HYPE, Cognizant, Bluefin Solutions and Intent HQ, has allowed me to carry out interviews with senior executives and managers responsible for transformation in more than 120 major corporations and to analyze the behavior of more than 500 companies on the issue of digital disruption and transformation.2

In an economy where structural innovation and productive investment have largely stalled for many companies, elastic enterprises defy all the conventions that have grown up around companies and represent a bright spot, a source of renewal.

The organizational techniques adopted by the best economic performers are spurring us on to a new era of growth, but there is a price to pay. The balance of power in the economy is shifting toward a small number of companies and very definitely away from policy makers and those in governance. We need to be more aware of these shifts and how to manage them for the common good.

The lessons in the book are applicable to companies of all sizes and should be learned by executives, employees, and the policy makers whose role is to stimulate growth and to develop and educate the workforce. Change is currently so fast that most politicians and even economic commentators don’t realize its causes and impacts. We’re too focused on questions of redistribution and inequality to realize we have developed a new way to create wealth. That new system is compelling, but it harbors dangers. Not the least among these is that we don’t yet talk about the new economy with a sufficient depth of understanding. Shift is meant to change that.

SHIFT I: Context

Introduction

There is a rule in the natural world and particularly in the biological world all around us. As natural systems scale, or grow, the energy nature expends to sustain them decreases proportionally. In other words, as a forest or wetland grows, the additional energy needed to sustain that growth declines proportionally. The natural inputs of light, water, nitrogen, and so on, are all proportionally less, providing there is sufficient diversity.

Scale in natural systems is usually efficient. Unfortunately, the same can’t be said for scale in human-social systems. Unlike natural systems, when human-social systems scale, they inevitably absorb proportionally more energy, which means that growing a human-social system like a business enterprise often costs disproportionally more with each unit of growth. Ultimately, the cost of scale usually outweighs the benefits. It becomes counterproductive to grow.

Statisticians from the world of complexity thinking argue that cities already represent a new type of scale, one where humans generally require proportionally fewer inputs as the city becomes larger. Somehow, cities manage to emulate the natural world. However, Nobel Prize-winner Elinor Ostrom has shown that the efficiency of cities relies on a special kind of management. 3 Cities are efficient when they delegate power to self-determining local groups, or ecosystems. The more that is delegated, the more efficient they become. This principle gives a strong clue about how a new economy is growing around us today.

The Western economy is growing and so is job creation. It mostly takes place, however, around these ecosystems, away from the conventional world of business where governments still hold sway. It is often impermeable to policies. It has its own life.

Research firm Gigaom estimates that the mobile app developer ecosystem in Europe employed 1.8 million people in 2013 and that figure will rise to 4.8 million in 2018.1 Not one of these jobs will be attributable to government policy.

Growth of this kind reflects Ostrom’s views on the power of decentralized, self-determining groups. It has profound implications for the future of work, for the economic responsibilities we need to take on as individuals, for government revenues and for how executives scale their businesses.

For decades, the problems of scaling business have handicapped Western economies with their large, legacy enterprises, many of which reach back to the start of the 20th century. But humans have now found new ways to handle scale and, in the process, are busy adapting the economy without intervention from policy makers. They are doing it too without the knowledge of many economic commentators who, like politicians, rely on an outdated view of how economic intervention works.

Companies like WordPress and initiatives like Joomla have quietly grown in scope and influence. Their software manages many millions of websites, more often than not for free. They are the most widely distributed content management systems in the world, bar none.2 Their nearest rival, Drupal, shares one characteristic with them. All three are open source (i.e. in some measure built in open communities and not enclosed within a company) and available for free to whomever knows how to work them.

Behind the stellar economic successes like Samsung, Alibaba, Google and Apple, companies like WordPress, and communities like Joomla, Drupal and indeed many more initiatives that never make the headlines have exceptional, enviable reach because they have extensive ecosystems of supporters and users. These are relationships of real loyalty and commitment. They inhabit a different philosophy from regular business. The players within them act differently. And their economics are a different world from anything in the classical canon of supply and demand.

In a recent study of social affinity online (a new measure of loyalty), researchers at StatSocial and I found that born-social companies like the crowdfunding site Kickstarter had as much affinity from users as companies such as Coca-Cola and Microsoft. So does TED, the online ideas site, and GitHub, the platform for open source projects.3 Behind the data we look at every day to measure the health of the economy, born-social companies have caught up with old brands.

The economy has already changed. We’re just not looking at it in the right way. There are other stark signs of change. Forbes.com does not pay 40% of the contributors who bring it income. Huffington Post is a wildly successful online news site and is similarly dependent on free labor. We have created a complex new set of interactions and curious motivations in the modern world that are often impervious to old-fashioned economic policy. Yet this is where growth takes place.

The philosopher Thomas Kuhn once stated that scientific theories were always vulnerable to criticism, but their adherents, instead of switching to a new paradigm, would add more scaffolding to keep the edifice of a theory from falling. The same is true of economics. However, the problem isn’t that economists and politicians are trying to maintain a building that is in danger of collapse. The fact is, they are trying to maintain an edifice when they should be building a hotel, a place of temporary accommodation where right and wrong have only temporary refuge, where we can continually update our understanding of a situation that is in constant flux.

Economists and politicians believe that economic performance can be improved by demand-side intervention (through public sector spending and the hope that consumers will spend more) or supply-side intervention (the improvement of educational attainment or of broader market conditions, such as the cost of credit).

These theories stem from experience of previous recessions, such as the 1930s Depression, but by their nature they don’t take into account fundamental changes to the wealth-creation process. Macroeconomic theory is in fact a way of sustaining conflicting political interests around an equilibrium point, left and right. That is the edifice.

The basic theories of economics, however, do not describe fundamental change nor can they capture it. Yet we are now witnessing a fundamental shift in how economies function.

The economy has entered a period of continuous change as the individual wills of nine billion people collide in new organizational forms and new individual desires. Within the currents of continuous change, some shapes are visible, some benign, some dangerous. Because governments don’t see them. They are spectators as the new economy emerges. Some are looking the other way, hoping their bets on old policy measures might pay off.

So too are many executives. Companies in the S&P 500 paid over $500 billion back to shareholders in 2013, while American corporations held over $1.5 trillion in cash outside of America. In total, that represents a $2 trillion innovation deficit. It reflects the fact that the resource allocation excellence of capitalism has frozen, largely because executives don’t understand the shifts they are living through.

Meanwhile, a new model of distributed risk and innovation is spreading as individuals and ambitious small firms seek new ways to capture and share resources in different ways. In a very important sense, the economy has moved beyond capitalism.

One line of analysis says it has become significantly more monopolistic and this alone spells danger.4 What is really happening, though, is that the economy is polarizing around platforms and highly distributed ecosystems, groups of people who create new forms of community around production and service delivery. The essence of transformation lies in the management of new relationships, in the way, for example, that large enterprises manage large ecosystems of application developers (as Google does), smaller companies manage large ecosystems of collaborators (as the web content company WordPress does), or even start-ups manage large social networks of people numbering in the hundreds of millions (as WhatsApp does).

When people think about significant shifts or disruptions in business they almost always think about technology. Technology often disrupts and politicians have an eye on it. But today’s reality is that people disrupt. It would be easy to write a book that said 3-D printing or programmable matter or the network will change society and the economy forever. Or that some other technology is primed to explode our experience of life. But the truth is, people make change.

THE SHIFT ECONOMY

The new political economy, the shift economy, is based on the devolution of risk and innovation from large enterprises and financial institutions to the small business and self-employed community, the start-up community, and, crucially, the global open-source development process.

A prime motivating factor is the widespread desire among creative people to shift opportunity away from large, legacy enterprises to the decentralized, distributed groups we call ecosystems, groups that we belong to or where we can exercise control.

To find the sources of disruption, look to the new social relationships that underpin transformation. They are creating a significant shift in political and economic power. They take power away from governments and shift it toward enterprises that create huge numbers of jobs and at the same time legitimately deny government access to tax revenues. The same forces shift risk and innovation toward the individual.

These changes are taking place without the participation of many business leaders who continue to run their companies along traditional lines—lines that involve apparently logical or rational decisions based on conventional accounting practices such as net present value (NPV) and discounted cash flow.

By and large, these widely used business metrics were designed for stable times. Their use grew in an era when the future was moderately predictable. They are still in use by companies that want desperately to change. These companies are now trapped by their own techniques. They use predictive accounting tools that depend on stability but these cannot be the right tools to guide transformation.

Management writer Steve Denning has pointed out that the practices of modern management often prevent a new and more creative economy from emerging within their own walls.5 Typically, the way management states profits in order to maximize shareholder value leads to short-term decision making and even (legal) financial manipulation.

The revolution we will look at together in this book creates a number of new ways to scale wealth. These are disruptive to the wider power structure governing the economy and politics. They make conventional management tools redundant.

Many executives sense, nonetheless, that something programmatic can be done to change their businesses and that they are lagging far behind—not behind the competition so much as behind a new reality. They privately acknowledge that the traditional business operating model is not producing the kind of results they believe is possible, the kind they’ve enviously observed in businesses that seem to be following a new set of rules.

These other, thriving businesses have adopted a platform and ecosystem model of wealth creation that offers a more adaptive and scalable way to work. It is an answer to a century-old problem—how to scale a business without it buckling under the weight of its own growth. But what is a platform and ecosystem model, the process model Nick and I referred to elsewhere as the elastic enterprise? What are the wider implications of shifting to this model?

The elastic enterprise relies on a different kind of economics that brings large and small companies and the creative commons into a dynamic new relationship. In fact, today, large companies are becoming increasingly dependent on small companies and start-ups, but at scale. Behind that innocuous statement they are also dependent on the commons, the initiatives such as open source, creative commons, open design, open engineering and related unenclosed economic communities. While that dependency grows, they are becoming more vulnerable to the commons’ creativity and anti-monopolistic mind-set.

Traditionally, in order to scale, businesses had to bulk themselves up. They had to make a case for capital investment; seek funding from their investment partners; plan new facilities; ramp up a recruitment program, and devise ambitious new marketing plans. All this would be predicated on a new or breakthrough product, and it would be built within the conventional economic context of specialization, or the internal division of labor.

The ultrafine distinctions in the tasks people perform are what originally allowed companies to capture economies of scale. Over time, technological innovation in enterprise platforms, capital investment, and organizational changes, such as supply chain management (and innovations like just-in-time delivery and vendor-managed inventory), has allowed enterprises to continuously push the boundaries of this form of business to grow larger. Much of that and what it implied for job creation, tax revenues, and government economic policy are all but at an end. Here is why.

The traditional operating model, what it takes to run a company, is typically mapped to the process of developing and selling new products—from R&D through product development, outsourcing and assembly, marketing, sales, and after-sales. There are a couple of problems with this traditional model:

• The cost of supervising scale—the investment in management and supervisory levels of a hierarchy as companies grow—has often offset many of the gains. Companies suffer financially as they grow. Even worse, as they get ever larger, they become increasingly inflexible and unwilling to process the decisions that will help them to adapt.6 Those problems over the past twenty years, though, have been addressed in a transformative way. Mostly in the supply chain and through large anonymous systems rather that in relationship development in the front office where companies interact with customers.

• A second issue is that the traditional model tends to be sequential, built on the idea of drawing in raw materials and gradually converting these for use, often reducing the unit price of products by seeking to scale every aspect of business. This traditional scaling model is losing relevance because the new business infrastructure is shifting the enterprise operating model away from materials toward relationships and free cash-flow. Value increasingly lies in dematerialized assets, even in such ephemeral concepts as community or affinity or relationships. These are people costs, and people costs are precisely what enterprises find easiest to cut and most difficult to commit to. An additional element of this shift is emerging in the monetization of relationships and the use of cash. Building a business is no longer capital intensive in a traditional sense. Success depends more on discovering ways to monetize relationships. At the same time, technology allows companies to free cash that lies within business processes but has been tied up in the system. Taken together these two transform the economics of the firm. If we were to look for one “post-capitalist” label for the new financial structure of the economy it would lie here in the decline of capital investment requirements, the growing importance of real time payments and the susceptibility of incumbent businesses, and Governments, to new ideas about monetary currencies.

The shift means that the ways most companies measure success and incentivize work must also change—crucially things such as how they calculate returns on investment. Although Denning’s major charge against companies is that they manipulate profits to satisfy shareholder value, there are very practical ideas to help businesses, whether start-ups or established, make different and better investment decisions. In this book, I will highlight some of those. It may well be, however, that the issue of shareholder value diminishes in importance as new methods emerge for financing business activity. It may well be, for example, that enterprises in general already need less investment money, and that is why so many feel able to distribute more to shareholders. In other words we could be asking the wrong questions of enterprises. More critically we could ask questions such as: if companies need less cash, what does this mean for broader measures of economic activity like GDP?

The key lies in creating more elastic enterprises that by their nature have broader economic remits than the classical product model. The elastic enterprise aims for growth at low relative cost and creates new types of economic activity by redefining economies of scale and scope (terms we will expand on later).

In elastic enterprises, companies typically externalize or federate many of the costs associated with growth. They engage in a new kind of externalization that is not outsourcing. In this new model, companies collaborate in entirely new ways with a broad range of economic actors at speed and at scale, updating their products daily and delivering value to hundreds of millions of customers in an instance. Central to this model is a new reality. Large companies need small companies and start-ups more than ever before. But they need something else: the distributed, disruptive, and often heretical intelligence of the creative commons, particularly of open-source communities.

This overall shift architecture is disruptive by definition. It disrupts old patterns of wealth creation (the typical business organization or the typical enterprise process model) and forces change across many dimensions, such as skill types, investment models, executive decision making, customer relationships, and the bare bones of organizational models. In so far as it is based on technology, however, it is also fairly predictable, far more predictable than many executives realize. In so far as it is more dependent on creative people with non-capitalist ideals, it is far more disruptive and disrespectful of old values.

The six million new jobs that apps will create in Europe will arise entirely from self-determining, small enterprise start-ups and creative individuals, not by governments. These developments will limit government’s ability to collect taxes as government fails to create jobs and as the economy drifts to more self-employment and more small enterprises where the tax net is not as effective as it is in the established economy. It will diminish the role of government, as self-determining groups take on more disruptive roles.

These groups are organized on platforms owned by companies like Apple, Google, and Alibaba. The ecosystems around these companies are bigger than whole continents. Apple has more than 800 million registered customer accounts—twice the size of the American population. Their impact on economic activity and employment is as important as its unwillingness to invest its $150 billion cash pile.

In the meantime, the open-source ideal is pushing the economy ahead by creating more tools for self-determination. The open-engineering website GrabCAD brings together hundreds of thousands of engineers who now share open-source designs. Their cooperation makes product development faster and cheaper.

Autodesk, a leader in CAD/CAM software and building-system modeling, has created a platform where companies in construction and civil engineering can draw on a growing number of apps created by an independent community. The aim is to help large construction companies simulate all aspects of a giant building project before breaking ground, so they can anticipate problems and better coordinate suppliers. The Shanghai Tower, the world’s second-largest building and perhaps the most innovative, was built this way.

Elasticity also reaches into marketing, where companies now make use of advocates in social networks or social media. They don’t outsource their marketing to fans on Twitter, but they do try to capture the attention and leverage the enthusiasm of their brand advocates. They externalize important parts of their messaging, and in the case of the Dutch airline KLM, they even use Twitter (an external platform) to sell flights.

When executed holistically across a range of competencies, this type of elasticity allows companies to achieve tremendous growth with only limited internal resources. But its consequence is that it also needs more and more self-motivated, self-determining economic groups of the kind Ostrom described in her work on cities.

The efficiencies of this system, though, are frightening in respect to job creation. The most effective enterprises of the near future will not need a large labor force. Already we see companies such as WhatsApp managing hundreds of millions of customer accounts with fewer than a hundred employees. WordPress supplies content management to nearly a quarter of the World Wide Web. It has 200 employees. As the economy becomes more reliant on small companies, it will become less dependent on numbers employed. This trend will force people to become more self-sufficient. It will change the concept of lifestyle. Lifestyle will become workstyle as we seek more and different ways to earn value. We are looking to a future where job creation lies with people who are prepared to self-educate, self-organize and act.

The new engine of growth is no longer investment as traditionally understood, either governmental or capital. It is personal investment, the rapid learning of the small risk taker, the intellectual development of the ideological commons, the common pool resource of knowledge and know-how, and the big platform where wealth is becoming increasingly concentrated and underutilized.

Look around now and you’ll see even old, established corporations, among them the very largest enterprises in old industry such as GE, setting out platform and ecosystem strategies that depend on engaging small-business ingenuity. In GE’s case, it has put $1 billion into growing a diagnostics ecosystem capability in breast cancer.7 It needs small, specialist data analysts to get involved. It is trying to grow a platform and ecosystem in industrial data. Who will be the ecosystem partners there, the risk takers who come to GE’s aid? The company is a major backer of Quirky, a platform that lets anybody with a good product idea take it to prototype. GE has offered to take a selection of those products to market and has put much of its patent chest into Quirky to help others invent new products, relying on unknown individuals to lift the fortunes of this global giant.8

This new enterprise operating model gained a lot from the transformation of Apple, and the growth of Google and Amazon in the mid-to-late 2000s, but it doesn’t start or end there. In fact, it dates back to the 1990s, when financial companies like Fidelity first adopted a platform approach to transacting index and mutual funds. The story then passed on to the technology sector via eBay and PayPal before it reached Apple’s iTunes in 2001 and Google’s AdWords a year later. Amazon became a major platform and ecosystem company in the mid-2000s, after which Salesforce.com, Netflix, and the smartphone apps stores took this model to a new level. But it is perhaps Google above all these that understood and implemented the platform and ecosystem model of business relentlessly. In that sense, the elastic enterprise was created by a generation of entrepreneurs influenced by Google’s success, techniques and dominance.

Much of the story also belongs to the hundreds of thousands of start-up founders, developers, and content makers who have tried, failed, and tried again to start or transform their small companies and who participate in the elastic enterprise as members of global, modern, business ecosystems, the lifeblood of Google-type platforms.

In the middle of this mix, many executives find it difficult to make decisions about innovation. They are encouraged to be more open, more creative, to take more risks or respond to disruption. By and large, their problem lies with their knowledge. They don’t understand what is driving disruption so they can only respond in piecemeal ways. They see the emergence of vast new utilities such as Apple and Google, who are reaching into an increasing range of industry sectors, but they are powerless to dream up the counterstrategy. They also see the emergence of new currencies such as BitCoin and don’t associate it with their own changing financial landscape, in effect the decline of capital and the rise of liquidity and real time business processes. Low friction business built around common intellectual property could also shake up the economy of physical goods but very few companies in the physical economy are anticipating radical price reductions.

As those who have sought to transform their businesses can attest, radical change is never easy, yet neither is maintaining the status quo. It’s an exciting and sometimes uncomfortable new world with many old boundaries blurred. In fact, there is one very large boundary that has begun to blur, and it is key to understanding the changes business has to address.

HORIZONTAL PRESSURE

The information technology (IT) industry and telecommunications industry are converging rapidly. IT is an industry with exponentially increasing computing power and a tradition of innovation driven by start-ups that seize the high ground and claim a standard way of operating (like Microsoft with Windows).

Telecommunications is creating a global, high-fidelity mobile platform that IT-generated data can travel along from our homes, our cars, our smartphones, and the workplace.

The joining of these two infrastructure sectors gives us new business platforms, a major topic of this book and an absolute necessity for executives to grasp. These new business platforms, in turn, are becoming the twenty-first-century utility. They are becoming monopolists in their own right, earning high margins from their substantial platform investments. Their monopoly power will create its own problems over time. But those investments are enabling business revolution and business opportunity. They are redefining economics and political power.

If CEOs understand these dynamics, they can strategize around them. There is no sector the new model will not touch. Everywhere it goes, it brings a wholly new dynamic characterized by greater levels of innovation; devolution of risk to smaller companies; a significant dependency on open-source communities that work with their own rules and values; and a vastly accelerated economic pace where commentators now talk of innovation as “continuous deployment” or “continuous delivery.” These developments are leaving policy makers and many businesses behind. They are about to disrupt the very basis of the economy—finance.

This new model will expose the economy to three major horizontal disruptions: the spread of mobile connectivity across all industries and a “business anywhere, anytime” logic; a platform and ecosystem model of business organization; and a rapidly changing financial environment with new sources and stores of value in digital crypto-currencies such as Bitcoin.

These horizontal pressures will go on to disrupt pharmaceuticals, manufacturing, energy, utilities, city governance, and more. The companies that win this shift in economic power will vie with governments for the right to determine economic regulations.

They will shift our concept of what constitutes legitimate government intervention, by eroding the tax base and resetting the labor force in a totally new pattern.

They will dictate how people need to be educated.

They will create vast new utilities, which is in effect what Apple and Google have become. These companies are utilities that enable the economy, specifically new relationships between supply, ecosystems, and customers. These vast organizing and transaction hubs are as central to the emerging economy as gas, electricity, and water have been to the one that is now receding.

The shifts this book concentrates on are taking place in economic structure and its power relationships, financial value and power, the growth of new utilities, customer power, productive ecosystems, political intervention, innovation strategy, education, personal lifestyles, values, and uncertainty and the overall effect on investment. But I am not going to tell the story as a discrete set of ten changes. It is more interesting to see the whole, the fabric and the weave.

According to platform and ecosystem specialist Delyn Simons at Mashery in Silicon Valley, these processes are now table stakes among the best-run companies. 9 What’s more, if you are not a pioneer in this area, your chances of attracting the talent to make it happen will decline as time goes by. Entrants to the platform and ecosystem model need to act fast. So let’s dive in.

CHAPTER 1

Shift Dynamics

THE SHIFT TO A HORIZONTAL ECONOMY

Ulf Ewaldsson is the chief technical officer at Ericsson. His company is not normally associated with the new business revolution; we tend to instead think of companies like Google, Apple, Amazon, and, if we have a global outlook, Alibaba, as the true revolutionaries. Ewaldsson’s company, however, supplies the networks for 40 percent of global mobile traffic. His department spends $5 billion a year on researching how to make mobile communications more capable.

Ericsson provides part of the layer of infrastructure that powers the shift. The major beneficiaries of change are IT companies like Apple and Google, but while we might think of the digital revolution as IT-driven, it is in fact enabled by mobile communications. Ewaldsson’s team is at the forefront of that, defining not just what’s next but how it is going to happen. He is about to test the waters with a new innovation.

Ericsson is a business currently built around globally agreed standards that took years to agree upon. In a highly standardized industry like telecommunications, Ewaldsson is nonetheless looking to the open-source community to accelerate the pace of change.

“We are thinking of putting some of our own code from our platforms into open source,” he told me recently, “so that we can generate more standardization.”1

This is huge news. As more and more global organizations turn to open source, the dependencies in the new economy are constantly shifting in favor of externalized business processes and non-enclosed value creation (major banks such as Goldman Sachs and Fidelity are now also turning to the commons, (a system for creating and managing a common pool resource).2

Both IT and telecoms, with their global power and super profits, are accelerating change by relying on the commons, the highly decentralized operating model of open-source computing. They often rely on people working for free in communities based on meritocratic ideals. In a typical open source project, work is accepted on its merits, code is critiqued openly, and workflow is based around democratic ideals. That’s quite a contrast from what we know about enterprise rules and processes. Yet the successful enterprise now relies on community. Not just on community—community with a different ethos built on meritocracy.

Open source is proving to be an effective way to create globally accepted solutions or systems that used to rely on standards. Why are standards important?

If you imagine how the automobile industry functions, as a point of comparison, it is almost entirely nonstandard: a Chrysler door will not fit a Ford car, braking system parts typically will not interoperate between different manufacturers, and many car models need different tire specifications. Even cup holders are all different!

In telecommunications, particularly in mobile phones, many of those problems of interoperability (one component working in many environments) have been solved—most phones now function on all networks, and all phones can transfer data and voice to each other on more or less any available network. There are about 250,000 patents in a smartphone but most device makers have agreements in place to treat many of these as de facto standards that anyone can use. And networks, the fixed or mobile infrastructure for carrying services like those we use on the World Wide Web, are highly standardized by agreement. Only a decade ago a mobile phone maker needed to make handsets to a network operator’s specifications. No longer. This is now a free market.

In contrast, IT has grown its interoperability through open-source software. Here’s an example. For many years, the Internet was dominated by Microsoft’s Internet Explorer, but gradually the open-source Mozilla Firefox browser began replacing it, in part because it appealed to an antimonopoly ethos among consumers and developers.

In fact, open source is the key that has unlocked many proprietary systems (a way of describing products that can only work on their originators’ technologies). The first major success of open source was Linux, an operating system for servers that undermined the Windows/Intel control of enterprise server markets (and eventually created the possibility of Cloud computing). Much of the work on Linux took place across a globally distributed volunteer workforce (although some was also paid for by companies such as IBM that had a vested interest in undermining Microsoft).

As more large enterprises devolve their innovation onto this commons, the commons’ ability to innovate and disrupt increases.

Looked at overall, the shift creates a polarized economy: large enterprises with huge concentrations of wealth and a large commons with unprecedented ability to generate and distribute disruptive ideas; economic actors who work without cash rewards, or work with high risk of impoverishment or success, almost like a casino.

There is another shift. In the past, resource allocation, the main function of markets, focused on the distribution of physical goods and the rewards that came from product margins (the gap between cost and price). Today, resource allocation is increasingly about service and nonphysical utility—emotion, sentient experiences, delight, connection, relationship, pleasure, and the skills it takes to deliver these. There is, in effect, very little cost-price margin in many services because cost trends toward zero. In these circumstances utility is a process under negotiation between service provider and customer. Price will come under attack not because of excessive margins but because of a non-price morality— insufficient quality of service, attitudes of employees, design quality or some other sentient factor.

In this sense-based world, more ephemeral measures of value are emerging. This is the case with crypto-currencies. There is no better illustration of the abstract, idea-centric times we live in than the invention of new ideas about money and value, and crypto-currencies like Bitcoin are an idea about value or money rather than an embodied, physical reality. They can only gain solid traction in an economy that is already divorcing itself from dependency on physical goods. They are the ultimate manifestation of an ideas and sentient economy, and they emerge from the open-source commons, which is an alternative value system running parallel to normal business practices.

Open-source communities are ideologically distinct from enterprises. They have entirely different working methods from those inside companies, particularly large companies. They are antagonistic to many large-enterprise working methods and objectives.

The best way to describe the difference is that they are meritocratic, self-learning, and critical. People earn respect through the quality of their work as coders, designers, and critics of other people’s work. The constant critique creates a highly effective way for people to learn, and because it is community based, many of the lessons are in constant circulation and freely available.

It is the most effective way to take on big computing projects but its basic principles are now extending to all forms of education, including universities (for an example look at Coursera.com, a site that offers free education from major institutions). Every large enterprise now relies on open-source code. However, large enterprises cannot enclose the ingenuity and the distributed nature of the commons.

Despite that apparent disadvantage, the mobile sector is looking for the same productive flexibility that IT has already benefited from and is turning to open-source projects to accelerate the development of key network technologies. These technologies are the real powerhouse of business process revolution.

When people talk about the digital revolution, they talk about the conversion of information to bits—for example, through the use of sensors in the Internet of Things. That is a very US-centric view, but that is understandable. It speaks to the information technology side of work. But what is really powering this revolution is connection—fault-free global telecommunications networks, especially high-capacity mobile networks.

Apple, Google, Ericsson, Huawei, Samsung, AT&T, Verizon, and Deutsche Telekom are all going to be major beneficiaries of the mobile Internet because they either power the applications (Apple, Google, and Samsung), or they provide the infrastructure and services (Samsung, Ericsson, and Huawei), or they provide different combinations of these (AT&T and Verizon). This competition is accelerating the road to global participation in one network. The fact that Apple, Facebook, and Google are now mobile companies (and Amazon is trying to become one) reveals something of the deeper story of business transformation.

Alongside the World Wide Web, mobile phones have created a global system for communicating a multitude of information. I talked with Ewaldsson while he was in Java, Indonesia, on vacation. We both dialed into an Ericsson conferencing service, him via his mobile phone and me via Skype (via the web, in other words). Connecting from across the world, Ewaldsson explained where he believes the essential dynamics of change lie. “The whole of the web,” he said, referring to its mobile component, as well, “is global. But banks, broadcasters, and many other companies are local. The disruption comes when a company like a Netflix, or HBO with some of its new plans, takes a global perspective and uses technology platforms to create a global service. We will see it happen industry by industry.”

The underlying argument here is critical to understanding the broader transformation of business.3 Mobile creates a truly global market for the first time and in doing so is capable of polarizing the large enterprise economy between mega-corporations that can truly act globally and those that fail to.

The economy is becoming horizontal. For most of the industrial era it seemed to fall nicely into vertical sectors: consumer electronics, telecoms, phones, computing, lighting, electricity, and so on. The convergence of IT and telecoms is changing that and reordering the economy around new capabilities.

It does this by creating a single infrastructure that is interoperable globally. In Cloud computing (remote, high powered storage and computing services that are rented out), we see the first real convergence of IT, with its dynamic entrepreneurs, coupled to telecommunications, and its history of interoperability standards and global operations (Ericsson is in 182 countries). This convergence of cultures, skills, knowledge, globalism, and competitiveness is already changing the business landscape. But it is the new globalism that will challenge Western companies to change or die.

In the West, companies have a separatist philosophy of business. Another way of saying that is they tend to stick to their core competency. They not only stick to their vertical. They stick to what they do best within that vertical.

Companies outside Europe and the United States do not think this way, traditionally. They are even more right to think differently when the economy is increasingly based on service, or how we feel, a value that can be transmitted anywhere.

INDUSTRY WITHOUT BOUNDARIES

The Chinese company Alibaba, partly owned by America’s Yahoo, doesn’t see itself as being in any particular kind of business. It began as an Amazon look-alike. Amazon has been an amazing case of a company that tries out adjacencies, or moves horizontally outside its core. But Alibaba is, by comparison, on another planet. It has its own payments system Alipay, which clears 80 million payments a day. It now owns a bank, MyBank. It has a wealth management service, Yu’e Bao, “with 570 billion yuan ($93 billion) under management; Zhao Cai Bao, a third-party financial services platform; micro-loan provider Ant Micro.”4 It is set on disrupting the global financial system, even though its roots lie in retail.

Samsung is another case in point. The mobile phone maker is also a leader in solar panel technology and has a strong position in medical imaging, particularly in gynecology. It makes some of the most advanced mobile computer chips; it pioneered the development of Organic Light Emitting Diode (OLED) screens, a technology it is bringing to the curved TV , but is also in other parts of the home. It makes kitchen equipment, such as refrigerators (this is actually where its innovation journey began, creating work-arounds of Japanese patents) and microwaves, and is seeking ways to connect domestic technologies (and no doubt its solar panel technology) through its mobile devices.

Alibaba and Samsung have achieved a broad spread of business extremely quickly. Alibaba was founded in 1999. In 2002, Samsung was still a contract manufacturer for Japanese consumer electronics companies. Their rise and the expansion of their scope of business have been astonishing. Velocity, in fact, is poised to be a new watchword in boardrooms that get the shift.

However, there is something else at work here. What Alibaba and Samsung realize is that all business is now horizontal. There are no market or category barriers other than those we imagine because most business will be global, most transactions will be Internet defined, and most advantage will come from how people feel in the economic relationships they enter. Enterprises will achieve results by appealing to customers’ changing sentient needs.

In fact, the winning formula is to appeal to our senses and in the broadest sense to make us feel good. That is a function of delivering what we consumers want as fast as possible, with as little friction as necessary, as well as delivering strong aesthetic or sentient experiences. Regardless of whether the experience is associated with a camera, a smart phone, a hospital imaging machine or an insurance policy, all businesses where Samsung plays a leading role. Experience is an intangible that can be delivered anywhere, anytime, at extreme velocity.

As an example of business velocity, consider Hewlett-Packard (HP). Once simply a computer hardware company, HP is now a leading advisor in business velocity, the speed that companies can update their services to customers. HP has noted another critical change beyond the convergence in infrastructure: there is a profound change in how products are created.

Paul Muller, a senior vice president at HP responsible for a line of business called the software-defined enterprise, explains:

By using software to “define” the experience [of a product], flexibility is increased, which drives increased expectations for change (in the way we all look forward to a new version of our smartphone operating system to breathe new life into our old devices, or an update to our favorite app). Smart product managers and marketers recognize this trend and use it to build and sustain category leadership, even when the physical device or service might lag behind the competitions’. We’re already seeing this manifesting itself in an uptick in tempo of software releases—researchers have extrapolated that trend line to 2020 and believe that we’ll go from an average of four releases per app per year to 120. That’s one every three months or so to over 120 releases per app per year or roughly one every three days!5

What does this actually mean? Although Muller doesn’t say it, this shift in the speed with which companies are now updating their products (and services) is an outcome of IT telecoms’ convergence, particularly around the mobile network.

At its peak, the streaming video service provider Netflix updates elements of its services several times an hour. It can do this because it has a constant connection with all of its streaming customers, wherever they might be accessing Netflix (and constant connection is yet another shift to expand on later). Remember seeing those additions to the classics’ catalog or changes in how your bill was summarized? When Netflix updates part of the interface or an area of its catalog, the change is visible almost immediately across the world.

Netflix can deploy change continuously. It can then quickly gather feedback on the changes it makes by looking at how users like you respond to new features in its services. It doesn’t need a market survey to get the point. It sees its users behaving differently, more or less in real time. Netflix is able to automate millions of instances of data collection on how a feature is used in order to determine whether a change is good or bad. And very often, the decision on what to do with that information is also automated.

Compare this to the old world of software before the meeting of IT and the network. In those days, Microsoft would update its enterprise platform, SharePoint, once every three years. Companies that understand today’s technology landscape get the whole package of change as one big shift—the Cloud, platform, velocity, connection, and more.

Netflix can do continuous delivery because IT and telecommunications are converging. Netflix’s Cloud applications are directly linked to all of its intermediary Internet service providers and therefore its customers, but increasingly those customers are out there on the move, accessing services via mobile and handheld devices.

Being able to address all these users in real time also means Netflix can adapt to new markets quickly without having to wait years to see how a service embeds. And it also means it can operate in forty-two countries and deliver to over 1,000 different types of device with fewer than 2,000 staff. In old industry, each of those end device access points would have its own product manager, a country manager, and its own development path, maybe even its own brand manager. But no longer—the move to interoperability makes it unnecessary and renders many ideas about product management irrelevant.

Add in the fact that an additional four billion people will come online between now and 2020, providing even more challenge and opportunity. As IT and telecoms converge more rapidly, the types of services that will be offered via a Netflix-type model will increase—we will manage connected cars globally, health services will not be bound by geography, and banks will have to learn to be truly local or truly global.

Yet there are many ingredients to succeeding in this environment. Companies like Netflix are increasingly using open-source software—in other words, using open collaboration—as a way to reach a kind of standard in some of the areas where they work. So, too, are the mainstays of mobile like Ericsson.

The questions about how to make this shift are numerous. How to scale quickly enough before new competitors emerge with a global business plan? How to make sure you understand the forces of disruption well enough? How to secure the talent to make this work?

If these are the main questions a leader might ask from within the existing resource allocation system, he or she should also ask what forces are likely to change that system. As dependency on small firms increases, business dynamism is in retreat. Despite all appearances that we are in a start-up boom, fewer viable small companies are actually surviving.6

A recent Kauffman study of the American economy explains the importance of firm replacement by start-ups but concludes:

A number of signs point to a secular decline in US business dynamism, which goes far beyond the more recent effects of the Great Recession. For example, the rate of new firm formation—a key element of business dynamism and new job creation—has been declining steadily for at least the last three decades. Job reallocation—the process that moves workers away from contracting or closing businesses and toward expanding or new firms—also has been declining over the same period.7

Yet, small companies are an asset. They are better able to adapt to the changing economy. Large enterprises are increasingly seeking them out as partners. Small is adaptive.

Over the past decade, mobile technology and the global mobile Internet, coupled to changes in IT technology and practices, have laid the foundation for rapid, low-cost business formation and growth. It is now cost effective for an enterprise to manage many thousands of business relationships (in fact many hundreds of thousands), as well as hundreds of millions of customer relationships. Whereas some companies are funding a few start-ups through their venture funds, they really need to be capturing the relationship benefit of dealing with many hundreds and thousands of companies in independent, self-determining groups, as, for example, Expedia has done with the Expedia Affiliate Network or WordPress does with its ecosystem of designers and web hosts.

The problem is there is not enough of this dynamism, and therefore the bulk of innovation resides inside the common pool resource. This larger change, the shift to dependency on small enterprises and the commons, is redefining the economy around new relationships and new technologies. That’s why the overarching theme of the shift is relationships, not technologies. Large enterprises must depend on the less predictable and instinctively disruptive movement of the commons and the whims of small companies.

Crypto-currencies, the origination of exchange value from ideas, emerge from the commons, and they exhibit the same character of speed and immediate global distribution as any product created by a conventionally incorporated, global company. In other words, the commons has the same advantages as the enterprise, perhaps more so because it is by nature globally distributed.

In this new world, disruption is a mix of:

• Shifting currents of power in the infrastructure and the trend toward open source as a method.

• The processes or platforms that support horizontal, highly adjacent business strategies.

• Being able to create multiple options to play in different markets.

• The rise of sentient products and services, or the new value of aesthetics and feelings.

• The arrival of velocity as a competitive weapon, due to constant, automated connection to customers on reliable, computing-rich networks.

• The devolution of innovation risk to smaller entities, the common pool and individuals.

• The growing power of the commons to convert mainstream and wildcard ideas into globally distributed products.

While these are some of the key elements of disruption, enterprises still have to find ways to use those inputs effectively. For decades, the idea of organizing resources down to the individual contribution among hundreds of thousands of people, self-determining small entities, and common pool resources would have been unmanageable and ideologically inconceivable. Highly-scaled relationships are now possible but many enterprises struggle with their own internal agendas and a lack of understanding.

FROM GROWTH AS A LIABILITY TO SCALE AS A COMPETITIVE ADVANTAGE

Why can’t more executives or enterprises get the shift?

Companies are judged by growth. A company that does not grow its profits is seen as a serious risk. For four decades, environmentally conscious economists have asked whether growth can or should continue, but for the foreseeable future we are at the very least stuck with growth as a value metric. Companies are judged by profit growth, output growth, sales growth, margin growth. But growth has always been problematic because of the risk of diminishing returns.

In traditional business models, an enterprise is structured to produce for a given capacity in any one investment period—the expectation of so many cars or smartphones or tablets, and so on. Efficiency often comes from wringing increasing levels of output from that capacity. A one- or two-percentage-point change in demand could leave the company with large capital investments to make in new plant and machinery (and R&D).

Gradually, beginning in the 1980s, a portion of that capital expenditure (CAPEX) and risk has been devolved onto suppliers, through supply chain management. This began a dramatic wave of externalization, a term I’ll use frequently. It has often been accompanied by outsourcing to offshore suppliers, but let’s keep the two distinct: to outsource is to give responsibility for specified functions to new suppliers (like call center management or IT support); to externalize is to seek elasticity and investment advantages through partnerships. Supply chain management was the first large-scale pervasive example of externalization.

In the average business model, many investment decisions are made at the tipping point where anticipated demand looks likely to push production capacity to a new scale. So when a company such as Samsung wants to produce an extra million Galaxy S5 phones, it might mean a supplier like Universal Displays has to make a much larger investment in extra capacity to produce the compounds that go into the Galaxy S5 screen.

Tied up in this calculation is a strong sense that, at some point, the marginal return on this type of investment will diminish—for every new dollar invested, the return will be less than for the last dollar invested. These calculations are also part of a larger nexus of constraint. They fit into a body of thinking called “diseconomies of scale,” an economic term that indicates the very principle we began this book with: at a certain point, scale turns against a company.

The classic example of this is a factory that reaches capacity. The factory owner anticipates 10 percent additional demand in the following year. At this extra margin of demand, he will need to build a new factory, yet the cost of this new factory will certainly exceed what a 10 percent increase in demand justifies. Furthermore, with this extra capacity, the company will also have to layer in more management and supervision, which automatically puts distance between decision makers, production, and the market. It brings in complexity as worker roles change. And almost invariably, it leads to some duplication of effort.

The cost of growth simply absorbs too much of the extra revenue earned. In addition, it is associated with a decline in innovation. Many of the labor inputs for a large global corporation are spent on administrative and managerial activity to manage and coordinate resources. For example, in the case of the management of IT, as the portfolio of information systems grows, more time and money is spent on maintaining those systems rather than creating ideas to support new business initiatives or ventures. The same is true in other functional areas. Growth becomes a liability.

Gregory Milano’s company, Fortuna Advisors, studied the comparative returns and growth performance of large, medium, and smaller companies among the top 1,000 US corporations. They discovered there was hardly a measure that showed size, as conventionally understood, to be an advantage. Total shareholder returns of large firms were nearly 70 percent below those of smaller firms over a ten-year period. Share price performance was 25 percent lower. Larger firms were also more likely to redistribute cash back to shareholders, which exposed the firm to future performance risk.8

Yet the burden of management is always growth. Large companies must grow, however unrealistic this proposition becomes. To illustrate, by 2010, consumer products group Procter & Gamble required extra revenue of approximately $8 billion a year to sustain double-digit growth as demanded by the markets. At the low end of start-up funding, that represents about 40,000–50,000 new seed-funded businesses per year.

How to profitably grow a business is the fundamental dilemma facing every executive team today. Added to that dilemma is the need to transform.

In the adoption of supply chain management techniques, the parameters were clear. Many of today’s large consultancies grew as a result of being able to help large enterprises implement a devolved supply chain system. In many cases, supply chains have hit a complexity limit and now expose their owners to excessive political and climate risk. For example, when Greece’s economy neared a debt default, businesses had to suddenly seek new sources of supply, a situation they also faced when disaster hit Japan’s Fukushima Daiichi nuclear plant.

Still, supply chain management, acquisitions, globalizing markets and certain types of innovation have helped keep companies growing. The greater emphasis in business investments to date has therefore been on what happens behind the company that “owns” the customer relationship—the vast, often global, supply chains that give us cheap products coupled to software platforms that enable the management of global complexity.

There is now a shift away from that emphasis to what happens between the enterprise and the customer. There is also a shift toward the relationships the company needs to build with third parties to make that customer relationship strong and profitable. Those customers and third parties number in the millions, and most of them have some form of continuous connection to the enterprise.

For example, it is not unusual for a modern business to see many millions of “calls” on its web platform every minute of the day. A company such as Apple, which manages eight hundred million customer accounts, is likely to see billions of calls on its platforms every day.

A “call” is an access to any piece of data, so one website visit might involve calls on many different types of visual information, such as product images, alongside information about apps, trial downloads, pricing information, product basket records, checkouts, mobile access to map requests, GPS signals, and so on.

Growing connections and relationships at an unprecedented scale is a major impact of digital business. But it is set against the seismic shifts we have also discussed in infrastructure capacity, capability, and cost.

It is a very different environment from ten years ago. It is much more highly scaled, especially at the point of customer interaction, yet it is also apparently open-ended in that it can scale further and companies can move into adjacent areas with apparent ease. This kind of scale and adjacency is strangely doable; it is affordable and valuable. Leaders of elastic enterprises have the technical acumen to grasp that. They are the change makers.

Apple has expanded from music into apps and is now moving into health, the home, autos, the enterprise, retail and more. This type of scale and radical adjacency planning are sources of strong competitive advantage.

Since Apple became a platform company, its operating income has risen from $324 million in fiscal 2004 to $51.3 billion in fiscal 2014. What, then, is a platform company? Apple’s experience defines it. The platform is a combination of software, services, transactions and relationships, bundled into a seamless identity. The App Store is a platform that manages hundreds of thousands of relationships and millions of customer relationships, many through some form of regular transaction. Apple has achieved its extraordinary success by making serious horizontal moves into adjacent sectors on the back of its platform. That it succeeds wildly in these radical adjacencies crushes the idea that a company should stick to its core. Apple makes the idea of conservative management laughable. But so too does a more modest financial success like WordPress. Less lionized in the business press, WordPress is an extraordinary success story, set within a narrative of modesty, cooperation, social ambition and huge scale.

Many executives comprehend neither of these examples. Their ideas about management arose when firms felt under threat from very specific historical developments such as the rise of Asian manufacturing. The planning tools, broadly used investment metrics such as Net Present Value, and the basic business outlook behind management culture, were created in that very different environment, one that can be a handicap today yet still has executives in its grip.

THE DIGITAL SHIFT

For the past two decades, IT and other business functions have been at odds in many organizations, often to the point where IT hinders the shifts that companies need to make and forces many executives to defer change.

In the early 2000s, however, management theorists began to recognize the problem. IT departments managed huge resources but seemed to add little business value.9

You may wonder how a rich computational culture could constrain business activity. Well, the IT systems of the past both mirrored the hierarchical nature of the firm and helped create it. IT systems were built against the background of the numerical control systems that were common in the early days of mass production and assembly. They were later redesigned to help control the vast new supply chains that were often spread across the globe.

Three new developments have altered the computational landscape for corporations and created an environment in which IT fuels dynamic activity rather than constraining it. IT now enables elasticity. IT allows companies to move well beyond the core, and to move with a new velocity. While the long-term impact of these developments will not be known for years to come, we know they are hugely powerful. They are driving a new phase of globalization and the wider business transformation that the business press refers to as disruption.

MOBILITY AND MARKET DIFFERENTIATION

There are currently 4.55 billion mobile phone users. Although most do not have easy access to the Internet, the continuing growth of mobile devices and related telecommunications infrastructure will soon extend individual access to computing and Internet-related services to virtually every location on the planet.

For the first time, due to mobility, the most significant changes in business are not taking place in the advanced markets. They are either global or they are arising in developing markets. During the period from 2000 to 2010, the number of Internet users grew at approximately 445 percent, with Africa leading the way at 2,357 percent, followed by the Middle East at 1,825 percent and Latin America at 1,033 percent—all despite two global recessions during this period. When considered in absolute terms, the number of users on the Internet is led by Asia with 825.1 million, Europe with 475.1 million, and North America with 266.2 million, with the remaining approximately 600 million users distributed across other geographies.

Much of this growth has been aided, particularly in developing countries, by the emergence of low-cost mobile devices and infrastructure as the primary forms of IT, bringing more and more people into the web of scaled interaction.

Today’s start-up hot spots are as likely to be Nairobi and Istanbul as London and Berlin. In a recent study of innovation in the financial community, my research group found that most of the social innovation in banking was coming from Turkey, while most innovation in mobile finance emerged in Africa and the Middle East.10 Today, Silicon Valley VCs are often investing in distant locations with indigenous entrepreneurs who are connected via mobile and wireless with the entire global entrepreneurial community.

Not only is this a globally interconnected world, but outside the newly developed economies, a new middle class is emerging with unprecedented speed. In India, for example, according to the McKinsey Global Institute, the “average household income will triple over the next two decades, and it will become the world’s 5th-largest consumer economy by 2025, up from 12th now.”11

That’s on top of a decade where extreme poverty has been eradicated in many parts of the subcontinent. India’s middle class is projected to grow from 50 million in 2010 to 585 million in 2025, reflecting a huge expansion of spending power. If that sounds dramatic, then consider this, too: the World Bank estimates that the global middle class is likely to grow from 430 million in 2000 to 1.15 billion in 2030. Furthermore, over 93 percent of the global middle class will come from what we now think of as developing economies, with China alone contributing 52 percent.

These Internet users and members of a growing middle class are all contributing to a connected world in which smartphones and associated technologies are commonplace. What they are also doing, though, is changing the structure and dynamics of markets.

With growing levels of global connectivity, the “scale value” of products increases. What one person loves can become a phenomenon that millions love in a matter of minutes or days. Product launches have become globally shared events accompanied by unprecedented adoption and participation rates (ten million people bought the iPhone 6 during the first weekend of sales.)13

Companies are finding it necessary not only to scale up their operations to meet the challenges of this new environment, but they must simultaneously manage smaller niche markets that might have been impossible to serve were they not part of a global market. 14

Coca-Cola, for example, now has over 500 brands and 3,000 products. It is under constant pressure to diversify further—for example, developing non-caloric sweeteners for some products; adapting to drink as a fashion (the 2014 fashion was toward mango, maybe next year it will be kiwi). The ingredients for these preferences have to be sourced and the final products juggled across different markets around the globe.

In fact, one of the underlying market conditions is a new degree of market heterogeneity that is both geographic and yet extends beyond geography. This phenomenon is mass differentiation—still producing at scale but having to find differentiators for submarkets across the globe.

In the background, banks have to devise new systems and relationships that allow enterprises to manage revenues in more diverse geographies. Corporate treasuries in companies of varying sizes, not just in large enterprises, now have to manage receipts and payments in real time so that cash is constantly available to fund new economic activity as they seek ways of finding and satisfying these more differentiated markets.

As well as having to serve many diverse markets, companies must also appeal to a new level of intra-market differentiation. People are demanding ever more differentiated lifestyles and are able to source “differentness” via the web. Companies need to think in terms of more product variety and more products that meet a wider array of customer needs. This is true for all products in a complex world of diverse global demands.

Pharmaceuticals are a perfect example of the modern need for mass differentiation and the cost of not building up a platform for delivering it. In the United States, all prescription drugs have to go through three phases of clinical trial before receiving FDA approval. In the case of cancer treatment, many trials show surprisingly good results for a small portion of the trial population. However, if three people from a population of forty enjoy positive outcomes for a trial medicine, the trial is considered a spectacular failure. No drug with such trial results would ever receive approval and make it onto the market.

Consider that those three people might represent some form of cure for patients like them. The other thirty-seven people clearly need another form of treatment. The three, however, represent a perfect case of mass differentiation for them and all people of that type globally; the trial medicine might well be a breakthrough.

In a world of massive computational power, the consequence of this finding should be to identify more people like the three rather than reject the drug. If that were to happen, it might mean that a population of forty people needs fifteen types of treatment (i.e., mass differentiation).

However, our mass production-era sensibilities prevent us from seeing the value of mass differentiation because we are programmed to drive toward a one-size-fits-all formula for most products. These sensibilities force us to reject that drug. We perceive only one type of patient, and so we treat the three successes as flukes.

Businesses must forge new business models and modes of operation that can reach a growing, diversifying, and developing world population of customers. They have to move beyond old classifications—old notions of customer income groups or class divisions—to serve a market of mass differentiation, often by aggregating niche markets in various parts of the globe. This can be a daunting task that includes trying to extend banking relationships, to pay and be paid, and to manage cash across continents and deep into the heartlands of emerging continents like India and Africa.

Elastic enterprises create economies of scope to make this possible. They do so by moving the focal point of competitiveness away from employed labor and internal structures toward independent contractors or smaller companies with the talent to solve customer problems, often in self-learning ecosystems such as GrabCAD, the open engineering community we touched on earlier, or to developer ecosystems. Or they develop other platform options, as has Netflix, which we will examine later. The key, however, is to devolve innovation onto the right, talented, motivated groups that will solve problems in a self-determined way.

THE NEW TECHNOLOGY AND SERVICES INFRASTRUCTURE

Cloud computing is a crucially important new way of organizing business processes. It has become a catch-all term for making computational power, capacity and services available to anyone, anywhere, at any time, at a cost that is closely tied to usage. In other words, companies no longer need to make a capital investment in vast computing facilities. Instead, they can rent processing power as needed and pay for only what they use, giving them elastic scale without disproportionate investment costs. While Cloud is important as a technical infrastructure its impact on business models is probably its most significant achievement. It drives the shift from product to service and therefore from well understood “margin” businesses (products with a mark-up) to relationships that need nurturing over time. in the process it requires firms to reset their investment priorities, transition their customer relationship management, adapt the time scales between cash out and cash in, the way they deal with debt requirements and hence their banking relationships, how they manage channel relationships, and more.

In turn, this shift is creating the opportunity for very rapid and low-cost business deployment.

Here’s an example. London Live is a new TV service for the British capital. It creates programs and transmits every day of the week like any regular TV station. However, it never has to touch the engineering side of its business. It does not “play out” TV or manage a “headend,” the intersection of its services with cable operators or other networks. All those issues are managed by telecommunications company Ericsson, allowing the TV station to function with less than seventy employees and with no deep technical knowledge, focusing on the entertainment, news and sales cycle that its people know so well. Crucially, it does not need to keep up with the fast-changing world of technology. It rents that expertise along with its broadcast management services.

In today’s environment, a company can rent just about any service needed to make its business work—the global services firm Cognizant calls this reality “everything as a service.”15

This is a significant departure from the recent past. For most of the last fifty years, corporations were forced to make private investments in large-scale IT projects to meet their business objectives. Often these IT projects required significant customization or new code written from scratch, in-house.

For example, in 1990, Wal-Mart revolutionized retailing with a just-in-time global logistics system, but it did so at a massive cost.16 The company spent over $300 million to create its much-vaunted system, with most of its code written by employees at its headquarters in Bentonville, Arkansas. It was a huge undertaking that covered electronic point-of-sale systems, electronic document interchange and the introduction of bar coding, value-added networks, and new real-time electronic ordering systems, all tied into fleet management. You can imagine how, in this complexity, software became the mirror of a highly disciplined and hierarchical system.

Even where existing commercial software packages might have met business objectives, most companies were forced to engage in an arduous effort to customize “out-of-the-box” packaged software to meet their unique needs and then continue to modify the code over its useful life as business requirements evolved.

Cloud-based platforms replace much of the pain and investment of this type of work. Though the Cloud was initially enabled by daring investments from companies like Amazon and HP, a new service infrastructure is growing up in the Cloud. The Cloud enables the development of real expertise around service frameworks, just like Ericsson’s media platform allows it to develop very specific innovation expertise in broadcast.

Innovation and accelerated rates of change are now subject to specialization, making it easier to accomplish. In place of many thousands of companies trying to run their own software development projects, a smaller number of companies are becoming more expert at creating frameworks for new solutions based in the Cloud.

That means modern Cloud-based platforms not only provide an opportunity to rent processing power, but they also increasingly provide much of the service and business process design infrastructure on a rental basis, making transformation much more of a purchasable asset. The flexibility to do so will only increase; according to analyst firm Gartner, by 2016 Cloud computing will make up the bulk of IT spending.17 But there are even wider ramifications. Because of its low CAPEX, and rental nature, the Cloud enables a much broader array of business models, principally a shift from selling products at a margin to renting products on subscription. It shifts revenue from the margin of sales to the continuous relationship of service and introduces an entirely new requirement for aggressively and skillfully managing liquidity as a significant asset. That in turn is what will open up business to new currencies like BitCoin or new payment systems like Ripple that enable real time value transfer.

THE SHIFT TO CONSUMER POWER AND SYSTEM USABILITY

Although Apple first pioneered usability in its Macintosh line of computers in the 1980s, it was only with the iPhone that Apple and the rest of the computing and mobile sector finally focused on the user’s experience as the key to scaling a product.

That might seem an unlikely statement: surely every computer company cared about usability? It would be nice if that were true, yet in most cases computing systems were developed for existing computer users, mainly programmers. The first thirty years of popular computing was really driven by engineering and by engineers building products they liked and admired (and understood). To get the most out of computers, you really had to understand the logic of the machine, the interface, and the application. Even apparently usable systems like Microsoft Word needed a training course if the user wanted to exploit them to the full.

One of the biggest shocks to IT policy in the second decade of the 21st century was the Bring Your Own Device Movement—in which employees began to bring their own smartphones into work rather than use an IT-designated model.18 Why would people do that? Usability! They were happy with the easy-to-use screens, software, and interfaces of their personal devices and didn’t want to switch to a corporate model every time they went to work. What’s more, a CISCO study of BYOD found that firms could save $1300 per person per annum with a comprehensive BYOD policy.19

Usability has not only disrupted enterprise device policies. Simple, consumer-grade storage and sharing systems like Box have stolen market share from large platform players precisely because they have consumer-grade usability (backed by enterprise-grade engineering).20

For a host of reasons, but primarily because of global, mobile networks, customers are also now far more accessible to many more companies. The breakdown of monolithic enterprise IT is partly a result of customers having an opportunity to interact with services and products via easy-to-use apps. Today, apps are global—even low-end phones have what are known as SIM-based versions. More and more of the world will have phones with smartphone features. By 2020, there will be over six billion smartphone subscriptions globally, with over 9 billion people able to access the broadband Internet via their mobile phones.21

THE MANAGEMENT SHIFT

Companies like Alibaba and Samsung, unencumbered by Western management philosophy, make bold market decisions. But there are Western heroes too. Platform companies like Apple and Google and Expedia and more. The idea of a business platform is little understood, but they are the organizing hub of vast new business empires. They are a tool for achieving oligopoly power, and extending the reach and wealth of select enterprise beyond what we have previously witnessed in any part of the industrial economy outside the large cartel systems of the early 20th century. These are companies that pay salaries of $80 million plus a year.

Platforms (and their brothers-in-arm, ecosystems) entice companies to make radical new moves in the market. These moves are radical adjacencies that I talked about earlier and that Nick and I introduced in the elastic enterprise. They differ in scope and scale from what companies have been advised to do for nearly thirty years: to stick closely to their core competencies. Core, we were told, is a way to excel in conditions of uncertainty by identifying what you are good at and uniquely qualified to offer to customers.

The idea of the core emerged in the early 1990s and was formulated in a 1990 article by C. K. Prahalad and Gary Hamel.22 There were very good reasons for emphasizing the idea of core competency in 1990, just like there are good reasons for saying that today it may inhibit change. From the early 1980s onward, companies were experiencing their first tribulations as they attempted to shift from largely production-oriented companies to service-oriented, and from monopolists to market competitors.

The beginning of this period was marked by unprecedented turbulence (executives seem to forget how often significant change comes around) as governments, beginning with those in the United States and the United Kingdom, forced deregulation onto a succession of different industries, including telecommunications, transport, energy, defense, and finance. It came about because of a justified fear of stagnation. In these Reagan–Thatcher years, there was a sense that Western economies as a whole were stagnating, and these two politicians were very radical in forcing change onto business.

Old manufacturing industries were under threat from Japanese and Korean competition. Companies needed encouragement to become more efficient and yet also more creative to face that challenge. Policy makers believed that these old companies needed to develop more new services so they could differentiate themselves from their low-cost Asian competitors. At least, that was the thinking in the 1980s. Companies needed to move away from their reliance on product and create a new product-service mix.

In 1982, the US government mandated the breakup of AT&T’s monopoly of US telecoms by splitting out local carriers (Baby Bells) into the regional Bell operating companies. It was the first step toward a more competitive telecommunications landscape. Prior to that, AT&T enjoyed a nationwide monopoly on fixed telecoms, during which it also supplied telecom equipment through its Western Electric subsidiary.

Beginning in 1980, in the United Kingdom, the Thatcher government ended the state telecoms monopoly previously enjoyed by British Telecom. Prior to that, British Telecom had been a part of the national Post Office—a super monopolist given control over all forms of communications. This was a typical arrangement in Europe. The first step in the privatization process was to establish British Telecom as an independent entity. Then in 1982, a license was given to Cable and Wireless to operate a competitor service. From there on, and especially with the arrival of mobile phones, competition grew (though in European telecoms, competition is still restricted to each of the twenty-seven countries of the European Union; there are no cross-border service providers in European telecommunications).

The effect of these changes runs deeper than is immediately apparent. Like AT&T, British Telecom had previously held a monopoly, not just over the network, but also over the supply of devices such as phones—only they could sell and lease phones to customers. It seems crazy in retrospect, but the stable economy of the postwar years was stable because of monopolies like this. If executives at British Telecom knew how to plan, it was because they controlled every aspect of their market by law. As liberalization progressed, more companies had the opportunity to create and sell phones directly to customers, paving the way to the mobile revolution in the 1990s and the rise of iconic names like Nokia, RIM, and Motorola. It also introduced uncertainty into previously controlled markets.

The process of deregulation and privatization and the break-up of monopolies (first in the United Kingdom and the United States, and then throughout Europe), created a wave of disruption and expanding economic opportunity. It was in this environment that classic business works on competitive strategy and enterprise disruption, like Competitive Advantage, on the basic tenets of competitive strategy, and The Innovator’s Dilemma, how strategically good firms are undone, first began to appear.23

A broader economic philosophy prioritized a transition away from monopoly to competition and away from manufacturing to services. This philosophy appears to have had a huge impact: in the United States, services now account for 84 percent of total employment, up from 60 percent in 1947.

The reality is slightly different, however. A large proportion of the growth in services can be accounted for by companies outsourcing their internal service needs, such as accounting, to local companies. In fact, 40 percent of the growth of service jobs in the United States can be accounted for by companies outsourcing services to firms of accountants and consultants within the United States.24 The idea of service innovation did not catch on in ways the data might suggest.

In a sense, for the first twenty years or so of this shift, the transition to a service economy was mostly illusion. Looking at the data, companies increasingly chose outsourcing as an option that made their balance sheets look better, with lower head count and lower overhead, rather than the development of new product-service combinations. They often managed to skirt around the product-service requirement, a fact that would come back to bite Western companies post-2007. Even where companies did attempt to improve customer relationships, it tended to be via software (customer relationship management) and call centers that kept customers at a distance and uninvolved. That meant the focus of strategy was on the brand and image marketing rather than on constructing entirely new customer propositions.

In this new environment, though, executives were adrift and often became captive to vendors that offered packaged solutions to the service economy, solutions like customer relationship management. Leaders had largely been trained in managing large enterprises with little fear of competition, and their cozy monopolistic business environment had been shaken up. What’s more, they had to do this new thing—introduce services that would genuinely enrich their relationships with and loyalty from customers.

They became a willing audience for a new generation of management thinkers who emphasized highly structured ways to recognize or track competitive conditions and who provided executives with the rules of competitive advantage.25 The idea of core competency was also born at this time.

The first generation of business gurus made their mark on competitive strategy just as four elements of today’s economic environment were set in motion: the transition to a service-based economy, the initial globalization of business, the expectation of less-certain business outcomes, and the need to manage externalized business processes.

It would be naïve to say today’s shifts are a continuation of those begun in the 1980s. Nonetheless, management may find some comfort in knowing their predecessors have walked some of this road already. The challenge, though, is that many of them avoided critical aspects of change, particularly around the product-service mix and its implications for devolving innovation. The challenge for management now is to ask if the ideas that supported them in the age of liberalization are the right ideas for the age of uncertainty. Clearly companies need to grow many new competencies to deal with greater uncertainty and more opportunity amid complexity. Yet, they are constantly tempted to react to change by reverting to the core, a paradox that has no long-term benefits.

They need to scale to meet the opportunity of that massively increasing, diverse, global middle class, and provide the scope of services to meet the different needs of these varied populations. Scale and scope are both critical, just like velocity. It is a hugely demanding environment. In the past, the demands of growth have either been self-imposed by ambitious entrepreneurs or have been imposed by markets to force high returns on equity investments and loans.

However, right now enterprises need to scale to meet the needs of mass differentiation across more geographical locations. They also need scale in order to compete with companies who do not see market barriers in the same way. The best way to meet the needs of scale is to shift from internal growth to externalizing the innovation and delivery of an unprecedented scope of products and services. They need to shift from core competency to a fluid core, from growth defined by the key performance indicators (KPIs) imposed by markets to the scale made possible by new infrastructure. The challenge lies in how to make the shift when most management holds to values that militate against developing optionality, scope and scale.

CHAPTER 2

Shifting Out Dying Models of Business

It can be challenging to make sense of change, especially when change begins with new ideas. The idea of a transitioning economy can very difficult to understand because it is impossible to render as a concrete, visible process. We don’t see economies. We can’t touch them or smell them. But think of an economy as the arrangements we voluntarily enter into to create wealth together. Put that way, it is really about a set of relationships that can be structured in a number of ways—in big companies that employ people, in enterprises where employment is fluid, in small companies and start-ups, and among self-employed people and in the economic commons.

The balance of these elements, the way relationships between them are constructed and the power that goes with them, is shifting significantly. While platform companies like Apple enjoy super profits and strong adjacency options, the majority of companies in the wider field of consumer electronics are financially stressed. According to a report published in November 2013 by recovery consulting firm Alix Partners, “Fifty-six percent of companies in this technology sector—representing a full 88% of overall sector revenue (excluding Samsung and Apple)—have already fallen into financial stress or are at high risk of doing so.”1

Only four broad-based consumer electronics players have their heads above water: Panasonic, Sony (which announced in September 2014 it would quit the mobile sector), LG, and Sharp (which in fact in 2013 turned for investment to competitor Samsung). A follower category consists of well-known names such as Harman, Pioneer, and JVC. The most stressed companies are in Europe and America.

Add to this the fact that the start-up ecosystem is actually significantly less dynamic than in the past. We simply hear more about it. Across the economy, there is an unknown degree to which labor has become significantly less secure.2 Meanwhile, we are seeing the development of business ecosystems we don’t know very much about. The economy is in disarray. When people talk about the 2008 crisis, they tend to treat it as a financial one, but it is increasingly clear that the restructuring of the economy is itself a case and consequence of real-economy crises. Part of that is attributable to the significant shift in economic organization that Nick Vitalari and I described in The Elastic Enterprise.

Compare the new organization with what has traditionally been accepted as a fundamental theory of why the enterprise exists, developed through the work of Nobel Prize-winning economist Ronald Coase. Coase states that companies exist to counter unsustainable transaction costs in markets. What does that mean?

If we were to conduct all of our economic activities in open markets, then we would suffer an overload of transactions—seeking out each skilled individual we needed every time we wanted to accomplish a routine task, negotiating the acquisition of their skills and time, negotiating access to each element of materials we want them to work with, and so on. Imagine having to negotiate a deal every time you wanted an assistant to set up a meeting. The firm gives us control over what would otherwise be open transactions that need to be negotiated every time we require a service or product. It reduces transaction costs, particularly bargaining costs. Imagine, for example, having to haggle every time you wanted a person with knowledge, such as the CFO, to give a presentation on your firm’s finances.3 The firm converts all those negotiations to set roles and responsibilities. It embodies them, so to speak, in roles and hierarchies, and reward systems.

Ultimately, firms enclose economic activity, uniting many human transactions under the control of an entrepreneur (or management team). That idea has been developed further by economists such as Nobel Prize-winner Oliver Williamson, who argues that the role of the organization is also to “infuse order, thereby to mitigate conflict and realize mutual gains.”4

Less and less productive business can actually be enclosed by the firm. The most potent source of innovation is the open-source movement, or the commons, which by definition is external to the firm. But many, many transactions and many aspects of business now rely on extensive external relationships in order to make business happen.

Order and mutual gains are also out of kilter. For example, tens of thousands of companies now struggle for advantage as they create online games and apps that may or may not be successful. This is an inherently chaotic situation that the firm itself does nothing to resolve. Many businesses have become “hits” businesses that depend on occasional major hits to support a series of relative failures—venture capital, Hollywood movies, the games industry and publishing all have that characteristic. In important respects, we have entered an economy where rewards accrue in unexpected ways. The famous computer game Angry Birds was game-maker Rovio’s fifty-third product. Often the ingredients that make such products successful rely on an unusual degree of luck.

The type of economics that Williamson advocates also depends on firms having access to foresight about market developments. With foresight, companies can plan, and planning gives order and the opportunity to reduce conflict. Today’s markets, however, are not susceptible to the kind of foresight that transaction economics and many traditional businesses have relied on.

It is also in the nature of transition, especially disruptive transition, that order and predictability are inherently compromised, rendering a lot of economic theories unhelpful. Theories that emphasized order out of chaos were very helpful as we transitioned from monopoly-defined economies to more competitive ones. Ideas about investment valuations, that relied on the power of foresight, helped executives replace the conventional planning process of former monopolies. These investment metrics were innovations in their own right. Net present value (NPV) for example is a way of projecting future sales and then applying a discount rate (in effect applying a risk weighting) to give a present value of a project in its early phases. Many projects literally have no value at the outset, but here was a way to account for them and to help managers create business plans that gave every appearance of certainty and value as new projects developed.

The economy is shifting, however, and it is at least doubtful that techniques like NPV have the same degree of solidity and forecasting power they once had. Through no fault of today’s management, key tools like this are losing relevance, even though they are still widely used. The reason for the decline in relevance is the changing relationship between firms and markets.

Traditionally, firms have been defined by a product development process where in an important sense, the enterprise is the product and where accounting principles like NPV give executives a sense of security about their decisions.

These firms developed hierarchies and strategies to internalize the cost of taking a product from invention through raw materials sourcing to production and assembly, then to market. The structure of this type of enterprise was best described by Michael Porter in the 1980s. When he was writing, R&D expenditure could be relied upon to deliver a reasonable ratio between money in and profit out.

Figure 2-1: The Value Chain Approach To Organizational Structure

In Figure 2-1, drawn from Porter’s work, it is clear that the enterprise’s major activities are product-centric: inbound logistics (components), operations (assembly), and outbound logistics (delivery) are three of the main activities prior to marketing and sales.5 These are supported by procurement and technology development, which are again product-centric, and by the firm’s infrastructure (possibly based on software, such as enterprise resource-planning software).

This type of model, in its strictest form, began to look outdated by the early 2000s as the dot-com boom created far more customer-centric markets. Because the World Wide Web makes it possible to develop continuous connection with customers, there has been a strong emphasis in the past decade on securing customers before securing revenues.6 This led to a free or “freemium” model where companies gave their services away in order to draw customers in.

Companies often did this to take advantage of a network effect. The telephone is an example of a network-effect product: it is useless for me to own a telephone unless some of my friends also own one. Given that the web is a network of connected organizations and people, many of its early services have been very connection- and network-oriented.

This strategy suffered a period of decline following the dot-com bust in 2000 but came back stronger in the succeeding boom. It has also been accompanied by a revival of interest in the business writer Peter Drucker’s idea that the purpose of the firm is to create a customer.7 Web-based firms are very good at creating customers, though many fail to create sufficient revenues.

In reality, what the web makes possible is the creation of relationships rather than customers. In future these will be relationships between objects (for example, cars and road-side sensors and the web) as well as between people and processes.

The possibility of creating connection in a relationship sense, and not just “creating a customer,” is a long-term strength of the web and an important driver of business innovation. The earlier example of Netflix and its highly scaled relationship with customers, one that is continuously active and updated, illustrates that it is no longer possible to sign up a customer and then forget him or her until it’s time for a subscription renewal or a product upgrade; the connection enables a continuous relationship, and competitive advantage comes from leveraging it.

This expectation means that infrastructure (the Cloud and platforms talked about in the previous chapter), which was a support service in Porter’s model, is now central to how leaders organize a broad range of activity (based inside and outside the firm) focused on strategic options.

In fact, then, the very concept of what constitutes a business is shifting. Infrastructure is everything right now, because it provides companies with cheaper and cheaper access to computing power and storage, readily available services, and scale. But it also brings them into constant contact with billions of people.

If you reflect on Ericsson’s calculation—four billion additional people on the net between now and 2020—then you can also grasp the speed at which business has to scale to meet opportunity (or alternatively how many new businesses will be needed). It is an exhilarating pace of change, but it also means more variability in the relationships that make up the economy (and the enterprise) as people and companies seek out the right balance of technology, services, partners, and ecosystems.

This idea of a new type of enterprise, though, can be difficult to grasp on the first pass. Elastic enterprises still have CEOs and CTOs and CIOs. In many ways they look the same. They still have accounts payables and car parks and offices. But the enterprise has shifted significantly in form and purpose.

In the past, infrastructure consisted of any of the physical, computing, and networking investments that supported the product-to-market model. That might have meant, for example, the investment in a data center, a data warehouse, the computing infrastructure to support access to that data, and the enterprise resource-planning systems that recorded transactions within an enterprise and between the enterprise and its suppliers and customers.

Such an enterprise was largely built around internal IT investments that supported improvements in efficiency (though often the opposite happened, as people struggled to come to terms with the giant software packages companies built for themselves in the 1980s and 1990s). Furthermore, in the traditional economics of the firm, there was a clear distinction between the internal, enclosed actions and the externalities that firms created in the course of production and sales. Externalities were usually thought of as costs, such as pollution, imposed on actors who were not directly involved in an activity. Pollution is an externality. Many companies suffer the effects of a polluter and these effects are externalities. However, today, it is important to think of externalities as relationship assets that cannot or should not be enclosed.

Today, infrastructure often supports the company in externalizing much of its activity. In fact, infrastructure becomes a competitive asset, but it is both infrastructure and exfrastructure, inside and out. The company can build new services in the Cloud, and it can draw on the Cloud for a variety of new services that it may want to use internally, such as file-sharing services that have a consumer-like look and feel, or services like Bill Pay that take large areas of responsibility away from internal processes.

In the elastic enterprise, the focus of attention is, in fact, split between internal and external factors with externalization becoming increasingly important. Already in the early 2000s, there was a trend toward a porous organization.8 But we have now crossed a threshold. We are not simply seeing firms that allow some additional activity to take place externally—we are seeing a wholesale move toward marketplaces.

In social media, the firm opendorse is creating a marketplace for firms to buy influence from athletes. This influence marketplace replaces advertising. It is multi-participatory and relational rather than a sequential purchase of advertising space.

Hired.com exists to create a marketplace for IT talent. Rather than hire IT talent through a traditional recruitment process, Hired provides a platform that lets tech companies compete for the employee.

Khan Academy (and many similar platforms) creates a market for education. Right now, participants in the formal educational system in countries like Ireland are using Khan Academy to help teach young people, especially in math. The enclosed educational system has no way to teach young people math in the way young people want to learn—in their own place at their own time.

The mechanisms that allow this externalization are the Cloud and connectors, connectors being technologies such as APIs—application programming interfaces (ways to make one piece of data or software work with another)—and identity management, which will be discussed later. The activities made possible by the Cloud and connectors are organized around a business platform, usually in conjunction with an ecosystem of self-determining economic agents like small firms.

I said earlier that a platform is simply a stack of software, transactions and relationship activity, the modern equivalent of a department, but one that stretches beyond the enterprise to manage external activity as well. Platforms gather diverse experiences into one combination of software and know-how, and act as an organizing hub of relationship activity at huge scale. They are accompanied by new marketplaces that represent much less of a transaction overhead than they did in the past. Suddenly, we can deal with hundreds of thousands, indeed hundreds of millions, of external relationships. Taken altogether, they are profoundly impacting the economies of scaling a business.

THE GROWTH OF NEW EXTERNALITIES

In 2012, I was invited to analyze the archives of the Harvard Business Review for a special edition on the topic of failure.9 I took over four hundred articles and blog posts from the preceding twenty years and analyzed their content to uncover the predominant themes of the periods 1990–1992, 2000–2003, 2004–2006, and 2007–2011 and onward. I was looking for patterns in how business writers had thought about business failings. The reason for that was simple. When writing about growth, there’s a tendency to overlook shortcomings. In writing about failure, the analytical mind is less forgiving.

The literature suggested a fifteen-year period, from 1990 to 2005, in which business writers were heavily focused on one specific set of failings: businesses were struggling to come to terms with the transition to a service economy.

Now think about this for a moment. This was the key transition of the late twentieth century, championed by leading politicians. The service economy was supposed to differentiate the Western enterprise from Asian competition. Yet it wasn’t happening.

In the earliest period, the writers directed the blame toward enterprise culture, claiming that companies simply were not developing a service-centric culture. They might well have outsourced major parts of their production requirement, but they were not shifting resources into creating the type of service value that would protect them.

A decade later, the problem was the same, but the writing community had shifted blame. Now, it fingered the employee as the culprit—employees were not developing a customer-centric culture. They were not buying into the change.

By 2004, the boom years, failings were laid at the feet of CEOs. They were not generating enough profit out of the boom.

And then around 2006/2007, the critical discussion began to change. No surprise, you might say—of course it would. We were entering crisis.

It’s true that a lot of the critical literature began to focus on the dangers of recession, but by no means did all of it. The real focal point was the external environment of the firm. Crisis was an external factor, but so, too, were developments like the rise of China or the more variegated demand schedule of a rapidly developing world, from Africa to India to Latin America and the former Soviet Union.

Critics talked about the rise of competition from those regions, as well. The rise of social marketing was a factor, as was the need to innovate much faster, perhaps to innovate differently, by making greater use of external expertise. The empowered consumer was another much-talked-about external factor. A new infrastructure option was opening up, too—the Cloud-computing infrastructure mentioned earlier. And there were concerns about content piracy as renowned academics began to advocate a creative commons as an alternative to copyright, that is, a freely distributed form of intellectual property as distinct from one that can lie holed up inside the firm’s vaults.

Essentially what happened during this period was a transition in management focus from the internal workings of the firm to the external environment. This reflected a growing recognition that much of the firm’s activities were now taking place outside of the traditional supervisory structure of business.

New realities like Cloud computing, social marketing, open innovation, and changing patterns of demand were all outside the control of the executive team. They were, in a sense, unplannable. Across a range of activities—from how to develop new products to how to sell them—management was losing its traditional levers of control. Because of this, some observers say competitive advantage is now history.10

Clearly, there are additional deeply embedded problems for companies. The writer Steve Denning has repeatedly drawn attention to the fact that companies divert resources to shareholders when they could be making much better use of resources by investing in more creative, customer-centric organizations.11

Denning has made a powerful case for the argument that market forces, as we currently construct them, are forcing companies into damaging short-term decision making. One valid piece of data for this is the amount that companies have returned to shareholders instead of investing in creative solutions. In 2013, according to the Economist, companies on the S&P 500 index spent over $500 billion on share buybacks.12

The pioneers of elastic business models, like Apple, have also been drawn into this process, because they sit on concentrations of wealth that are not being productively invested. There is a clear ambiguity here. There are enterprises that avoid risky investment and manipulate results to make their performances shine. And there are companies that are excelling like never before. The latter is being drawn into the same orbit as the former for the wrong reasons. Companies that have made the shift to elasticity are simply scaling ahead of their peers, and because of their speed, are creating wealth and imbalance. They are investing long-term in infrastructure-as-strategy and eliminating or at least radically reducing the old organizational overhead (which is why WordPress can support 23 percent of worldwide websites with fewer than 200 people). In doing so they are also redefining the purpose and nature of the enterprise, and the structure of the economy. We need to look at how.

CHAPTER 3

New Ways to Create Wealth

TRADING UP

The new, elastic enterprise operating model is a combination of advances in how we organize to create wealth. Its most dramatic examples originated in Alibiba, Apple, Tencent, Google, and Amazon, but the model has taken a variety of forms, each of them drawing on their individual influences.

The dynamics that helped Apple and Google grow and made Amazon a colossus are evident in the financial sector and are emerging in medical diagnostics and prosthetics, hardware, product design and manufacturing, and bioengineering. Expedia has taken this model into travel, GE is taking it into medicine and industrial data, Autodesk is incorporating it into design and manufacturing, and American Express has done the same with finance. Oxford University Press is one of the oldest publishers in the world, part of one of the oldest educational institutions in the world. Their business model? The elastic enterprise.

Implicit in the elastic enterprise is something absolutely crucial to the new way of business—a much cheaper way of organizing people and a more efficient way of allocating resources for a platform owner (we have to reserve judgment on its efficiencies within the ecosystem).

This new system is characterized by the principles of self-organization and self-determination. Surreptitiously, these principles shift the burden and risk of innovation onto distributed groups outside the enterprise. In turn, that creates an anomaly. Power and wealth are concentrated in the hands of the major platform owners like Apple, but innovation and the power of disruption are passed back to the commons, as is most visible in the case of Bitcoin and Ripple.

Yet platforms and ecosystems all differ substantially. The Apple ecosystem is not the same as Google’s or Expedia’s or any other ecosystem. Complex human dynamics define the culture and outcomes. But what is clear is that conventional management levers cannot control these ecosystems. They can influence them profoundly, but members of an ecosystem are not employees. They are free agents and we have a lot to learn about how and if they can be influenced to do anything at a predictable degree.

In all its forms, the elastic enterprise model’s most defining characteristic is the ability to fuel growth at an unprecedented rate, relative to the cost of scale (the new scale economics).

Figure 3.1: Elements of an elastic economy

In an earlier diagram (Figure 2.1), we saw how enterprise structure maps to the process of taking a product from invention or design to market and after-sales support.

In the shift, the elastic enterprise reflects the dynamics that are shaping the economy as a whole. Because there is less enclosure of transactions, i.e., more externalization outside the enterprise, a description of the new firm is really a description of new economic structures, global economic connections, stronger economic actors and the management of relationships.

The key organizing dynamics of the elastic enterprise are those on the left of Figure 3.1: platform, ecosystem, connectors, and the Cloud. These are essentially bridges that link the enterprise with different parts of the economic system. These will be discussed in more detail in later chapters, but briefly, these are both the key disruptors and the major dynamics of a new economy:

Platforms are the constellation of software, business rules, and enabling technologies and services that allows a company to control relationships and transactions of highly scaled populations.

The Ecosystem is composed of third-party relationships that gather around a platform, which becomes a productive, innovative community, often investing resources at its own risk, and usually with a high degree of self-determination. The ecosystem owes its existence to the open-source movement and the commons and is frequently part of a larger common pool resource. As more innovation responsibility is devolved onto the commons, the commons becomes more disruptive, easily generates and popularizes radical new ideas, and finds the mechanisms for their distribution. It is in this sense that disruption is now becoming systemic.

The Cloud is the storage, computing, and service framework that enables the rapid deployment of new business propositions and services at unprecedented scale and velocity. It has wide repercussions on business models but can easily be replicated or replaced by peer-to-peer, distributed models that propagate commons-derived innovations (like Bitcoin) ultrafast. So while it is a key dynamic, it also lives in tension with alternative systems.

Connectors are the technologies that make business assets of all kinds available for sharing and hence for assembly into new services in the Cloud. Here’s an example of these factors at work.

Like many large civil engineering companies, global construction giant Bechtel works increasingly in distant geographies (e.g., Australia, the Middle East, and China) and from the mid-2000s onward has been replacing retiring US-based employees with employees based around the world.

To manage this transition to being truly global while running forty major projects a year, Bechtel has a platform and ecosystem approach. It has reengineered its IT around an internal Cloud system and has converted many of its legacy systems to more modern apps. The apps give users real-time access to its internal data. Since the iPad became available, Bechtel has used the iPad as a kind of onsite apps repository. Employees on site get an iPad preloaded with all the apps they need to do their job—but with only the apps they need to do their job. Bechtel called it the Project Services Network, and it was one of the first times a large company had adopted a consumerized approach to IT.1

Since those early days, an increasing number of companies have deployed platforms externally to manage key aspects of the business environment. Expedia is a great example of the platform deployed in support of marketing. Until 2010, Expedia followed marketing practices that were common to companies born in the web era: they had an affiliate network. Content producers were encouraged to put an Expedia logo on their site. If a consumer visited, for example, a blog with an Expedia logo on it and clicked through to the Expedia site to buy a travel product, the blog owner would get a commission from the sale.

In 2010, though, Expedia began offering its affiliates the opportunity to run their own travel websites with Expedia as the transaction engine behind them. Its own site was reengineered to become a platform. To do this, it opened APIs to the platform, which allowed the affiliates to write code connecting their sites with Expedia’s travel information and transaction processing capability. Before long, 8,000 businesses were running on the Expedia platform via the API.

In Figure 3.1, the API is called a “connector” technology. It is under this umbrella because APIs are not the only way to automate business relationships. RSS, or really simple syndication, is another. So, too, is identity management, that is, the automated logging in to a website through third-party approval (like Facebook or Twitter).

So as part of an elastic enterprise, we have platforms that traverse the boundary between the interior and exterior, connectors that help build automated but tightly coupled business partnerships, and we have the Cloud. But we also have the ecosystem.

Natural ecosystems are highly efficient as long as they are diverse. Human ecosystems are playing an increasingly important role in scaling businesses, the most dramatic example being Apple’s ecosystem of developers (500,000 strong). Expedia’s, at 8,000, is impressive, too.

It would be wrong to suggest we understand ecosystems fully or perfectly define terms like “platform” and “ecosystem.” People use the term platform in different ways, and when they talk about an ecosystem, they often mean “community.” I will bring more clarity and business perspective to the terms, but for now, let’s just focus on their importance.

The platform and ecosystem structure is becoming more and more widespread, even without many companies having a full understanding of the business mechanisms. In the middle of Figure 3.1, you can see four more terms that need further explanation: externalization, federation, radical adjacency, and constant connection. Each of these is a new way of looking at business. Underneath these is one element that people struggle to understand—the information layer. Below that are new metrics and leadership roles, subjects for part III.

THE INFORMATION LAYER

All business activity, as well as all reputation building, needs information in circulation. That role used to fall to newspapers. Now it is largely carried out on the web and in conferences. Information has proliferated and yet people have limited attention. Many websites are very swarm-like—they write about the same issues and compete to gain attention from sites that can catapult their readership numbers like Google News, Yahoo News and search engines.

In the past business critical information was also generated by industry specific newsletters sponsored by an anchor company—for example, Philips sponsored material on CD-ROMs when it pioneered CD storage.

Over time the information layer passed from websites like CNET and ZDNet in the 1990s and early 2000s to start-up blogger sites like TechCrunch, around 2005. TechCrunch was successful at creating more attention for itself and became the start-up bible, whereas start-up content sites like Gigaom, ReadWriteWeb and TheNextWeb, claimed the wider tech developer scene, and Techmeme became a very popular aggregator of technology stories.

In parallel, the blogger community grew substantially and later migrated to social networks like Facebook and Google+. Post-2010 websites like The Verge and AllThingsD emerged. Right now the baton seems to be passing to sites that help writers syndicate material among each others’ audiences (medium.com). Alongside all this there have been many changes in the wider business press (Forbes becoming a general purpose website with 1200 outside contributors; The Huffington Post, Business Insider emerging, etc.). All of these represent the information layer and it is always changing.

The information layer also consists of the relationships that exist between ad hoc groups and information.

Figure 3.2: A comparison of two information layers in finance

Figure 3.2. shows how information and people connect around two distinct areas of finance. On the left, is the information layer around cash management, treasury management and trade finance; on the right is the information layer around crypto-currencies. It is clear that the latter is much more dynamic and intense (compare the number of interconnection at 75,600 with those on the left at 933) and hence, probably, more prone to disruption.

Being able to use the information layer skillfully is the best chance a company has of expanding its word-of-mouth marketing and its market competitveness but an intense inforati layer also signals a likelihood of profund change. Sometime in 2013, this activity became known as “growth hacking.”2 Growth is not the sole objective of performing well in the information layer but often the best exponents are start-ups looking for high growth, or individuals seeking higher reputation. They set the bar for word-of-mouth marketing and their techniques are worth following.

NEW CONCEPTUAL APPROACHES

Externalization—all instances of a company conducting its business through external agencies, including outsourcing, structured supply chains, social media advocates, software or platform as a service, Cloud computing, crowdsourcing or crowd labor, external design agencies, open-source software, open-source hardware and design, creative commons, and so on.

Federation—businesses are now highly externalized, but they are often also “federated.” Take as an example the Guarulhos International Airport (GRU) in Sao Paolo, Brazil. Sao Paolo is a typical modern megacity, difficult to reach by any transportation route, overcrowded, and despite what systems thinkers believe, inefficient in many senses.

The airport, on the other hand, is a greenfield site with opportunities to grow. In view of Sao Paolo’s downtown congestion, the government privatized the airport under a twenty-year contract. This comes at a time of rapid growth in air travel in the region and also intense urbanization in the major cities. GRU is seizing the opportunity to take on many city attributes. As well as increasing the size of its malls, it is building conference and tourism facilities. The airport is attempting to become the platform for a wide variety of businesses, including car rental, car parking, business services, currency exchange and ATMs, security, Internet access, conference organization, hotels, office rental, restaurants, baggage handling, and, of course, air traffic.

Normally an airport would just be a rentier collecting monthly paychecks from its concessions, but at GRU, the ambition is to help the airport tenants cooperate more, cross-promote, and seek mutual opportunities.

As a platform partner to all of the businesses at the airport city, GRU has a complex billing issue—much of its revenue is predicated on revenue share. The more people it can attract into the airport city, the better for the vendors. The better the performance of the concessions, the more revenue goes to the airport. It needs to expand its airport city partners very quickly; it has limited time to prove its model under a twenty year license to operate. In this situation, GRU has created more of a federation than an ecosystem.3

In contrast, in an ecosystem there would be some active building of the airport’s core business by its partners. Ecosystems tend to be co-productive around a technology, device, or project, whereas federations are quickly collated partnerships with common objectives without the need to actively cooperate. As another example, banks typically have federated websites that are dominated by the bank logo and branding but rely on a variety of third-party services to invisibly provide much of the site functionality.

Radical adjacencies—the key tendency of elastic enterprises is to move quickly into adjacent markets. Many companies make bold strategic moves when they have a solid platform foundation—think of Apple moving into mobile phones or Google moving into cars. It seems that the new ability to organize interactions at scale, based around the four organizing dynamics, allows companies to accomplish what is generally regarded as one of the most difficult business strategies of all—moving into adjacent markets where an executive team has limited knowledge, experience, and skills.

Business case studies are full of attempts to create successful adjacencies: UK cement maker Blue Circle Cement’s ill-fated move into lawn mowers, Cisco’s defunct move into video cameras (Flip), Sears and Roebuck’s dilutive attempt at retail finance and real estate with the acquisitions of Dean Witter and Coldwell Banker, and Kodak’s unlikely purchase of Sterling Drug. A five-year study of adjacencies by Bain and Company of 1,850 companies showed that only 13 percent achieved what Bain called “even a modest level of sustained and profitable growth from adjacencies.”4

But look at Expedia. It now rents its platform to its competitor Travelocity. Look at the Guardian newspaper extending into product areas like search. Forbes is moving into book publishing and conference organizing. Google and Amazon are moving into anything and everything. Even Nike became, briefly, a hardware producer with FuelBand.

Naturally, to make a success of radical adjacency, these companies must either hire executives with expertise in new areas or quickly forge new partnerships. But every company plotting adjacency moves can hire. Most adjacencies fail due to cultural misalignment. The company’s culture simply is unable to cope with a new market or geography.

Unlike so many, Apple is able to succeed in this area because its culture has been reframed by its leaders to make radical adjacency seem natural. And its core competency has shifted out of computing into the development and management of highly scaled interaction, as well as into design and retail. Amazon, too, has been a poster child for radical adjacency.

Constant connection—many companies are seeking constant connection with customers to replace old customer relationship management. Constant connection enables a totally new range of innovation activities around data and automated innovation; even companies whose history lies in business-to-business activity, like chip-maker Intel, are now chasing direct, end-consumer relationships so that they can collate data and gain insight into the needs of users.

The ability to maintain continuous connection with customers is an entirely new opportunity, one best illustrated by the actions of Autodesk. In 2012, Autodesk moved away from selling software to leasing it. That was a good decision even from a conventional point of view, but a brilliant decision from a platform standpoint.

On a conventional analysis, Autodesk now has recurring revenues tipping into its bank accounts each month without requiring the usual efforts involved in making new sales. More importantly, it now has continuous insight into how its products are used. By providing its software as a service in the Cloud, it logs all instances of use. That means it has instant feedback on the parts of its software that work well and those that don’t. With this feedback, it can plan its updates more effectively and also deliver them instantly.

Amar Hanspal, the senior vice president of Autodesk responsible for platform development, summarizes the advantages of this continuous connection:

How you connect is transformed. You know how customers are succeeding with your product. You don’t go out every six months and interview them. … You have direct privacy, security, and success insight every morning, and that goes well beyond what CRM [customer relationship management] gave us. How customers interact with us is becoming much more immediate and more continuous.5

On a related issue, Autodesk now has 120 million consumer users, in contrast to twelve million business users. Impressively, that consumer audience has grown over the span of only three years. According to Hanspal, that growth has come in part through acquiring companies or networks like the MIT spin-off Instructables. Autodesk has partnered with LEGO to provide consumers with software for LEGO robot design. And they have a community of 6.5 million students and teachers in education. These all represent an attempt to build on and with the maker community.

By having constant connection and securing continuous feedback, companies are now able to provide continuous delivery and updates of their products.

Finally, on the right-hand side of Figure 3.1 are some of the major business model effects of the elastic enterprise. Here’s a quick summary of each effect:

NEW BUSINESS MODELS

Utility value. Elastic enterprises are likely to adopt a utility position in their markets. The term “utility” often holds bad connotations in business—it smacks of businesses that function with low margins and very low intellectual dynamics, things like gas, electricity, and water. But modern utilities, companies like Apple, Google, Alibaba, Expedia, and Autodesk, are in fact creating a new business infrastructure and enabling business activity. Their power comes from their enabling role.

In this context, the idea of the utility is very powerful—it reflects the control these companies exert over hubs for the creation of end-user utility. These elastic enterprises are intellectually adventurous and design-centric. Their role and (often substantial) profit come from being an essential cog in an entirely new wheel. They find that being a utility protects them from competition. It can be, as Google knows, a coveted position to achieve.

Google is a search utility, Facebook a relationship utility, and Android a platform utility for producing and selling smartphones and phone apps.

Being the utility takes these companies into safe territory. They become quasi-monopolistic. To get there, they may need to be radical with price (after all, Google gives Android away, just like it gives away search), and they need to be inventive with their business models. A whole new generation of thinking has arisen lately around how to do business-model innovation in utility businesses.

They will often be beneficiaries of network effects (like Uber and Airbnb, utilities in rides and room renting, respectively), or they will achieve such mastery of their platforms that they take on utility roles for competitors (as Expedia has done for Travelocity). Utility is a strong competitive position in the elastic economy and should be an aspiration for executives.

Mass differentiation. The corollary of scale is that elastic organizations can and do serve micro-markets. Unlike in the past, they are not scaling to serve mass markets through mass media communications. Their operating principles allow them to grow through mass differentiation; they are able to serve the narrowest requirements in large global markets—that is, serve on mass and micro levels at the same time.

Mass differentiation is a new phenomenon that acts as a counterpoint to mass customization. In mass customization systems, companies like Nike and Adidas create products that consumers can then customize. In mass differentiation, the manufacturer takes responsibility for serving multiple niches with the same basic platform that is then varied extensively through hardware and software, such as apps.

Google, for example, had over 2,000 apps lined up for the launch of Google Glass. No single customer wants all 2,000, but many millions may want some variation of the apps offerings. The smartphone sector has done a lot to introduce this idea of mass differentiation and a portfolio of offerings. This phenomenon is so profound that we have yet to grasp its full implications.

To a significant extent, it is a result of globalization (and, of course, global consumer empowerment). Global business is hypercompetitive, fueled by fast, new, developing markets and production centers like China. And global markets are a major source of enterprise growth.

To be successful in this environment, an enterprise needs to have global information and global relationship-building capabilities. But that global market is fragmenting at the consumer level into many thousands of micro-markets or hyper-channels. And it is happening fast across the globe, pushing the demand for mass differentiation.

Indirect revenues. In 2009, Chris Anderson made a splash with his book Free: The Future of a Radical Price. Anderson makes the point that the giveaway has a proud history and probably a glorious future as more companies go in search of the “network effect.” Amazon and smartphone maker Xiaomi are both known to give product away at or around cost price. Google does something similar with its tablet devices. Actually, a version of “free” is becoming easier to justify as company CAPEX expenditure heads downward due to the Cloud, software as a service, and platform as a service.

But that creates all kinds of pressures, especially for hardware companies that cannot very easily give products away. At consumer lifestyle giant Philips, the digital innovation department has been experimenting with connected cooking and lighting products, for example a connected cooker they want to build a food community around.

The problem with this, as relayed by head of digital innovation Alberto Prado, is that the company is founded on excellence in hardware engineering and innovation.6 Yet, suddenly, leaders are telling those engineers that the company will not make a profit margin on their devices because it wants to grow its revenues from soft skills like community.

The conflicts that ensue are the effects of a growing need to create direct connections with customers and the opportunity for lifetime revenues that go with it. Philips’s aim is to own a direct relationship and reap the benefits that an Autodesk or a Netflix reaps. The challenge lies not only in learning how to develop community and harnessing it for indirect revenues, but it also lies in managing the cultural conflict that goes with the shift to indirect revenues.

Scope: Modern platform businesses have created a new type of scope economies that fit the principle of mass differentiation. The purpose of scope is to create as many versions of a product or service as satisfies the optimum range of customer needs. Smartphones do this superbly well by delegating apps innovation to the ecosystem and proliferating options for consumers.

Hardware-centric companies are doing something similar. Samsung’s decision to produce five variants of its flagship phone the Galaxy S5, alongside the Note series, gives it a broad range of products and margin exploitation opportunities. It is not only that the five variants segment the market; the variants themselves have sub-variants with different processor and display components that allow Samsung to vary the cost of a product and deliver it at different price points, depending on the geographical location of the customer. These are extreme economies of scope made possible by Samsung’s unique position as a manufacturer of various components—batteries, chips, displays—and its ability to source alternatives to its own products.

Velocity. Velocity refers to the extraordinary change that software companies bring to product development (sometimes offering updates by the hour). The continuous connection that modern companies maintain with customers gives them a unique ability to modify products to suit the types of users they are serving. In the old model of business, customer connection fell under the rubric of loyalty programs or customer relationship management.

At the time of writing, many of the early proponents of these systems are actually downgrading them or not enjoying much success: At Delta and United Airlines, the value of an air mile was recently halved. Qantas, the Australian airline, has done the same.7 Tesco, the UK retailer most closely associated with a highly successful loyalty scheme (the majority of the adult British population participate in it), saw profits slump in 2014 and its shares hit a ten-year low.8 As an aside, Club Card points and AirMiles are, in effect, currencies—the fraudulent website Silk Road trafficked Club Card points.9

The problem for many of these companies is that they haven’t or can’t embrace the idea of constant connection. Maintaining a continuous connection with customers allows companies like Netflix and Autodesk to continuously modify services before problems arise. It is a different use of the customer relationship. Continuous connection allows companies to compete through the sheer velocity of the moves they make in the market. They update their products continuously, but they also adapt their internal workflow so they can experiment and test what might work well with customers. All of this is happening very close to real time.

Old loyalty schemes will live on as big data and will no doubt continue to yield some value. To best address the evolving new personal data environment, companies must be ahead of the customers’ needs.10 They have to think of velocity as a key element of their business model.

Taken together, these dynamics, concepts and business models make up the building blocks of the emerging economy. They are not necessarily easy to understand, nor is it readily apparent what skills are needed to make them work. We should engage leaders in a deep discussion of the disruption landscape because we are facing change on an unprecedented scale and at unprecedented speed. Before looking for answers, we need to understand the choices better. What does each of these dynamics imply?

SHIFT II: The Enterprise

Introduction

In the elastic economy, companies need to shift their processes to create flexibility and durability. They need to meld an ability to make internal structural changes with an ability to address markets in new ways, often through highly scaled relationships built around platforms. At times that process means sacrificing immediate profitability for longevity, the opportunity to serve shareholders over a long period of time.

Executives know they face disruption but they see it in terms of threats to short-term revenue rather than the obligation to change how they do business. IBM’s 2013 Global C-suite Study found that two out of five CEOs expected their industry to be disrupted by outsiders (creating radical adjacencies).1 In this context, though, what is meant by disruption and how does the elastic approach help address it? A contemporaneous Accenture analysis revealed that C-suite executives felt their companies were disrupted when as little as 2–3 percent of revenues were under threat, in any line of business.2

This perception is understandable when you consider the long-term effects of management by rationalization. Looking at sectors like banking it is clear that margin erosion is underway. Alternative banking services (like cheap currency exchange Currencyfair.com) don’t kill banks; they simply eat at the margins. If that sounds like more of a nuisance than a serious problem, consider that Nokia was still the world’s largest maker of phones (even of smartphones) when its phone business entered its terminal decline (some say at the hands of Apple, but Google was the real stalker). Companies feel threatened by start-ups because margin erosion can have devastating effects.

However, those same companies need to develop many more new products to meet changing market conditions, even though they are clearly operating on such tight margins that they feel threatened by a 2 percent loss of revenue.

In a 2014 study of the financial industry, my research team and I found that at least half the institutions we studied had the capacity to transform to a more elastic model.3 Outside the top ten (out of sixty institutions), however, they lacked the leadership capabilities to make those changes. The leadership capabilities were often down to know-how, simply awareness of the types of changes needed.

The strategy debate, however, gets stuck at another layer. IBM’s C-suite Study showed that CEOs see technology as their biggest challenge.

It is almost naïve to focus primarily on technology. It is also clear, however, that the decision-making capacity of leaders is often inadequate because they do not understand the range of choices they face. There are usually serious internal relationship problems too. In almost every organization, there are fundamental relationship challenges between IT leaders and marketers, IT and strategy, or between the organization and the changing needs of customers. These need to be ironed out. The biggest threat to an organization can arise from not reconciling IT-business relationships and from being unable to manage relationships at scale, in the way that social companies do.

Born-social companies get relationships right because they often emerge from cultures, like open-source software, where meritocratic guidelines already apply. They also have the example of companies like Google where responsibility is highly delegated. Google gives managers a minimum of thirty direct reports to prevent them from micromanaging.4 The born-social culture also tends to rely on peer leadership or the belief that a good leader must be a first among equals.

Large companies however have responded to the need for process change before. Supply chain management, the business revolution of the 1980s and 1990s, created new corporate structures and a new context for customers.5 With a good supply chain management strategy, companies held next-to-no inventory; they reduced their CAPEX and R&D spending by devolving these to tiered supplier relationships (or production ecosystems). Supply chain management allowed executives to focus on a smaller range of core tasks. In theory, it also improved responsiveness to customers.6

Today’s customers, however, require scope, or choice, from companies, often through a significant range of ready-made applications or content. Scope refers in this case to the range of utility needs it can satisfy—many single products are a portfolio of services (think of the iPhone as an example). Enterprises require a new generation of restructuring to meet these requirements.

There is something else at work that executives are aware of but pay too little attention to. Success or failure also stems from being able to create enough interaction with users in global markets, in part to make a new product big news, but also to engage with users as equals. Around the major structural changes we are about to look at in this section, companies also need to become more peer-like. Their product portfolios are increasingly going to be defined by customer relationships. While constant connection is important, so is information and awareness.

This relies on managing the information layer around a product or brand properly: engaging tech bloggers, social network users, newspapers, YouTube visitors, and the like with compelling content and vision for the platform. In fact, the information layer, or consumer awareness of new possibilities, is a key factor in disruption, and yet most companies ignore the process of seeding interest in their brands and companies well ahead of any change.

There are two essential elements to doing this.

The first is being profoundly social. Companies need to belong in the social space to a degree few have yet realized. (Even Apple is still growing here, as shown by CEO Tim Cook’s recent arrival on platforms like Twitter.)

The second is to have a wide-enough range of products or apps (through mass differentiation) to pick up all kinds of micro-markets. Increasingly, it also means being able to launch a product as part of a wider family of goods, services, and software.

Companies struggle to deal with economies of scope. In the ailing consumer electronics industry, virtually every major company has a bench full of new products it is not launching. It is par for the course. They are anxious to avoid launching a product that becomes a high-profile failure, but they have few insights into customer demand that will prevent such an occurrence, precisely because demand is generated in social environments, often online. Demand can scale quickly (virally) or it can fizzle. But it can rarely be affected just by budgets. Successful products are often those that develop attention even before launch, which means the product owner has to have strong social connections.

There are now many new social mechanisms for developing that front end of attention to cultivate the information layer of a product. These include making use of crowdfunding sites that replace investment with cash gifts (Pebble Watch, a wrist-wearable computing device, secured 88,000 online mentions within forty-eight hours of launching on Kickstarter); buying into ecosystems (Autodesk’s acquisitions have helped it secure a consumer ecosystem of 120 million users); developing significant content development (Red Bull now thinks of itself as a media firm); and giving product away or selling at near cost.

In the past, a company could expect to launch a high-price version of a new product and cascade down the market as it reduced the price. Very few companies can still work with a product-only cascade model. Today, companies often don’t know what to launch from their R&D portfolios, but they are too hidebound by old practices to use social mechanisms like crowdfunding to find out. They stand aloof from the customer base. Not all companies reject these mechanisms: in contrast, the Google Glass project is wholly crowdfunded.

In other cases, companies are stepping out of their areas of expertise to launch radical new products that demand their moment in the spotlight. For example, a range of companies has now tried to launch heads-up displays (like Google Glass), including printer maker Epson. (You might want to pinch yourself—that’s a printer company competing at the leading edge of wearables.) Yet, it is almost impossible for a company like Epson to saturate the information layer with just one innovation when Apple and Samsung both boast a scope of relevant products spanning software, services, and hardware that are all very well known.

Once again, a fundamental change is taking place here, making information and its associated relationships a major source of new competitive advantage. Jeff Immelt, CEO of General Electric (GE), is one of the leaders recognizing this change. Immelt heads one of the largest industrial concerns in the world, with 300,000 employees and $150 billion in long-term service contracts. Despite being “old industry,” he has pushed GE into new areas like social media, crowdsourcing, platforms, APIs, and data. Immelt has also opened up a Silicon Valley center of excellence where he is tempting data scientists and social media experts to work on the future of GE. All of this is an attempt to transform GE into a modern, relationship-based elastic enterprise.

To compete, GE has to innovate in turbines, health care, lighting, and manufacturing. Yet in all these areas, GE also needs transformation. Its competitive metrics remain important, but for the company to survive, it also needs to become something else. That means leaders like Immelt need a transformation strategy alongside their competitive strategy. They need a new operating model like the elastic enterprise.

What does this mean for GE? By thinking of the product as a platform, GE can generate new services for its customers, often from the data and processes that already exist and certainly from data they can generate by interacting with customers and the ecosystem. But in this process, new relationships have to evolve, particularly with smaller companies. As a step in this direction, GE’s industrial data platform has a set of APIs that open up the data to the start-up and specialist data community. This one move alone changes the product development cycle.

The simple view of product portfolio development in the past was that growth products became a “cash cow” that provided all the resources needed for product innovation. One cash cow gave the enterprise opportunities to work on several new products, which taken together, made up the new product pipeline.

While portfolio management was never quite this simple, it’s even less so today. It is complicated by the need for companies to generate many more new products and services. The significant change is to create relationships that can solve the product scope problem. In the process, companies can also create the conditions for long-term durability of the firm by being as responsive as possible within the overall constraints of the business.

The big issue is, what are the real constraints? When Google developed Google Glass, it did so entirely at the expense of a user community prepared to pay $1,500 each to be Glass Explorers. They pushed the cost of product portfolio expansion onto lead customers.

Executives need to explore just how much scope their customer and ecosystem relationships give them for expanding product portfolios rapidly while creating sufficient information. The structural changes we are now going to discuss are based on the enterprise working equally hard on its long-term durability through real customer relationships and portfolio expansion.

CHAPTER 4

The Platform for Disruption

THE CHARACTERISTICS OF THE NEW BUSINESS PLATFORM

Platforms have been around for most of the life of modern IT, going all the way back to the IBM mainframe. SharePoint is a platform. So are the enterprise resource-planning systems, sold by vendors like SAP and Oracle, which companies use to log all their transactions. But these new platforms are specifically IT-centric platforms, an IT model for how to manage transactions. The business platform is where IT and business come together, the focal point for where the old administrative IT becomes an enabler of new forms of business.1

The change has been described as a transition from systems of record to systems of engagement.2 The engagement model refers to platforms like social business. These are platforms that allow employees to communicate more naturally and effectively. However, this idea of platform is quite narrow and applies to internal changes within the enterprise.

In the 2000s, the platform came to mean a technology platform that helped power innovation across an industry. For example, Intel is exactly that kind of animal. It was a platform for innovation across the software and appliances sectors. Its challenge for many years was having a technology where innovation occurred very rapidly—processing power doubled every eighteen months—and a dependent community (software developers and device makers) who might not feel compelled to radically update their products at that level of frequency. In the second decade of the 21st century, Intel has paid far more attention to stimulating new demand among end customers but for most of its life it relied heavily on encouraging innovation within its partner ecosystem.3

Another slant on platforms is that they are primarily about a core software stack that gives developers access to data, or operating systems, through which they can create new products. This is the version best understood in the tech sector. This is another definition that limits our understanding of a platform’s scope. Platforms offer more than connection.

Companies such as Uber and Airbnb are not successful because they have APIs. They barely engage developers in their platforms at this stage. Netflix had an open API but reverted to closed relationships once it had achieved what it wanted to by being open. These companies succeed because they create utility value.

Platforms that create utility value allow other parties both to create and to consume preferences. These “preferences” are not necessarily products or services. They can be things like reputation or kudos. Klout, for example, aggregates social activity across the web and gives people a kind of sociability score. In creating that reputation value, Klout also opens up the opportunity for many more businesses to create reputation-based services. It has created utility; it has enabled further business activity and consumption options. Reputation plays a big role in a platform like Airbnb.

Utility value, or preferences, can be product, service, reputation, connection, or any value that satisfies a sentient need.

While the new platform can be a system of engagement and a creator of utility value, platforms used by elastic enterprises are also much more. They have an additional set of unique qualities. These platforms:

• are a hub for ecosystem or federated business activity; they themselves can often be incorporated into another business platform (such as iOS and Android being deployed in car platforms and home platforms)

• have a strong dependency on self-determining groups like developer communities

• create economies of scale and scope in new ways

• have unique assets they can open up to create new utility value

• define clear parameters for building and sustaining trust

• are designed to be extensible

• often have strong, usually seamless, transaction engines

• have a preference for automation

• change the rules of partnership—partnerships become automated, low friction extensions of a core business

• are systems of engagement and build strong communities of advocates, not only among members but also in the press and blogosphere by providing leadership and rules of the road

• change how we create and consume value

The primary objective of the new business platform is to create, manage, and transact utility value across diverse, and highly-scaled, communities. It is only through platforms like this that enterprises can manage the complexity of vast ecosystems of partners and an extraordinarily large customer base that typically has widely divergent needs. It is distinct from the Intel platform approach or that of ARM.

The literature on platforms is often confused by a related discussion. There is an underlying assumption that platforms create what are called network effects.4 (See Chapter 5 for a more detailed discussion.)

A network effect happens when a service gets its value because it takes two or more people to use it—like the telephone. This imperative for the device or service to spread, increasing utility value as it goes, has a dramatic effect on sales.

However, the platform and ecosystem model does not rely on network effects. It relies on giving people the opportunity to create and consume utility value and on the distribution power of online groups like open-source communities.

In short, the business platform becomes a critical unifying mechanism for the strategies and operations of a broad base of economic actors that now includes customers. It is a medium for creating utility value, and it performs this role by bringing people together at scale.

The current generation of platforms is increasingly relying on trust, which in turn makes the content layer more important. This is not trust that is bought through the marketing budget. This is trust as an integral part of platform use and it is created by all parties to the service. What makes platforms like Airbnb and Uber possible is that trust is a multiparty unwritten contract developed on the platform. This is the new customer relationship management, brokering trust between many parties in a community of transactions.

In the background, Airbnb develops architectures to mitigate the risk of a dangerous encounter or of fraud. Lending sites and Kickstarter all have the same responsibility, to engage people in activities that entail risk but are made safe by high trust levels. But this is a multi-sided relationship. It is unlike a customer trusting a bank with her money. While the platform bears a responsibility for trust building, so, too, do all the users on it. Trust is developed in the ubiquity of relationships and the common will to make the service happen.

In many ways the platform becomes a global meeting forum for ecosystems on both sides of the supply and demand chain. That might be its most ingenious characteristic in phase one of its development. But in phase two, companies are going to find it indispensable as an organizing hub.

For example, Apple is continuing to extend its major partner networks. Apple’s platform is integrated with its telecommunications carriers’ partner platforms and with inventory systems throughout the supply chain. It automates many of the business processes between these parties. The company is now established in autos and is moving into home and health services and enterprise software, with partnerships in hand.5 This is a huge range of economic activity, all happening as the company gears up for new product launches, a major operating system upgrade, expansion of its retail chain, and continued registration of hundreds of tech and design patents in areas as diverse as interface design, form factor design, and display technology.

Apple’s platform (the App Store or iTunes) makes this complexity manageable. Apart from being a single hub for focusing employees, it is also as a very powerful “intelligence network” on user preferences and actions. Additionally, it serves as a data acquisition engine for finding usability and technical issues, diagnosing problems and identifying new-use cases with its products, spurring the continuous deployment strategies referenced earlier. On the reverse side of this, it is a powerful connector to all kinds of advocacy channels, including well-visited websites like AppleInsider.com and journalists who actually specialize in covering just Apple.

Some analysts estimate that from when the iPod was introduced in 2001 up to 2012, Apple invested $8–10 billion to develop and implement its business platform. Some analysts have also estimated, based on a review of its published financial information and investments, that it now costs more than $2 billion per year to operate the business platform.

Although these are enormous sums, the company has increased its quarterly profit from $8 million in fiscal Q1 of 2003 to $13.06 billion in fiscal Q1 of 2012 and put nearly $150 billion in cash on the balance sheet since the iTunes business platform was introduced. Not a bad return on investment.

The platform is a forum of social engagement, a source of new utility value, and a hub for orchestrating complex economic activity. The power to coordinate the activities of hundreds of thousands of third-party businesses, across hundreds of millions of customer relationships, in a trusted environment, at a profit, is the crowning achievement of platforms like Alibaba/Alipay, Apple or Google.

THE PLATFORM LANDSCAPE

Business platforms have progressed through four main stages.

At the beginning of the Internet, companies like Fidelity created significant multiparty platforms, where brokers and funds could offer financial products to consumers (through the newly initiated 401(k) retirement system in the United States). The 401(k) brought a flood of cash into the system, from millions of households to brokers and funds, and Fidelity was one of the first to manage that process. This was also the point where open source technologies began to make a commercial impact, notably via Apache, Linux and MySQL. They can’t strictly be described as platforms individually but cumulatively they constituted the platform for the web.

In a second phase, companies like Google and Amazon created a more generic infrastructure to make the World Wide Web a monetizable environment for business—through AdWords, AdSense, and Amazon’s affiliate and Web Services platforms. These were pioneering attempts to create a pervasive e-commerce infrastructure. There was, of course, a generation of payment platforms but Google and Amazon pioneered generic platforms for enabling business. At the same time Salesforce began working with external developers on its Force.com platform marking a distinct turning point where a young company began to take on the might of estbalished ERP vendors such as SAP. Apps scored a victory over systems (Palm had already pioneered apps on hand-held devices).

The third phase has been more mobile-centric and involves companies creating platforms that facilitate the growth of mobile apps, for example Apple and again Google and a host of Apple imitators. The apps phase has been incredibly dynamic and reaches into many sectors of business. It will continue to pressure whole business sectors to change as we have seen with AirbBnb and Uber.

In the fourth phase, the emphasis is shifting once more, and almost unseen, the platform is becoming a disruptive force in the basic infrastructure of finance in part because of the wild card nature of open source cryptocurrencies. We are moving to what we referred to earlier as the Internet of Finance with wide repercussions for how business is transacted and for how financial trust is mediated. Fidelity, for example, is now looking at ways to provide complete wealth management services via its platform through the integration of more third-party suppliers but also possibly integrating BitCoin’s block chain technology. At the same time we see more finance projects and more open source projects.

Figure 4.1: The platform landscape

Figure 4.1 gives a brief history of business platforms between the mid-1990s and 2014. The period from 2002 to 2008 was dominated by Google and Amazon but also included eBay. It’s here that the platform and ecosystem model truly bedded in and that Google began to use it extensively. Apple had launched iTunes in 2001 and then developed its transaction engine in music sales and distribution but without much emphasis on its developer community until 2008, six months after the launch of the iPhone. PayPal, Force.com (the Salesforce.com platform), and Netflix were important additions to the landscape at different points along the way. Although PayPal had been around for a long time, it wasn’t until 2010 that it truly opened up its platform to third-party developers.

After 2008, a run of telecoms and smartphone makers like Nokia, AT&T, Samsung, RIM, and Orange tried to copy Apple—often too late—which was particularly the case with Nokia and its launch of a new cross-device app store, Horizon, in 2009. Meanwhile Google and Android went from strength to strength, gradually dominating mobile sales and advertising, eventually approaching Apple’s success in apps.

The well of initiatives was also broadening between 2008 and 2012 with more content initiatives like Forbes and the Guardian; renewed innovation in finance through Thomson-Reuters, Bloomberg, USAA, and M-Pesa in Africa; innovation in the graphics sphere through Avid and Adobe; data through GE; and the extension of platforms into maker communities with Etsy.

Finally, several horizontal developments, such as Bitcoin, have introduced entirely new and global possibilities in financial platforms, beginning as early as 2009 but really coming into their own from 2013 onward.

Not all of the initiatives have succeeded. Samsung continues to struggle in establishing a position at the heart of an ecosystem, and RIM has faded from the center of the smartphone scene. The general themes are clear, though: the tech titans have provided much of the framework for the platform and ecosystem model, but they were preceded by financial services, which have continued to play a role; the availability of services to assist the development of the new platform-based business process model is increasing; and the range of industries adopting the model is expanding. That’s not all. Platforms offer firms the option to accelerate process innovation by reframing departmental silos as interdisciplinary platforms. Crucially, what we are also beginning to see is that two horizontal platforms—IT/telecoms, and now finance—are going to create even more disruption.

The timeline also suggests a useful typology.

Table 4.1: Types of business platforms

Each of these is a legitimate business platform. Starting at the bottom of Table 4.1, delivery platforms tend not to have multiparty services attached to them. They are designed for embodying and improving the skills base of a service supplier. As a platform, they are latent and their owners can open them up at any time (Netflix could be positioned here).

Two-sided markets are the simple buy-and-sell-type markets represented by companies like Uber and Airbnb in their first phase of development.

Federated platforms don’t serve a two-sided function—they represent companies that come together with a more negotiated relationship to fulfill specific market needs (a bank website that integrates many third-party offers).

Aggregator platforms are relatively new: they are able to steal the customer base of platforms that lack interoperability, like the various music platforms (iTunes Radio, Spotify, Pandora). They aggregate music, in this case, on behalf of the consumer.

Multiparty platforms are those like Fidelity that bring together a wide variety of service providers. In Fidelity’s case that means third party funds and brokers as well as information providers. These platforms set broad-ranging rules of the road, and any legitimate agent willing to comply with the rules can enable or create business on their platforms.

The academic literature on business platforms tends to classify multiparty platforms differently. There, the emphasis is on the benefits to one class of users (say, people who want to rent rooms) of there being a large group on the other side (people who rent rooms). The assumption in economic literature is that the simple fact of scale has a benefit.

Researchers Andrei Hagiu and Julian Wright define a platform as “an organization that creates value primarily by enabling direct interactions between two (or more) distinct types of affiliated customers.”6

That definition seems a little incomplete. Interactions need to create value. The key question is what utility value does scale help to create, and the answer is in its scope—it helps users, the paying side of a platform, to self-select utility from a wide range of choices.

The big disruptors, though, are the horizontal platforms (for example payment platforms that have relevance across many sectors). In this category, we see companies and initiatives that will influence essentially everything. This is partly because they are perfectly aligned with the technological base of modern business: drawing on mobility, exhibiting huge scale, and operating at unique velocity. But it is also a testament to the vision of their leadership. Bitcoin might appear an anomaly here, but that’s simply because it is decentralized. Within four years of its launch, Bitcoin looks like it will disrupt the financial services industry in very fundamental ways.

For the most part, platforms also create different types of utility value. Horizontal platforms are the perfect utility, with apparently no limit to the breadth of value they can provide (visible as Apple and Google move into autos, the home, health, education, the enterprise, and more).

Multiparty platforms have a less profound claim on disruption, but they clearly offer a substantial form of market utility, enabling a wide variety of activity across product, information provision, and service.

Aggregators create social utility, bringing people together around their interests.

Federated platforms are really designed for business velocity, getting new offers to market in the shortest possible time.

Two-sided markets have a special emphasis on the utility value of trust in transactions where people who are party to a transaction have not met, yet need high trust. Delivery platforms have yet to define broad utility or a path to third party enablement. Twitter falls into this category (see Chapter 5).

HOW DO PLATFORMS BECOME DISRUPTIVE?

It is important to have a model that helps executives anticipate the disruptive effects of platforms. It is not enough to say they are caused by digitization or the Internet. That hardly touches on the causal roots. There is also a variety of descriptions about how system-level transitions take place—for example the shift from sail-powered ships to steam. These are useful background for understanding disruption—we are in a similar shift toward mobility, away from static places of work. Within this structure there are distinct moments where the conditions are right for disrupting whole business sectors. There are many contributions to thinking on business disruption. Do they capture the essence of change that we face in the current transformation?

1. In the 1930s, the Russian economist, Kondratieff explained disruption as sixty year cycles (or waves) in which commodity prices become too high for incumbents to sustain business-as-normal and therefore needed radical innovation.

2. Schumpeter, famous for the term creative destruction used this wave theory to suggest that capitalism becomes increasingly corporatist; corporatism would alienate key thinkers because it would produce an economy that made entrepreneurism impossible—this would lead to creative destruction or, in other words, an ideological attack on capitalism.

3. In the 1990s, the American business writer Clayton Christensen described disruption as a process where even good companies could be hoodwinked by smaller companies with low cost products picking off low-end customers, meanwhile gaining experience, resources to broaden and change the basic conditions of the market and customer needs, and compete by changing market structure.7

4. More recently, writers Larry Downes and Paul Nunes have described a new form of disruption that they call Big Bang; one example is Twitter, that began as an experiment and grew into the world’s largest broadcaster of personal information. Big Bang disruptors can be strategically inept and accidental yet still very powerful.8

5. There is a fifth school of thought that has been given less attention, economist Steven Klepper’s work on firm survival and new entrants to a market sector.9

Let’s look at Klepper’s ideas in more depth.

Klepper found that firms, and the sectors they work in, tend toward oligopoly. The modern problem of oligopoly is overlooked—Hemphill and Wu point out that its main effect is parallel exclusion (policies that keep competitors out of the business area).10 Klepper made that observation about businesses historically—they gradually reduce the number of competitors through merger and acquisition (M&A) and become one of a smaller group of survivors. Once in this position they will continue to survive for as long as they keep the barriers to market entry high, through raising the industry-level of investment in R&D, and maintaining strong returns to the investment community that funds innovation. These oligopolies fall short of being a cartel, but they can have cartel-like characteristics, such as implicit price agreements and even price fixing (as happened in airline competition) or exchange rate fixing in banking.

Oligopolies, however, can be their own undoing. The complex decision processes at the senior level of these very large firms make it difficult to respond to competitive pressure from outside the oligopoly group. Those moments where everybody knows there is a better way but the pattern of tacit agreements with competitors, coupled to political infighting among institutional owners and high profile leaders, as well as a loss of confidence among a company’s funding institutions (particularly as profit margins slip), can stall important decisions. Such a combination of factors has led to the decline of whole industrial sectors (such as the US tire industry in the 1930s, and more recently, TV and autos), typically in the past under pressure from low-wage, high technology markets (first Japan, then Korea, then China).

Klepper also found that in many instances competition arises from the employee-base of an oligopoly. People denied resources internally, because of decision-making inertia, tend to quit and start their own competitive enterprise, often with better ideas and newer technology, as happened in the US West Coast silicon industry.

What Klepper’s work did not take into account is the possibility that whole industry structures can be transformed in ways that destroy oligopoly power very quickly from within. The industries he studied were marked by serious external competition from what were then developing economies—such as Japan, followed by South Korea.

Drawing on his thinking and Schumpeter’s (the idea of an ideological movement against the main actors in the economy), it is possible to suggest a five-step process that leads to structural disruption that affects all firms in a sector.

1. Concentration and hubris. The consolidation of market structure into an oligopoly, with satisfactory margins, but often accompanied by the growth of hubristic management and parallel exclusion opportunities.11

2. The experimental era. Early horizontal pressure from excluded actors (often employees of existing oligopolists leaving to create their own companies), increasingly drawing on open-source technologies and work principles that democratize access to an industry (this is especially powerful in the age of Cloud computing when capital is not a barrier to entry in many industries).

3. The new content layer. The growth of awareness, or a new content layer or changed information market, as consumers experience alternatives to oligopoly offers, often as co-creators or participants; this content layer can also be a battle for mindshare, particularly over which companies are seen as innovative (in a 2013 study of 5,000 companies across eight stock markets Richard Petersen and I found that innovation reputation was strongly correlated with future stock price improvement).12

4. Ecosystem consolidation. The consolidation of a durable start-up community with continuity of personnel and objectives over time, often represented as an ecosystem that takes on innovation risk. This also reflect an increasing externalization of core processes in major companies and the increasing visibility of a new industry structure.

5. Platform. The arrival of a platform company as an organizing hub for a new industry creates severe horizontal pressure and triggers multiple random adjacencies.

The relevance of this model is its ability to predict the point at which industries become very susceptible to very rapid change, or when disruption reaches its tipping point. In many industries, incumbents fail to spot the moment to make radical change. In this model it is clear, when start-up activity becomes evolved enough to organize, and when customer experience has evolved enough to adopt new trends, the opportunity is ripe for a platform company to move in and become the new dominant player. In today’s economy a new factor has emerged. As enterprises become less dependent on investment capital and more on liquidity, access to new payments systems also becomes a disruptor. Liquidity reduces an enterprise’s dependence on the market for capital and cash, allows executives to make more autonomous decisions, and frees it to be more adventurous in its business model choice. quite how liquidity management and new payment and currency system will help restructure the business landscape is not yet clear.

4.2: From a vertical to horizontal structure in telecommunications

The diagram summarizes disruption forces in the IT and telecoms space from the late 1990s onward (see Figure 4.2). Reading the diagram from the left, it says that telecoms was a highly consolidated sector comprising network carriers (AT&T, British Telecom, etc.), network infrastructure suppliers (Ericsson, Nokia Siemens, Alcatel) and device makers (Nokia, Motorola).

The relationship between these organizations was cartel-like. It was almost impossible for a small company to do business with them or to enter the market. Big company talked to big and together they controlled the specification of products, the supply and distribution chain, and the rate of innovation. Arguably those companies also became complacent and, contrary to Christensen’s findings in the disk-storage industry, some element of blame for the demise of, say, Nokia, lies with corporate attitudes becoming hubristic.

The rich cash-flow of the telecoms industry inevitably attracted entrepreneurs who sought opportunity and found themselves repelled by the club-like barriers to entry.

Consolidation also applied to IT. The IT landscape was dominated by Windows and Intel and by large systems providers like Oracle. Their vertical structure was tightest around the server market, the first place that an open-source alternative arrived (Linux).

The entrepreneurial community attempted to bypass Microsoft’s control of computer operating systems (OS) by gravitating to a new type of online device, an Internet appliance (1995-2005) that would need its own OS. This was one route that the highly experimental BeOS tried. Be also tried to offer interoperability between Macs and PCs.

It’s important to note that Be was started by ex-Apple employees. Be employees in turn went on to Android and into the PDA industry where the leading company, Palm, eventually bought Be. Android started its life as an attempt to build an OS for Internet-compatible cameras. There was strong continuity of personnel and ideology across operating systems and devices, and between companies.

Go back to the left of the diagram and you see that as Internet appliances’ entrepreneurs tried to gain traction, there was also a burgeoning and often struggling PDA market. The PDA market had been growing since the late 1990s and by this stage, the mid-2000s, was Internet enabled.

An apps developer ecosystem was also well established. Palm had 50,000 applications before Apple ever thought about having an App Store and the independent app store Handango launched as early as 2002 (at which stage Palm had around 8,000 apps). There was a well worked out information, or content, layer created by these products.

Throughout this time, the open-source software movement was growing in scale and scope, providing a ready supply of highly skilled people experienced in working in highly critical, self-educating and productive teams.

In parallel, established mobile device makers like Nokia had begun making something akin to a smartphone back in 2000, the Nokia Communicator, modeled on successful but niche products like the Psion, made by a small British company that still has its adherents. Nokia developed an interface platform, the S60, built on Psion’s operating system, Symbian. Eventually all Nokia smartphones became Symbian powered.

This was the situation when Apple launched the iPhone in 2007 and when Google launched the first Android phones a year later.

Nokia, the smartphone leader at that time, was producing 450 million handsets (all types of mobile phones) a year. Within four years it would be more or less finished as an independent company after it entered a relationship with Microsoft and switched its OS to Windows, eventually selling off its handset business to Microsoft. That disruption was visited also upon Sony (which by 2014 was contemplating exiting smartphones), Motorola and many other handset makers.

The catalyst for change—applications written for mobile devices—were only manageable at the scale they reached (by now millions of applications) by companies that had a strong platform background, as both Google and Apple had through iTunes, Search and Maps. Their arrival presaged a period of what is now six years now where the rule of management has not been to stick to the core—it has been to seek out the adjacencies. The sector walls are broken down and economic activity is horizontal across industries.

There are many questions around why incumbents like Nokia could not become the platform for the mobile ecosystem, rather than cede to Apple and Google. In fact Nokia tried.

Although it is commonplace to attribute Nokia’s decline to Apple’s success, its profit decline coincided with the launch of Google’s Android in 2008, not with the launch of the iPhone in 2007. The significant drop came between Q3 2008 and Q1 2009, almost two years after the iPhone’s introduction.13 By 2009, Nokia was competing with both Apple and the Android ecosystem but was also struggling to introduce services into its product offering. It began to produce more low-cost phones and effectively commoditized its brand. Is there something in this story, though, that enlightens the field of competitive platform strategy and disruption theory?

PLATFORM AS COMPETITIVE STRATEGY

A remarkable part of Google’s success as a company is its platform strategy around the Android operating system. Bought for $50 million in 2005, Android yields about $1 billion in profits per annum to Google through mobile advertising. When Google made the acquisition, it had no role in hardware or mobile. It was still primarily a search engine and had only just consolidated its position as the market monopolist. Android was a wild card.

The project began life as an open-source attempt to gain traction in Internet appliances, such as cameras; in other words to bypass the desktop monopoly of Windows and create a more open environment for non-PC devices. It was staffed with people from projects like Be, many of whom were ex-Apple employees, or with people who came directly from Apple. This group had an ideological approach to the Internet, believing it to be an open medium. Android began life with staffers who had operating system experience and motivation. They combined a web and mobile-centric view of opportunity and a desire to see a different type of Internet.

When Google acquired Android it could still have been regarded as an odd move. There was momentum toward more computer-like handheld devices but there was no indication that Android could become a mobile operating system, especially the system of choice for major manufacturers.

Google had been experimenting with its own SMS service (providing weather forecasts and location information) and had acquired a company called Dodgeball, a mobile social network service, that was ultimately a failed initiative. A year later Google bought YouTube for $1.9 million and that seemed to signal its future lay in web-based content. Android continued its work in secrecy.

The project only came to life four years after it was founded when Google formed the Open Handset Alliance (OHA) in 2007 (and invited Nokia to join; Nokia refused).

The OHA was made up of some of the biggest names in mobile, which proved that Google had significant convening power. Its OHA partners included giants like global carrier Vodafone; handset makers Motorola, Samsung, LG, and Sony Ericsson (it now includes many more, such as HTC, ZTE, Sharp, Fujitsu, ACER, etc.); and chip makers like Qualcomm and Intel. The idea was to use this federation to promote the use of Android as a free resource. None would be charged for the operating system, offering all of the device makers a significant cost benefit.

Many of these companies also had formal and profitable business relationships with Nokia. In June 2008 Nokia announced it had acquired all the shares in Symbian (its operating system supplier). It then set up the Symbian Foundation in order to open source its OS code, in much the way Android was open source. The Foundation began functioning in April 2009, and offered all other device makers an opportunity for a free open-source operating system.

Nokia also turned its attention to countering Apple’s App Store with a version of its own called Ovi. In fact Nokia announced Ovi in August 2007 and began releasing services in December, but it relied on large third party partners like telecoms operators and services such as Flickr to provide content. It did not become a true platform until July 2009, when it released APIs for its mapping services by which time it has given Apple a year’s start in app development and store marketing. Its concept was more of a web portal than an app store. Ironically, as computer and Internet companies Apple and Google were moving into mobile, Nokia wanted to establish a web presence.14

Now, though, Nokia had a strategy in place. It would build up Internet-based services to compensate for falling device prices; and it would open source its operating system and build a high level ecosystem around it.

During this period the average selling price of mobile phones was going down but Nokia was still a highly successful company, owning a third of the overall mobile market.

Unlike classical cases of disruption, it was not facing threats from start-ups or low cost producers. It faced threats from radical adjacency moves by two large, cash-rich companies who were staking their future on winning Nokia’s business.

The critical elements, though, were the fact that both Apple and Google had platforms that already managed many tens of millions of business relationships and had seamless transaction engines that already paid, in Apple’s case, musicians, and in Google’s, content providers. They brought highly-scaled business platform experience to the fight with Nokia.

In Nokia’s case most of its business was traditionally conducted via telecoms carriers rather than with a large customer population. It had no platform and instead reverted to a portal, a concept that was well-embedded in web culture but was also in decline as the world gravitated toward social networks and Q4.

Yet, for all that, in Q3 2007 Nokia’s mobile phone profitability was soaring as smartphones began to take off and as its global consumer base grew. Profits grew slightly again in Q1 2008 and although they fell back in the next half year, they showed new signs of growth by Q3 2008. Entering 2009, there were few signs of what was to come—a dramatic collapse in profits that far outpaced the drop it experienced in revenues.15

Throughout this two year period, Nokia could be accused of acting slowly. Yet it launched its own app store in 2009. It launched an open-source community for its operating system Symbian (at that time the largest open-source project ever attempted in the sense of moving software from closed to open) and it even had an installed base of some 325 million Symbian phones by 2009. This was more than Apple had three years into the distribution of the iPhone. Nokia also tried high profile alliances like an agreement with Yahoo for email and search service, but Yahoo was in crisis and Nokia really needed to turn to the developer ecosystem, not to major companies that shared some of its own failings.

Nokia struggled despite having convened the same players in the Symbian Foundation that Google had in the OHA (Samsung was a member of both). Android, not Symbian, became the operating system of choice for manufacturers, as well as consumers.

This is how Lee Wiliams, director of the Symbian Foundation, sums it up, reflecting on one large technology tipping point, the arrival of consumer wireless data networks in the United States:

Now the North American market was primed, non-American companies did not have the same level of brand, or product recognition, and they were playing by the rules, the old rules. As an example, at Nokia we were producing products to compete in this space, but while adhering to thousands upon thousands of product requirements that were unique to each (network) operator, we were “not allowed” to reach out to the consumer directly. It was considered a huge violation of the “partnership” we had with the channel owners, the network operators.

Apple and Google were playing on a different playing field and by different rules. They chose which operator requirements they adhered to, they approached the consumer directly, they viewed them as their consumers and they owned that experience. More than that, they were able to extend value by giving the consumers apps.16

Although Android was a manufacturers’ alliance in one sense, in another it can be considered a born-social project that thrived through good ecosystem management. Google was in daily contact with millions of web users and was able to reach out to potential consumers of Android phones, as well as rapidly form the ecosystem of developers from the prehistory of Android, its partner network and the open-source community.

A critical factor in this was the information layer that surrounded each platform—in other words, the information, blogs, and websites that advocated their use. After the launch of the Ovi App Store, the influential technology website TechCrunch described it as a complete disaster.17 Developers were also critical, especially those in Silicon Valley, a group that Nokia failed to win over.18

Google on the other hand became exceptionally good at attracting people to the transformational story of Android. It had to learn fast. The first Android phone—manufactured by the relatively small Taiwanese producer HTC—launched in October 2008. Google and HTC gave the launch as much exposure as possible. This was the birth of Android as a device, yet during the three-month period on either side of the launch, Apple was attracting twelve times the number of online references as the HTC.19 Google was struggling to help its partner make an impression. Two years later, Android had become a key brand name and suffered only a three-to-one deficit with the iPhone. That’s far from a lead, but it shows that Google was becoming better at securing attention for what was a sub-brand. More recently, we (the original authors of the HTC paper) have calculated that Android has in fact surpassed the iPhone in the number of online references.

Another factor in Android’s emerging dominance was its ecosystem approach. Nokia’s customers were telecoms companies that approved its products for use on their networks. It produced phones for the carriers and not for users. It did not develop end-customer relationships, and without access to end customers, Nokia’s business logic was profoundly hampered.

Google created a business platform and business ecosystem that included a range of partners that manufactured different hardware devices but also incorporated their developer communities—it went to the grass roots. As time has gone by, Android has been adapted for wearable devices, too. And a new ecosystem awaits Google—Android might also be adapted to robotics.

Unlike Apple, Google allows its partners—software developers, hardware vendors, and customers—to modify virtually any component of the Android software. Android also involves hardware companies like Motorola (since purchased by Google), Samsung, LG, and Sony Ericsson, to mention a few (in total, the original equipment manufacturer pool includes over a hundred different devices using the Google Android operating system on a wide range of devices).

These multiple vendors have agreed to a structure that effectively means they share the ecosystem of developers around the platform and, to a lesser extent, the ecosystem of customers for Android apps. They are both collaborative and competitive around the devices and the optimization of apps and components for those devices.

The alliance and the platform have been hugely successful in the sense that Android smartphones have been the fastest-growing product class in mobile smartphones over the period 2009–2011. Google claimed in December 2012 that Android activations were approaching 700,000 per day, another example of business acceleration from an elastic enterprise strategy.

In contrast, Nokia began its open-source and app journey as the incumbent. Here is Williams’ (Executive Director of the Symbian Foundation) take on the crucial decisions, particularly on building the platform, ecosystem and partner relationships:

The Foundation and our ecosystem didn’t get any support for those initiatives, despite sitting down with leadership at key moments, quite the opposite, the rug was pulled out from us at almost every turn. And the message it sent to other Foundation participants cannot be underestimated, e.g. Samsung, Sony-Ericsson, Vodafone, DoCoMo, AT&T, etc. Almost as quickly as the company had decided to form the foundation and execute the new Symbian growth strategy, it appeared to have changed its collective mind.

What are the lessons for disruption?

1. Concentration and hubris. Nokia was a company that built relationships with other big companies. It had little experience of working with start-ups or radical thinkers who eyed disruption in an ideological way that Schumpeter suggests will happen when industries concentrate. Lee Williams’ testimony also suggests it had entered a period of hubris. Nokia’s continued profit growth, particularly its spike in 2007 during the first part of the Google/Apple entry into mobile must have added to its sense of invulnerability.

2. The experimental era. For those reasons alone, it missed the opportunity to ally with radical thinkers in the 2000–2005 period, when developers were looking at new operating systems and new web-based appliances. In effect Nokia was the device industry leader and sought ways to define its future, by looking toward options that the web was moving away from.

3. The new content layer. It proved to be poor at anticipating change, opting for web-based strategies, and missing the platform and ecosystem movement,20 which proved a serious disadvantage in the information layer. Nokia was talking the wrong game and the technology press probed these weaknesses. But there was an entirely new systemic element here - the move away from supplying devices as defined by carrier networks to direct customer contact, aided by improvements to the US mobile infrastructure. An element of structural change is critical to the Nokia story. The market could, suddenly, be organized at great scale; Apple and Google were prepared for this by their recent history. Nokia simply failed to anticipate the need to interact with end customers.

4. Ecosystem consolidation. In the critical 2007–2010 period, Nokia was running about a year behind the critical disruptions in the industry, but only a year. There is constantly a sense that it could have caught up and didn’t need to cut its losses and make a radical move of its own. Hence it created partnerships with more big firms like Yahoo. The ecosystem of entrepreneurs grew rapidly as it pursued new opportunities in apps. Nokia switched its attention to mapping software with the $8 billion acquisition of NAVTEQ, ironically a far sighted, long term move when it needed short term actions to grow support in the developer community. Crucially what we are also beginning to learn is that the ecosystem model shifts price and revenues in two directions. Extraordinary revenues flow to the platform. Yet price to the consumer trends downwards sharply (as is the case with apps, the majority of which are free).

5. Platform. Nokia was late with its platform and tried to launch it simultaneously in 35 countries. It lacked the background engineering skills to create a platform seamless enough at such scale and to make it usable by the developer community. It failed at platform.

That brief overview of events fits the model of disruption that platforms bring but it also introduces this new wild car element. What are the structural changes that redefine markets? Here’s how it fits another under-threat sector, financial services:

1. Concentration and hubris. Bank consolidation preceded the Great Recession and was an integral part of the solution as governments arranged shot-gun marriages between stable and failing banks. Since the crisis we have seen considerable evidence of hubris (such as Libor fixing).

2. Experimental era. The finance sector has been surrounded by innovators seeking ways to pull down the walls of the oligopoly since the early 2000s (e.g., PayPal; some forms of high frequency trading; personal money management systems like Mint.com: low cost security brokerages; alternative payment systems, or P2P loans).

3. The new content layer and structural change. The growth of awareness and experience among consumers of different forms of substitute products and services has grown over a 10–15 year period (e.g., Instant payment via PayPal, online banking, mobile banking, Square, P2P Lending. More recently we have begun to see structural pressures emerge, built on global telecommunications networks. As companies switch business models out of product into service, and rely more on cash, new instant payment services have and with them the new wild car, cryptocurrencies, which promise, or threaten, global instant money transfer at ultra low cost, possibly with much less need for hedging. These developments will transform how financial services work and where banks derive profits.

4. Ecosystem consolidation and price reduction. Over the past three years Fintech (financial technology) funding has grown three times faster than all venture capital funding; BitCoin and Ripple have now introduced the disruptive threat of open source and banks themselves are seeking a role in open source projects.21 Here too we are seeing radical price reductions with, for example, currency exchange margins dropping by as much as 80% and payment margins trending towards zero.

5. Platform. Will AliPay (Alibaba’s financial arm), a bank, or a start-up like Ripple provide the platform to consolidate this activity and renew finance?

Beyond the mobile phone sector, the concept of applications has now entered most industries. In many industries, there are enterprises that have taken on the mantle of the sector platform (such as Autodesk’s Revit platform in construction). In each of these cases, the devolution of innovation to self-determining small entities is characteristic of the changes taking place. Even more disruptive, Apple, Google, and Alibaba are also entering new industries.

As the disruption effect hits finance, we will see more turbulence as the very basis of transactions, value proxies like money, also changes. The most important lesson in all this is that we are dealing with a very different type of disruption from the past. The classic definition of disruption is that companies are hit by low-cost, often low-performance, technologies that gradually realize a following and satisfy needs that higher specification products don’t satisfy. In effect, the disruptor creates a new market. Today’s disruption results from the cumulative effect of a new business philosophy, a new commercial structure, cheaper business infrastructure, and the devolution of risk onto self-determining entities whose actions are organized around platforms.

Something else is also happening.

BRANDS, UTILITY VALUE, AND TRUST

On top of the organizing principles and the power to create relationships at scale there is also the broader concept of utility. The elastic enterprise invites companies to take on a utility position in their market.

In economics, the term “utility value” is a way of describing the subjective value that people might put on a product or investment risk. The utility value of anything, in this sense, is different depending on who is using the product or making the investment. It’s really a statement of individual preference. Having a sense of the trade-offs people make when they think about utility is one way companies can set a price for their products. However, that is always just one step toward aggregating preferences and setting one price for a product in a mass market.

In the present platform era, there is recognition that customers have highly individualized needs. Creating broad content and service reservoirs, for example, through app stores, allows customers to choose what suits them.

Here is a new economy of scope to go alongside the economies of scale. Economies of scope occur when a company creates variations of a product that share similar characteristics. BMW famously enjoys economies of scope because many of the components of its 3, 5, and 7 models are shared across these marques. Economies of scope refers to savings companies can make by reutilizing a component or cost factor (such as marketing collateral). Today, economies of scope are beginning to look a lot different. App stores, for example, give smartphones enormous economies of scope. The basic smartphone as a device is important, but the many thousands of apps it accesses give user control over the eventual utility of the phone.

In this situation, the platform—not just the store but the whole suite of activities around a phone—has to create conditions where utility needs are met without actually knowing what those utility needs might be. It has to create enormous scope without necessarily having the insights to preselect on behalf of customers.

Typically in the past, economies of scope underpinned conglomerates. They were also typical of the behavior of banks that liked to cross-sell loans and insurance. In both cases, scope crippled companies by stretching executive capabilities too far, by forcing them to think across too wide a range of options.

Today’s economies of scope are twofold. First, they are integral to the platform. It is what platforms do, which is permit products to launch with vast scope. Second, they enable executives to focus on new areas of product development because scope is more easily managed via the platform.

Take Apple’s App Store and Airbnb’s renting platform as examples. In both cases, the platform company is creating circumstances in which people can make decisions about products and services, yet neither Apple nor Airbnb actually create apps or have rooms to rent; their role is to create the conditions for utility value to be created and consumed. But they attract audiences by providing scope.

Likewise, Soundwave aggregates activity from music platforms and creates a utility platform where customers can self-serve their own music discovery. The scope of its service is the aggregation of all other music platforms plus its own conversational setting.

This is vastly different from the principles that govern supply chain management. It does not involve trying to control and funnel data on customer demand. It instead creates systems where customers self-surface and self-select for their utility needs. The platform creates the opportunity for utility value to be created, offered, and chosen. The platform also underpins the transaction process. In Apple’s case, it underpins the process of producing and distributing apps, collecting and distributing revenues, and making the utility widely known.

There is a big difference between Apple and Airbnb’s utility strategies. Airbnb’s utility value is simple: convenient rooms in a trusted environment at a good price. Apple uses its App Store to promote its iPhone and iPad products. It takes for granted that the majority of the apps it hosts will be downloaded for free, without generating revenue for Apple.

Even in this situation, the App Store—and Apple’s utility strategy—is highly profitable. One estimate puts the 2013 App Store revenue at $10 billion (compared to $15 billion since 2008), with Apple earning close to $1 billion per quarter by the end of 2013. A 2014 report from analyst firm Macquarie estimates that total revenues from software services, iTunes, and the App Store now account for about $30 billion in annual revenue for Apple, and by 2020, they will make up 20 percent of Apple’s profits. The report states, “That is almost equivalent to its iPad business and is more than Facebook, Twitter, Yahoo, LinkedIn, and Netflix’s combined revenue (on consensus 2014 forecasts).”22

Creating utility value is clearly something that companies do if they take a platform position. They are not necessarily originators of the product or service, but they most likely have accumulated insights into the utility people are seeking, how to uncover options for those people, and how to persuade them to make choices.

Utility can be a scary term for executives. It makes many of them think of actual utilities—the gas, electric, and water companies that we refer to collectively by that name. Typically, these utilities have been low-margin businesses with little innovation potential. Innovators often want to run away when you suggest they seek utility value. That’s because many companies have to tread a fine line between utility value and commoditization.

This is what the start-up company eXo Platform is doing through the use of open-source software in social business. Benjamin Mestrallet, founder of the eXo Platform, states:

We believe that social collaboration can really transform enterprises, so we strive to make it accessible to all businesses—no matter their size, resources, location, or activity. We offer our clients a fully featured, affordable, open-source, social collaboration platform to use, build on, and innovate with. We are building a fast-growing, profitable business, based on low margins and mass distribution resulting from the enterprise social market commoditization.

Exo Platform is lowering its prices to stay in the game. While executives generally might fear this, in his classic books on competitive advantage, Michael Porter says that every market has the potential for low-cost competitors. It’s their choice. This is an era when much is given away for free, after all. But utility value does not automatically mean commoditization. It means taking a utility-enabling position in the market. It is almost a neutral position with regard to the products and services being sold.

PLATFORMS AND BRAND POWER

Whether the platform owner is Fidelity or Apple, Autodesk or Soundwave, the issues the successful platform company solves are usually those of scale and scope. Platform companies not only scale with relative ease and create access to scope, they also continue to grow if they achieve strong utility value. They have eased their way out of the day-to-day competitive environment in order to host value creators like apps developers or room renters or fund managers.

Not all companies have the culture to achieve that position. Samsung has never achieved utility value with its software and services; the jury is still out on AT&T and Orange, though most telecom companies consistently seem to fail to attract the loyalty of their users. Fidelity has utility value in connecting investors to funds and advisors. Ericsson is pursuing it in mobile money and media. Airbnb and Uber are achieving it in their respective fields.

Can they maintain the pace? The strategy model we began with would predict that Airbnb and Uber will be challenged relatively quickly with look-alike competition, as Apple was by Samsung and then Xiaomi.

In the world of Web 1.0, their first-mover advantage would be critical in sustaining growth. But in Web 2.0, the advantage lies in the trust the brand can build in novel ways other than just by “branding” or buying loyalty.

In the old business model, trust and loyalty were confusing terms. And in many instances, they are still thought of as a part of brand value. The problem with this view is that it is extremely vulnerable to shifts in the current climate. Most brand value is bought through a combination of advertising and product and service quality. Advertising, however, is the most significant factor since many products have low integral value—Coca-Cola, after all, is just a sugary drink, yet it remains a powerful brand.

If you look at brand value through a different lens, then you see how platforms are changing people’s perceptions. Table 4.2 details the most valuable brands, using both traditional brand metrics (the Interbrand top 100 is a widely used index of brand value) and a new social metric developed with the data firm StatSocial.

Table 4.2: Top twenty companies by brand value and social affinity

Interbrand Top 20

Social Brands Top 20

1

Apple

Google

2

Google

Twitter

3

Coca-Cola

Instagram

4

IBM

Apple

5

Microsoft

NASA

6

GE

Amazon

7

McDonald’s

Microsoft

8

Samsung

Facebook

9

Intel

Disney

10

Toyota

Starbucks

11

Mercedes

TED

12

BMW

Comcast

13

Cisco

Whole Foods Market

14

Disney

Samsung

15

HP

Pinterest

16

Gillette

Nike

17

Louis Vuitton

Cisco

18

Oracle

Android

19

Amazon

Etsy

20

Honda

Lonely Planet

The left column shows the top twenty companies in the Interbrand 2013 list of most-valued brands. The right column shows the top twenty brands drawn from a study of brands and social affinity, a different measure of loyalty that is drawn from social media activity.23

Social affinity is a measure of persistent sharing and reuse of a brand name or product name by people online. It is drawn from a database of 100 million Twitter users, and analyzes the past 3,000 tweets of each. The scores have then been indexed to create a list with scores one to one hundred.

The social affinity list shows that brands with an inherent communications role have very high social affinity. So naturally, Twitter, Instagram, Facebook, and Pinterest appear. But if you look beyond these, you will see Etsy, the maker platform, and Lonely Planet, the travel guide platform. If we removed social communications sites, like Twitter and Pinterest, from the list, then names like Kickstarter and GitHub would appear in their place. They rank alongside Coca-Cola, the highest spending brand. Note also the presence of Android.

The point is that these born-social brands—Kickstarter, GitHub, Lonely Planet, Etsy, and Android—are mostly platforms with a social role. They are wholly commercial products, and yet they are not buying affinity. Social affinity is built into their offer; their existence is bound up with promoting socially engaging activities either in professional or consumer communities. They are affinity engines that generate brand value through use.

It is a peculiarity of current brand valuation systems, like the Interbrand lists, that they prioritize high-spending brands over social brands, but there is every reason to argue that the social affinity metric is more appropriate and bought loyalty will dwindle in relevance because it is inefficient. In terms of financial performance, the born-social brands have achieved recognition that costs other brands billions of dollars per year to sustain. They have done it in the normal course of their businesses.

What’s more, as data paradigms change and a more customer-centric paradigm develops, the most important intangible asset will be relationship based. Intangible asset values are key to corporate valuations, even though, as yet, they don’t measure social affinity.

The Organisation for Economic Co-operation and Development (OECD) points out that intangible asset investment in the United States is now running at close to $1 trillion per annum. Companies in OECD countries now spend more on intangibles (including software) than on physical assets, yet relatively few companies collect and report data on intangibles,24 because financial metrics dominate enterprise reporting. Data on new assets like social affinity often doesn’t exist. The consequence is that most large companies continue to “market” products while smaller start-ups build platforms, utility value, and affinity.

It all begins with the platform.

WORK AS A PLATFORM

The platform idea is now migrating to the very center of the enterprise. However, the concept is being used to focus employees on tasks where a departmental structure might inhibit innovation. To grasp the significance of this, it’s worth thinking about the nature of knowledge work.

Most of the work we do in organizations is brain work or knowledge work, with more and more of this being creative or problem based. But organizations have a challenge with knowledge work and creative work, which is what they now need most. Neither is easily controlled by a hierarchy because the creativity is, by nature, often subjectively acquired and selectively shared. Furthermore, neither is easily measurable in transaction-based systems or records of work done.

Figure 4.3: Internal platforms at Netflix

In Figure 4.3, you see a number of platforms Netflix uses to run its business. It can deploy teams from different disciplines to work on each platform. The benefit of thinking of “work as a platform” is that executives can create innovation as a process. They do this by making creative and knowledge work much more visible, observable, and targeted at concrete goals.

A huge problem of knowledge work is making it visible and observable. Knowledge work often takes place in someone’s head and isn’t shared. It needs to be made more observable or, at the very least, its outputs have to be more frequently observed. Otherwise, the enterprise is stuck with the problem of not understanding what it is paying for or how best to improve it. The problem of observable work has surfaced from time to time, but has never gelled as a mainstream topic, even though it is central to many of the changes taking place in high-performing enterprises.25

In software development, that problem was resolved in the open-source community where people earned kudos through the quality of their code as judged by a community. Until people exposed their work, they didn’t belong. A specific objective of this process was to create public critique and, therefore, improve the quality of work. Scrum software techniques use frequent team meetings to do much the same thing.

Netflix has made knowledge work abundantly observable:

• The internal network of platforms and teams means everybody’s work is open to scrutiny.

• An established, small set of imperatives—all work has to be tested before being deployed, all work should trend toward automation, and all work should be low maintenance—provides teams with guidelines for judging each other’s work.

• By delegating to small teams, Netflix recreates some of the self-determination drivers seen in the ecosystem, particularly human scale and kudos.

• By focusing work on platforms, everybody has a visible, though fluctuating, responsibility as platforms interconnect and teams work across different platforms.

Historically, the enterprise has used departments to manage a relatively stable state, as in R&D, different branches of production, accounting, marketing, sales, and after-sales. In contrast, companies like Netflix focus staff energy and ingenuity on how platforms should function in the pursuit of a better customer experience rather than on how departments should execute in a (relatively) stable state. The goal of work becomes invention, with the target being new practices that improve platform performance. It is as close to reinventing the enterprise as we can get.

Overall, the value of the platform to the enterprise is realized by devolving responsibility as work becomes less measurable. Given that most platforms relate to an outside group (customer or partner), it helps to create focus and new performance goals based on platform innovation targets.

THE PLATFORM SOLUTION

The examples of structural disruption reviewed so far draw on Steven Klepper’s work as well as Schumpeter’s. They suggest there is a pattern in structural disruption, at least in what we see happening now, and that pattern forms around the business platform.

As per Schumpeter, the model argues that entrepreneurs destroy industry structures, often for ideological reasons; as per Klepper, it argues that oligopoly is really its own enemy because it leads to poor innovation decision-making.

The disruption effect:

• Pulls down the walls of vertical industries like telecoms and creates a more democratized business environment with an entrenched antimonopoly philosophy, driving horizontal opportunity for the commons and small firms and individuals

• Is normative and not just commercial, driven by people who want to see business done differently

• Radically reduces barriers to market entry. It creates ease of access and proliferates competitors in the application and customer relationship spaces

• Is cumulative. Arguably, it took fifteen years to change the IT and telecoms sectors, but the disruption effect is spreading because both are horizontal industries that impinge on all other sectors

• Creates its own information markets and demands that companies play their strengths out in the information layer around a product or service

• Changes the basis of brand value

• Creates utilities based on platforms and ecosystems

Executives who want to anticipate disruption need to look closely at these factors. What are good solutions, though? As we’ve learned, solutions include:

1. The platform. Certainly one solution lies in adopting a platform approach that allows other parties to create opportunity and wealth (see Chapter 12). This has to be done against a background of high expectations and scale in self-determining ecosystems. It requires new management levers particularly around the information layer; it requires peer leadership; and it also requires near seamless performance. Platforms will increasingly be used to focus internal innovation processes.

2. Externalization. It is clear also that companies need to embrace innovators inside or outside their own walls; this is beginning to happen as companies use more start-up competitions and corporate venture funds to back start-ups, but in many of these cases there is a disconnect between corporate strategy and the innovation tactics. The real winners organize ecosystems that already exist or are nascent.

3. Playing horizontally. Companies need to recognize the new horizontal nature of disruption and seek opportunity outside their core, a reality that means adapting old management ideas about core competency.

4. Mastering the information layer. They need to master the new business of information, to anticipate trends better and create the impression of being on top; that in turn means being able to tell a transformational narrative. People also need to recognize the shifts in content over time. From magazine-type formats that ruled in the 1990s (CNET) to the more dynamic websites that cover today’s technology (TechCrunch, Mashable, The Verge, Ars Technica, AllthingsD, Apple Insider), to new newer business sites (Forbes, Business Insider), to very new collaborative sites that are helping authors gain traction (medium.com), content on the web is always mutating. Increasingly it is tied directly into social marketing platforms like FunnelFeed.

5. New decision processes. But companies also need to revise their decision processes. I mentioned in passing that Google was looking at WebTV and mobile, and in the process backed failures. Companies should learn that they need more options. It is impossible to create options without experiencing failure, and without changing decision metrics (more of which later).

6. New metrics. They need to move away from traditional ROI and KPI benchmarks to re-conceptualize the firm around its own potential rather than the industry’s limits.

7. Being the utility. Finally they need to seek a utility position, a way of enabling value creation by third parties.

Platforms are far and away the most successful form of enterprise organization, evidenced by Apple and Google. However, there are absolute failures, evidenced by Nokia and to an extent Twitter. They are a historical development. There is not necessarily a formula for creating a platform but important lessons can be learned from looking at who has done it well and who has failed.

CHAPTER 5

Disruption and Renewal Strategies

Not many executives are taught platform and ecosystem management. However, there are many examples of successful platform and ecosystem strategies for executives to learn from. The key is of course to learn.

For example, Google acquires companies at an early stage in their development and then uses them to create platforms of extraordinary scope—such as MAPS. Ericsson has chosen to acquire companies that have a platform component but that also have an existing customer base. Platforms vary and are often a work of invention. Their character is defined as they grow. They have a working model behind them but executive teams need to master what sportspeople refer to: playing what is in front of you (rather than drawing on the playbook). They must back themselves to learn quickly—that’s an obligation that is coming full speed at all of us. In this chapter we will look at platform strategy examples, beginning with the most accomplished player, Google.

GOOGLE—THE SERIAL PLATFORM STRATEGIST

Google has employed effective platform strategies for search, Android smartphones, Google Maps, and enterprise apps (in this case, slightly less successfully), and soon will deploy a strategy in robotics. At its origins, Google is a platform innovator.

In the early 2000s, when Google began to emerge as a strong contender in search engines, a business it now monopolizes, there was still wide choice for consumers. The available search engines included, among others, Alta Vista, Yahoo, Northern Light, Dogpile, Lycos, HotBot, Excite, Ask Jeeves, Infoseek, MSN Search, and Alltheweb, popular in Europe.

At the time Google launched, Alltheweb competed well with Google on information coverage and relevance. Yahoo was the dominant name in the business. Alta Vista was popular.

Essentially, there was little difference between these products in terms of usability and relevance. Google did not win this battle because of superior search technology—search is something of a commodity. What it did do very well was to win the argument around relevance. How it won that argument is the key to its success as a platform business. It did so by winning over an ecosystem that already existed in search marketing. It then scaled the ecosystem and drove the search business horizontally into different markets.

Pre-dating Google’s arrival on the web, marketing pioneers such as the MultiMedia Marketing Group (MMG) had already established the informal ground rules for making sites more visible online through search marketing techniques. While MMG and a group of search engine optimization (SEO) experts convened to discuss best practice, the web was also becoming a target for spam-type sites or Black Hat (manipulative) search techniques.

The web was vast and fascinating, but it also caused frustration and uncertainty—which information was genuine, which was really important, which search returns actually mattered?

In this environment, web search engines interacted fractiously with the SEO community, who might advise clients to create poor content that nonetheless performed well in search, so the client could easily be found.

From 1995 to 2003, the big issue online was how to guarantee search relevance, with a related, secondary issue being how to monetize it. Google created a solution to both these issues, or rather it built consensus around the first of these and then scaled the latter.

It introduced PageRank. PageRank was a way of guiding users on the relevance of web pages. Monetization followed on from this, but it is doubtful whether Google actually improved relevance. What is undeniable is, its leaders won the argument.

That gives a good clue to Google’s platform strengths. Being smart Stanford grads, Google’s executives are very good at exciting other smart people, and at winning debates. They are also ultra-smart at creating platform strategies.

PageRank is named after Google CEO Larry Page, who developed the algorithm while at Stanford University. In fact, the patent for PageRank belongs to Stanford, not Google, although Google has an exclusive user license (Stanford took shares in Google in return).

PageRank awards points to web pages based on the number and quality of inbound links it has. More formally, it assigns a numerical weighting to each element of a hyperlinked set of documents, such as the World Wide Web, with the purpose of “measuring” its relative importance within the set. The popular interpretation is that PageRank gives a kind of peer vote to web pages. The more high-quality links it has (or votes), the more relevant the page.

Prior to PageRank (and indeed after) webmasters stuffed their web pages with keywords that search engines would find and then rank. Google’s innovation was, in a sense, to make this word-stuffing game harder but still achievable. PageRank could be manipulated by SEO experts who second-guessed how the algorithm worked, but it took considerable investment to build high-quality inbound links and to maintain good content. The game was tougher, particularly as major publishers moved content online and almost automatically achieved strong search returns by virtue of having huge amounts of content people would link to.

The development of PageRank led to the rapid expansion of the SEO industry, as companies sought advice on how to be visible in this highly competitive content-crowded, environment..

SEO companies offered to create link-building programs for companies so they would show up on the front page of Google search results for particular terms and that meant link spam began to build up on Google. Even as it began pulling away from competitors, ad relevance was back in doubt.

Danny Sullivan, editor of Search Engine Land, marks 2003 as the pivotal year for SEO. Throughout 2003, Google announced a series of changes to combat link spam rendering a lot of previous SEO redundant.1 Google also launched a set of tools to help webmasters and SEO experts better manage and analyze their site performance. In 2003, it stepped up its efforts to inform people how they should create sites to make them search engine efficient, even suggesting that companies ask themselves if they really need an SEO expert.2 It effectively redefined SEO and, with it, the way people use the web. People began creating Google-friendly content with site structures that Google could navigate easily. The game was becoming not so much tougher as different, like going from handball to basketball.

Link building, and later content design, become the “relevance game” with Google lending a helping hand, as well as periodically admonishing website owners for amoral strategy or for going beyond the game’s rules.

One aspect of this game had momentous implications for search as a business: it became beneficial for SEO companies to promote Google as the best source of relevant information. They bought into the idea that links were currency, in no small part because Google provided a source of straightforward income that also had a barrier to entry (people needed to develop expertise to be good at SEO). These companies became Google’s advocates.

PageRank in effect gave birth to the modern platform and ecosystem play: it gave Google an abundance of smaller companies as advocates at the point of consumption, it created an ecosystem of self-learning, self-determining companies who pushed the boundaries of what search-related advertising could do and who therefore sold search advertising to clients, enriching Google in the process.

As Google introduced its updates, it also bought Pyra Labs, which had created a new service called Blogger. Blogging fast become a source of democratized online content, which was directly relevant to Google’s business and created another part of the overall ecosystem—bloggers. Google’s promotion of blogging was helped in no short measure by AdSense, the practice of creating small text ads next to content. It meant blogging came with a potential revenue stream attached.

With AdWords—the text ads that appeared alongside search returns—already in the mix, the benefits of SEO became even stronger. Now, with good SEO, clients did not have to run a paid search advertising campaign. They could develop earned media (create good content). Or they could run paid and earned together. Strategy became more complex and advertisers needed more advisors.

As Google also launched AdSense, the blogging community became pro-Google. And so from 2004 onward, Google began its spectacular profits growth. It also introduced Maps, creating another community of advocates and broad exposure in those websites that used mapping technology.

Going still further, Google has introduced more (undisclosed) content indicators to signal relevance, moved in to search return personalization, and incorporated social signals. All these have reduced the relevance of PageRank but have increased the complexity of earning “good points” or higher rankings (with ranking itself becoming less relevant). In 2011 and 2012, Google introduced a range of “penalties”—behavior that it punished websites for engaging in (the company demoted them in its rankings or even excluded them from returns). The updates were known as Panda and Penguin. Today, it takes an extremely sophisticated online player to “game-up” a site, but by the same token it takes sophistication to ensure that good content is treated fairly.

It is highly questionable whether Google has actually improved relevance throughout this process. In the past, search engines like Alltheweb consistently reflected search results similar to Google’s. Even now it is debatable whether Google is actually a better search engine than, say, Bing.

To evaluate a variety of search engines, PC World conducted multiple tests in 2007, concluding that “variation among the top four general web search engines (Google, Alta Vista, Alltheweb, Yahoo Search) was minor, with Microsoft’s Live Search coming in a distant fifth.” Yet most users in the tests perceived Google as superior. Commenting on these results, Jeremy Liew of venture capital firm Lightspeed Venture attributes Google’s perceived superiority to its branding.3 He explains, “When stripped of UI [user interface] and branding, most users couldn’t tell the difference in quality between search results. When UI and branding was [sic] returned, most people thought that Google was the best search engine.”

Google’s branding has never been fully homegrown—its ecosystem does a lot of the lifting. Its platform and ecosystem are stronger than all other search engines and have helped maintain its market position through the interplay of good technical quality and the dynamic business interests of its thousands of unofficial partners.

Far from offering a guarantee of relevance, PageRank actually created a worldwide game and, in doing so, complicated elements of search that SEO companies could then exploit. It also allowed Google to dictate the shape that websites took, even down to the way articles were constructed. A huge but unmeasured industry grew up around Google and, as in many cases of ecosystem evolution, became global advocates of Google’s search engine and relevance.

Whereas Apple is a guardian of its developer community, Google has existed in constant potential conflict with the SEO ecosystem. SEO experts need to fathom Google, and Google has relied on the SEO community for its growth. Today, most advertising agencies have SEO expertise, content marketing expertise, and other Google-specific skills.

The fascination lies in this relationship, though—Google has to be at arm’s length and keep the game evolving in much the same way that video game designers do. In the meantime, everyone associated with advertising has succumbed and most, in one way or another, are Google’s advocates.

SEO services are probably worth around $20 billion annually around the globe, based on projecting the UK market (worth £500 million). Google ended up dominating search, owning close to 70 percent of global market share. That’s the platform that allows it to earn revenues of over $40 billion annually on advertising.

The broader Google platform is now extremely diverse. Google provides many different capabilities through its platforms, such as Google Maps, Google Apps, Google Voice, Google+, and multiple specialized programs and tools that developers can use and leverage for profit.

Like Apple, Google actively develops its business platform and uses it as a major engine for strategy and operations.

A comparison between the two companies suggests platforms can be closed or open and that neither will necessarily inhibit success. But it’s clear also that extraneous conditions have to be ripe. The platform’s role is to scale a business in the market, but the market has to be ready. The wisdom of that is often only available in hindsight.

What are the overall lessons of Google’s platform activity? They include:

1. Platform by acquisition. In Android (bought in 2005) and Maps (six companies bought by Google in 2004/2005), and robotics (it acquired six companies in robotics in 2013, which it will use for its growing robotics ambitions), Google has shown itself to be very adept at buying companies from which it can assemble platforms. Few companies take the time or realize the benefit of acquisition in platform construction (Salesforce is a notable exception).

2. Multiple tactics. Google adopts different tactics depending on its platform. In search, Google has constantly iterated a game-like strategy with its dependent ecosystem; in Maps this hasn’t been necessary. In wearables like Google Glass, perhaps an attempt by Google to refresh its opportunities in mobile platforms, it has used its prestige to attract a lead-adopter community. Platforms do not have a single strategic playbook.

3. Focus on prestige. Prestige plays an increasingly important role in Google’s thinking. It constantly draws on the founders’ background at Stanford and on the idea that its business innovations are like moonshots. No company talks up its innovation strategy as successfully as Google (as its core ad revenue price goes down, its share price goes up).

4. Constant adaptation. Google’s experience in search is that platform and ecosystem relationships can be reset very regularly, every year or two years. It is highly adaptive and has all the hallmarks of operating a continuous active strategy (more about this later).

APPLE—DISRUPTION AND DOMINANCE

Apple is the most astounding implementation of elastic principles. To illustrate the incredible scale of change that took place between 2007 and 2010, just look at Apple’s figures from these pivotal years: In fiscal 2007, the company generated over $24 billion in revenue and $3.5 billion in net income. During Q4, Apple sold about ten million iPods and just over one million iPhones.4 Less than a year later, the iPhone had pushed Apple to number three in the global smartphone league. The phone was suddenly the single most profitable product in Apple’s portfolio.

Only six months later, after the launch of the iPhone 3G, the Wall Street Journal quoted Apple’s CFO: “Response to the new iPhone 3GS has been tremendous with over one million sold by the third day after its June 19th launch. We are currently unable to make enough iPhone 3Gs to meet demand, and we’re working to address that.”5

Apple’s retail stores were attracting 23 percent more visitors year over year, and it was on its way to becoming the most successful retailer measured by revenue per square foot.

By Q4 2010, Apple was again reporting record revenues, partially based on 14 million iPhone sales. In July 2011, iPhone sales were up 142 percent year on year. The recently launched iPad was growing at 183 percent. Profits were up 125 percent.

In the third quarter of its 2010–2011 fiscal year, Apple’s revenues were up 82 percent, and profits were up 125 percent. This would be a supreme achievement at any time, but the summer of 2011 was not even a holiday period when sales tend to be strongest. And the economy teetered on the edge of a second recession.

Apple was not just succeeding wildly. On its way to record profits, Steve Jobs’s team had created monumental disruption in the smartphone product category, which the company had inhabited for a total of only three years. Shortly after entering the smartphone market, Apple then single-handedly created a new product category with the launch of the iPad tablet.

This was unusual success heaped on top of astonishing corporate performance.

Of course, Apple has a history of introducing disruptive products, but its original disruption in desktop computing was thirty years earlier when Apple was founded. Never in its history had Apple sidestepped the opposition so frequently and effectively as during the five years prior to 2012. It did so by creating partnerships at huge scale through the App Store, illustrating for the first time that a new kind of economics could drive a hardware business.

Apple showed something else, again, often overlooked. Apple showed how to make the transition from an old product to a new product-service company. Whereas some companies, such as IBM, had abandoned large areas of production as it transitioned to a service company, Apple created complementary hardware and service businesses—the iPod and iTunes, the iPhone and the App Store. If you are looking for a case study of a company that made the transformation to services in ideal fashion, there it is.

As in the telecoms sector as a whole, Apple owes a substantial debt to it precursors and to the self-determining members of the broader ecosystem. This is much truer of Apple than it is of Google. Apple created a significant platform with the iPod and iTunes, but it was isolated from the rest of the burgeoning open-source, commons-based economy growing around it. Apple was more or less a straight digital reseller of music until the App Store revolution, and app stores predated Apple’s App Store by at least eight years. Even at the release of the iPhone, Steve Jobs refused to countenance a third-party developer community. He relented and allowed the iPhone to grow.

From a strategic point of view, here are the lessons of Apple as a platform:

1. Investing for the long term. Under Jobs, Apple was effectively a privately run firm and made decisions based on long term, continuous renewal. Jobs had been looking at apps since the 1990s, and his iTunes investment only came good in spectacular fashion six years after its release.

2. Adapting rapidly. Though certain of himself, Jobs quickly relented on the App Store and then made third-party apps central to his whole project.

3. Making adjacency risk central to strategy. Apple has changed from a computing company to a mobile one and to a broadly based lifestyle company, as it now moves into wearables, expands its retail presence, and even hosts live-music festivals. It has moved into autos, the home, the enterprise, payments, and services to other retailers. It has been slow to make some of these moves and sometimes appears as though it is stumbling, but gradually it is making use of its pure utility position to move sideways again and again. The sheer breadth of its activity is testimony to the power of the platform and ecosystem approach.

AUTODESK—BUILDING ECOSYSTEMS TO EMBED KNOWLEDGE

Autodesk began life as a producer of computer-aided design/computer-aided manufacture (CAD/CAM) software. After about thirty years in business, it now has around twelve million customers worldwide. In recent years, the company has diversified into animation software—they helped award-winning moviemaker James Cameron by custom-building software for Avatar. As well as CAD/CAM and animation, they also do simulation software particularly for the construction industry and have numerous products in development, including 4-D printing and biological design. Some of that may seem futuristic, but what’s very present is their platform and ecosystem strategy.

Autodesk is in the process of building out a platform and ecosystem in the creative industries where it has Adobe in its sights. It also has a huge ecosystem in the maker business (sophisticated DIY production), largely based around its acquisition of the maker community at Instructables. Additionally, it has a platform and ecosystem evolving in building information modelling (BIM) and construction.

In the maker and 3-D business areas, Autodesk owns or is closely associated with end-user communities that number about 120 million people. In that community, it has the makings of an ecosystem, one that is linked to an immediate market about a fifth the size of Apple’s registered consumer community

The company is inching its way to what senior vice president Amar Hanspal calls the fact pattern, or the definitive strategy for how to make a true elastic enterprise play. To get there, it needs a subset of the community to become active producers of something relatively new to the ecosystem world—hardware.6

Future platform and ecosystem projects will have a strong hardware component. It’s just not common right now to see hardware communities behave in the way open-source or apps developers do. But it is coming. As we move into the Internet of Things (IoT), ecosystems will incorporate software and hardware engineers and designers.7

It is not as simple to activate and motivate the makers in large ecosystems, as it is developers who only need their labor and a software development kit to start innovating. You can see why Autodesk is taking small steps toward the right solution. It is trying to figure out the future of manufacturing.

What is the right platform for this?

It will have to combine access to low-cost engineering designs, so communities like GrabCAD will come in handy, as well as initiatives in product modularization, like Google’s Project Ara, for the modular smartphone. There will have to be more ecosystem proof of concept, more testing of the human capacity to collaborate on projects that crosses the hardware/software divide.

One senses there also needs to be a big problem to solve. Apple solved digital rights management with iTunes and a consumer need when it delivered 99-cent tracks. It solved a usability problem in smartphones and got lucky with apps. Google solved a different problem with Android—low-cost access to smartphones.

What problem does the maker community want to solve? It is still an open question. But Autodesk has started to answer the same question in the construction business, where its BIM platform Revit seems to be reshaping the power dynamics of the construction industry.

Construction has huge risk profiles. Most projects are one-offs and contractors need to assemble new teams to meet the challenges of each new project. They are the exemplary collaborative environment with multiple trades stepping up at different times to complete their part of the overall puzzle of a large building.

That process can now be modelled in a BIM environment, allowing Revit to help reshape work practices and the relationships between trades and project leaders. In the past, the architect was typically the knowledge holder on any building project. Now the knowledge can be modelled in advance by all parties involved. That can mean HVAC and plumbing, carpentry and fittings, electrical, architect, quantity surveyor, contractor, and more, all pitching in with data on how they see a building evolving over time. The BIM is a simulation of the process.

With Revit, Autodesk is solving two big problems: the cost of complexity and the knowledge structure of a large, one-off project. Its platforms let teams anticipate the complexity of a large project even to the point of allowing them to plan for on-site customization. Because teams have been able to anticipate many complexity issues and iron them out ahead of construction, they are now taking new design capabilities into the field and deploying real-time design decisions on site. Its platform also continuously embellishes and expands the knowledge base of the global construction industry, nesting new experiences into the software simulation environment.

Not surprisingly, a developer community is evolving around it. For example, the SysQue app allows mechanical engineering trades to model building systems, such as HVAC, with materials and sizes based on actual manufacturer products by name and part number. That means it extends the Revit BIM data to include material costs, labor values, submittal documents, and procurement part numbers, as well as Excel reporting ready for cost analysis, prefabrication, manufacturing, and installation.

In its additive manufacturing business, Autodesk has also extended its reach through a platform and ecosystem model. Every day, Autodesk is becoming more of a services platform and ecosystem player. The big lesson from Autodesk is that platforms can be living entities, embodying human experience and knowledge and supplying a new set of capabilities back to complex business environments. As Autodesk’s strategies evolved during 2013 and 2014, the stock markets were responding favorably. Its stock price almost doubled in those two years.

At Autodesk the platform is a consistent strategy across all of its businesses. The lessons?

1. Platform thought leadership. Autodesk is able to concentrate executive talent and time on figuring out the future of platforms as a strategic asset. Autodesk aims to be a leader in platform and ecosystem deployment across various businesses.

2. Working with specialists. The communities that figure in Autodesk’s ecosystems are often specialized communities with design and engineering talent. A key insight in this world is the willingness of specialists to pool resources just like developers do.

3. Platforms generate huge qualitative and innovation impacts. Projects like Shanghai Towers are moving the principle of real-time design into sophisticated and high cost single projects like skyscraper construction. At the Shanghai Tower construction project, many of the important structural design principles were worked out on site with components, then built to order during the course of construction. It was made possible by BIM.

FIDELITY AND FINANCE—A MULTI-PARTY PLATFORM LEADER

Fidelity operates investment funds that total $2 trillion, administers funds totaling $5 trillion and represents the retirement portfolios of 20 million Americans. It is also one of the best examples of a company that has grown up with the Internet and evolved a platform approach as Internet technology itself developed. Its funds platform now hosts 10,000 independent products, and its first iteration of a strategy as a platform goes back to the mid-1990s and the birth of e-commerce.

At that time, Fidelity was the number one record keeper of 401(k) business (the management of corporate retirement programs) in the United States. Fidelity made a wider range of information on mutual fund investment opportunities available to large-company employees and administered the records of transactions that followed as people began to invest for retirement.

This approach of bringing together multiple funds on one platform has been called a funds “supermarket” and has now become commonplace in financial services, but when it was launched, it was based on an idea that was as radical as any platform today.

The company made the decision to build its broader business by allowing any fund to sell on its platform. Think of that as the equivalent of Apple allowing any developer to build apps and sell them through the App Store. It is the same principle, but it anticipated app stores by a decade. In place of apps, Fidelity managed direct broker relationships through its platform.

According to Roger Stiles, who works in the Fidelity CIO office and is a long-term employee at Fidelity, the company realized as early as the mid-1990s that it needed a singular platform. “In the mid-1990s, we had a lot of different record keeping systems but needed a simple view for the customer to understand their position. We consolidated all the funds and brokerage into one view for customers even prior to the Internet and sent them out as quarterly statements.”8

Once it had built its e-commerce platform, Fidelity began selling a kind of “platform as a service” long before Cloud computing and platform as a service became popular technology terms. It now has two subsidiaries (IWS and National Financial) that are dedicated to selling platform services to other financial institutions. It provides platform services for financial services firm USAA, for example, and previously provided the same for Bank of America and JP Morgan. The company is thought to invest upward of $1 billion a year on developing and maintaining the platform.

It is currently experimenting with the idea of building a complete wealth management service into its platform, according to innovation SVP Sean Belka. The company sees a time down the road when customers will want to see all of their financial records, from wills to bank statements to investments, all in one place and supported by some form of artificial intelligence that will aid their decision making. The platform will become accessible selectively to third-party developers and more partners and become a more effective utility as Fidelity reaches into these new areas of customer service.

The lesson from Fidelity, as from Apple, is to invest in the long term. Belka asserts that the platform is Fidelity’s competitive advantage, but it was made possible by the company being privately owned and able to make decisions based on long-term positioning even though the investment cost could look high.9 Some estimates put Fidelity’s annual platform expenditure at over $1 billion.

Here are the lessons:

1. Fidelity is a great example of major platform requirements:

2. Long term view and commitment

3. Extraordinary openness to third parties

4. Continuous innovation

USAA—A FEDERATED PLATFORM

USAA began operations in 1922 as an insurance company devoted to the risk management needs of US military service men and women and their families. Today, the company is a fully integrated financial services firm that serves 7.7 million military and nonmilitary members.

In 2009, USAA wanted to create a new car-buying service for its customers and prospects. USAA envisioned a service that would provide detailed information on automobile features, deals, prices, and trade-in values, real-time loan origination services, preferred services via prequalified auto dealers and customers, and a mobile app to support customer inquiries. At the time, USAA was not known as a car-buying resource in the general market, so it had a lot of work to do.

In the past, USAA would have developed an in-house custom system to deliver these services, as many companies did. That system would have run entirely in a USAA data center, requiring significant investment in new technology and software and considerable time for development. But instead, USAA built their new service, Auto Circle, using a platform approach—one that provided it with new capabilities, credibility, and the ability to extend its existing and new business rapidly into new markets with new customers.

Auto Circle is built on a business platform that USAA assembled through a combination of its existing information systems (e.g., financing and auto insurance) and a series of external platforms that support research, inventory, auto transaction services, used-car evaluations, and other important aspects of the business. The combined business platform offers an unprecedented level of service and convenience for the consumer. In fact, it established a new benchmark for auto-buying services in the United States and possibly the world.

Remarkably, USAA was able to assemble Auto Circle in approximately one year by integrating existing platforms and capitalizing on prior experience developing iPhone apps. Auto Circle cost a fraction of what was required to set up a platform only a few years earlier, because it could integrate third-party platforms and because of the growth of Cloud-based services.

Launched in August of 2010, Auto Circle was well received and recorded high customer satisfaction from the start. In the fall of 2010, USAA saw a 77 percent increase in visits to the Auto Circle car-buying site, a 15 percent increase in auto loans, and a 23 percent year-over-year increase in automobiles sold. Immediately after the introduction of the Auto Circle service, USAA used its experience in business platforms to introduce Home Circle. What Auto Circle does for car buyers, Home Circle does for real estate through a combined range of services for homebuyers and renters.

Three key lessons are worth noting in the USAA experience:

1. Federated platforms are an accessible route to a new strategy. The recent growth in standardized interfaces and the entrance of third-party providers of platform-based services enable any company interested in initiating a platform business to do it quickly and at much lower cost than before, if its aim is to federate (or create partnerships around) the capability.

2. Platforms help transform assets and competencies. USAA built upon and transformed existing assets and core competency, using IT strategically in cooperation with multiple partners.

3. Platforms trigger radical adjacencies. In less than a year, USAA rapidly used its success with the Auto Circle platform to move into an adjacent market with Home Circle, further leveraging its new competency, establishing a larger customer and partner ecosystem, and potentially disrupting existing players in the residential real estate industry.

EXPEDIA—TAKING UTILITY TO THE EXTREME

Expedia is a fifteen-year-old travel marketing and transaction engine, best known for providing users with access to low-cost travel and hotels. It began life in 1996 as a division of Microsoft and now owns companies such as Hotwire, Hotels.com, and eLong, the second-largest travel company in China. Expedia operates in the most commoditized online industry, and its challenge is to understand how it can move and adapt its utility proposition.

Like many players in the travel business, Expedia built part of its business through an affiliate network. Affiliate networks were popular when the web first began to make an impact on e-commerce. Companies with a globally relevant product, like travel, needed to reach big, new audiences with their branding. One way to do that was to offer publishers, or content owners like bloggers, a kickback for putting brand collateral on their sites. The web proliferated publishers; every blogger was and is a publisher. And every company had a website, too. These were all potential billboards for an ambitious brand. And to facilitate that, start-ups began offering affiliate network-building services. Expedia, like every other travel brand, had its affiliate network of thousands of publisher sites.

In 2010 Expedia decided to convert that affiliate network into something else. Think of those app developers that Apple and Google associate with. Might it be possible that there is a community of people who, in fact, want to run their own travel sites, rather than just be affiliates, or sandwich boards, for Expedia?

Expedia opened APIs to its data and services and offered members of the Expedia Affiliate Network (EAN) an opportunity to brand their own travel services with Expedia providing the back office. By early 2013, 5,000 EAN members had taken up that challenge and begun their own businesses, and an additional 3,000 major travel companies used the site.10

The rules of the road at EAN are different from those at Google or Apple. Every affiliate member has to pass site review before they can set their site to go live. That makes for more business friction than Google Play, but it has still enabled Expedia’s expansion. API-related revenues by 2013 had reached $1.9 billion, and Expedia was itself investing in a small family of new start-ups. Gogobot, for example, is a social trip-planning site using Expedia APIs.

Perhaps most remarkable, Expedia now provides the platform for the Travelocity websites in the United States and Canada, while providing Travelocity access to Expedia’s supply and customer services, leaving its former competitor free to concentrate on brand building. There are two important takeaways from Expedia:

1. Being the utility. Expedia is becoming a supreme utility, investing hundreds of millions of dollars in advancing its platform capability. It is vital not to rest on the laurels of a successful platform. This capability has to evolve continuously.

2. Converting people to ecosystems. It offers a supreme example of converting partners to an ecosystem model of interaction. In the early days of the web, companies built affiliate networks as a cheap marketing channel. Now they can create and manage large third-party partner networks.

TWITTER AS A STRUGGLING PLATFORM

Twitter is a stellar business success and even managed to create an ecosystem of approximately a million developers. However, it has not managed those assets very well. Developer Conor O’Neill, Director of Product Management at Irish company FeedHenry, explains some of the why–and in the process gives some valuable insights into how a platform should partner with its ecosystem.11 I’ve taken the liberty of quoting Conor at length:

As a user, I remain shocked at how bad both Twitter and Facebook are at targeted advertising. I have close to 50,000 tweets and years of Facebook updates but I’ve never once seen an ad on either site that I would click on. I’m pretty clear with my purchase intent on Twitter, heck I even Tweet what KickStarter projects I back. But nothing useful, ever.

This can be solved by a platform approach where third party developers deliver specific demographic groups to Twitter which generates the relevant ads and offers rev-share. TwAdsense. And the thing is, this doesn’t even need logged-in Twitter users, just vertical apps that use the platform to grab information of relevance for their user niches. Whilst Twitter’s MoPub has a classic Ad Network model, it looks like the developer has to do all the work around targeting. It also doesn’t seem that related to Twitter per se, just mobile in general.

Twitter doesn’t have to worry about “doing TV” or “doing sports,” it just has to provide the platform base to allow others to do that. In the specific case of TV, the on-screen integrations have become ridiculous. Two examples— MythBusters where Twitter was taking up 1/3 of the screen and Big Driver where we were encouraged to Tweet along, whilst a woman was being sexually assaulted in the TV show. Twitter is a second screen experience and trying to dominate the first screen is deeply intrusive, like the worst kind of product placement.

I’ve always been amazed that Twitter or others haven’t built big-data analysis of the geo-movement of storm/hurricane tweets and then layered useful commercial information on top. So many useful tweets crying out to be Hadooped, used and monetized. Watching popular hashtags or search terms in the early days was a joy. Trying to do it during any major event in recent years has been ridiculous. I end up back watching human-curated stuff elsewhere. Twitter was supposed to replace that with smarts. And if it can’t, let others do it without putting roadblocks in their way.

Seriously, Twitter, Digits + Direct Messages = WhatsApp. It’s time to make DMs a first class citizen in the platform. You want stratospheric user growth? DMs! My 72 year old mother has just joined WhatsApp on her Nokia S40 phone so she can IM from Kilkenny in Ireland to her grandson in New York. He’s on Twitter. She could be on Twitter with Digits+DMs.

You can see from that list that even a company in Silicon Valley staffed by smart people can get platforms badly wrong by not tuning in to what third parties want.

NINE KEY LESSONS

These examples show there is a good deal of flexibility in platform strategy. There are nonetheless consistencies across successful strategies that are worth noting:

1. There is no single platform strategy. Platforms deploy through a wide variety of strategies. They can even be driven by gamification.

2. Long-term time horizons are a necessity because platforms need to expand horizontally through radical adjacencies. The best platforms have a decade-long history at this stage, having graduated from delivery to perfect utility.

3. Acquisition can drive platform strategies very quickly.

4. Platforms are based on deferred revenue, which is very difficult to construct from simple NPV calculations. There will be more to say about ROI later.

5. Platform owners can and should always be seeking new communities and new horizontal applications—this is a major source of competitive advantage.

6. Platform owners can also extend utility value, as Expedia has by renting out its platform to competitors, allowing them to focus on branding.

7. Increasingly, platform owners manage the link with customers; they are accumulating vast knowledge banks on consumer decision making, and the right set of internal platforms can give the platform owner a unique position in expanding its services.

8. Platforms enable new kinds of innovation, as evidenced in construction. Platforms create new types of work environments where it becomes possible to establish better value propositions experimentally, for example at the Shanghai Towers.

9. Finally, platforms are living entities that can capture human experience and embody it in tools that make complexity more and more manageable. In instances like construction, this is opening up dynamic new methods for innovation.

The essence of good platform strategy is to use the extraordinary access to talent and creativity in the ecosystems that form around exciting services. Companies routinely fail to understand that they are moving into a new type of ideology when they set up platforms. Old command leadership can work for as long as a platform delivers cash quickly to developers. Over time however, platforms force change on the owner, not on the ecosystem. The online world provides all the information necessary to get the strategy right, but look closely. Even thought leaders like Twitter can fail. That might be because as yet we have an under-developed literature on platform strategy and success factors. What about the ecosystem?

CHAPTER 6

The Ecosystem Metaphor

THE ORGANIC GROWTH OF ECOSYSTEMS

For the past forty years, the metaphors of competitiveness have made business sound like warfare, with troops encouraged into battle by charismatic leaders, strategies providing “air cover” for the people on the ground and the view of markets as a battleground.

Ecosystems represent a significant change in the metaphors we use around business. Ecosystems are a natural phenomenon. They are the life teeming in a lake or a forest or underground. They are generally regarded as stable systems, which makes them sound idyllic but within these self-adapting organisms, life often changes dramatically. Their composition is forever shifting; species come and go, rise and fall.

This sense of rise and fall is particularly important in a business ecosystem, especially since in the future we are likely to see such change happen more frequently.

Take Nokia as an example. It has shed its main business—mobile phones—and is now a player in mobile networks and mobile data, particularly around maps. Few people realize that Nokia has done the same thing before. It had previously been involved in paper, consumer electronics, footwear, and rubber goods like cable production. Currently, the company is on most people’s list of classic business failures, but it may yet become a case study of long-term adaptation. It may be that it learns the ecosystem model and prospers in another guise.

How good a metaphor is the natural ecosystem? In a natural ecosystem, inhabitants compete for the energy resources that sustain life. It is a do-or-die kind of place where species devour each other or crowd each other’s space. That they tend to be stable is the ultimate expression of competition and mutual dependency at work.

Business ecosystems can also be a sump for the energies of individuals and small companies who sink time, skills, and ingenuity into projects that don’t go very far. For them, the ecosystem is experience, a learning process, or temporary kudos. We should not be seduced by the soft, organic image of the ecosystem. They are tough places. Ask anyone who has tried creating a hit app for a major platform.

At the same time, they do offer the possibility of efficient growth for a platform owner. Whether this makes the ecosystem more efficient overall needs putting in context. Most forms of organization have inescapable redundancies and ecosystems are no different.

Early writing on ecosystems stressed the co-opetition element, cooperating with competitors to achieve mutual gains. Co-opetition is a valid interpretation of many of the large industry ecosystems that rely on complex and expensive technologies (such as mobile microprocessors), where a common platform (like ARM, the mobile chip consortium) can embed and develop experience.1

In these circumstances, ecosystems have become an essential way around the lengthy work that a standards’ process would entail for everyone. They accelerate innovation just like platforms do. The platform and ecosystem model, however, generally describes a less formal environment and one with a backdrop of unenclosed creative activity.

In today’s ecosystems, there is almost always a commons at work, developing some element of a product in an open, non-proprietary environment.

Chart 6.1: The Growth of Open Source Code Lines^2

Here is an illustration of the commons at work (see Chart 6.1). The chart shows the increase in the numbers of open-source code lines. It shows open source doubling in volume of code every thirteen months or so in the period January 2004 to January 2008 when this study was concluded. That growth is faster than that predicted for computer processing power by Moore’s Law, the law that says processing power doubles every eighteen months. Since then a variety of surveys suggest the rate of growth has actually increased even further.3 Open source is no longer seen as a cheap alternative to proprietary code; it is seen as the most innovative approach to change. The involvement of large enterprises, like Ericsson, small start-up projects and public sector participation are all driving growth. Open source is also driving growth in online communities as well as earning more and more consumer recognition. North Bridge Ventures and Black Duck, who conduct an annual survey, believe there are now more than 100 billion lines of open source code, and ten million people involved in around 1.8 million open-source projects.4

The chart is an indicator of what ecosystems really stand for, as represented by open-source software: they are vast, self-teaching, self-learning organizations that truly power intellectual property development. They are, by and large, based outside of any one enterprise. Yet they provide the resources and the personnel development that many of the world’s leading organizations depend on.

The public sector is also leaning more heavily on open source. In a 2012 PricewaterhouseCoopers’ study of global city attributes, the city of Paris scored highest in intellectual property development, beating London, San Francisco and Beijing. The Parisians themselves attribute that score to their investment in open-source assets in the city.5

Concepts like “free,” “commons,” and “open” are critical to today’s ecosystems. They are part of a wider movement toward a commons that is part hedged in by large enterprises and part free, part owned, and part wild.

Yet, there is a strong groundswell around the idea of freely available resources, as well as around crowd-based resources. The new source of funding for many companies is the crowd; crowdfunding is a commons of sorts. People do not invest in projects on crowdfunding platform Kickstarter for a financial return. They do it in a gift environment. When the owners of Oculus Rift, the Kickstarter-funded virtual reality project, sold the company to Facebook for $3 billion, there was outrage in the broader web community that it could have taken gifts from Kickstarter members and then cashed in.6

There is, then, a shifting moral framework to business, and ecosystems typify this change. The new ecosystem is likely to function with much lower guarantees of returns to its participants than business approaches in the past. And yet there are other types of inter-company ecosystems in the market where broad-based collaboration is replacing formal standards setting and accelerating business velocity.

THE EMERGENCE OF BUSINESS ECOSYSTEMS

Our understanding of business ecosystems is evolving, but the discussion is still heavily reliant on two texts that are respectively twenty and ten years old: James F. Moore’s The Death of Competition,7 which is a strangely recurring theme in business writing, and Marco Iansiti and Roy Levien’s The Keystone Advantage.8 Both are essential texts to understanding how we currently think about business ecosystems and why that needs to change. (Moore has recently updated his work in this area in a shorter book: Shared Purpose: A Thousand Business Ecosystems, A World Wide Connected Community, and The Future.9)

Moore originally defined the ecosystem as:

An economic community supported by a foundation of interacting organizations and individuals—the organisms of the business world. The economic community produces goods and services of value to customers, who are themselves members of the ecosystem. The member organisms also include suppliers, lead producers, competitors, and other stakeholders. Over time, they coevolve their capabilities and roles and tend to align themselves with the directions set by one or more central companies. Those companies holding leadership roles may change, but the function of an ecosystem leader is valued by the community because it enables members to move toward shared visions to align their investments and to find mutually supportive roles.10

Moore’s purpose was to describe how business was changing, or should change, at a systemic level.

As an example of this, you could look at the auto industry and say change of real substance (e.g., much lighter cars, radically improved fuel efficiencies, fuel-cell technology, or broadly available electric cars) will only come about if the industry standardizes many of its components, so that a Volvo door fits a Ford car and a Renault gearbox can be repaired using a Porsche part.

That will reduce costs and increase efficiencies. It will permit the industry to turbo-charge change. The lack of interoperability is the systemic issue here. To call the existing set of activities an ecosystem, though, would be unhelpful. It is really a set of silos, and it is not truly coevolving its ideas to a point where efficiencies increase dramatically. Twenty years after Moore wrote, not much has changed in that regard.

Iansiti and Levien wrote ten years later and made a different point. In witnessing the growth of the software giant Microsoft, they observed the almost military phalanx of companies that served Microsoft. Microsoft’s success stemmed from a movement rather than just its own credentials and capabilities. The movement took the form of solutions and value-added service providers who used Microsoft technologies with their own customers.

In the late 1990s and 2000s, Microsoft had become a dominant force in business. Its vast influence created the world’s richest person in its CEO, Bill Gates.

A ruthless operator, in the 1990s Microsoft was capable of killing off its competition, for example, in web browsers and document formats.

In the web browser business, only Opera, a small Norwegian competitor, was left standing after Gates decided to make Internet Explorer a primary focus of Microsoft’s web strategy by integrating it with the Windows operating system. From that point on, the company held a monopoly on browser use. This was the very tool people used to access the World Wide Web, making Microsoft the web’s gatekeeper not only for users but also for talented entrepreneurs who wanted to make web access easier or more rewarding. To innovate, they had to get past Internet Explorer.

That monopoly couldn’t last forever. As we saw in the last chapter, concentration attracts entrepreneurial antagonism. After peaking in 2003 as the choice of 95 percent of Internet users, Internet Explorer began losing market share to Mozilla shortly after and then to Google Chrome.11

In the enterprise, Microsoft had an equally dominant hold. Microsoft ascended to an incredible position of power and influence in enterprise software because it grew a highly disciplined business ecosystem. At its peak, that ecosystem comprised some 75,000 partners, or value-added resellers (VARs) and independent software vendors (ISVs), in almost every corner of the planet. Each of these partners underwent some element of Microsoft training and might have received Bronze, Silver, or Gold accreditation by Microsoft. Many also attended the giant conferences that Microsoft organized around the world.

Each of the VARs/ISVs in the ecosystem had a dual role. The first was to become an expert in Microsoft software technologies such as .NET or SharePoint. That expertise had to be sufficient to customize Microsoft technologies for clients and prospects and/or to propose business solutions to them. The second role was to advocate for Microsoft. Microsoft had a huge following to sell on its behalf and was a precursor of modern ecosystems.

Wherever they were deployed, these well-marshalled ecosystems became a source of considerable income for what Iansiti and Levien called the keystone company or, in effect, the ecosystem owner.

The organizational power of the keystone company created a global army of resellers, but far from Moore’s vision of a more collaborative world, these ecosystems were a new form of ultra-efficient, almost ruthless global competition. While the system is still alive, it exists alongside new relationship matrices that are fundamentally different from how Moore, Iansiti, and Levien viewed ecosystems. What is different about the ecosystem today?

Elinor Ostrom wrote about ecosystems in the context of common pool resources, that is to say, a commonly created asset where there is no enclosed ownership. This concept best fits the nature of today’s business ecosystems. It applies to areas like open-source software, the creative commons and many creative products.12 When James Moore recently returned to this topic he made a further important point:

Nobel-prize-winning economist Oliver Williamson writes that 100 years ago the profession of economics split along two lines: one group went toward a price model, the other toward achieving shared purpose in organizations. The price mechanism had the great advantage of making our economy more friction-free because you and I can trade freely even if you and I don’t agree about values and purpose.

Moore argues that modern business requires more dialogue around purpose because the economics of pricing are not solving enough problems, a view that’s been expressed earlier by GE’s Jeff Immelt. Ostrom’s finding, that the management of common pool resources was most efficient when devolved to the people whose lives are most affected by them, is also a good motif for today’s business ecosystems, even if it is arguably incorrect. Moore and Ostrom are stressing two sides of a coin: the need for a broader purpose than price-based efficiencies and the power of devolved relationships to make that happen.

ECOSYSTEMS AND THE FUTURE OF LABOR

Platforms bundle software and business processes and permit scale and scope, connectors reduce business friction, and the Cloud shifts the investment paradigm for business away from CAPEX or capital expenditure to rent-by-use.

While all of these are vital aspects of a more elastic enterprise, ecosystems are what drive some of the greatest changes. They have powerful effects on the production, distribution, and consumption of services, and, to an increasing extent, products. The truth is, though, human ecosystems are good and bad: vibrant, dynamic, good for scale, but as likely to be tough and impoverishing for people who don’t manage their presence in them skillfully. Their impact on labor needs careful consideration, a task we will get to in Part III.

The idea of business ecosystems is that human economic activity is beginning to parallel natural world ecologies. By being less organized, people can enjoy the same economies of scale as those found in nature (although I pointed out earlier that loosely-coupled human activity in cities can actually be more efficient than natural ecosystems). The salient point is that lack of organization can be good. Devolved responsibility can replace bureaucracy.

The platform that powers online business magazine Forbes is built on WordPress, an open-source content management system. WordPress now powers over 23 percent of all websites globally, many of them for free, ranging from the official website of the world’s fastest man, Usain Bolt, to the US Air Force.13 That has happened with very little formal organization. WordPress employs very few people (under 200); its marketing is minimal; it does not go out and sell its technology or services. It has simply managed to develop a very strong ecosystem of supporters, advocates, designers and users, benefitting from one of the ecosystem’s primary tools—the free price point.

This fits what we instinctively feel about ecosystems—efficient, supporting scale, and low cost. Since adopting WordPress, Forbes has developed a contributor ecosystem of 1200 writers and increased visitors from 15 million to 45 million monthly. Metro.co.uk, the fifth largest newspaper in the United Kingdom, is a user of WordPress. Metro used WordPress’s familiar interfaces to develop a contributor ecosystem that took it from 5 million to 25 million monthly unique visitors within a year.

In reality, nobody has tested labor ecosystems for real economic efficiencies. It took perhaps thirty years for the open-source software movement to become efficient and it has the benefit of strong informal self-learning mechanisms (developer tool kits, online forums, peer review). Most of the communities we call ecosystems today do not have these self-learning capabilities, but they should have, and could have, if their importance was sufficiently clear. What is clear is that large platforms like Google and Apple, Joomla and WordPress make use of them very effectively and devolve labor tasks and risk to them, and are able to scale by making use of them.

However, it could easily be the case that many companies or individuals in these vast ecosystems of developers and content producers actually lose money or go about their businesses well below any rational optimum. The ecosystem is a very significant organizational development, but its internal efficiencies are uncertain. Its ability to help companies scale is much clearer and so too is the willingness of individuals and companies to take on risk on behalf of large enterprises.

To get a clearer idea of that, let’s again look to Google, specifically Google Glass.

A heads-up display fashioned like regular spectacles, Google Glass works in conjunction with a smartphone to send messages directly to a user’s field of vision, and it allows for functions like video recording, music play-out, and voice control from the heads-up display. It is a new way to interface with computing power.

When Google began developing Glass, it immediately tried to nest it within an ecosystem. A year before a limited beta launch, Google offered Glass to the developer community, where it already has deep roots, at $1,500 per pair and also opened an API and provided a software developer kit so developers could begin building apps for it.

It also launched a public campaign on Twitter called #ifihadglass.14 The reward in the campaign was that Google would give 8,000 people the opportunity to win a place on the Glass Explorer (a user-adoption) program. All they had to do was tweet a suggested application area for Glass and attract support for it on Twitter. You can imagine that many tens of thousands of people tweeted about Glass! It gave Google Glass a huge head start in creating the information layer around wearable computing. Arguably that has been its greatest impact because as a product it stalled badly within a year of peak publicity.

But Google is a great ecosystem developer. It put in place an ecosystem of developers and lead adopters from the outset of this project. But look closely and you will see Google had people paying to do its work. It is the most glaring example of how labor inputs can be distorted in the modern ecosystem.

The company relied on two common techniques here—crowdfunding and crowdsourcing—to generate support for Glass, yet nobody thought of these as routine crowdsourcing and crowdfunding implementations. In fact, in Google’s hands, these techniques lent prestige to the project and helped them raise $15 million to run the Glass development program. The company aimed to launch with 2,000 apps ready for market, and it had 10,000 advocates before it even had a post-prototype product. Google has demonstrated how techniques like these, traditionally the domain of start-ups, can be used to great effect by a global enterprise.

Earlier, I referred to attempts by Epson to create a Glass-type product, the Moverio. The Moverio BT 200 smart glasses launched in January 2014, joining at least a dozen Glass competitors on the market, including Intel-owned Recon Jet.

The challenge for these competitor products was that they lacked the scope of applications that Google was lining up. Instead, they had very specific-use cases, such as aiding in assembly line production or providing data for sports stadia or high-performance cyclists and skiers. They had no real ecosystems in these application areas. Nor did they have a broad scope of applications. Google, on the other hand, had 2,000 developers working on Glass apps. Yet the competitor production teams have deployed their own resources and their own ingenuity in developing use-cases and applications. They are more focused on real businesses cases and have some recognition for that.15

It remains to be seen who will ultimately grab the most market share in this area, but it is almost certain that, by now, Google has superior data on how Glass-type products are used—it had 10,000 people experimenting with them and hundreds of journalists writing about those experiences. It enjoyed constant connection with all 10,000 users.

Compared to the number of references to Google Glass during its peak of interest in May 2013, references to Moverio and Recon Jet during the same period (through the end of June 2014) were generally negligible. In June 2013, Recon Jet registered its highest number of mentions online, yet there were still sixty-seven references to Glass for every reference to Recon Jet.

Both Epson and Recon have great products, but neither has the ecosystem experience or the strength Google has in the information layer. Google is simply a master of ecosystem strategy. In an age where marketing is divided between old broadcast methods and new crowd-centric methods, Google consistently proves that real engagement, even when driven by transparent business goals, wins out.

Google has also shown how to integrate users and developers into a self-marketing community. This is the power of the ecosystem and externalization working together. Many of the key functions of product development in Glass were externalized to those 10,000 people, including ideation, app development, customer feedback, use-case development, data provision, and resource allocation. Externalization in these subtle ways is a complete rewrite of the old rules of business. Whoever wins the market share in heads-up displays, Google has demonstrated that the new product development process can exclude even highly skilled employed labor. What’s more, it encourages people to work for free. Ecosystems are a strange bundle of motivation and relationships.

For example, many of the apps that reach a platform like Apple’s App Store or Google Play receive hardly any downloads and are often given away with little economic benefit on the producer side.

Figures from a 2011 analysis of the App Store suggested that 83 percent of all apps were given away and the average selling price of the remaining 18 percent was only $1.44. In contrast, iPad apps had an average selling price of $6.42. A year later, estimates of future app prices had trended down to 8 cents per app overall. By that time, the average revenue for paid apps had declined from $26,720 in 2011 to $19,560 in 2012, according to researchers at Research2Guidance. Tristan Louis in Forbes estimated that an app developer working on the Apple platform would earn just over $21,000 annually for producing five apps.16 This excludes revenue created through in-app advertising but, even with that in mind, being an app developer and selling in App Stores can be a tough business.

When people signed up to a Microsoft VARs program, they did so knowing that once qualified, they would have access to a Microsoft-installed product base, which needed solutions on a lucrative fee basis, and huge customer demand. Today’s app developers can work in the same way for third parties—one of the big growth areas for developers is creating apps for smartphone platforms, like the iPhone and Android, on behalf of large companies. But the likelihood is they will be independent businesses or entrepreneurs looking to strike a hit.

Chris Stevens was one of those. Stevens was a three-time app developer before he hit the jackpot with Alice for the iPad. Alice couples the text of Alice in Wonderland with animated drawings (created using the original Alice illustrations). Nothing much happened for Alice until Stevens made a video that he uploaded to YouTube. It was only when the collateral was in place that the app began to move. He secured coverage on Oprah and ultimately racked up 500,000 downloads within a year.

Stevens’s background is a typical reflection of apps culture. He’s a former writer at The Times in London and also worked for the BBC and CNET before moving into apps. He went on to write Appillionaires, a book about the people who had struck it rich in apps. Stevens explains, “There have been huge success stories from people working in small teams often in their bedrooms and that has massive appeal, that idea that intellectual vigor and the strength of the idea is enough.”

He also claims that apps developers are a breed apart, not necessarily driven by typical business objectives. “The game StickWars was created by one person, who now happens to be in the US Navy. He created the app coming out of college, joined the Navy, and works on nuclear subs, and now jokes that he can only do the updates when the submarine surfaces. He seems to be unmotivated by money, but that’s not unusual.”17

This is not the VARs or ISV community at work. This community is more diverse, vibrant, risky, and dynamic, and includes companies with highly structured business plans, as well as those who leave more to chance.

Most often, though, it brings in companies that are comfortable somewhere between the boardroom and the casino. The game company Supercell, for example, is one of the most successful on the planet, coining $1.5 million a day within three years of its launch. But its formula is built on the expectation of multiple failures. Its competitor Rovio hit the big time with game number fifty-three.

It is vital to understand this change in the business environment.

Remember when failure was not an option? Now failure is integral to the broader business ecosystem.

Today’s ecosystems are coproducing communities where wealth creation takes place within specific guidelines or rules of the road set by the platform owner. New cultures have quickly evolved around them, from the meritocracy of open source to a more laissez-faire culture around platforms like Facebook and app stores.

These cultures are dependent on the core platform that is supporting the community. They often operate in an atmosphere of trial and error to get their products right; failure is a frequent occurrence. Self-organization and self-management are paramount. Financial returns can be high risk. A key feature, too, is many participants earn very little revenue but the majority can build or contribute to their long-term reputation and portfolios.

THE BUSINESS CULTURE OF ECOSYSTEMS

Strong communities are a singularly important business resource. Technology has certainly been a key factor in the development of the new elastic business model, but without the communities that surround it, it would have had little effect. Community now dominates software production, especially in large enterprises, as evidenced by the launch in 2014 by of the TODO group (Talk Openly, Develop Openly).18

Writers who focus on technology tend to overlook the seismic and formative effects of the open-source software movement on modern business, yet the movement is the prototype for much online activity, including business ecosystems and the freemium business model. Open source, though, is not a technology in itself. It is a set of relationships and agreed-upon rules.

Much modern technology is based on open-source software: the Linux operating system; server virtualization; Amazon Web Services; the database technologies behind big data such as Hadoop; open-stack and Cloud standardization, software as a service, platform as a service; and the Android operating system that has made mobility so easy. It is a source of low-cost, often free, labor, but it is also a model for how to organize human endeavor. Its outputs have transformed technology in general.

What’s remarkable is that most companies involved in the great transformation to a more devolved economy rely on extreme openness but also on the dramatic cost-reduction process of near-free labor. Today’s software revolution, from apps to the Cloud, would never have happened without the open-source community.

Its effect has been profound also on business models. Companies that built their wealth from open source had to find new ways to sell, given that most of their products were commonly owned. The evolution of Red Hat, a pioneer in the field, foreshadowed the business model innovation of the past five years. Red Hat, the product, was one of the original compilations of a commercial version of Linux, the open-source server operating system that now dominates many data centers. Initially, the company tried to make its revenues by selling books on Red Hat Linux and charging for telephone support for its products. But neither would allow it to scale. Then in 2001, Red Hat began work on an enterprise version of Linux, Red Hat Enterprise Linux (RHEL). From the start, it took a different approach, selling this new version as a subscription rather than a one-off sale. It continues to give away RHEL source code for enterprises to self-compile, but it sells subscriptions to the version that it compiles and prepares for use. Red Hat also provides services, such as bringing customers into open-source communities to work on specific projects.19

According to Ars Technica: “While RHEL would bring in the money, the spirit of the original Red Hat Linux lived on in Fedora, a Red Hat-sponsored community project, which was first released in 2003 and initially based on Red Hat Linux code. Fedora and RHEL have a mutually beneficial relationship, with Fedora code serving as something of a testing ground for the enterprise features delivered to Red Hat’s paying customers.”

This brief account of Red Hat shows it experimenting with several models that would become commonplace: first, the free product; second, the subscription service; third, the add-on services; and finally, a version that is more experimental that allows Red Hat to understand future requirements.

Open source is a vast, self-learning community that has laid down the guidelines for creating excellent code by being transparent and meritocratic—it is an ideal model for modern ecosystems. The question is, how far are these principles traveling in the wider ecosystem world?

TYPES OF BUSINESS ECOSYSTEMS

Can we build a typology of ecosystems to help understand more about them? Here’s one set of possibilities:20

Scale ecosystems—rapid scale. These ecosystems are largely a vehicle for scaling business, and they are designed to effectively remove transactional friction. In the case of Apple, the apps developer ecosystem helped sell the iPhone to a broad public and is now spearheading Apple’s enterprise growth. In the case of Android—a much lighter touch, open ecosystem originally based on open-source principles—the Android platform attracts developers through the core principles of openness. Google invests lightly. However, it has its own agenda to exploit the Android ecosystem initially via ads, then apps, and now through device sales.

Creative commons ecosystems. Creative commons is a legal framework for sharing copyright. Many writers and artists share work under creative commons licenses, as do corporations. In the ecosystem approach to business, creative commons licensing is being used to take friction out of intellectual property development. Autodesk provides a classic example, now that it has branched out into consumer ecosystems and begun to release versions of its software as apps for consumers and makers. New, spontaneously generated communities have evolved around this maker software. The growth of such communities is leading rapidly to an open-hardware movement with Autodesk at the center, ready to monetize this activity. All that activity, however, would be impossible without creative commons licensing.21

Customer ecosystems. These tend to be thought of as a social network of end customers who participate in activities around a brand of product like LEGO. However, customer ecosystems are also prevalent in business-to-business companies because they improve agility and accelerate development times.

The mobile microchip design company ARM has built a significant business by being the repository of design and innovation in mobile processors.

ARM does not make processors. It does develop partnerships that enable its customers to co-design processors at the leading edge of mobile applications.

James Moore makes the point that these types of ecosystems are characterized by demand disruption—customers becoming powerful enough to demand breakthrough results from their suppliers.22 Why does anybody belong to the ARM ecosystem? Because it is the only environment within which they can continuously push the barriers of processor design, because this is essentially a relationships-based system of demand and supply.

Contrariwise, Intel for a long time had customers who simply could not keep up with the pace of innovation set by Moore’s Law (arguably the company lost out in mobile because of its focus on persuading and cajoling the old ecosystem). Intel was constantly in the position of pushing demand forward. ARM is always responding to disruptive demands.

Nokia Solutions and Networks, the telecoms infrastructure group recently bought out by Nokia, has traditionally been a labs-to-market company building its own intellectual property and then using its profound marketing power to sell to customers. Today, it is much more likely to try to bring customers into the development process and to find partners whose intellectual property they can mix with their own. This is an ecosystem of peers, pooling resources for rapid development and improved agility.23

Systemic ecosystems. Systemic ecosystems are the totality of an industry structure. In this sense, they are the system where a given range of businesses operate. The electric car industry is a tiny part of a century-old, gasoline-based, auto service ecosystem that innovators had largely failed to change until recently. The gasoline-based system has garages, gas stations, high-speed motorways, and strong cultural roots. Electric cars have made some headway in creating a comprehensive alternative system, but they haven’t been able to get a critical mass of customers to change their behavior. Hydrogen fuel-cell cars, which have more benefits than electronic, are even further behind. These are all part of a struggle to change the system, to shift the ecosystem to a new, raw materials base.

The lesson for other innovators is that many business sectors are in fact systems, and competition often has to arise at the level of new systems rather than just new products.

THE NEW AGE OF SELF-DETERMINATION

Companies that want to develop business ecosystems must be prepared for substantial investment. Though it is possible to develop new services by combining assets from different partners, as USAA has done, it is also worth remembering that all ecosystem investment is a way to scale business. Implicit in that idea is the need to grow computing assets and data analytical capabilities, but the missing ingredient lies in community development, how to build an ecosystem of self-determining economic actors.

Companies can be reluctant to take on this task. They are used to their relationship management being automated through systems like CRM. They believe that the platform and ecosystem model should give them a network effect or significant market acceleration.

The network effect is referenced often in platform literature, but I think there is some confusion. The network effect is a power law. It means that under specific circumstances, businesses scale rapidly, usually once a given threshold or critical mass of users is met. The classic case of the network effect is the telephone. It is pointless for only one person within a social group to have a telephone. It requires other people to own a telephone before it can ever be used.

The effect of that is to make the first owners of the phone external agents for propagating the use of phones. Something similar can be said of Facebook. If you were the first user of Facebook, it would have a very low utility until your friends joined, and each friend could increase utility by adding in more friends and broadening your network.

At this stage, many of us badly manage our social networks and suffer a form of network congestion as a result—too many friends and too much noise. But prior to congestion, the actual logic of the network effect is that there can only be utility value if a person’s own network of friends and colleagues also join. That makes users necessary advocates of the service.

There are diluted forms of the network effect, where a service can improve in value as more people use it. The telephone is again a good example. While the first phones were expensive, the more people who owned them the more it should, in principle, have driven down the cost of the device and network traffic (while this happened it was also hampered by monopoly ownership of networks and device distribution).

Hotmail, one of the first, web-based e-mail services, had a network effect in that one user of Hotmail needed to encourage other users in order for the system to be useful to any one of them. New messaging systems like WhatsApp follow a similar principle. Microsoft created artificial network effects with Microsoft Word.

The platform is said to have a network effect, often for two-sided markets or multi-sided markets.

A two-sided market is different from what we have just discussed because the user base is not homogeneous. In other words, users might be credit card holders and merchants who accept credit cards.

Airbnb and Uber are also both thought of as two-sided markets. Both the room renter and room owner are users of the Airbnb platform, but both have different objectives: to rent out rooms and to find rooms at a reasonable price with some discount for risk (the more Airbnb or Uber can mitigate risk, the less the discount needs to be).

By way of contrast, retailers prevent two-sided markets from evolving. They are not a platform intermediary (though Amazon is to a certain extent). The nightmare scenario for retailers is to put customers in direct contact with suppliers, but that is what happens on Airbnb and Uber (also on platforms such as Booking.com).

The theory of two-sided markets suggests that a platform owner can achieve scale through the kind of network propagation effect common to networked products. But this is not true. Network effects occur when a product has to be used for a set of activities to be completed. For example, it is essential to own Adobe Reader software in order to read Adobe pdf documents. This creates a network of Adobe Reader owners. In the case of two-sided markets like Airbnb and Uber, there is no necessity to use either, not as a room renter or as a room owner, not as a rider or a driver. Because there are alternatives to both, Airbnb and Uber have to compete through better service, higher trust, and/or lower prices.

It is quite feasible for Airbnb to have 20,000 rooms available to rent and nobody to rent them. It is inconceivable to have 20,000 people on Facebook with none of them being linked or communicating. Likewise, there is generally no imperative for people to own iPhones and download apps. It is a choice. On the other hand, it is imperative if people are to use credit cards that merchants are able to accept them.

What often happens with two-sided markets is quite different from an imperative. It is the joint effects of the ecosystem and social affiliation in the context of the new economies of scope.

Because it is now possible to offer vast scope at very low cost, users can select the type of service they want to use. In effect, my iPhone will be vastly different from others’ phones because of the apps I choose, just like my profile of use on Airbnb will be different from other people’s. I might self-select a service that is built largely around, say, shared rooms in inner urban areas of North America, whereas other people might select rural properties. The missing ingredient when people talk about two-sided markets is this interaction between the range of choice and the users’ ability to construct a service that suits them.

The growth of two-sided platform businesses like Airbnb and Uber appears exponential, when it is really the result of hard work and marketing dollars. In July 2013, Y Combinator (YC), the accelerator that backed Airbnb, posted an article called “Do Things That Don’t Scale.”24 The author Paul Graham’s point was that Airbnb’s founders did not hit upon a secret formula for scaling their business (like a network effect). For weeks on end, they went out knocking on doors and asking people to join their service as renters, even going as far as to photograph the properties for them.

Marketplaces are so hard to get rolling that you should expect to take heroic measures at first. In Airbnb’s case, these consisted of going door to door in New York, recruiting new users and helping existing ones improve their listings. When I remember the Airbnbs during YC, I picture them with rolly bags, because when they showed up for Tuesday dinners they’d always just flown back from somewhere. Airbnb now seems like an unstoppable juggernaut, but early on it was so fragile that about thirty days of going out and engaging in person with users made the difference between success and failure.

The Airbnb formula was to try to increase listings and customers by 10 percent per week. The point about Airbnb and other two-sided markets is they don’t enjoy a network effect. They benefit from mass differentiation and economies of scope, and over time, they build strong social affinity in markets that are often fickle. That begins with the hard work of finding participants and skillful use of social media, word of mouth, and trust-building factors. What companies really need is social affinity, the willingness of an ecosystem to recommend and replicate its collateral and messaging. It can’t be replaced by a network effect.

BUILDING ECOSYSTEMS AND SOCIAL AFFINITY

Building ecosystems is a commitment of time and energy, as well as an exercise in constant interaction to discover ways of satisfying utility needs at the right scale and price. In the period 2011 to 2013, Autodesk set out to acquire companies that were linked to or had built strong business or consumer communities.

At the same time, the company forged partnerships with 3-D printing companies like 3D Hubs. 3D Hubs has a network of 3-D printers around the world. That means anyone in the Autodesk ecosystem can now access the precise functionality they need in 3-D design tools via Autodesk’s apps; they can access open-source engineering designs so they can play with, simulate, and prototype different components; and they can print components or finished products via 3D Hubs’s network.

They can also go one step farther. Communities like GrabCAD are natural meeting points for people who might back a Kickstarter or similar crowdfunding campaign. If a designer/maker has a hot idea, she can now get it to prototype and jump onto a crowdfunding site with the power of a network already behind her. In the process, Autodesk has built up an end-user community of 120 million people.

When Google set up the Google Map ecosystem, it first acquired half a dozen companies it could compile into a viable mapping platform. It gave the outputs of the mapping service (the online interactive maps) away to websites and quickly generated an end-user community of one million websites. The company secured interest from its wider developer community based on the momentum it created. Like most large tech companies, Google holds an annual developer conference to share its plans. It also has a prestige system—Google Experts—to reward and honor developers who take a lead in community projects. It maintains a dedicated site for developers, another common practice among tech companies. Google is an active player in many open-source projects. Right now, it is active around the Robotics Operating System, a candidate for becoming the standard operating system for robotics, an area where Google has managed to deploy many aspects of Android.

What’s noticeable about both of these examples and Google’s experience with search is that they are considerable investments in a particular way of doing business. There are no shortcuts and they are not cheap. These companies are investing in relationships with highly skilled people with their own astute business acumen, who have many options to choose from. In a world suffering a developer shortage, these are highly prized skills. But they are also evolving relationships, and as we saw earlier with SEO, these relationships need managing by intuition as much as by a playbook.

They are devolved and self-determining. They are social ecologies that demand a different approach than what enterprises have previously used to deal with employees. The ecosystem forces a new sociability onto enterprises and the route to profitable goals is social affinity.

ECOSYSTEMS AND CUSTOMERS

Simultaneously, a new phase of externalization has evolved: crowdsourcing. Crowdsourcing can function in creative environments and is sometimes the baseline for open-innovation projects. But it can also function in routine processes. We are about to witness the rapid growth of crowdsourced routine tasks. Amazon’s Mechanical Turk, which breaks work processes down to ever-smaller units that are outsourced to very low-wage economies via game-centric or mobile text-based tasks, is finally taking off.25 But even companies at the high end of adding value, like GE, are using crowdsourcing to address complex tasks. In GE’s case, it has been crowdsourcing hospital enterprise management and airline fuel consumption models using the crowdsourcing platform Kaggle.26

On the customer side of the business ecosystem, change is just as rapid. In a 2011 study of the British economy, the MIT open-innovation theorist Eric von Hippel noted that in some economies (notably the United Kingdom) consumers spend more than firms on improving, changing, or innovating products.27

The growth of customer ecosystems, or the integration of customers into business ecosystems, has gone farthest in print and broadcast media. In print, readers have always had the option of sending in letters to the editor. Today, their comments on newspaper websites are integral to many of the stories that journalists tell. The number of comments might also signal the importance of a topic to other readers.

The advent of cheap videophones has deepened and broadened customer involvement in media. American broadcaster CNN, however, has gone much further than most with its iReport service, a dedicated channel for viewers to send in news content.

At the time of writing, CNN has announced its intention to convert its iReport consumer participation reportage segment to a social network. This will more fully blur the line between viewer, reader, and producer. CNN has also expanded its commitment to support multiple end-user devices that now include a range of traditional screens and mobile devices. On websites like YouTube and Instagram, the integration of the customer as producer and viewer is already complete.

Across all sectors of commerce, communities and coproductive ecosystems are underdeveloped resources. More importantly, we are headed to dramatic changes in how companies interact with customers. Developing community and building ecosystems smack of soft skills that many companies would like to avoid. But these skills are a core capability in the new economy. Power is shifting to companies that have this engagement, but perhaps power is also shifting against many of the people within the commons.

To summarize, ecosystems:

• Are often a way for companies to acquire resources and assets at low cost or for free, even though they are essential to the platform’s profitability. The platform’s scale is often key to profits, but the availability of free resources is also important.

• Can be large coproductive partnerships between networks of major companies involving cooperation and competition.

• Fuel economies of scope but not necessarily by duly rewarding people at all levels in the ecosystem, and indeed, companies that build ecosystems are under no obligation to reward participants.

• Are often part of a new business model and are always part of a complex new business morality.

• Require platform owners to also think carefully about instigating appropriate reward systems, even though these might be nonmonetary. Ecosystems are essential advocates of a service, a role that gives the appearance of a network effect.

• Are a way to devolve or share innovation and risk onto self-determining groups.

• Often involve high levels of chance that can be very rewarding, but rewards come in different forms.

In more complex inter-company ecosystems, partners are increasingly willing to make investments that improve competitor positions, which in turn makes investment decisions more relationship focused.

Participants in grass roots ecosystems need to construct and review the reward system for themselves, asking questions like: How much is reputation-building worth and in what future context? What value will the new relationships that I make have and when might that value be realized? What is to be learned from a project, and do those lessons have tangible value?

These questions are important, but few people put a value on them or construct their participation with a strong sense of planning the outcome of participating for no cash. It should be a new responsibility of the education system to teach them how to perform better.

While it’s always tempting to be a cheerleader for a new way of doing business, ecosystems are a complex phenomenon. They emerge from open source and creative commons’ thinking and have a different moral outlook from many routine businesses. But they can also be enclosed by strong platforms and in the process become impoverishing for many members. It is also transparently obvious that platforms need modern ecosystems to have depth, which they currently lack. Ecosystems often lack the structured learning environment of open source and they lack a culture of peer critique.

In other words there are policy measures that can help people understand better how to make them work; and policy measures that could support education and cultural growth inside ecosystems. Those problems tend not to affect the large inter-enterprise platforms like ARM but they are a feature of the ecosystems that many individuals depend on for a living, so need looking at further for policy support.

The skills for developing these ecosystems begin with hard work and simple metrics (add 10 percent more customers a week). They also involve strong social media skills to build awareness, involvement and loyalty, and a structure that allow real participation like mutual trust building. The ecosystem needs to be its own reward for users.

CHAPTER 7

Friction Free Business

THE SHIFT TO CONNECTION

Ericsson is a nineteenth-century company in the midst of converting to a series of twenty-first-century platforms. It had already been buying into service delivery firms in broadcasting and financial services when it bought MetraTech in July 2014. MetraTech is a flexible billing platform. Its software allows companies to collaborate through different types of incentives through their billing systems.

Think of it like this. If companies are involved in multiple partnerships, maybe there are ways to vary the billing and the terms of invoices so that each is better incentivized to make a project work.

Partners can create multiparty agreements where the financial rewards are easy to adapt to specific needs. For example, an airport can use differential rents and revenue sharing to ramp up new activities for example favoring rents in one part of an airport that is getting less footfall in a holiday period. MetraTech’s software lets these adaptations take place on a daily or weekly basis. Conventional ERP or enterprise resource planning makes it very difficult to be flexible and in fact forces companies to agree to rigid contract documents that the system can replicate easily.

Flexible reward systems are important where companies federate their offer to a customer base. Now Ericsson can promote its platforms as federated business enablers. Billing becomes a key connection enabler at scale, a fluid piece of business strategy in place of static, old rentier behavior.

Connectors are an essential part of the new business infrastructure. In the past companies have depended on standards to agree on interoperability. Open source plays a part of that role today. But there is also a burgeoning new type of connectivity that provides seamless, friction-free business. It derives from business externalization, as described earlier, creating the bulk of economic activity outside the corporate walls

Today’s software environment is snapped together and regularly repurposed because the connection points between systems and software can be mediated by new software tools like APIs. Businesses, too, can now be snapped together.

Business processes like marketing campaigns, partnerships with dozens or hundreds of developers, the acquisition and integration of content from a variety of partners, the integration of multiple applications on a third-party platform—all these can be built as if they were modules.

All around the technology of interconnection, new relationships need to be built. Connectors fill this dual role. Connectors are ways to automate business, but they also change the social dimensions of business through scale and flexibility. They make modern ecosystems possible, and they contribute hugely to the feasibility of platforms.

Think back to the early days of the World Wide Web. Suddenly, a small symbol or acronym began appearing on websites, particularly those that hosted original content. The acronym was RSS and for many people it is still a mystery.

RSS (really simple syndication) would eventually be used to create powerful new web content products like The Huffington Post, sold in 2012 for more than $300 million. It means that with a few mouse clicks, people can access and use any content that has an RSS symbol next to it. When it arrived, RSS was a new form of syndication initially pulled or subscribed to by end users. It allowed The Huffington Post to snap together a daily newspaper online.

RSS is a powerful example of new business relationships at work, relationships that wouldn’t be possible without universal connectors. Those relationships are often anonymous. They represent business built between people who don’t meet, but whose output is connected automatically.

Four broad categories of universal connectors now exist:

Content. The first category is content connectors like RSS, Twitter, SMS (i.e., texting), calendars, or instant messaging that enable almost anyone to interconnect and exchange information.

APIs (application programming interfaces). This category of connectors is used by programmers and software architects to make a plethora of dissimilar systems and technology interoperate. They provide the capacity for entire systems to interconnect in a standardized way and interact productively under many conditions.

Identity and social networks. Identity and social networks now assume many roles of universal connectors by integrating all communications within one platform (though this was eventually found by Facebook to be too limiting). LinkedIn, for example, is a giant experiment in low-friction interconnection of people. New types of universal connectors have developed around social network platforms like Facebook, LinkedIn, and Google+.

Universal connectors propagated by identity and social networks simplify the transfer of information about many of the events, communications, and content broadly related to individuals and companies doing social business. The sophistication and variety of the universal connectors available through social networking platforms have increased significantly in recent years and will continue to advance for some time.

Marketplaces. New marketplaces allow companies to place both physical and intellectual processes in the open market to be executed by third parties. That can mean marketplaces like opendorse.com, or it can mean crowdsourcing and crowdfunding. These two areas are relatively new, and business people are gradually learning how the dynamic of broad groups of people can be managed to best effect. Nonetheless, these are new resources for achieving business objectives, and the participants are outside the firm.

Let’s examine each of these connectors in turn.

CONTENT CONNECTORS AND FREE WORK

RSS has a few characteristics beyond those that helped The Huffington Post succeed. From its inception, content syndicated via RSS has been free. (Free is a constant refrain in the born-social era.) Yet there is a contract in this arrangement, even with The Huffington Post. Implicit in RSS is an agreement not to abuse copyright (although this agreement is often ignored).

The contract is implied through generally agreed-upon terms of use rather than being negotiated by content creator and content user. The contract also implies the content creator should be honored or cited for what he or she produces. Content aggregation is not supposed to be a complete giveaway. Instead of receiving payment for their work, content producers receive kudos.

This method of building business relationships is both anonymous and easy to scale. The reader, aggregator, and creators of content never meet each other and the creator may never know who the aggregator or re-user is. The relationship is anonymous even though they have a contract with each other. The aggregator is generally free to use the content in some form, either in an online reader or by integrating some of the content into a blog or website.

Scale is very relevant to RSS. Some authors have thousands upon thousands of readers pulling their RSS feeds effortlessly, which can then be reused. The contractual basis of the relationship is real and important. By taking the feed, the reader agrees not to misuse the information, and the creator agrees to relax his or her copyright protection, knowing that the content will be used in some format.

This proliferation of free work has forced content creators to think how they should monetize their writing, or what alternative value they could derive from having an engaged audience. That, in turn, led to a very different view of how we build and treat reputation and monetize work (bearing in mind that in open source a good proportion of work is absolutely given for free).

Concurrent with these developments in RSS, in 2001 the Creative Commons movement was founded and helped produce licenses for sharing and attributing works that were provided without a monetary charge. There are now 130 million creative commons works online. Major companies like Al Jazeera use commons licenses even for significant video reportage. And when people upload material to YouTube, there is an implicit creative commons license attached.

Another development that promoted free content was Google’s introduction of AdSense, the text ads that Google serves up alongside third-party content. Evan Williams, who later cofounded Twitter, launched the blog platform Blogger, one of the first “push button publishing” platforms, in 1999. Google bought it in 2003 and began promoting blogging heavily. Simultaneously, platforms such as WordPress also entered the new publishing arena.

From these content platforms, a new class of company grew that used other people’s content to create new products, strictly speaking in contravention of copyright. But attitudes to copyright were changing, as they had with open-source software. It was not such a big deal for most content creators to see their content used so long as they received reputational value in return.

This shift had significant repercussions.

Just as the web represented a modular form of software at work, content also became modular and reusable. The product of a writer’s imagination, the result of research, an expression of painful reflection—all content went into the pot and simply became a component for other people to use. Distribution of ideas in some sense replaced the origination and production process, just as curation is now replacing creation on websites such as Upworthy.

Companies that stayed in the origination and production paradigm, like newspapers, lost out—they could continue to create print-only content or create firewalls to try to charge for online content. But very few such companies found global audiences this way, let alone benefited from the new economics of communications based around universal connectors.

Some of the most successful newspapers functioning online lose money. The Guardian, with a monthly unique visitor audience of more than 70 million readers, lost in excess of $45 million in 2013, even as its online business grew.1

The reasons for this disparity are that RSS radically changed the content industry, creating new social and economic structures. Google’s AdSense shifted revenues to smaller sites and gradually unleashed programmatic auctioning of advertising. Coupled to the drift to mobile access, this forced ad revenues down. At the same time, content publishers have become increasingly dependent on the shift away from monetary reward to public reputation. In the meantime, readers also came to view content as free.

It is characteristic of universal connectors to transform what they touch and perhaps its most dramatic impact has been the erosion of paid labor and living standards. Of the 1200 contributors writing for Forbes only sixty people—just 5 percent of all writers—made or exceeded the average wage for journalists of around $45,250. The US national average was $48,800 (some estimates put it higher), which means a proportion of these sixty would have been below that, too.2 The connected economy is exciting but it can also be a hard place to grow a career with a living wage.

APIS: THE CORE CONNECTORS IN TECHNOLOGY

APIs, put simply, are ways to connect two elements of business that might not have been designed to work together. They help to connect dissimilar software across companies and institutions and connect analytical software with assets like data. They enable mobile phones to access data that was designed for storage in different “silos,” only to be accessed by complex query languages. APIs underlie much of the workings of the Cloud. They facilitate mobile apps to interoperate across dissimilar networks as users go in search of different types of data, perhaps from their banks. They allow gamers to collaborate or compete across dissimilar personal computers, game consoles, and mobile devices.

APIs specify a uniform way of interfacing software functionality, which means all kinds of software can work together, even when not originally intended to.

Developers and other specialized programmers have formed an entire sub-industry dedicated to creating APIs in response to the global demand to interconnect systems and technologies.

APIs have led to a new type of economy of scope and enabled the growth of a new business model. Apple, Google, USAA, and other elastic enterprises would not have been able to deploy business platforms or form business ecosystems without the emergence of APIs. Like RSS, they let business relationships scale anonymously, without the need for bilateral negotiations and specially constructed bilateral legal contracts.

API growth is astounding. Mashery—one of the major players in the API management space, having built APIs for Expedia, USA Today, Hoover’s, Netflix, Klout, Dun & Bradstreet, The Guardian, The New York Times, and Best Buy—estimates that the average individual has 147 connection points, from social networks, to mobile phones, to television sets and consoles and gaming platforms, to cars, home mashups, even LEGO pieces, which can now be programmed. Each of these represents multiple automated API transactions with a remote platform.

Figure 7.1: Projected Open API Growth Rate. Source: Craig Burton and Phil Windley. 3

Figure 7.1 shows that open APIs are currently growing at around 100 percent per annum (like ecosystems, faster than Moore’s Law). They are born of the principle of interoperability, the trend that has been driving disruption across telecommunications and IT. The principle of interoperability is spreading, and the greatest significance of APIs for business planners may lie in what comes next.

In hardware production, engineers have now created communities to share creative commons’ libraries of engineering designs. These community platforms become, in effect, a human API where the designs of others can be mashed into a single new product. That trend will continue as hardware apes the successes of software and as the Internet of Things (IoT) expands. The IoT is highly dependent on APIs automating business processes and relationships through software.

The IoT could be an overblown tech fantasy—we’ve seen plenty of them before. The claims made about its revolutionary impact are not testable in advance, so we will only really grasp its significance in practice. But here is a foretaste.

The most demanding environment in business today is innovation. Companies know they need to innovate, but they can’t really be sure about the detail. There are too many imponderables:

• Does your staff have the right insights to make the right calls on where to innovate?

• Do you know enough about market evolution to second-guess events in twelve months’ time, when a product will be ready for launch?

• Will you be disrupted?

• Are your teams really competent enough for the next generation of innovation?

• What will the returns look like?

As these questions have become more pressing, many company leaders find themselves managing a growing gap between a company’s profitable businesses and its innovation activities, particularly when they adopt fail-fast, fail-cheap methodologies. They take on a lot of innovation without clear purpose because fail-fast, fail-cheap is a trial-and-error approach. It is almost anti-strategy.

The IoT can help solve that problem by automating this creative but vexed process. Imagine that your company’s products automatically record how customers use them. It happens with software—every time you use a search engine, your search is recorded along with basic data about other sites you visit, your location and search history. That pattern helps score the relevance of sites served up to you. But imagine that principle applied to hardware. That’s what the IoT does. Suddenly, you know how your product or device is being used—if it is a vacuum cleaner, how often it turns badly on its axle; if it is a car, how often a particular driver (and all drivers) stand on the brakes just after accelerating. You no longer need to go out and interview customers about their preferences because you can see them in their behavior. You suddenly know a lot about usage patterns and can fine-tune your innovation program to control that behavior better or improve product performance in relation to it.

Now apply that to shift all innovation.

Suppose every component in your product is supplied with a sensor. Suppose that product is an electric car and you begin to record data on usage patterns. You find that in cold, wet conditions, particular uses of the car leave the batteries exposed to certain new types of risk behavior that drains the battery very quickly.

At the same time, you find that driving is interrupted in what looks like an aggregate pattern across all drivers in the rush hour, as the lack of engine noise makes the car interact dangerously with pedestrians, and drivers are compensating with a lot of braking, leading to rapid brake deterioration. Multiply that data across multiple components, weather conditions, and time zones.

What you have now is a set of data streams that will allow you to automate your company’s innovation priorities. The data is reflecting what needs to be changed in your product to make it perform its functions in ways that are better suited to different types of users. You have an opportunity for differentiation (and perhaps a small price hike) and higher customer satisfaction through tailoring performance to needs.

You know precisely how many customers will be affected by your innovations, and you will have good information on what the innovations must achieve. You will be able to roll them out in a version of an A/B testing program as you try specific modifications in some populations before a general rollout. (A/B testing programs allow companies to introduce a change in their products or services but only roll it out to one group in order to get good feedback data.)

But of course, the chances are you will not be making many of the components. Instead, you will shift to being the orchestrator of a systemic approach to innovation, scheduling improvements from your suppliers, which is where the universal connector comes in. The likelihood is that innovation programs in the future will be much better coordinated within a system, and they will be dictated by algorithms rather than human decision making. The algorithm will be an important carrier or connector in the ecosystem. It will shape relationships and determine investment patterns in product development.

How close is that? Milind Pansare is VP Product Marketing at Mindjet, a company that acquired innovation platform Spigit in 2014. “We have re-architected our APIs around the semantic web,” says Pansare, “so they can integrate innovation into things like machine self-learning applications. … So, for example, an idea or innovation stage is represented in the APIs. The physical ecosystem could both describe and signal the need for innovation. Applications will call on other applications.” MindJet, in other words, already has the platform to shift innovation decision making into automatic.4

Specialist API developers are also moving companies like Expedia to a hyper-channel future. For all of its existence, Expedia was one company, one brand, serving one market. Then it opened an API for its affiliates and transformed itself into a multichannel, hyper-local business delivered via local partners with particular local market knowledge and customer relationships. In other words, via the API, it was able to create and deliver mass differentiation. Between 2009 and 2013, the top ten sectors (by number of API projects) launched more than 8,000 new API projects.

All this means that business decision making and relationships are becoming far more automated with less friction.

IDENTITY AND SOCIAL NETWORKS

Identity is the new universal connector, the technology that is making seamless, friction-free business happen in a more customer-centric way. Previous (and continuing) examples, such as RSS in content and APIs, are important, but since the advent of the smartphone, identity is shifting commerce in to a more friction-free mode.

By encouraging users to have one identity, companies will have a single view of the customer across multiple applications. In return, the user gets rid of multiple passwords and can use one sign-in to access many services. The lead in this area belongs to the enterprise. Already many companies are improving efficiency by allowing single sign-on across different enterprise applications.

RSS transformed content. APIs are transforming many enterprises that adopt them. Identity is having a similar impact, helping to enable the development of seamless federated businesses in enterprise applications.

Take a site like Comcast.com. Many of the services offered via the site are not actually offered or managed by Comcast. The experience of these services for customers, though, is seamless. They enter a world of service possibilities when they sign in to Comcast. Federated services, like those offered on the Comcast site, extend to a variety of industries. Andre Durand of single sign-on service provider Ping Identity relates that one of America’s top banks now federates ninety different services on its website, all provided by third parties.5

If companies can guarantee the identity of a user, then they can offer users access to dozens, even hundreds of services to make their jobs easier—and without multiple sign-ins and sign-outs. Identity gives employees and suppliers the ability to traverse across the applications that are relevant to their roles, creating the possibility of silo-free organizations as assets become accessible from anywhere. And it gives customer-facing platforms the ability to extend their offerings in a seamless way. It means one enterprise can actually integrate the services of, say, ninety or a hundred organizations.

Social networks provide another example of a dramatic transformation fostered by universal connectors. Instead of seeking to connect via RSS or APIs, social networks like Facebook initially created large, self-contained worlds where all the connections were internal to the platform. At one stage, it seemed as though social networks were trying to create their own mini-webs. Gradually, though, they opened up via APIs and now enable the interchange of many different types of content: user-generated posts and other content; commercially created content from news organizations, advertisements, opinions; and so on.

While social networks have had a strong effect on the sharing of content, identity may perhaps have the larger impact. In the meantime, data collected within social networks is leading to a broader vision of how web platforms should treat customers.

One of the major developments in business today is big data, but one of the biggest changes is posed by the question of who owns data and how data ownership can benefit customers and companies in new ways.

These two questions are overshadowed by the rise of social media. In its current shape social media is split between individuals who wield influence and enterprises that try to emulate them, hoping to re-establish their power to broadcast messages to as many people as possible. They all communicate via Twitter, Facebook, Pinterest and similar sites, building up their followings and influence. Social media of this type is really a staging post to much more sophisticated and deep-rooted change.

That change is probably going to take place in three overlapping phases. The first is social media as we have it today. The second is the implementation of platforms like Sprinklr that allow enterprises to merge their CRM systems with complex customer social management. That needs a little more explanation. Sprinklr is an example of a social platform that helps companies identify people who are talking about their companies (the listening strategy that is common today), recover all kinds of data on the people who are critical (a new step in data gathering), and propose ways to resolve customer problems, in real time, depending on that customer’s perceived degree of influence over others. While some of that is in place in any socially aware company, the full package is new. Enabling anybody in a suitable role within a company to communicate with a customer, and to propose solutions in real time, is a serious step toward scaling intimacy.

But at the same time there is growing movement toward managing data on behalf of customers, almost in opposition to the enterprise.

The current data paradigm, where most data rests with large organizations, arose because, historically, only the largest companies could afford all the data technology and skills. But the price of large-scale data collection, storage and analysis is now low. If autos were to drop in the same proportion, a Rolls Royce would now cost 10 cents.

The price drop has encouraged a new start up community to offer data management on behalf of customers, who can then aggregate their intentions to buy products or services. Potentially, customers could dictate to businesses what they should make and at what price. At the very least the power asymmetry is balancing out. Already in the United Kingdom, one such service manages more than 1 million energy accounts.6

The upshot of these changes is unclear but a clear direction would be three new parallel approaches to marketing: the continuation of social media influence programs; the use of big data refined by the presence of new social platforms; and a reversal of the data paradigm where customers become increasingly empowered.

Through the identity shift and data shift, business will be able to relate more closely to customers who use different services, but perhaps on customers’ terms. Already, though, identity is helping employees access applications from wherever they may be. It will help create seamless networks that we still need to imagine how to best utilize. But perhaps it will do even more—as enterprises become exhausted by their data collection options, it will spur a new industry in data management that sits between customer and enterprise. Identity is a friend to both, helping users understand their data and options and brokering relationships with enterprises in place of sales and marketing.

MARKETPLACES

Elance is a marketplace for hiring labor for all kinds of tasks that can be performed and delivered virtually—graphic design, website building, writing and editing, and software coding. Hired.com is a marketplace where companies compete to hire the best software talent. Opendorse is a marketplace for buying product endorsements from professional athletes. New labor marketplaces are part of the bigger shift we are witnessing.

This is a shift from transactions that previously took place within the firm to transactions that now take place in an open marketplace.

As stated earlier, the works of Ronald Coase and other economists who have dealt with firm economics promotes the idea that the firm’s role is to reduce transaction costs. They do this by enclosing transactions in the processes and roles that make up a firm’s hierarchy and work flow. Each role in a firm could be fulfilled following an actual negotiation. For example, it could be possible only ever to assess cash flow when a new deal is struck with an accountant to compile a cash flow report. However, that would make business unworkable, and rightly many such activities are enclosed within the company’s hierarchy, processes and culture.

In the open marketplace, deals are made under a common set of guidelines that applies to every transaction. As distinct from bilaterally negotiated contracts, deals can be made quickly, almost as easily as booking a hotel room on Booking.com. That means you can secure labor services, talent services, even endorsements with a “booking.” As transaction costs are trending toward zero, the role of the firm is reduced.

Connectors go a long way to creating the externalized environment that companies increasingly operate within. They change the nature of the firm by creating low friction and by facilitating scale at low cost. But they need to be allied with the other dynamics of change, not least of which is the Cloud.

CHAPTER 8

Recalibrating Profits

ALIENS AND VIRTUAL WORK

Some technologies have innocuous names. The Cloud is one of them. Cloud evokes the idea of a pleasant but maybe challenging day, weather-wise. But the Cloud computing paradigm is a much bigger deal than its name suggests. Its biggest impact is it shifts the way businesses make profits; it opens up new ways of thinking about revenue and how these relate to cost and long-term customer relationships. It can be revolutionary for business models if nontechnical executives can capture its essence. It shifts the emphasis of investment downstream, and in the process, it creates new conditions for the enterprise under the new conditions. People and relationships become significantly more important because value is created after products are sold or after customers are signed up. Here is another way to look at it.

All over the world, there are people searching for signs of alien life. Out of this group, one community is not looking for UFOs; instead, they are listening. They are helping NASA interpret radio signals across the sky, across the globe. This is SETI@home, the search for extraterrestrial intelligence, a project that pools spare computer processing units on domestic computers to search for alien communications. The project is an impressively large exercise in distributed computing (or using computers spread out across large distances). The organizers claim that 400,000 people had pre-registered for SETI in 1999/2000:

In May 1999 we released the Windows and Macintosh versions of the client. Within a week about 200,000 people had downloaded and run the client, and this number has grown to over 3.83 million as of July 2002. People in 226 countries run SETI@home; about half are in the U.S.1

San Francisco-based company BitTorrent launched a similar distributed project, Sync, in 2013. (At least the principles are the same, though there are no aliens involved.) Sync allows people to take advantage of spare storage capacity in a peer-to-peer (P2P) network. Imagine one million people all with spare storage capacity on their devices that can be used by other people to store their encrypted data. That’s what Sync does. It is a highly decentralized file storage system.

BitCoin, the digital currency, bears some resemblance. It too is a P2P system but in place of aliens and files, it is a way to record the exchange of value ownership. The World Wide Web is a similarly decentralized virtual system and projects run along those lines have proved extremely disruptive in the past (think of Napster which began the revolution in music distribution).

The Cloud, by way of contrast, is a centralized and virtualized computing system, but it shares a common goal with SETI@home: it takes advantage of huge efficiency gains from treating computing power as an abstraction rather than a machine. The Cloud shares some of those characteristics. It tends to be centralized rather than distributed, but its effect is similar to that of SETI—it draws companies into a long-term search for the real lives of their customers. It makes product sales the beginning rather than the end of a relationship. In place of customer-by-exception planning, companies are investing in systems and people who can scale intimacy or build strong relationships, maybe with millions of people.

The Cloud is now the new pervasive digital infrastructure for business and is expected to be the predominant infrastructure of choice for business by 2016. It is one reason for the convergence of IT and telecommunications. For the Cloud to be effective, vendors need good pipes for their customers’ data to travel down. For end users to benefit from this more abstract environment, they need good wireless connectivity to access company data,services and applications from the Cloud anytime, anywhere.

The Cloud is as much an economic solution to a business need as it is a technical one. And because it stems from pressing economic needs, it harbors many economic benefits.

If we go back to the beginning of the 2008 Great Recession, the sale of IT hardware and services faced a crisis as large- and medium-sized companies embarked on a series of cuts. Large IT vendors reacted to this initially by rolling many of the add-on services they provided (like system customization and business advisory) into the price of IT hardware and then leasing the whole package. That way they could keep business rolling without forcing a large CAPEX onto their clients.

Concurrently, software engineers began to see opportunity in machine virtualization, which allowed companies to think of computing power as separate from any particular desktop device or server. What companies needed was the power of computing, not the metal of the machine. Two years earlier in 2006, Amazon.com had launched the first of its Amazon Web Services, or remote access to computing power, based on the expertise it developed while growing the world’s largest e-commerce business. By 2008, there were already 180,000 developers using the Amazon Web Service, according to Amazon.2

One of the most public and dramatic examples of Amazon Web Service’s impact was Amazon’s agreement to support Target Stores. In 2002, Target’s e-commerce infrastructure, customer relations, and fulfillment were all served by Amazon. In fact, Target.com was to all intents and purposes an Amazon operation, though Target had its own Internet marketing team. By 2011, that relationship was over, but prior to Target going it alone online, its Amazon-supported e-commerce arm enjoyed revenues of $1.3 billion in 2010—all while markets were still hit by recession.3

Amazon had already supported the online businesses of Toys “R” Us and the late Borders prior to creating an end-to-end service for Target. While developing these relationships, Amazon continued to externalize its systems and enrich its range of services. It launched Mechanical Turk in 2005, Amazon Web Services and Amazon Payments in 2006, and Amazon Flexible Payments in 2007. In two short years, it had laid the foundation for a new way of doing business, long before Apple and Google moved into Cloud services.

During the same period, the computer community had begun reworking ideas around software virtualization, a process that initially lets one server run multiple operating systems and applications. In itself, this is a breakthrough in scalability of resources because of its superior efficiency over the older paradigm of one server, one operating system. It also allowed for the desktop computer to be abstracted—or seen as a resource that needn’t be confined to a desk like a PC is.

Some of these concepts were already available through the P2P environment. Both the Cloud and the P2P environment allow a frictionless business economy to grow because businesses can now rent computer processing and storage at an ultra-low cost. But it is the Cloud rather than P2P that currently dominates the landscape.

While the Cloud currently enjoys dominance, P2P might well grow in stature on the back of governmental spying revelations leaked by Edward Snowden in late 2013. These revelations showed that the US National Security Agency and the UK Government Communications Headquarters maintained substantial surveillance on individuals, including many senior politicians and companies across the globe. The result was to create distrust in US Cloud vendors. According to European Union observers, “The surveillance revelations will cost the US Cloud computing industry USD 22 to 35 billion in lost revenues over the next three years.”4

In the immediate aftermath of the Snowden revelations, P2P provider BitTorrent began to get rapid pickup for its asset-syncing platform Sync. According to BitTorrent, uptake of Sync soared to one million users within days of launch:

We’re staunch believers that the Internet can work better by being based on distributed technology. HTTP just wasn’t built to handle the workload. Smartphone use continues to climb as does the amount and size of data moving across mobile platforms. Streaming video apps like Netflix are crushing the network, and it will only get tougher with 4K (ultrahigh definition TV) on the horizon.

And we’re seeing development of distributed technologies from other camps as well. In recent weeks, we’ve seen Google unveil a decentralized web browser, called uProxy. Researchers at Cambridge University recently announced a project named Pursuit that aims to end server (and storage) dependencies.

We feel this is the start of the next great wave of innovation for the Internet and we want to lead the way.5

As both the Cloud and P2P continue to develop, these technologies will spawn further innovation as they connect more diverse parties and their ideas. Most importantly, the availability of the Cloud and P2P is one of the critical elements that support the development and operation of the modern business ecosystem and the mobile enterprise. These technologies also enable business at an entirely new rate of velocity.

BUSINESS NEARING REAL TIME

The impact of the shared economics of the Cloud infrastructure is huge. In most accounting systems, the Cloud will appear as an operational expenditure that is distinct from capital investment or CAPEX. It potentially releases a portion of CAPEX that was formerly spent on IT. It should fund more innovative approaches to business because innovative service development will be cheaper to create and deploy.

As the Cloud gains traction, IT departments are therefore under increasing pressure to take on new roles, and create results that justify huge IT expenditure by adding to the company’s output more directly, for example, by supporting the rapid development of new services. That, in turn, is leading to new developments in IT processes.

Two of those are worth mentioning.

The first is simple enough—DevOps. DevOps (a portmanteau word from development and operations) is a new IT process designed to accelerate development processes. The second and more significant is the software-defined enterprise. An increasing number of companies have now spotted the service development potential of the Cloud and have set off down the acquisition trail to buy start-ups that will give them a competitive edge in software development.

This pursuit takes on a number of forms. For some companies, software acquisition is a fast track into a more experimental enterprise model. For example, the global events company Live Nation is using its software acquisitions to set up a Silicon Valley-based “lab” that can accelerate its innovation practices.6 Staples bought the personalization start-up Rune specifically to enhance its capabilities in customer relations. In both cases, the companies are acknowledging the fundamentals of speed. They cannot take the time to develop new skills; they have to buy them. And those skills are needed to change the responsiveness of the firm.

Speed or velocity, is essential to the software-defined enterprise. It is what will differentiate many companies in the future, specifically the velocity they can bring to deploying new product and service features. Part of the reason for this is the term we used earlier—mass differentiation. Companies are constantly uncovering new customer needs that they must address in the short term.

Companies with strong IT capabilities now deploy software updates by the hour or even the minute. Software companies talk in terms of continuous delivery.7 Think back to 2010, when Microsoft was on a three-year update cycle with its SharePoint platform. SharePoint was deployed in most Fortune 2000 companies, all of whom were content to wait three years for an update. Driven by changing expectations, today we talk of minutes rather than years.

This acceleration of change also ties into the Cloud and a company’s bundle of services and products. As products and services draw more on the products, software, or components of others and become integrated into a larger “ecosystem” offer, it is necessary for each party to keep up the pace across a range of functions.

Paul Muller of HP sums it up:

Think, for example of Microsoft Window’s XP operating system, which simply became irrelevant to the world it was initially designed to serve. This situation is called “technical debt”… this requires the biggest shift in thinking compared to the post-industrial revolution world we’ve lived in. Despite the fancy language, the idea here is pretty simple, that every change in one part of your software (and even hardware) has a ripple effect up and down “the stack” of apps and operating systems that rely on each other to function. The longer you put off changing one piece, the more likely it is that other elements will start to build in lots of assumptions about the pieces they in turn rely on, the effect of which is that when you go to change a component, this ripple effect results in the whole system failing….

This situation can often last for decades before the customers become so frustrated that they are faced with a massive upgrade cost, which, ironically, creates a situation where switching to another supplier or platform becomes economically viable and their original supplier loses them to their competition.8

What Muller is referring to here is the increasing tendency for products to be part of an ecosystem of their own. To be more accurate, they tend to be part of a product/service suite. The iPhone and its apps are one example. Because both the iPhone device and platform are owned by Apple, there is a closed system where one vendor (Apple) controls the product and the service platform. That makes it expensive for Apple customers to switch, say, to Android. All of their personal expenditure is invested in the Apple ecosystem. However, if Apple were to let its guard down and the Apple platform were to underperform in one area (e.g., security), those customers who move out take not only their expenditure on the device (the iPhone), they take a lot of lifetime expenditure on content, apps, peripherals, and other objects.

To maintain its position, Apple needs to be diligent about a wide variety of product and service issues—the phone’s operating system, the phone itself, services, and so on. To date, the company has done well with each of these issues. Apple overhauled the iPhone design in late 2013 and introduced the 64-bit processing power more normally associated with powerful desktop PCs. It is belatedly introducing a phablet after introducing a lower-cost iPhone version in 2014. It is constantly bringing in new features like the fingerprint sensor and improved camera capability, and it introduced iTunes Radio at the same time it introduced the iPhone 5S and 5C. Meanwhile, the ecosystem and Apple have to keep in sync, too—for example, developers had to redesign millions of apps in 2013 to fit in with the new design language.

At the same time, Apple was opening up new deals with automakers like BMW to get the Apple “stack” embedded into autos as well as giving away productivity apps to gain traction in the office market.

These are different product/service ecosystems that involve increasingly complex relationships. Apple, along with many other companies, has learned to manage relationships at scale. Now, all companies need to learn how to constantly synchronize change. They also need to know how to recognize technical debt in the companies they acquire, or it can render an investment worthless. These are not specifically Cloud issues, but they become Cloud issues when companies plug their products and components together. They also send a warning signal to all enterprises as software becomes more central to the services that they offer: unless you can manage continuous deployment, the chances of slipping behind and incurring debt are real and potentially very costly. Those types of problems are amplified by the Cloud.

THE REVENUE IMPACT OF THE CLOUD

Xiaomi is a Chinese smartphone vendor, founded in 2010. Xiaomi’s mission is to broaden the market for highly functional smartphones available at low cost. It therefore sells at a very small margin, assuming it will make its money on apps and content.

This used to be known as the Gillette model. The razor-blade company makes shavers that rely wholly on their own blades. Give away a shaver and people have to buy your blades. Nespresso does it with coffee machines. The difference today is that neither Xiaomi nor Apple has blades or coffee-type equivalents. They rely on being able to develop the ecosystem that produces the apps and content that drive desire for the phone. That’s why we say the firm is so heavily externalized because those apps and that content are external products.

This process takes place increasingly in the Cloud. What the Cloud does very well is enable downstream revenue as a viable option for companies. By downstream revenue I mean revenue from subscriptions or advertising or services, after the sale of a product. In the old business model sales had to yield high margins to justify R&D budgets and high capital expenditure on manufacturing. These costs can now be avoided by using the Cloud and sophisticated supply chain management.

Cloud therefore allows companies to shift their saved CAPEX expenditure to an ecosystem development program, assuming they know how to create one.

The shift is a big one, however. By relying on downstream and indirect revenue sources (like subscriptions, service contracts, and advertising), companies force a difficult cultural change upon their organizations. This cultural change takes a variety of forms, none without difficulty.

In product-centric cultures, the effect of shifting to non-product revenues leaves engineering disciplines feeling forced out. The whole organizational culture in these product-centric companies has typically been built around engineering skills, the adaptation of basic science, and engineering ingenuity, workflows, and values. The engineering contribution to profitability is a large part of their sense of cultural superiority. Typically, in building a product/service mix, the organization also has to bring core engineering professions closer to soft trades like design and marketing. The conflicts that ensue are at the heart of many failing corporate change programs.9

Conflicts can also happen when hardware companies try to introduce more software. The software-defined enterprise typically looks to a variety of revenue sources. To succeed in this change, companies must have strong leaders who know how to build a narrative, a story of change that paints a picture of opportunity for employees and those in the stakeholder community.

Most companies recognize a need to change their culture to become more innovative. Yet this is, in many ways, a red herring. They need to change organizational structure and personnel so they can improve software and customer–community development because the future lies in becoming integral to customers’ needs. But more than that, the Cloud accelerates the shift from capital investment to liquidity needs. It shifts the underlying investment consideration from capital projects to people. It raises new questions about what a company gets back for the investments it makes in the people it employs. Of course it also begs new questions about what those investments should be.

NEW IDEAS ABOUT RETURN ON INVESTMENT (ROI)

Changes from the Cloud as a CAPEX-free form of investment to software dependence and to the dependence on downstream revenues require a new way of looking at a company’s ROI. Being adaptive with ROI is far and away the most difficult change for most organizations. ROI equates to profit and any challenge to quarterly profits is heretical. Nonetheless, companies are beginning to make the shift.

The vast majority of them plan future business through fairly simple ROI calculations. At its simplest, these calculations say that for a given amount of investment, we expect X percent of annual returns, but we know business is risky so we will discount that rate for risk. This formula is the basis of many business decisions.

In product-centric companies, the calculation is relatively straightforward: amortize the cost of developing a product over so many units of sale, calculate the cost of production, and then build a profit margin into the price.

But if profit is unlikely to come from the product then you have to make complex calculations about the risks you are prepared to take to secure a community of apps developers or customers, or to secure content or other services, or to create the capabilities to create scope and affinity. You don’t stop doing ROI, but you push the returns into a different time horizon and look at a different mix of capabilities and related inputs (like network and community costs). In today’s business environment, this is necessary for many facets of products and service. It means managers have to shift the way they look at returns—start-ups, for example, posit returns only after they have built community, which requires investing in different capabilities.

The platform and ecosystem model, particularly in regard to the Cloud, permits and fosters the search for new revenue downstream from product sales. That should also lead to new ways of looking at returns.

I explored the strength of conventional ROI thinking in a 2013 study of thirty companies that were in the process of some form of transformation.10 They should have been planning to invest in new capabilities as well as some form of federated components, software, services, or content, and how to bundle and market these.

The findings were startling. When it came to applying a major criterion to decision making, I explored whether companies still applied discounted cash flow or NPV or whether they made investment decisions based on exploring new revenue streams or adding to the company’s foundational capabilities.

Half of those who responded to this option said NPV or a variant was the main criterion.

Surprisingly, only five companies made investment decisions based on exploring new revenue streams, while nine made decisions based on adding to the company’s foundational capabilities. Only about 12 percent of respondents made positive decisions about innovation projects based on a long-term transformational strategy. For most, the trigger for action was either a desire to be first to market or to be a fast follower. Even those companies with a transformational agenda liked to talk of themselves as having a strong core competency that they were reinforcing (and these were companies in transition).

Companies typically draw on decision-making criteria from a stable, predictable past. Changing decision processes involves a behavioral shift they find very difficult to make. At the same time, executives find it impolitic to stray far from convention in what they say, even if they are trying hard to change. If they are not sticking to the ROI/financial metrics, then they are sticking to the idea of core competency.

That leads them to build out a business case that is highly stylized along traditional lines. They plan on market conditions being predictable, even though they know this to be a false assumption. Rationality in fact turns against them.

What companies actually need is a set of methods for capturing that uncertainty rather than pretending they can predict profits (more on this in Chapter 11). They also need to devolve responsibility down to managers with sufficient business maturity to make good decisions in conditions that remain uncertain.

These changes should drive companies to establish stronger customer ecosystems. The consumer lifestyle company Philips is trying to do this with its kitchen products, to move its culture away from hardware design and engineering to a service orientation and at the same time build downstream revenues through customer communities. “Digital allows us to completely reinvent how we engage with our customers, allows us an ongoing dialogue, and allows us to redesign the monetization principles and restructure the money flows,”11 Alberto Prado, VP of digital innovation at Philips, told me. Prado confirmed a reality that Paul Muller also mentions—products are never done. In a more elastic economy, products are always ready for a new update. That makes conventional ROI calculation very difficult to carry over to the new era. A product with high levels of service does not have a net present value the way a product with a fixed margin used to. The product/service is dependent on other products, liable to being undermined or promoted rapidly by its place in a chain or suite of products in the marketplace, and must always be kept alive with a sense of new options. Its value, in other words, is often in flux and needs to be accounted for in new ways.

EXTENDING ACCESS AND ENABLING ELASTICITY

The Cloud also democratizes start-up and small company growth financing. It is now possible for small companies to buy significant computing and storage capacity at the rate of a few dollars per hour. The low and elastic cost of the Cloud capacity means individuals and smaller companies can offer services to larger companies based on a Cloud infrastructure. Start-ups can get out there looking like a larger, accomplished company, equipped with the computing power of any larger rival.

That means a larger range of talent and services becomes available to everyone on this common elastic infrastructure.

In fact, the effect of that has already been felt. From 2010 onward, large corporations began noticing their employees were using third-party applications to conduct some of their work. They might use Box for sharing documents with agencies or Huddle for online meetings or Basecamp for project management—all Cloud-based services. From the start-up phase onward, each immediately began penetrating the large corporate software and services market.

The Cloud not only allows smaller companies to create enterprise-grade services but also makes temporary business relationships more feasible. In the past, the cost of establishing a business relationship required a long payback period to make it feasible and productive. No longer!

As enterprises rebalance their priorities between hardware engineering and soft sciences like design, community development, and indirect revenue production, they are bound to meet with challenges. The Cloud has serious social consequences. Yet working to find solutions is worth the effort. The shared economics of the Cloud contribute significantly to the friction-free elements of the new elastic approach to business formation, business models, and business operations. In the process, they make enterprises of all sizes more elastic. But there are threatening developments in this process. What are the real consequences of making the shift?

SHIFT III: You

Introduction

Imagine a stack of innovations. At the bottom, new protocols for mobile broadband create a global, fault-free, place-agnostic network that performs exceptionally everywhere. Geography is no longer a source of advantage. The benefits of the network are everybody’s to claim. At the next level, a high-capacity computing environment with new database, search, and analytical technologies enables the interpretation of trillions of human actions. One level up, a growing number of companies create services that enable utility value across tens of millions of connected customer relationships and take a dominant position in their markets. They do this at low cost relative to the scale they enjoy. Their ecosystems help them flesh out the infrastructure for enabling customers to seek and find new utility; the ecosystem absorbs much of the risk. They become quasi-monopolies in their field. Meanwhile new financial system emerges geared towards immediate value transfer, using new payments infrastructures and, even, newly invented currencies.

At one end of this new system, low-cost labor drives down labor prices everywhere. At another extreme, highly ambitious knowledge workers compete for reputational advantage and eschew monetary rewards beyond their basic needs as they build their careers. In the mid-ground, experienced knowledge workers continuously adapt their ideas and their personal platforms to consolidate some advantage over their peers. Sudden eruptions of economic activity around new ideas create new millionaires; new ways of conceiving value send shocks into the system as often as business cycles once did. In fact, the business cycle is replaced by continuous shifts in the underlying structure of the economy. There is a constant bidding war for the next big idea.

This is more or less where we have arrived. What shifts do we need to make to keep pace and prosper? What will enable us as individuals and organizations to navigate these new waters? There is a debate around the shift that focuses on the moral responsibilities of companies to do more to help society as we make this transition. There are three variations of the argument:

• Companies have a duty to create “shared value,” not just profit.1

• Companies should no longer focus on the financial ratios that deter productive investment.2

• Companies should no longer focus on shareholder value.3

These arguments had been developing over the period 2011–2014 when attention switched to a more obvious argument—the large scale redistribution of wealth. In Thomas Piketty’s book Capital in the 21st Century.4 Piketty’s data seem to suggest that there is a historical trend toward wealth concentration, in that returns to capital always exceed the rate of economic growth. However, all three business articles referenced above, that preceded Piketty’s, stem from the same desire to see enterprises perform a broader role, or at least to use their assets more productively for the wider benefit of society.

The challenge with this debate is that each proponent takes a static view of what enterprises are and how the enterprises deal with capital. It is arguable, in contrast to all of them, that we cannot mean one thing by an enterprise. The enterprise has transformed into many types of social arrangement, and it is less dependent on capital than ever. In fact, capital is pushing itself onto enterprises like Apple and WordPress (Apple has taken out loans to fund share buyback, and WordPress has accepted VC money to expand its control of web content) because capital has a much reduced role in the productive economy. In the shift period, indeed, extreme concentrations of wealth may be pernicious but the concentration of power in large platform enterprises might turn out to be more dangerous.

That is not to dismiss the wealth redistribution argument. My argument here in Shift, though, is that we are undergoing a sea change in how we create wealth. Many people and most governments have not adapted to the shift. The arguments for redistribution of wealth or a new enterprise ethos both have merits but more important is to educate people rapidly about this new economy and their potential roles in it.

The traditional enterprise, the place we relied on for work and pensions, has not survived the present mutation. It has changed dramatically. It has suffered disintegration, and its leaders are struggling to make significant structural shifts, often because they do not understand change and cannot move out of old ROI models. Platform and ecosystem businesses are already a light-year ahead of them. But even these will mutate.

The tools for more mutation exist also in a new commons-based idea of value epitomized by Bitcoin. Who controls money also controls the destiny of whole societies. But who will control money in future? The answer to that is not at all clear. The question is an urgent one but we are distracted by the redistribution and moral responsibility arguments.

Platforms prosper by enclosing some part of the commons, the value systems like open source that represent alternative production systems, but the commons is fighting back in initiatives like Bitcoin and threatening to wrest away control of the financial systemat just the moment when cash is becoming vitally important. These are epochal changes in the making and they will profoundly affect how we work and what we make and sell.

Perhaps the friction between commons and enclosure will create disruptions we have not yet dared to imagine, such as an historic shift away from democratic governance into a more anarchic and corporatist world. The 1920s provided that kind of example as the 19th century chemicals industry grouped together into cartels that went on to more or less define German economic policy.

Cartels were considered a legitimate form of economic self-governance in the 19th century and we are beginning to hear arguments once again in favor of the self-regulating monopoly.5

The idea of value and how to allocate resources evolves over time. We take it for granted that resource allocation can be characterized by an Adam Smith-like invisible hand or, as lately described, “free markets.” But over time the way in which we allocate resources passes into the hands of various power groups, like government, cartels, oligopolies and, yes, competitive forces but also self determining groups like the ope source movement and developer communities.

For most of the era in which businesses have dominated the economy—say, from 1850 onward as agriculture declined in economic importance—the organization of wealth-creating activities has taken place in the context of enterprises with stable operating models and clear rules of governance, or in business cartels with inter-company agreements.

Gradually, the dominant legal form of individual enterprise became a joint-stock company with shares traded in a public exchange. The innovation of joint-stock companies goes back to the seventeenth century and the Dutch, French, and English East India companies. These organizations consolidated a longer-term practice of seeking investment in single voyage risk as the master of 17th century ships sailed in search of new sources of wealth. Before that, “exchanges” already existed for spreading the risk of government debt. The joint-stock company, however, is the bedrock of post-1945 capitalism. Shares in them are mostly traded in open markets where people, often institutional investors or activists, can signal expectation or disappointment by buying or selling stock.

These are the market indicators that, along with the price of goods, we accept will signal how best to allocate resources across a range of economic activity. By creating market pressures, we, the market participants, persuade the business community to make at least satisficing decisions (those that meet the minimum required for a consensus) and preferably rational ones that maximize opportunity.

However, various parts of the economy are subject either to public ownership or heightened regulation, and many have been oligopolies over long time periods. In reality, the West had already developed a hybrid resource allocation system long before platforms and ecosystems evolved.

The resource allocation in the West began to break down in the 1970s with the rise of the Asian economies and the broader conglomerate-type of organization that typified Japanese and then Korean business (large enterprises with business interests across many sectors, who maintain very close relationships with suppliers and government). More recently, a new form of organization has emerged in China. By common consent, these Asian systems have successfully innovated and out-competed Western companies in many areas of manufacture and are now engaged in their own turf battles.6 The platform and ecosystem model, though, has similar characteristics to the Asian model. It is becoming a conglomerate that is tightly integrated, spans many sectors, takes ruthless advantage of insecurity in the labor market through the ecosystem, enjoys high margins, and is global.

The structure of Western organizations began to change first as supply chain management advanced in the 1980s and 1990s. This was a reflection of management’s newfound power to move away from their status as national champions with obligations to the communities in their country of origins. Supply chains were global from the outset. They were complex and they gravitated toward cheap sources of labor and specialty raw materials. They were accompanied by business process reengineering, a movement that swept through business in the 1990s.7

As organizational complexity and scale grew, a new range of suppliers like Oracle and SAP created the enterprise resource-planning systems that made a more distributed form of manufacture both possible and a source of innovation. The downside to this, perhaps from the loss of labor power, was that large organizations designed operational systems that reduced human discretion even in knowledge-intensive tasks.

The most recent mutation of the enterprise, beginning around 2000, has seen complexity multiply even further as companies have begun to externalize a much wider variety of functions.

These have included externalizing marketing via social media, consumer-centric innovation via apps and APIs, the computing infrastructure through virtualization and the Cloud, the service infrastructure (such as document sharing and document management) through software as a service, and even innovation itself through innovation platforms and challenges. This coincides with more stockholder activism, and is affecting even Apple’s ability to pull together the internal resources to innovate.8 Enterprises must now manage vast relationship pools among suppliers, partnerships, and customers.

The platform and ecosystem, though, is as much a historic point in capitalism’s development as the joint stock company, the cartel or the oligopoly. It is underpinned by superior infrastructure and executive teams that understand how to exploit complexity.

In this mutation, the core driver has not necessarily been a search for cost efficiencies. These changes have been driven in part by the phenomenal success of exemplary companies like Apple but the new enterprise structure is also a consequence of many social collisions and the crosscurrents of different cultures within the business realm.

It is important to recognize when projecting ahead that change is being driven by the growing needs of individuals to recapture autonomy, as through Bring Your Own Device and Bring Your Own Applications but also in crypto-currencies, market platforms, peer-to-peer funding mechanisms and work processes. These are the worker movements of the technological age.

In a technology-seduced era, where start-up culture is lionized, we often fail to realize that innovation and disruption occur because of profound dissatisfaction at a personal and social level or because people have found a better way to satisfy needs than the enterprise has offered.

As we saw earlier, the open-source movement has been multiplying year over year. This is now a huge commons, or as Elinor Ostrom called it, common pool resource, with high levels of autonomy for the people involved. By its nature, it is an alternative form of organization with meritocratic decision making. Much of the new infrastructure of business originates in open source.

Open source already shapes the economy. Linux, Apache, and MySQL underlie the core World Wide Web. They are all open source. Nominally, Android, the dominant mobile operating system, is open source. The ROS (Robotics Operating System) for the next generation of hardware is open source. Increasingly, hardware is taking the open-source route through open engineering or the sharing of component designs. Hadoop and MapReduce, key technologies of emerging global data infrastructure, are open source. LinkedIn’s search engine, an engine for connection, is open source. Google’s growth has been highly dependent on the commons, and if Apple is less open in that sense, it is no less dependent on a commons for the infrastructure that enables its business.

While open source has graduated from the days when projects were largely driven by a non-commercial ethos, large enterprises (because of their wider involvement) still rely on the gift of time and ingenuity from the commons. Large enterprises traverse the open and closed worlds, selecting their own opportunities to commercialize what they wish, while developers and other individuals follow a “pay-it-forward” ethos of giving now in order to get back later.

The modern pay-it-forward economy forces many people to spend their time exploring new opportunities that will benefit the platform at their own expense. They have to help engineer the early phase of platform projects at their own risk. Google Glass is a prime example. The commons, meanwhile, is creating powerful new resource allocation mechanisms, particularly by making the claim that it can create and control the ownership of value through crypto-currencies. That is a difficult concept to grasp—what crypto-currencies do is record who owns what shares in the total value of a currency. Think of it as a cake that can’t be eaten and gets more valuable as people look at it. The BitCoin system simply measures who has what slice of the cake and when.

Crypto-currencies reflect the build-up of pressure in the overall system of allocating resources—resources are not being allocated fast enough or to the right ends. The new currencies are an expression of a need and desire for a broader range of opportunity that is not constrained by the regulated economy or by the overcentralized wealth of the platform and ecosystem model.

These initiatives have introduced an entirely new horizontal pressure to break down the walls of finance, and by breaking down finance to transform the system of exchange in all vertically integrated industries.

In an economy stalled because of overregulation of finance, and where financiers and enterprises have forgotten how to invest in productive activities, the commons has reinvented money. That brings pressure to recalibrate the economy and the financial infrastructure. These pressures emerge from the commons, but they are intertwined with a different narrative.

Increasing levels of surplus wealth are accruing in a small number of companies like Apple and Google. In 2013, American companies were sitting on $2 trillion of cash that they could find no way to use (they gave $500 billion to shareholders).

This is a sign that traditional resource allocation mechanisms are not working. Yet the best of those companies are expanding the base of their power and influence, legitimately denying their competitors access to resources and making it more likely that resources will not be allocated to competitors by the market. The companies (Alibaba, Apple, Google, etc.) are becoming increasingly influential because they are reshaping the economy through their success. But the commons and its claim on the big shift will not go away. Something in this new system has to give. More turbulence and disruption is on the way.

CHAPTER 9

The Shift Away from Price

THE SHIFT TO A SENTIENT ECONOMY

In the book The Zero Marginal Cost Society, Jeremy Rifkin argues that the cost of producing additional products is trending toward zero.1 In his view, this means enterprises face a future of dramatically curtailed revenues because there will be fewer barriers to entry in many industries; competition will become ultra-pervasive as, in effect, anybody can make anything.

One consequence of zero marginal costs, so the argument goes, will be a move away from a product-sales paradigm toward product sharing. In place of sales and traditional profit margins, enterprises will increasingly create access to products. People will buy less and share more. This is the sharing economy.

There is a fundamental truth in what Rifkin is saying: investment needs are shifting from capital-intensive projects to people and relationships.

However, it is important to understand where this concept is flawed. The big problem is that enterprises are becoming increasingly people-dependent, yet habitually cut people-related costs where they can. There is a shift needed in corporate investment priorities, and the next three chapters suggest a number of ways to make that shift, ranging from new investment criteria to changing the way investment and value are conceived. But the shift is not a simple one—it will not be met only by changing financial reporting or by refocusing away from shareholder value.

At the same time, executives are being forced by the more service-oriented economy to understand and legitimate their investments in new ways. In the service economy many propositions hold no promise of an immediate product-like return with a margin. Enterprises have to change their investment horizons, the assets they invest in, and the rationale for being in business. These changes are driven by people.

Starting with simple shifts, people are reinventing their need around connection and interaction; they are expanding the scope of sentient—or sense-based-needs they wish to find fulfillment for. They are reluctant or unable to make the number of consumer goods’ purchases that keep businesses afloat. Many of their new needs can be met without the intervention of a paid service supplier—connection with remote friends, for example. The sentient aspects of connection are not products or services whose demand and supply can be defined by a price. The value of sharing a photograph online is outside the blunt sensibilities of pricing mechanisms—which is why most photo-sharing services are free. (Yet photographs are the mainstay of the Internet economy. Facebook’s users have uploaded more than 250 billion photographs and now upload 350 million per day; Snapchat’s users share 350 million per day.)2

This is not altogether unfamiliar territory. Sentient goods abound—music is a key to emotion and shared experience and it drives online services’ innovation; color is a key to enrichment in human experience and smartphone makers compete to provide the best color experience; sexuality can be a traded commodity but it is most often free. Sentient needs have driven commerce for centuries. The distinction today is that they are taking place within an expensive infrastructure like telecommunications and they are abundant. (This also makes me think that the thesis of The Attention Economy^3—that the most scarce resource is attention, not capital—has to be qualified. People are overworked but still have abundant time for photo-sharing and emotional engagement. They are experimenting with a new consumer economy.)

Another way of framing this is that customers are raising doubts about what they want from the transaction economy, and are spending more time outside it in the world of senses, connections and feelings. Consumer-facing companies confront these new preferences and new models using old ideas about how to relate and how to create value in a changing world.

A lighter version of the Rifkin argument is made by Rachel Botsman and Roo Rogers, who both draw heavily on earlier work from Don Tapscott and Anthony Williams.4

The crux of Tapscott and Williams’s argument is that all enterprises and organizations can and should make more use of mass collaboration like crowdfunding and crowdsourcing. Their argument has the distinction of being fairly predictive as they were writing well before these concepts became mainstream. It’s not clear, though, how much a crowd system relies on collaboration. Tapscott and Williams use the example of Netflix.

In 2007, Netflix ran a public competition to source a 10 percent improvement in the accuracy of its recommendation engine. But this was a competition, not collaboration. Social business platforms are designed to increase collaboration in enterprises, but there is little proof that they do that pervasively. Examples exist where it has worked and where it hasn’t, leaving an unknown degree of truth in the mass collaboration argument. What seems to be as, if not more significant, is decentralization, the dispersal of responsibility and tasks to self-determining groups.

Building on Wikinomics’ thinking, Botsman and Rogers claim that society is becoming more access driven—more willing to share than to own. They cite examples such as Freecycle along with ridesharing and may now add in Airbnb. Michel Bauwens at the P2P Foundation has created a summary of a much broader set of collaborative activities.5

These are all novel services. However, giving away unwanted products, giving people a ride to work, and providing a bed and breakfast are hardly unusual behaviors. The argument makes more sense in relation to music and other digital assets. Buy a CD or rent a song via Spotify for a month? People with low disposable income or insatiable music tastes would often shift to the latter.

In fact, there is another shift taking place that is every bit as important. That is the shift toward investing in people more than non-sentient assets. The reality of the modern economy is that its infrastructure and development costs are now negligible in relation to people costs. Over the past 40 years, labor productivity has been forced up and labor has been forced out where managers have deemed it to impede productivity (for example in France there are currently 1 million young people who have no active role in the transaction economy). That situation is now changing. It’s not that labor is costing more in wages (most often it is not). The additional cost to the enterprise lies in equipping people with the skills to deal with the new economy, a cost they are often reluctant to meet.

A 2012 article on The Next Web asked experienced developers to estimate the cost of creating modern services such as WhatsApp, Pinterest, Uber, and even Facebook. Their answers were: $250,000 for WhatsApp, $120,000 for Pinterest, $1.5 million for Uber, and $500,000 for Facebook.6 As their educated estimates indicate, capital investments are low in virtual (digital) service development.

On the other hand, the cost of investment in devices is larger than ever. Samsung’s development of organic light-emitting diode displays was an eight-year initial R&D program, with each factory a multibillion dollar investment. It is unlikely that we will trend toward zero marginal cost in hardware for decades, but those huge, capital investments are being made in Asia rather than Europe or America as part of the new global division of labor. Western economies are actually becoming capital-lite.

The capital expense of digital service development is low, not least because of the Cloud. But costs begin to mount up in areas where management teams have little experience in judging investment allocations.

The highest costs are, first of all, in iterating a user experience. That takes a lot of sophisticated user testing and redesign and painstakingly building credibility with users, assuming the start-up or large company has the right people for these tasks. Most managers find it difficult to make assessments of this type of human-oriented investment.

It is closely related to the sentient economy. Products, devices, services only work if they are pleasing. Functionality has become secondary to quality of experience. These sentient experiences are where the cost of products, services and skills’ development now lie.

Service design and build costs are low relative to traditional capital costs, but they are not negligible—Uber took $258 million in one funding round.Erik Brynjolfsson and Andrew McAfee point out that the human side of any IT investment can be as much as nine-fold the technology investment.7 But when that also involves developing market-facing systems like building out new information markets around products, then the calculations are much, much higher.

Further lines of cost involve the development of human networks (or processes) capable of developing, selling, and reinventing a service frequently. The next cost is developing the skill needed to access the information layer in order to attract people to a service and to promote it as it passes through various phases of viability.

This information layer, with its blogs, news sites, and social media sites, is democratized; it can no longer just be bought. A new set of skills is needed to activate it in a company’s favor.

In reality, large companies still have an advantage in being able to scale a user base for a new product since they have existing customers. But it seems that smaller companies, especially start-ups, are much better at making themselves attractive to users who will swarm to a new experience, for example, by promoting it as a movement on Kickstarter.

The problem for many larger companies when faced with new service development is that they have to invest outside of their existing process model (in new services that imply a new way of functioning) and at the same time invest in changing that model (to bring the benefits of change to the wider organization). Those investments have no easy NPV. They are the risk investments that great leaders make when they are sure of their teams.

The challenge, however, is often represented differently. We see it spelled out as a behavioral dilemma—do we continue to execute (run our existing lines of business) or do we divert executive energy into innovation? The reality in a more horizontal world is that the two merge. However, enterprises tend not to have made this shift. Instead they turn innovation into its own silo or into a doctrinaire approach to growth. What they need to do is acknowledge the human cost of business and the sentient needs of customers and employees. What is the NPV of that? The question is difficult for many executives even to ask.

THE SHIFT TO OPTIONS

Santander Bank is a successful global bank with its origins in Northwest Spain. It fashioned its growth path through the 1990s and 2000s on using IT to integrate acquisitions and reduce costs. It is a process that required a highly doctrinal form of innovation. The company turned itself into one of the world’s largest banks by developing (or inventing) a core model and methodology to grow rapidly through M&A (merger and acquisition), integration and cost shakeout.

To do that, it invested in routines and systems that made integration rapid and assured. To move at speed and with certainty it created an immovable doctrine of how to grow its business and make the desired margins. Its whole investment cycle was dependent on this process and it made Santander one of the largest companies in the world.

This is not an untypical innovation structure from the past thirty years, particularly in the service sector. Create aggressive process models and then apply them in different locations, through merger and acquisition.

Two problems arise with it now. The first is, it entails a commitment to a doctrine at a time when executives need to lead employees toward a more flexible culture (doctrinal innovation limits options). The second is, it takes little account of sentient needs (either of employees or customers). It is a process innovation and it is entirely logical. But can it survive?

In business literature, we have focused mostly on the distinction between disruptive and sustaining innovation. Disruptive innovation is the big move that shifts markets into a new mode. Sustaining innovation is the tinkering that keeps the business going.

There are, however, a number of additional innovation paradigms. Doctrinal innovation is one of them. It refers to a situation where an enterprise has an innovation model and drives it for all it is worth, without blinking when circumstances might need some circumspection.

At Santander Bank there was a doctrine for growth and a core of people who proselytized it and benefited through careers and bonuses. However, in 2013, Santander announced a $26 billion write-off from its Spanish bad loans and real estate assets; profits declined 35% the previous year and the company has been mired in a succession of legal problems. Customer satisfaction in some locations is very low.8 Yet the bank still follows its doctrinal innovation model.

Doctrinal innovation is difficult to shake off because its methods fit an entrenched view of how companies ought to grow.

One challenge for companies is that change is not just difficult to enact, it is often difficult to perceive. When companies have a growth doctrine they find it hard to see options. They might say they have to focus on execution not innovation, but that is often a way of overriding the need to think in terms of optionality.

There are good reasons for resisting optionality. The cost base of a transformed or transforming company is totally different from a traditional one. Many of the costs of developing new services are mostly human and social, such as developing relationships, building a presence in the information layer, building thought leadership and influence, making non-NPV-driven acquisitions to bring in new cultures or to test new approaches, creating new human capabilities, and investing in companies for talent acquisition.

These are the very costs and behaviors that leaders had sought to squeeze out through IT efficiencies, even though human factors are intrinsic to a new way of doing business. They are costs that sit outside the doctrine. They are almost disease-like to doctrinal companies.

Human costs also make business less predictable, and they make growth dependent on social interaction rather than on conventional investment or process models. The cost of change is becoming increasingly vague and difficult to represent as a certainty. The way companies calculate investment return actually reinforces these problems.

However, in order to change, companies have a strong need for metrics that reflect investment in human performance and just as urgently companies need to create options, that might even become throw-away investments (like Google’s investment in Motorola).

In fast-changing markets with problems of technical debt and high velocity, having limited options is a pronounced weakness. The mobile phone maker RIM found this out when it focused on its device strategy and ignored its pioneering position in embedded operating systems with QNX. The CEO at the time, Thorsten Heins, backed his reputation, and the company, on being able to make a device to rival the iPhone. He failed and lost his job.

Options have to increase for many reasons. Disruption is broadly based. Simply to keep up in a fast-moving, unpredictable game, executives need new cards to play at critical junctures without having to wait on a new product development program. What’s more, competition can come from adjacent markets and arrive unnoticed and at speed. Companies need also to figure out how to play out their own adjacencies and they need options for that. Further, economies of scope demand that an enterprise multiply the options it offers customers. All of this entails making investments that are not always going to work out. Some will; some won’t.

Options’ thinking is a way to acknowledge a lack of clarity and certainty in this investment process. It makes managers feel disempowered to build options and to keep the future fuzzy. Yet old-fashioned NPV estimates are often only educated guesses—in fact, they are often worse than this. They are guesses dressed up as probabilities. But this is how managers have been educated.

The argument that companies create attractive financial results by playing with ratios like return on assets could hold water, but at the coalface, the problem is more mundane. Managers are not accustomed to building out multiple options; nor are they given the tools to measure their progress in ways that financial officers can incorporate into accounting standards. Options’ thinking is an alternative, but it is a complex form of project accounting that needs plenty of adaptation, an issue I will look at later.

Making matters more complex, the market value of options is currently aberrant. Facebook’s $19 billion acquisition of WhatsApp and Apple’s $3 billion acquisition of Beats are both apparent cases of seemingly distorted valuations. Yet both result in new options for the purchaser.

If you think of customer attention as a scarce commodity, then these valuations make a bit more sense. From an enterprise standpoint, they provide options on customer attention (Apple is now in streaming music; Facebook has options to monetize a six-hundred-million-member audience aside from its core social network platform). And they deprive competitors of customer attention.9 Even with this in mind, the sums were considered by many as wide of the mark, illustrating the acute problems executives face in valuing their possible courses for future action.

Companies like Apple and Facebook are barely answerable to shareholders (in Apple’s case until recently) and make bold decisions they don’t need to legitimize outside of routine reporting norms.

Other companies that stray too far from NPV will be called to account by the markets, unless they have a strong transformational narrative to fall back on. Implicit in these narratives, their stories of change, has to be the assertion that normal investing rules don’t apply. Investment in an alternative scale model with vague and risk-laden NPV is necessary but it requires different modes of innovation. And it is happening today in some areas of business, even in hardware.

THE SHIFT TO PRICE-FREE ENGAGEMENT

Xiaomi, the Chinese smartphone maker, is a good example of placing a big bet on a strategy with no clear NPV. Xiaomi was founded in 2010, and yet by Q1 2014 had sales of around 11 million units. In 2013 alone, Xiaomi’s overall sales grew roughly 160 percent and reached 18.7 million devices. The company had expected to retail around 40 million smartphones in 2014, but soon analysts were ramping up expectations to 60 million.10 It has applied the principle of selling a high-quality, low-margin product in order to grow a user base for its increasing scope of services.

An additional factor behind the growth of smartphones is that pricing is divorced from cost.

In many jurisdictions, phone suppliers are able to sell at below cost and recoup the losses through the buyer’s data contract with his or her carrier company. There is an almost universal gap between cost, price and acquisition in smartphones, as if half the buying public has surreptitiously been hooked into hire-purchase agreements with absolutely no transparency on the interest they are paying. The arrangement also bypasses many consumer credit laws that give people taking out loans an opportunity to cancel a contract in a given time period.

In a separate development in the smartphones market, Microsoft, following its acquisition of Nokia, has returned to the company selling cheaper phones in order to build up a larger consumer base. This will attract more developers to create apps for the Windows 8 family of phones, which will, in turn, make Nokia/Microsoft phones more appealing.11

The significance of securing an end-customer base and building new types of relationships has never been more pronounced. But the path to doing so involves new risks. There is a trend now of virtual products and services declining in price regularly and consumers are alert to it. Taking a risk on low-cost, high-quality hardware, means also relying on fickle consumer behavior over the longer timescale between sale and profitability.

In all these cases, companies might produce NPV-type business plans, but their actions are actually a leap of faith as they try to create a new pattern of business around new economies of scale.

What is also important in this process is scope. Under the old rules, companies built economies of scope (launching extensions to products to decrease unit costs) on the back of successful projects—often just through brand extensions. So an iPhone is now actually three separate products (currently the iPhone 4, iPhone 5C, iPhone 5S, and the iPhone 6, each with varying memory capacity) that have evolved over a two-year period. Samsung launched multiple variants of the Galaxy S4 within months in 2013. These are typical, though profound, brand extensions.

Today, hardware has to launch with scope built in, and smartphones are a good example of that. iPhones are beautifully constructed, and they sell in such large numbers, not because of the brand extension, but because Apple has the most substantial array of apps.

In a parallel example, there is nothing in the Amazon store that cannot be bought elsewhere, other than a few Kindle titles. But Amazon succeeds because of its huge inventory and willingness to invite infinite scope by creating a marketplace around many of its products. In this system, if Amazon does not have your product at the price you are prepared to pay, you can buy from a lower-cost Amazon partner.

In traditional economics, the idea of an economy of scope referred to a company’s ability to reuse its know-how to create two or more variants of a product at a lower average cost than creating one product. In today’s economy of scope, the platform enables vastly more variation by devolving scope onto the ecosystem. We have called it mass differentiation to point out how vast the scope of new options in a platform can be.

Profound changes in human behavior lie behind these strategies but many companies fail to see the disruption that human interaction is storing up for them. They expect disruption to arise from technology or competitors, but changes to economies of scale and scope stem from human needs—the need to be more connected, more adaptive, to self-educate, or to be more creative, to experience beauty more often, to differentiate, even to work in more varied ways.

People are often flooding their attention with many options. They are renewing the scope of experiences they want to engage in, often with no end logic. They have entered the game of reinvention as they jump in and out of websites and networks. The problem that creates is second-guessing what they will value sufficiently to pay a good price for.

Health care provider WellPoint’s CEO Joseph Swedish believes that companies like Amazon and Uber have changed the way customers think about services and products forever. “We need to put the end consumer at the center of everything, and these end consumers calibrate their expectations of us against the likes of Amazon and Uber. We need to innovate to remain relevant and to defend our business against untraditional new entrants that may disrupt our industry,” he says.12

Even this doesn’t capture the core of what is happening. It is not just the end-consumer expectation that has changed. It is the relationship that companies need to build with them. People in general seem to be evolving new sentient needs around communications and self-determination as well as a different perception of value.

They are trending toward the use of services that have little actual cost to them (in addition to being hooked into smartphone stealth pricing). For a monthly Internet subscription of under $50, most people in the United States and Europe are now passing a significant portion of their time engaged with online services that have no additional price—for large periods of time they engage with each other in a price-free zone.

As of February 2014, Americans spent an average of thirty-four hours a month with the mobile Internet, twenty-seven hours accessing the Internet via a PC, and sixty hours overall per week in front of a screen. The standout winner in this scenario is the smartphone. The number of American households with a smartphone increased from 19 percent in 2009 to 65 percent in 2013. Users spend over 84 percent of their mobile time with apps, the vast majority of which are free; furthermore, 45 percent of that time takes them into social networks where they do not transact.13 The smartphone is also the medium with the least effective engagement from advertisers.

In other words, people are seeking time away from the transactional economy, forcing the unit price per minute of digital value into continuous decline. And within those minutes, people are creating value with activities as simple as being in contact with each other.

The creation of extra value within a consumer’s existing price point for goods is referred to as “consumer surplus.”

On aggregate, consumers acquire more value as the price of a product or service decreases. But they are doing more than that. Consumer surplus usually refers to the value acquired by price discounting or when prices fall as companies release new versions of products. For example, a consumer may be willing to pay more for some features on a BMW than BMW actually charges because competitive pressures are forcing car makers to include more features as standard. The development of products with additional features that are not priced in, is referred to as consumer surplus, because the consumer makes (an often unwitting) gain.

What’s happening today is that people are creating value between themselves on platforms like Facebook. This is non-traded value. They are also creating small portions of value on unregulated platforms like Airbnb or ride-sharing sites or platforms like Etsy or eBay. They are being asked at the same time to contribute more to the commons, knowing that enterprises that enclose that value for their own use are reaping huge rewards.

This whole process is awash with anomalies. Investment is switching from hardware and hard margins to people and indirect revenues built around community or the commons (through advertising, for example).

Within this changing landscape, people are also changing their sentient needs, which means companies have to get closer to them.

The shift to a transaction-free, price-free economy is significant for many reasons. It is not absolute, of course. Price, supply, and demand still function across most areas. But when selling products near to cost, companies are inviting customers into a lengthy tangle over which ads or services or apps will appeal to them.

The elastic enterprise, platform and ecosystem model lies behind these changes. It does not necessarily drive them but it is an integral part of the overall story.

CHAPTER 10

The Shift to the Personal Economy

So far, we have looked at the shift to a less capital-intensive form of enterprise, the presence of pervasive disruption risk from the commons, and the shift of individuals toward a new set of sentient needs that are not transaction based. Within this overall picture, there is also the growing concentration of resources into platform companies, the risks associated with being in the ecosystem, and a general drop in business dynamism that companies are compensating for by strengthening the commons.

We have also noted Coase’s view that companies exist to enclose transactions, and Williamson’s view that the enterprise brings order to what would otherwise be unmanageable chaos. In fact, more transactions now take place outside the enterprise because platform enterprises are able to manage complexity on a scale that would have been unthinkable a decade ago.

Because these changes stand on the shoulders of common activity, they also change the way people want to be treated. The modern economy forces people to give labor away and insists on their adoption of a new range of personal, often reputation-based, incentives in place of money. These social changes, along with growing polarizations in the economy, are building pressure for additional change. Let’s look at that in more detail.

THE PERSONAL ECONOMY

The new elastic economy is fundamentally about people and more specifically about the individual. However one frames strategy, its success depends on whom you can convince to be part of your project (customer, partner, developer, content creator, etc.). The new economy is loaded down with projects, and desperately short of spare attention.

The elastic enterprise, built on platforms and ecosystems, is a powerful way to organize networked innovation and ingenuity, and is perhaps the answer to the process innovation models that companies need. The mix between cost, scale, and manageability are all improved in the elastic enterprise, and companies can now benefit from relationships even with individuals and micro-entities around the globe.

This change is not necessarily good for policy makers, however, who generally curate a particular geographical region (nation, city, or region) and collect stronger tax revenues from paid employment than self-employment (The rate of tax leakage—or underreporting—in paid employment can be as low as 1 percent, whereas in self-employment and small businesses it can reach 18 percent or more).1 Self-employment and borderless economics are both features of the elastic economy.

Governments and other economic development agents face the twin dilemmas of geographical leakage and revenue loss. The ecosystems that gather around platforms have no necessary geographic location. They do not need to be anchored in a place; they are instead anchored in a software, services, and product stack with global contributors (many of them unpaid). Yet these systems are essential to job creation and small-firm growth, traditionally core provenances of government.

Ecosystems, however, are difficult for government agencies to generate. They are difficult to create through policy. Brynjolfsson and McAfee pointed out in 2014 that the next big shift in computing involves digital technology taking over more knowledge-based tasks from people.2 Governments face having less power to create jobs just as structural unemployment is set to increase in knowledge-based work. Even further adding to government problems, some of the best-known platforms are also the most ruthless at using geography to avoid corporate tax.

There is a broader cultural shift underlying and contributing to these changes. The modern economic infrastructure is built on free labor: most open-source ecosystems still rely on 25–50 percent of their participants contributing without being paid any cash rewards.3 That tendency has now shifted to other areas of economic activity. People in the marketing ecosystem for the open-source web browser Mozilla contribute largely for reputational gains with next to no opportunity for earnings. Writers for news and information websites like The Huffington Post do likewise. And the many of the apps in the Apple and Android app stores earn their creators next to no revenues.

Along with rising free labor, one of the most important personal economy trends is the drift toward self-employment and small-scale businesses with high rates of failure.

The current wisdom is that by 2020, every third worker in the United States will either be self-employed or running his or her own small (i.e., personal) business. Already there is a self-employed population in excess of 17 million, and the self-employed labor force grew by 12.5% between 2011 and 2013 compared to the overall labor force growth of 1%.4

There is a hidden degree of complexity in this changing environment. In Europe, according to the Financial Times, “About 14 per cent of the UK workforce is now self-employed, compared with 10 per cent in Germany, 11 per cent in France and 13 per cent in Belgium and Switzerland.” The figure jumps to higher than 20 percent in Southern Europe. The UK figure grew by 8 percent in 2013 alone.5

Evidence from the United States and Europe suggests self-employed people are increasingly content with their status, though to be successful they need to be well networked, a gift more often possessed by the older worker than one young, unemployed, and in need of an income.6

While the self-employed may report satisfaction with their situation, the real financial returns to self-employed people have fallen by about 65 percent over the past forty years, some of which may admittedly be due to underreporting of income. 7

Data on the UK economy shows that incomes are also falling in real terms. According to the Bank of England in October 2014:

Growth in real wages has been negative for all bar three of the past 74 months. The cumulative fall in real wages since their pre-recession peak is around 10%. As best we can tell, the length and depth of this fall is unprecedented since at least the mid-1800s, said Haldane. This has been a jobs-rich, but pay-poor, recovery.8

Between 2010 and 2013, wage rates across Europe dropped substantially, even as employment rates began to recover. Over the longer term, American median salaries have stagnated for much of the past thirty years, though data on earnings is often difficult to compare over time.9

So, more people are taking more personal responsibility for their work in the context of declining real incomes and the imperative to do free work. People are seeking noncash utility from their work. Studies have shown that people will pay an economic price in order to be their own boss or to have work where they are of value to others. In a study of the independent American workforce, MBO Partners, a specialist firm supporting independent contractors, found that 73 percent of respondents would take less money in exchange for doing work that they like. Sixty-two percent said they would choose flexibility over money.10

People are also engaging less in traded services. To engage with virtual environments, they simply need to pay two access charges—the cost of a device and the cost of a network subscription, both of which trend downward in price. (This is evident, for example, in the United States where T-Mobile now offers free music streaming on its 4G networks. AT&T and Verizon followed suit.)

The traditional tools of the economy—inputs like labor exchanged for cash and capital investment with clear ROI—are breaking down. There are wider markers of independence and self-determination.

Crowdsourcing and crowdfunding give individuals a new set of options in pursuing their own objectives, but in a context that lies outside the normal transaction environment. Crowdfunding platforms, such as Kickstarter, are gift platforms rather than investment vehicles, and they are growing exponentially. In Q1 2014, funds raised were doubling every sixty days. There were more than 20,000 open projects at any one time with about 4 million participants, and 75,000 projects total in the three-month period.11 The total sum raised for 2014 will still be small in the context of the overall economy, perhaps comprising less than 1 percent of Apple’s $160-billion cash pile, but the growth rate is impressive.

In addition, platforms like Airbnb and Uber give individuals options to create new sources of revenue, which is also difficult to capture with conventional economic metrics. There is even a cohort of self-employed workers who provide welcoming services to Airbnb guests, pretty much like hotel receptionists. An ecosystem is evolving on the sell side of Airbnb, and it seems to be in the alternative, non-taxed economy.

Facebook is now home to 25 million small businesses worldwide, potentially giving each business access to its own global network. In 2014, the e-commerce site Shopify estimated that 65 percent of all visits to its 90,000 store partners came from Facebook, with Facebook generating 85 percent of all orders.12 Just as important, people are also generating domestic revenue through Facebook for small, home-generated or craft products.

These trends suggest the manifestation of a systemic problem rather than simply a cyclical one. That is, people are being forced to renounce the paid, salaried economy. At the same time, companies are harboring resources that they don’t know how to spend.

This systemic problem has three components: the increasing concentration of wealth in the reserves of companies with no practical way to invest it, the increasingly untaxable nature of wealth creation, and the withdrawal of people from traditional transaction activity (into self-employment, mixed economic activity and free leisure).

MACRO CHANGES

On the larger stage, business transformation is taking place in the context of increasing government impotence. Most Western countries are laboring under huge debt burdens. European governmental debt as of 2014 stood at over 11 trillion or €22,800 per capita ($14 trillion at 2014 exchange rates). Across the European Union, debt averaged around 90 percent of Gross Domestic Product (GDP), even though the reference target under the European Treaties was 60 percent.13 And that is just official debt. The UK government officially owed about £1.37 trillion or $2.3 trillion at this stage, yet a 2010 Office of National Statistics estimate puts the overall figure at £5 trillion ($8.3 trillion) when pension obligations and bank bailouts were added in. A 2010 PricewaterhouseCoopers report estimated that the possible total of all debt—household, business, and government—could rise to over £10 trillion ($16.7 trillion) by 2015 and at 2010 stood at five times national income.14 US federal debt stands at just under $18 trillion.

Compounding these problems, governments are now competing to offer low-tax status to corporations, particularly in Europe. That has a direct effect on the US tax revenue, evinced by the number of companies in the United States that are trying to relocate to Europe. Ireland offers a 10–12 percent corporate tax rate, which US corporations use in conjunction with Dutch incorporation to move assets to zero-tax environments.

The Netherlands, Luxembourg, and Belgium offer 5 and 6 percent tax rates to companies that locate intellectual property development in their jurisdictions. Their logic is that they can capture R&D and related intellectual property development, often associated with high-growth start-ups. This may give them an advantage in the creative economy, yet it is also fueling new tax management policies by large companies and fueling competitive tax policies. The United Kingdom offered a similar “patent box” scheme in 2014. Portugal has a special income tax regime for individuals who have skills that the country has a shortage in. Romania offers a 16 percent tax credit to technology workers in expert industries.15

Unemployment has weakened government treasuries but they also fear a future without the right talent as economic dynamism slows.

The US Bureau of Labor Statistics is projecting declining US labor participation rates until at least 2022:

…the next 10 years will bring about an aging labor force that is growing slowly, a declining overall labor force participation rate, and more diversity in the racial and ethnic composition of the labor force.16

It took six years for the United States to recoup the jobs it lost in 2008 in absolute numbers, but the new jobs have a variety of origins. In the state of Texas, jobs have been created by a new oil boom and a drive to oil self-sufficiency. More generally, jobs have been created at the lower end of middle class incomes. Manufacturing has added fewer than 10,000 jobs per month on average since the recession ended. A sign of the overall trend is visible in publishing where 400,000 jobs were lost in the recession and only 76,000 have been replaced by their digital alternatives.17 State revenues were hit hard by the reduction in the tax base, and that had a disproportionate effect on jobs in education, which will impact future talent availability.

This all reflects a background of government impotence, a period of generally lower tax income and an increasingly competitive approach to tax discounting revenue. The elastic enterprise could add to these problems.

The elastic enterprise is a global organizational structure. Its largely virtual nature, existing at the convergence of IT and telecommunications, means that its benefits are widely distributed rather than confined to a geographic location. It favors smaller enterprises, start-ups, independent contractors, and even domestic entrepreneurism where trade economic activity is in decline. It is global. Developers and content producers generally take the same risks for the same rewards wherever they are in the world. The best way to guard employment is to promote learning and talent.

The picture on talent investment is mixed at best. Tech entrepreneurs bemoan a lack of IT talent, but a survey of tech industry employment patterns and wage levels in 2014 indicates otherwise, as it found little evidence of wage pressure in computer programming.18 What is happening, instead, is that companies need employees with a mix of business acumen and tech or programming knowledge. They need people who can adapt quickly to changing circumstances and even anticipate the course of change. Yuvi Kochar, former CIO at The Washington Post Company, says:

Finding the talent that can deal with all these technologies and be savvy without increasing our costs is a huge challenge. We are up against all the mobile companies and the product companies. Although we have an interesting brand, talent remains a huge challenge. It goes beyond technology and relates also to product developers who can also deliver a new business model. …

There’s a more general issue of how you prepare people for jobs as jobs are changing so quickly. Kids coming out of college are already underprepared and don’t have the skills needed. That’s why ongoing education is so much more important. People must take on the responsibility of educating themselves.19

Perhaps that responsibility should be a shared cost, though, not just between people and employer but also with government. Talent costs are increasingly difficult for enterprises to justify now that more value is created outside the business. But the problem cuts deeper. Many companies remain starved of resources and are forced by accounting norms to record profit growth or by shareholder activists to distribute more cash to shareholders. In the absence of capital investment needs, it is difficult to justify people and process investment.

The Financial Times (FT), in its recent report on American corporate reserves, notes that “Apple’s spending on plant, equipment, and acquisitions has used up just 10–15 per cent of its operating cash flow in recent years.”

This is the most effective company in the world, the one with the biggest cash hoard, and it has little need to risk a substantial portion of its reserves on acquisitions, process development, or plant and machinery. When referring to Cisco, another company with huge cash reserves, the FT notes the company’s desire to spend on foreign acquisitions. However, they also observe that “the scarcity of potential targets has limited its effectiveness.”

Meanwhile, the FT reported in January 2014, “By the middle of last year, the concentration of wealth in the hands of a few tech winners had left just six companies—Apple, Microsoft, Google, Cisco, Oracle, and Qualcomm—with more than a quarter of the $1.5 trillion held by US nonfinancial corporations.”20

That system is clearly hitting a crisis point. The digitization of the economy is touted as one line of oxygen for a dying patient. Yet, in reality companies are not increasing their investment in IT. Global IT spending (all spending by corporates and households) stood at $3.5 trillion in 2011, according to Gartner analysts. In mid-2014, they projected little had changed at $3.7 trillion, or 5.5 percent over three years. That is flat in the context of a global economy rushing at speed toward digitization. In contrast, Gartner predicted an 8 percent rise in Middle East IT spending in 2014 alone.21

The new aristocrats of the modern economy are accruing unusual amounts of cash while other areas of the economy are in crisis. These aristocrats enable change and they create jobs by creating radically new economic conditions where pressure for more change builds up. The best way to counter these imbalances is through educating and training people to improve participation in or alternatives to these new systems. The problem is that governments can no longer afford to develop policies that radically improve individual economic performance or creativity, and technology companies have no need to. This deficit can be overcome, a topic for Chapter 13. However, it is worth also saying that education is trending back toward a free commons through websites like Coursera and Khan Academy. The reality for many people is that the future lies in the commons or in ecosystems that demand an investment in self-teaching, continuous networking and diligent reputation building. Few have been educated for what the economy now expects. The big change can be summarized in one word–options. How do you build your options?

CHAPTER 11

Options

MEET JACK

Jack is learning to live with his mistakes. A thirty-three-year old social media junkie, a freelancer working mostly with a small local company, he has just blown a year of his career development path after taking on a failing project. Rather than manage a diverse portfolio of projects he took on a 12 month gig that was badly managed by the contractor who hired him. He has lost visibility in the market and after curating his portfolio well for two years previously he now feels as though he is unemployed. How can he learn to translate his opportunities into better options in future?

It is inevitable in the new economic environment that we are going to be wrong more often than we might have been in the past in handling critical career and life decisions. A young CEO of a Silicon Valley start-up talked to me with the usual life-changing rhetoric about his company at the Dublin founders conference in 2011. Within a year he had abandoned it for a new one in a different field of activity. A woman at a conference in Chicago talks about her passion for changing the face of education even if she has to rebuild schooling one brick at a time. Her passion has been overshadowed by Khan Academy.

People have to make more decisions but in conditions of uncertainty and increasing complexity. Their judgments will be more often wrong and often only partially right. In fact it might be that we dispense with the concept of right and wrong in our careers and seek new ways to balance the impact of decisions and what we can learn from them.

It was so much easier when all we had to do was get a job and climb the ladder. But increasingly we will all be prone to making poor judgment calls as we traverse this new crag of an economy, taking on work we regret; cooperating with groups who we realize have the wrong ethos; being out-negotiated by unscrupulous clients; retreating from the climb before maximizing an opportunity, defeated by fatigue or confusion.

The tendency to exaggerate the positive outcomes of our experiences is a sticking plaster. Ex-Google chief Eric Schmidt said in 2014 that the $10 billion or so his company lost on Motorola Mobility (MM) turned out okay because it meant they could sell MM (bought for $12 billion) to Chinese company Lenovo (for $3 billion) and get one more company working on the Android platform.1 Nice try—it would have been easier to invest a billion in Lenovo from day 1, and save $8 billion (in fact Schmidt is eloquent about how MM gave his company options).

It is far better to learn how to adapt to a less forgiving age and to embrace its lessons than to pretend everything is okay. The vast majority of us have to make our way in turbulent ecosystems we inhabit, for work and leisure. We can find balance there in the complex web of excitement, confusion, connections, deceits, networks and isolation, successes and failures. The point about this world is that each experience has the potential to make us rich, without the pretense of success where we in fact encountered failure.

Every exemplary company at the height of its game in the modern economy has made major mistakes. That means the really smart folks like Zuckerberg, Page, Jobs, Cook and more made seriously bad calls as well as good ones. For Facebook, it was missing out on mobile (which has cost them dearly in acquisitions like WhatsApp). For Google, it was the acquisition of Motorola Mobility and before that the failure to spot the rise of social networking. For Apple, it was its early failures in tablets and phones and not continuing its ascent after it reached a momentous peak in 2010.

Often these companies did not recover from those errors until years later. Adaptation is a slow process. For the majority of us taking the wrong route, building relationships that go sour and missing out on good mentors, are just some of the ways that our careers and lives go off course, and can also create long-term deficits that we might live to regret.

The current vogue is to pivot when things look like not working out. The idea of a pivot is to be able to switch course very quickly while keeping the value of work-to-date intact. The pivot can be a strong tactic—it is very difficult to get good teams together and if one is well-established, then the alternative to the pivot is to disband and then look for a new project. But in our personal lives and in business, a project or social network is a stronger commitment than the idea of pivoting implies. The better personal and business strategy might be to maintain a sense of positive uncertainty or possibility. To be a worthwhile colleague, we need to be there when the going gets tough, rather than to pivot and change direction.

The writer Charles Handy approached this concept various times in his career and referred to it as the need to manage paradox.2 Uncertainty is replacing the sense of ambiguity and contradiction that Handy believed was pressing on us twenty years ago. There are few paradoxes to manage when the uncertainty levels are high.

Options’ thinking is a significant antidote to these problems. There are different definitions of options’ thinking—a reflection of how busy people are trying to find the right concept and ideas to figure the new economy out. In my view options’ thinking replaces the idea of the portfolio career. It is a shift from simple to complex portfolios.

Here’s a brief explanation. The idea of the portfolio career is that people will change course four or five times in their lives, giving them an overall portfolio of experiences. Within that portfolio there will be times when they take on two or three roles simultaneously. Every freelancer understands that challenge.

It gets complex when trying to create a more modern portfolio. Let’s look at the career of Jack. Jack writes, has social media skills and knowledge of corporate change mechanisms.

Jack has to work for nothing during some part of his monthly time budget; he has a long period early on in his career when working for free is almost overwhelming, so he has a tendency to be grateful to clients for paid work.

Within his free work, Jack has to make decisions about when work is strongly positive for his reputation, or strong for his network, or where it is paying forward in the expectation of a favor being returned later.

Jack has some low paid work, about a week a month. It is regular and guaranteed so he banks it and regards it as stability. In addition he writes for a website that gives him visibility, but pays poor rates, adding to the downward pressure on his income.

Meanwhile in one area of this work, social media, he gets a chance to speak publicly and picks up a few thousand dollars, occasionally.

He has the option to go into an agency where the wages are good but he’s been hit twice now by shakeouts and he is reticent to foreclose on his independence.

About three times a year he manages to secure reasonably sized contracts that really lift his economics but to deliver he has to work extraordinary hours. He’s the guy on the late night tube bashing away on his laptop.

All the time Jack is trying to find the energy to look ahead and see where this portfolio can be upgraded or diversified in ways that give him leisure time.

This is a tough but regular experience for people in the ecosystem. What they have, in abundance, are choices. They need ways to manage choice better but few people are trained for that. They revert to what they know. That’s why jack took on a project that looked like regular work. But those 12 months are now biting him hard.

Companies are in pretty much the same boat. If they are not the platform owner, then often they have the same choices of what work to take on and how to deal with varying economic rates and problematic clients. The shift is really that these concerns now hit individuals as much as they do companies.

In this new business environment, it is important that companies and individuals are able to develop many more options than they would have thought necessary in the past. For example, they may work with a technical-debt risk that needs mitigating through options planning; they may work as well with new projects that need time to evolve; and they may need to hedge against an array of adjacency risks posed by platform companies that might invade key customer communities.

An additional issue is customer acceptance of new products. Companies need to place options in front of customers, as well as develop scope for highly differentiated markets designed for long-term, downstream revenues. Investment can no longer be described in relation to a fixed cost and a margin return. It has to be described as an ongoing cost and a variety of possible developments, routes, and outcomes. We all face that set of issues, though. That’s where options’ thinking can help.

STRATEGIC OPTIONS BUILDING

We need to think in terms of a strategic options’ portfolio, which is to say we must produce a broad portfolio of options knowing that many will not be used. That requirement is a Darwinian one—in the past companies could succeed by raising the cost of innovation but today innovation will emerge from the ecosystem and very likely change the cost base of an industry. Without options there is no defense against this.

What that also means is that companies need to broaden the conversation around shareholder value. The idea that shareholder value simply equals profit is entirely wrong. Shareholder value can be described in terms of risk avoided, enhanced social affinity, readiness to change, and longevity—ask the shareholders of Nokia.

Equally we need to broaden the conversation around career value so that individuals are better able to make decisions about their timelines and the commitments they should make to optimize happiness.

Options’ thinking is a way of adapting techniques from the financial markets to these new needs.

In the financial markets, investors take put and call options on the future value of an asset. These financial instruments allow them to buy or sell shares in that asset at some future point. To think in terms of options, investors have to understand and balance future possibilities. They often have technical data that gives them a stronger sense of probability than is available for broader enterprise or human activity but nonetheless these ideas are adaptable.

Their great value lies in getting us to think downstream, and getting us to recognize a range of possibilities there. The norm is to project ahead from where we are today—or if you are in business to look at a future total market and then guess what size of that you are going to win.

Options’ thinking places far more emphasis on the contours in the rocky road. It forces us to prepare for unexpected turns, to have options at the ready when the road forks.

This kind of preemptive and hedge-like thinking can help make the real risks and opportunities in a project much more obvious. For individuals, too—when was the last time you thought through the consequences of a career move in any detail, or looked at the relationships developing around you and assessed their potential impact on your ambitions downstream? If the career ladder is your thing, then this is not such a problem. However, if you are building a portfolio, then extrapolating each aspect as it exists now and making the impact of connections and turns explicit can help you make better decisions.

Suppose, for example, Jack gets a new gig that makes it unnecessary to do free work any longer—at least that’s the immediate impact. He has a chance to earn more and maybe save a little. He goes with that opportunity and begins easing up on himself and enjoying life more—at least spending more cash. The problem arises twelve months down the road when the contract starts to wind down. He looks at his online work, the blog and contributory writing he used to do, and realizes his reputation is in limbo and his network has cooled. He is a year down the road but no better off. Was Jack’s goal only ever to fill the month with paid work? Is that a good idea?

The ultimate goal of the portfolio is his to set, but it might have been better to have other goals, like finding more fulfilling work; achieving a high level of autonomy by building respect levels with clients and colleagues; nurturing the relationships that would always repay.

This type of thinking can also force management to explore the scenarios where they might stop a project, sell an asset, or invest more.

This is particularly important in innovation projects. The routine in innovation assessments is to force projects to defend their progress at different stage gates. It’s even known as a funnel and stage-gate process. Because they are locked into the funnel thinking, projects are often isolated from overall strategy. They have to stand on their own rather than be judged by their potential contribution to an evolving market, a broadening portfolio, and a hedging strategy.

This alternative way of thinking is often referred to as “real options” because it is not a financial option but a decision about real-world events. It is an alternative to thinking about Net Present Value, which companies arrive at by taking the hypothetical, future revenues from a new project and then applying an even more hypothetical risk probability and discounting the revenues back to today.

In it strictest form, real options’ thinking helps make decisions about large capital projects. In an elastic setting, its role can help generate more options and hold open foreclosure decisions so that projects get a chance to prove themselves before being terminated.

Options’ thinking is visible in decisions by companies like Google and Facebook. Both have become accomplished at creating options. Facebook’s strategic options are often bought in through major acquisitions of companies already succeeding in their field (Instagram, WhatsApp, Oculus Rift).

Google manifests multiple options the whole time through its X labs initiative, often smaller scale acquisitions, and its venture fund. The corollary of its big options portfolio is that it also divests projects quickly.

A short list of Google divestments includes Wave, its e-reader product; Motorola Mobility, an aborted mobile initiative that was a $12-billion investment; Health, its e-health records project; and Orkut, a global social network. Many of its existing X-lab projects are high profile and high risk, particularly Project Loon (a high-altitude, balloon-type satellite). Google launches them and closes them. It exercises options.

Real options’ thinking is real progress. Many organizations have begun launching captive investment funds, accelerators or start-ups in order to multiply the range of innovations they engage with assuming this to be a path to optionality. This is not necessarily an options-based approach. Many of the initiatives companies invest in are not tied into strategy. In that sense, they are not strategic options. They are left in a swim-or-sink status, adrift from the company’s desire or plans to transform. Before this situation arises, think in terms of options but do it with discipline.

Companies that currently build good options’ portfolios include Autodesk, from CAD CAM to bioengineering design, 3D printing, open engineering, building information modelling, and movie animation; Intel, from chips to consumer robotics and network information management; Samsung, which has options almost everywhere; and of course Apple and Google.

THE STRATEGIC OPTIONS PORTFOLIO

In the new economy, the need for innovation exists alongside high levels of uncertainty and far higher levels of optionality. Together, they create a new need for executives to abstract themselves from operational planning, so they can take a look at the array of options they face.

Real options planning can help them map the twists and turns in the road ahead. Yet, Finnish financial expert Mikael Collan has pointed out that real options are inherently complex to describe and also tend to design certainty into outcomes.3 Options’ thinking is progress over NPV but it is far from perfect.

A strategic options portfolio is a register of options in progress. The challenge for management is that the portfolio is often huge and gets bigger as its relationships expand. The option itself could be simply an idea or an existing product, a technological capability (e.g., a factory, production process, or information system), an invention, or an investment in a company. For each strategic option, the enterprise or executive team has the possibility of making a further investment of time, talent, capital, or partnership (where partnership also entails reputation, credibility, and attraction resources).4

All companies have some form of strategic options portfolio and play those options out using scarce resources, even if they are unaware of it. In fact, many companies play out their options as opportunity costs. That is, their options are lost opportunities that simply pass them by. Sophisticated companies might have an extensive portfolio, but chances are that even by traditional standards, successful companies spend more time at the executive level managing existing brands than they do developing their options. The really smart ones, though, are options focused.

Below is a small options’ table for future bandwidth supply in the mobile communications space. It shows two unlikely candidates innovating in mobile infrastructure. Samsung and Google have different objectives in creating the options below.

Samsung wishes to lead the next generation of mobile broadband infrastructure (5G). It has already announced 1Gbit transmission rates for its experimental 5G technology.5 It is installing WiFi on domestic devices like TV sets to make in-house communications easier (and to give everyone an opportunity for free broadband mobile). It is involved in the White Spaces consortium, an IT affiliation with designs on creating an alternative broadband infrastructure to that provided by telecoms companies like AT&T. Its experience of the silicon industry has allowed it to become a leader in solar panel design and production and it is using that know-how to create broadband enabled schools across rural Africa. And it has a substantial consumer electronics business with which it will build connected homes.

Table 11.1: A small options array for Samsung and Google in broadband mobile.

Google, by way of contrast, is not a player in the standard broadband mobile infrastructure but it is involved in White Spaces. In addition, it is building out urban fiber-optic cable networks and hooking these up to WiFi hotspots to create free mobile access in public places. It is experimenting with high altitude balloons as a way of bringing mobile broadband to rural areas and it has bought into home control systems through the acquisition of Nest Labs.

None of these initiatives is the main line of business for either company. They are just part of the long list of options that they are constantly dealing with, each in its own right containing many sub-options for how to fund, build, deploy, and earn returns. These two companies are options machines.

One of the key features of an elastic enterprise is its ability to generate new strategic options in abundance. Looking at elastic enterprises in action, you can see options proliferate, perhaps over-proliferate in some cases. Let’s look at examples of companies growing options portfolio over time:

• Dolby, the experts in all kinds of professional sound systems, is finding that its products are buried deeper and deeper into products like smartphones. That takes it farther and farther away from the customers it wants to serve. It has responded by entering the video market, bringing top-line products to the consumer market and by creating online sound experiences.

• USAA, long a user of the adjacency strategy in financial services through M&A and internal organic initiatives, employed radical adjacency as they moved into automotive buying and home buying services with Auto Circle and Home Circle, Cloud-based business platforms.

• McLaren, well known for its automotive racing prowess, is applying its real-time, race vehicle telemetry experience to develop new solutions and human telemetry apps for health care diagnostics and remote patient health monitoring.

• GE, with over 100 years of experience with adjacencies, has now established an entire business platform services division and is using elastic-type capabilities to support not only its aviation business, but also its medical equipment, energy, rail and leasing businesses.

THE GOOGLE GENERATION

Of all the companies active today Google is the most comfortable with options building. There are other examples: companies like Shell in oil discovery and refining has the free cash flow to develop a strong options portfolio. And Samsung is an options machine. Implicit in all three is an exceptional degree of comfort when taking risks.

In its early years, Google had one line of business: search. Google invested heavily in its search platform and, through its innovative advertising model, generated significant free cash flow. As it continued to invest in the capacity of the search platform, it also began to identify new strategic options: it started Google News, Google Apps, and Google Maps. The company began to develop a new overarching strategy, based in part on wishing to extend search opportunities but also on something less articulated, a desire to be an agent of change.

With News, Apps, and Maps, Google decided to exercise some of its options and invest in developing new capabilities. In the case of Maps, it meant acquisitions. Maps was in effect a compilation of start-ups (i.e., ZipDash, Where2, Keyhole, Endoxon, ImageAmerica, and Quiksee). Google also invested in developing and buying subject matter expertise; investing in platform development, new hires, and ecosystem development around Maps (the ecosystem reached one million websites using Maps—that’s one million businesses benefitting from Google’s investment!); and new marketing strategies. The Google Maps acquisitions also enhanced its geospatial businesses (such as contextual advertising). The Google Maps business platform allowed Google to enter local business markets to compete with the likes of Groupon.

With each new capability Google added to its business platform, it also generated new strategic options. For example, Maps generated serious new options in location-specific and mobile business and probably convinced Google it needed to go deeper into mobile, even to the point of owning a device company. Many of the advantages of Maps were not at all apparent when Google acted on its initial option. Within a few years, it also began investing in driverless or automated cars, a move that created huge innovation credibility for Google. This was built on its Maps databases.

Google Maps added an entirely new business ecosystem and also led to more users, new markets, new ad revenue, and new licensing revenues by deploying Google Maps on other platforms (e.g., Apple iPhone and RIM) and products (e.g., automobiles). The result: the expansion of Google’s strategic options portfolio.

Google is a great example of an elastic enterprise proliferating options and of a management team unafraid to make radical adjacency moves. Compared to a traditional firm, every elastic enterprise is equally able to become an options generation machine.

As these enterprises become better at creating real options,6 they also advance their capacity to put a value on the entire strategic options portfolio. Ultimately, the strategic options portfolio will help in calibrating the future value of the firm.

For evidence of this, look at how well Amazon—with its wide array of options—was treated positively by analysts, even when its profits were lower than expected. Compare this to analysts’ treatment of Salesforce.com, which had a much less robust strategic options portfolio. Analysts realized that Salesforce.com’s inability to grow its strategic options left it vulnerable to copycat competition in the customer relationship management space. By way of contrast, you hardly know which way Amazon might turn next. Their strategic options portfolio is multifaceted and dynamic, factors reflected in Amazon’s share price.

The markets will punish companies with an underdeveloped strategic options portfolio, those that do not reflect strategic options in their acquisitions, and those without a demonstrable ability to implement radical adjacencies. Conversely, they now reward companies that take risks, like Google and Amazon. It’s a new phase in market valuation.

But these comments apply also to individuals. Creating personal options and guarding those, along with a strong sense of portfolio planning, is going to become more and more important. Creating options tables must be at the heart of any executive conversation these days. It can also help those of us who have to manage career moves that have complex options consequences in the future. 7

TRANSFORMATION STRATEGY AND THE UTILITY POSITION

One of the hidden elements of the elastic enterprise is the importance of utility value. In traditional competitive strategy, companies aim for monopoly rent, that is, to be so dominant in their markets that they can set the price of goods and services. Monopoly margins, however, will always tempt in new blood, which in turn drives prices down for consumers.

Those are the basic laws of competition. The big change in the more highly externalized economy we now are creating is that many businesses around us are new utilities.

Mark Zuckerberg of Facebook made a pertinent observation when describing Facebook as a utility. Talking to Time in July 2007, he said, “I think there’s confusion around what the point of social networks is. A lot of different companies characterized as social networks have different goals—some serve the function of business networking, some are media portals. What we’re trying to do is just make it really efficient for people to communicate, get information, and share information. We always try to emphasize the utility component.”8

Utilities exist in many parts of the new economy. We are creating a new infrastructure around communications, data acquisition, data management, payments, the management of identity, real-time advertising distribution and auctions, the management of APIs, and perhaps even security.

Facebook is now a utility in social media, while Google is in search, WhatsApp and Skype in communications, PayPal and Amazon Payments in payments, and Autodesk soon in construction. In each case, the company is taking on the same enabling role that the gas or electricity supplier does but with much higher margins. Android and Apple’s iOS are both utilities, though only Android achieves pervasive utility value. They are all part of the infrastructure of other businesses.

The following are good questions for leaders to ask: Where is the utility value in our segment of the market? What process is so central to its functioning that we can capture utility value from it?

In the recent past, we have seen utilities emerge in data warehousing and customer relationship management. However, it has been rare to capture real, sustainable utility value. Perhaps only Microsoft has been able to do that with SharePoint because of its pervasiveness within enterprises. While it’s difficult to achieve, seeking utility value is an essential element of an enterprise transformation journey.

There are some essentials in establishing a utility presence: companies that make successful utilities have and preserve a reputation for neutrality (hence Google’s promise to do no evil and organize the world’s knowledge and its subsequent problems once it bought into Motorola in smartphones and began competing against its ecosystem partners).

A modern utility is also able to establish a standard in some form—a document standard like the pdf, for example. Increasingly, utilities will be called upon to take a moral position on personal data, though for now, data is still a free-for-all. It is awaiting the first true utility. Eventually, the role of the personal data manager will be to provide identity verification across a range of web-based services—identity as a utility.

In building strategy, companies need to probe where they can add and control utility value. The reason is simple: the new utility has embedded and ingrained competitive advantage.

PROCESS INNOVATION AND RISK MANAGEMENT

Companies face risks associated with economies of scale and economies of scope. The way to remove risk from economies of scale is to focus on redesigning business processes. Leaders have to reengineer their organizations to deliver continuously on platform updates, product updates, or service promises. According to Sangeeta Narayanan, who leads the Edge Architecture team at Netflix, it means investing in risk mitigation; Netflix does that by iterating every aspect of its innovations while keeping a healthy respect for failure. Big, scaled systems will sometimes fail, and the art of management is ensuring they don’t fail at the customer’s expense.9

If companies step back and think about this formula of continuous delivery and the likelihood of risk, then a core purpose of the firm becomes the willingness and ability to focus on experimental process design. Firms must invest in process design at various levels, but the key is to have systems in place that allow innovation delivery to take place without interrupting service quality.

In Netflix’s case, with forty-four million members, process design takes place in a wide range of areas—in billing (how to bill, remind, stop accounts and restart effectively across forty-one countries), in messaging (how to create and time messages that result in an action); in architecture (how to deploy new code, among which sub-sets of the audience); and content acquisition (moving from e-mail communications to automated ingest).

Elastic enterprises have shown themselves to be capable of moving swiftly into new markets and driving radical adjacencies—Google, for example, from search into mobile and productivity apps, and Netflix from DVD deliveries to global video streaming and then original content production. These were huge changes, which involved leaders in redesigning processes to make significant new capabilities possible. The innovation requires a shift from product innovation to big questions like, how does an enterprise function effectively?

Each adjacency, each layer of the platform, product, and service stack, is filled with risk. But the risk is also a reflection of a new optionality. At the level of transformation strategy, new processes need to be de-risked.

A significant way to de-risk economies of scope, or adjacencies from other companies, is to create options and to have the innovation systems in place to help manage multiple innovation strands. The strategic options portfolio is itself a de-risking environment. To make it work an enterprise needs to upgrade its innovation maturity model.10

The innovation maturity model rests on work I did with HYPE, a global innovation management platform, in the early part of 2014. The model looks at the different capabilities a company brings to innovation over time and divides the growth of innovation capabilities into three phases.

Many firms have underdeveloped innovation models, i.e., the developmental process that they use to grow innovation skills and capacity. They approach innovation at an immature level of process, such as seeking out new ideas. In fact this is characteristic of phase 1 of the model. Companies believe their innovation challenges lie in generating new ideas. They often forget to install new ways of evaluating ideas or of devolving responsibility to managers who can then make quick decisions about a new project.

In a fully de-risked environment, they will mature their innovation capabilities to deal with hundreds of new projects, in each case using some form of discovery method to test a process or product’s viability. Many of these innovations will be process-oriented and will have a formal experimental basis—i.e., testing against pre-agreed criteria. In that way the enterprise becomes truly a lab, inventing the future.

OPTIONS AND INNOVATION

Innovation seems to be in a renaissance. It is hardly possible to open a business paper or read a blog without its mention. But after further exploration, innovation emerges more like a fairy tale than a renaissance.

Innovation is currently misunderstood. According to HYPE, many companies initiate innovation projects with the primary aim of saving internal costs rather than discovering new ventures.11 Typically, they engage at the next level of innovation by generating ideas without a strong sense of where those ideas might fit the corporate strategy—it’s only over time that they are able to mature their sense of where innovation reflects enterprise needs. Even at this more mature stage, the innovation strategy is often misguided in companies with no strong innovation tradition. Their initiatives tend to be substantively different from innovation in the start-up community, and therein lies the clue to their failure.

In the start-up community, there is a far greater emphasis on creating companies that exist within the convergence points—in other words, they look to capitalize a part of a broader systemic change. By way of contrast, innovation inside established organizations is often targeted at existing process and or the operational product map. Because organizations are ruled by cost-reduction strategies, those projects are “lean,” meaning low cost, rather than being iterative and evolutionary. They need to live by normal stage-gate rules, which is to say, prove their NPV periodically. They are often trapped by the conventional ROI culture described above.

In an attempt to offset these deficiencies, some established companies are turning to:

• captive funds to invest in start-ups

• start-up challenges that keep the cost of investment low

• setting up innovation labs

• accelerator programs to buy into or co-opt high-growth start-ups

There are strengths and weaknesses associated with each of these.

Captive funds have to run along conventional ROI lines, and the risk is they are limited in their grasp of new processes. They take on good-looking investments rather than projects with transformational potential.

Start-up challenges are far more risky as a way of finding a road map to change. They are the roulette of innovation. Their organizers presuppose an ability to capture significant change at the presentational level of a start-up, when in reality they need to capture new processes by acquiring companies that do things differently.

Companies that set up labs also presuppose that same ability to capture process change, when what they are setting up are often user-centric labs. They are valuable, but in some cases they also push innovation outside the organization because the orthodoxy says innovation will always be rejected by the corporate immune system.

There are interesting experiments going on in accelerated innovation. One such experiment is run by Citrix. Citrix has several strings to its innovation bow—it runs a lab (of course) and it has a strong R&D focus on the CTO office. But the real innovation lies in its accelerator.

The Citrix Startup Accelerator is a seed-stage program with continuous enrollment that includes funding, a deep advisory panel, office space in Silicon Valley, close collaboration, and a focus on enterprise market validation.12 It’s actually a little more than that—Citrix is embedding its seed companies within its own business, so the projects continuously inform Citrix staffers about the direction disruption is taking. The seed companies are a proxy for external research into an uncertain world. They constantly inform the company about the potential new services out there. While perhaps that’s what all accelerators should be doing, by and large, accelerators and start-up challenges aren’t fully utilized, instead existing on the fringes of an organization rather than as an integral part of its day-to-day life.

The advantage of the Citrix accelerator is that people get to see what’s coming next and understand the reasoning of a new generation of entrepreneurs who are setting out to capitalize on change. Even though this kind of insight is a step in the right direction, companies still need to prepare people to take on more demanding roles at the forefront of innovation.

What lies behind all these examples is the move to a broader options portfolio. Executives, managers and all of us planning our careers need portfolio management skills. It used to be said of many organizations that they had an immune system that rejected new ideas or new initiatives, especially those from outside. Like a lot of negative behavior, the immune response stemmed from fear, particularly the fear of lacking capability. The immune response problem is waning and in its place we can see much more clearly that people need to learn a portfolio approach to career and to work life. Things will change, options will multiply. The deficit lies in our ability to broaden our perceptions of the world and embrace the complexity of options. We need to think differently.

CHAPTER 12

Thinking and Acting Differently

The factors covered in Chapter 10 show a brittle economy where people’s livelihoods are under serious pressure. Our behavior is changing. We are moving to less transaction-based lifestyles, and price is becoming a less reliable lever for engaging with us. At the same time, the power of government to bring order to the economy and society based on the old model of intervention, is also in decline. In the midst of this, the way in which we choose to work together (the enterprise) is shifting. We are more dependent on the organizing power of companies, yet strangely isolated in huge ecosystems. We are forging a new deal without the help of policy makers but it can be a tough one to understand. We need to think in terms of options, and to get better at projecting our portfolio opportunities into the uncertain future.

How can businesses get started on this? What tools do they need in order to start acting differently? This chapter focuses on five ways to think differently about the economy and our roles in it. In the final chapter of this book, I’ll look at the wider shift in the economy and society. These all imply shifts for individuals as their work and civil society conditions change.

Given that a shift to relationship investment needs to take place, enterprises have an opportunity to redesign their internal and external processes so they can create a better balance between the structure of their organizations and how they deliver value. They need to make their processes the focus of innovation.

In the past, innovation and execution (delivery) have been explicitly separated. Executives still say a business must focus on delivery and not let innovation get in the way. This is hardly a sustainable line of argument any longer, yet leaders are still grasping for the right way to bring innovation into the core of what they do. My own view of this is that many leaders do not think in terms of options, their own or their firm’s.

Very often executives also believe they are insulated from the bigger changes around them. It is human nature to seek sanctuary. In the 1990s, huge change took place in supply chain management, but companies were able draw on a reservoir of consultants and software vendors to help them through. The need for change today is no less urgent, but it doesn’t come packaged as software or as a consulting proposition.

They need to integrate and develop:

• Platforms that become an organizing hub for vast ecosystems of customers and partners and help enable new utility value. Platforms will increasingly host new systems of exchange (such as new types of money, new relationship values, and sources of affinity with suppliers and customers). They change the relationship between large and small firms. They prime large companies to work more often with small companies and with the commons. Along with the ecosystem, they make the larger economy increasingly dependent on new firm formation.

• Ecosystems, which allow highly-scaled innovation and economies of scale and scope by devolving responsibility down onto self-determining groups. Ecosystems reflect the new relationship between large and small firms, but they are made up of thousands of entrepreneurs who are eager to disrupt established business models and to increase economic and social advantage for themselves, possibly at the expense of the large firm.

• Connectors, which create friction-free business and velocity and enable the relationship between large and small firms to function at a low cost. Crucially, they also change the relationship with work, enabling more automation of knowledge-intensive tasks.

• Cloud capability, which reduces the investment burden of innovation and reduces the cost of scale and scope. The Cloud also shifts work away from products to services and toward indirect revenues, which in turn forces companies to multiply their innovation projects. The Cloud forces companies to move away from conventional NPV-types of investment.

This is a potent combination. The idea of reinventing utility, of being able to create a market for assessing and serving customer preferences, is central to it. So, too, are ideas like mass differentiation (scope), velocity (the speed of responsiveness to customers), radical adjacency (the power to enter new markets quickly), externalization, and the move to indirect revenues.

How do leaders orient themselves around these new structures?

Typically, at present, they tend to proliferate their innovation projects through labs, accelerators, and start-up challenges, without looking at how to adapt systemically.

What companies really need to do is develop systemic change capabilities. In the past, it was difficult to incorporate change and execution. Today, the two are closely intertwined. They consist, at a minimum, of the following capabilities. Each of them resets how we all work within organizations:

• Developing platforms that will help them manage externalized communities to serve customer needs better, and to serve them downstream where margins are a part of a continuous relationship.

• Reinventing process on the fly and reengaging middle management in ways that make the enterprise more externalized and better able to respond to velocity.

• Assessing value differently to help develop longer-term business plans.

• Constantly reviewing the understanding of disruption to anticipate change and promulgate options.

• Making uncertainty an operational advantage.

DEVELOPING PLATFORMS

In the early 2000s, Cusumano and Gawer drew up a list of four levers that management could use to develop platform leadership.1 These were: scope, product technology, relationships with external complementors, and internal organization.

• Scope, in this context, refers to the balance of internal and external innovation (particularly important in industry ecosystems where IP and patenting can be the key to future revenues);

• Product technology refers to the architecture of the technology and how closed or open it might be;

• Relationships with external complementors refers to the degree of competition and collaboration that might take place;

• And internal organization refers to the way the company supports the platform development process.

Although the four levers were drawn up for a different audience—technology cooperation among established enterprises—they are still relevant to the broader and more chaotic platform and ecosystem model we see today. For example, all platform owners will ask themselves questions about the balance of internal and external innovation. The levers nicely capture also the danger of technical debt and unsynchronized development in industries where technology is fast developing. There are differences, however, and I have tried to take account of these by updating them. The new inclusions are: market readiness, process readiness, and information or content readiness.

Scope—increasingly this now refers to the extent to which a company or platform can offer a broad scope of utility benefit to customers (perhaps through apps, content or services).

Product technology—is often an easier choice for companies not involved in complex technology stacks. It is an easy choice, for example, to adopt a version of Android for new areas of technology. The vexing question is whether a company has the capability to readily update its offer as the different members of a product ecosystem innovate. It is a rate of innovation issue. It is also increasingly difficult to build barriers to entry in the ecosystem, although there are examples of companies that do that by “forking” Android (creating bespoke versions as Amazon has done for its devices).

Relationships with complementors—at issue today is scale. Can a platform scale relationships very rapidly? Does it have insights into what this takes and is it prepared?

Internal organization—today’s equivalent would be a Netflix-style process of building out platforms in place of departments.

Here are the three additional levers. Under each heading you will find a number of attributes or capabilities that a company needs in order to excel in platforms. It is possible to score a selection of companies against those attributes to give some sense of competitive positioning. The table is best read in conjunction with the four levers above, in effect making seven levers of platform management:

Table 12.1: Platform readiness assessment form

Companies can use these levers to assess their readiness to take on a platform role. The table is a self-assessment form and can be used to compare internal capabilities with those of competitors.

PROCESS REDESIGN

Kevin Hartz, founder of Eventbrite, insists that his company is part of a new wave of businesses that are enabling people to create new economic activity.2 This is a point reiterated by many young leaders—they are not just innovating a new service, they are inventing part of the new economy.

The biggest shift for companies with any long tradition is to move their business into this enabling mode. The seven levers above are a good guide to the questions that need answering at the planning stage. They’re also a guide for people who are looking to fashion their careers around platforms.

The shift requires leaders to behave differently. There is a strong tendency for enterprises to exaggerate the prestige and esteem associated with leadership. But leaders in modern enterprises are respected as peers. They are the people who can lead change and invention because they are first among equals, not because they command the budgets. They are able to forge a new pathway for their people to walk along.

Netflix’s Reed Hastings is in many senses the perfect engineering leader. He decided to convert the company’s API program from open to closed once he had enough developers doing great things with the company’s assets. He needed more focus on the company’s substantial process redesign as the business scaled. Going against the grain at the time, he decided to cut back the open API program and instead refocused his engineers on continuously inventing a new structure for the enterprise.

Hastings retains the support of the external developer community, however, because of his continuous attempt to redefine how the organization can work best and how it can best deploy engineering talent.

People in the mix at Netflix see an opportunity to develop a global platform for consumers to select video on demand from any device that represents huge IT scale and efficiencies. It wins respect because of its vision, and it attracts talent because partly realizing this vision involves reinventing process.

Talented people want to be in the invention business. They don’t want to be told to keep quiet and bank the paycheck. It is imperative to their creative instincts to invest in change. That is a vastly different situation from the past when people feared change. Today people are more eager to get going on it but need the right leadership.

Many executives are deeply familiar with supply chain management and therefore know enterprises can change radically.

In retail, for example, Amazon dominates the landscape in the United States and Europe because of its economies of scope—it has the largest inventory of products of any retailer outside China. The capacity to create such an inventory is difficult for traditional retailers who typically prefer meeting suppliers and engaging in personal relationships. Automating inventory development, though, is now possible. Platform vendors like SPS Commerce provide that capability. Over time, they have connected with a large total universe of suppliers that can be used to create Amazon-scale competition.

Yet the issue for retailers remains complex. As they begin to expand e-commerce platforms, questions arise about their core functions and purpose. Amazon has already evolved from a retailer to a marketplace. The next logical step is to create market platforms that link suppliers and customers without the retailer’s presence, something which YOOX Group comes close to by providing white label platforms for luxury brands. In the space of a few years, retail could be transformed into marketplaces.

The winners will be those companies that empower their middle management to make the essential process shifts that facilitate this change. The shift for executives is to think of innovation as integral to process design. If executives don’t learn how to adapt process, they are going to lose out to companies led by people who make process redesign their major focal point.

The Netflix example (covered in Chapter 4) is probably the most dramatic one of a company that is turning innovation and invention on itself. It consists of five key factors (see Table 12.2):

Table 12.2: Five factors for rapid process innovation

Can it be that easy? There is a lot left implicit in the Netflix model. There are two crucial elements of it, though:

Extreme talent: The company actively seeks ways of shedding employees who cannot work productively in what is a high-risk, high-reward environment.

Self-determination: It apes the self-determining characteristics of ecosystem and start-up culture in the context of employment security (assuming an employee has the ability to stay the course).

By definition not every company can have the most talented staff. They can, however, look at improving key capabilities, constantly.

VALUE AND METRICS: THE KEY CAPABILITY INDICATOR (KCI)

As pointed out in an earlier chapter, companies do not measure and report on the state of their social affinity with customers in their financial accounts, though they might reflect a net promoter score or a brand valuation. The same is true of their capacity to orchestrate and influence an ecosystem; they do not actually measure and place a value on it. One of the big shifts every company and every individual needs to make is away from traditional metrics to ones that really matter for change.

In January 2011, Michael Porter and Mark Kramer argued in the Harvard Business Review that we must move toward some measure of shared value. Speaking on modern enterprise, they noted, “They continue to view value creation narrowly, optimizing short-term financial performance in a bubble while missing the most important customer needs and ignoring the broader influences that determine their longer-term success.”3

Their reflection on the enterprise’s external role is important and is being echoed by other writers like Clayton Christensen. These external metrics or rather the reporting metrics are, in a sense, the gloss on the cake. What really matters in terms of how people work together are the internal metrics, the way companies judge the performance of their people.

One of the big challenges companies face is the need to push back on the strict financial ROI calculations that are still central to their culture. These are now pretty much glorified guesswork. Leaders need to shed doctrine and change how and what they measure; they must invest more in people and in human interaction; and they need to multiply the number of options they have at their disposal. Asking to measure a return on those factors as a net present financial value is really a dereliction of leadership. Leaders need to take risks on softer but more accurate metrics.

Net Present Value is often closely tied to key performance indicators. I have recently been developing the idea of key capability indicators (KCIs). And here’s why.

In a highly externalized economy, companies are at risk from adjacency moves from nontraditional competitors and from the start-up community. The traditionally minded company can be off tune with the changing customer ecosystem, and lack the systems to manage a highly externalized business structure. It is clear they have to generate new process models (including many more options in the marketplace) that won’t be reflected in quarterly earnings for years to come. The types of investments they need to make are in key capabilities. It is almost certain that in the process of transforming themselves, some enterprise KPIs will slip.

The reason is simple. The shift has to be crafted to meet the threat of horizontal competition from new industries and competitors that often work with an entirely different cost base or customer interaction model.

The only way to make a continuing self-assessment in the early stages of preparing for these threats is to understand the capabilities that you have to develop and measure progress toward them. That’s the idea of a KCI. Markets hate the impression of decline, but they are more distrustful of a lack of measurement. The more an enterprise can measure transformation, the easier the ride with potential critics. Plus, it is good sense to measure change.

The following figure shows a KCI comparison between fifty banks and other financial institutions (see Figure 12.1). It reflects the average score of five groups of banks. Going from left to right along the bottom axis are the top ten companies by innovation capability; then 11–20, 21–30, and so on. On the vertical axis is the average score of each bank on three types of capability: leadership, strategy, and process externalization.

The analysis was done using a 38-question analysis of enterprise capabilities in content, platform, leadership, strategy, and externalization. These questions are then weighted to reflect the importance of factors like platform capability versus, say, patenting intellectual property. To date, I have assessed over 450 companies and a general finding is that the most adaptive do think of themselves as a platform business. In the financial sector, the chart shows how quickly various category attributes (content platform, leadership, strategy and externalization) fall off outside the top ten to twenty companies.

Figure 12.1: A comparative study of capability in the financial services sector

This is the principle benefit of the Key Capability Indicator; it is a way to measure capacity to change, rather than the outputs of routinely incentivized behavior. It is a measure of value, but the value is attributed to capability rather than to outputs like patents or new products. It is clear in finance that the top 10 are much more capable of adaptation than the group 11-20 but that some capabilities then begin to plateau.

New metrics like these have to be deployed rapidly if companies want to change. New metrics should also include the development of social affinity scores (how to attract brand value without undue cost; how to create born-social relationships with customers) or the capacity to create options (the topic of the next chapter).

To legitimize new metrics in the eyes of investors and analytics, companies need to tell a story of change. They need to generate a transformational narrative. An exemplary version is Autodesk, whose executives simply talk about designing the future, or more specifically, designing the future as a commons-based economy where self-determination is key. With this narrative, Autodesk has justified its investments in maker communities that now number 120 million participants but that don’t yet return profits.

It is inevitable in this shift that Autodesk will have to find ways to engage and reward those participants. They are part of its future value potential. Ultimately, its value may not be reflected in stellar earnings growth. It might instead be reflected in longevity, the promise of being around for its shareholders in fifty years’ time. It is surprisingly scary for executives to contemplate longevity. But their value as custodians of value resides in staying around.

DISRUPTION MAPPING AND OPTIONALITY

Few firms invest in modeling the disruption vectors surrounding their businesses and threatening their survival. Many industries have oligopoly structures that breed complacency. Disruption is quite different from the way we actually conceive of it. It is no longer about the growth of new, cheaper, and often inferior technological alternatives that take an incumbent by surprise. Disruption involves the rapid breakdown of industry structures and is now part of the normal run of events, exacerbated by alternative economic value systems like the commons.

Understanding disruption is vital to acquiring the right capabilities for long-term survival but many firms have eroded their strategic thinking capability as part of cost reduction programs.

To function effectively, companies need to map the crosscurrents swirling around them. Below I present a disruption analysis adapted from the research firm Gigaom.

Think of this as a disruption mapping exercise. What we’re going to do is crowdsource a list of possible disruption vectors and then weight and score them. To do it, it is necessary to construct a crowd, a cross-section of internal and external people.

How might a company go about choosing people to judge their options or vulnerability to disruption?

The temptation is to go with experts—the so-called Delphi method. Philip Tetlock has written extensively about the weaknesses of expert judgment as prediction.4In reality, experts are not necessarily better at predicting the future than lay people, especially when their views are aggregated. When Pierre Levy wrote the original crowdsourcing bible, Collective Intelligence, in 1999, his key observation was that large-scale social interaction would create better judgments than expertise, given that expertise is often hostage to a particular kind of peer framework or bias.5 By using the “crowd” to make judgments, we can, at the very least, cast issues in a new light.

Disruption is very much a “what’s next” issue, so basing our judgment on experience (the past) is a shaky proposition, unless we are on a trend or in a predictable sequence of events. The idea of Disruption Mapping is to let the crowd have its say and to cast the discussion in terms of possibilities—this is also a new way to view economic planning.

Typically business plans draw on probability—the probability of a new project yielding results. But as Mikael Collan points out, there is rarely a basis for probability estimates in business. Collan has developed his own investment assessment techniques in his book The Payoff Method.6

Probability involves observations of repeated behavior. From observation we calculate a percentage likelihood of an event repeating itself. The classic probability calculation is how many times a coin might land on heads or tails. That event (tossing a coin to see how it lands) repeated often enough will yield a 50-50 probability. There are very few business plans with that kind of probability analysis behind them. In fact, the idea of probability is a screen for educated estimates.

Instead of pretending we have access to probabilities, we should admit we are dealing in possibilities and work with the uncertainty. That way we can access the crowd with some assurance that its wisdom will not be very far wide of the mark. There’s another benefit in that, apart from its frankness. Possibility analysis should open up a sense of real potential.7 Many companies plan their future around key metrics drawn from industry benchmarks. For example, they might measure performance based on an industry norm. That is not such a bright move when industries are under threat.

Disruption mapping can be used in small teams or at a high strategic level. The first round involves drawing the map with broad brushstrokes. That means, asking people, in an informal setting, to describe the kinds of disruption threats they feel they, their department or company faces. Here’s a possible list drawn from a project on the future of hardware design.

POTENTIAL DISRUPTION VECTORS:

1. Additive manufacturing. Many people think of additive manufacturing as 3D printing. It is much more than that. Additive opens up the possibility of incorporating many new combinations of materials into a component. For example, it is possible to incorporate different metals into a car door where it has no role in crash protection and tougher materials where it is essential. The combinations are likely to grow as designers get used to new materials, like graphite, new alloys, and new composite possibilities.

2. Mass differentiation. Customers increasingly seek out ways to differentiate themselves. Couple that to global markets and the degree of differentiation necessary begins to create new possibilities to be first in customer satisfaction.

3. New materials. Advances in areas like printed electronics, nano-scale materials, and programmable matter would change the complexity of design, component sourcing and product performance.

4. Radical adjacency. The big competitive threats come not from our oligopoly or known competitors. Horizontal developments in other industries are a threat to us, in particular companies who can capture customer attention could make us less relevant in key areas like trust.

5. Crowdfunding. Using the crowd to validate products is a fast track to getting products to markets. It is not yet the done thing for a large organization, but it has worked well for Google with Google Glass. Most companies have plenty of products ideas and prototypes. Will crowdfunding become the accepted route to bring some to market while finding out quickly which ones have no real market?

6. Designing for devices. Most companies think of their products in a standalone way. But hardware will increasingly become a device; that is to say, it will be connected and have device-like service possibilities, connection and emotion potential, and downstream revenues.

Those are six possible disruption vectors. After a list like this has been created, the crowd needs to score two aspects of the list. First, it needs to give a weight to each disruption vector, usually a weight that represents the comparative importance of each vector (out of one hundred what are the respective percentages to assign to each vector?). Second, it needs to give a score to each vector on a scale of 1–10, signaling how important or possible they are as threats and opportunities (depending on whether the activity is meant to seek out opportunity or threat or is meant to create information).

Here is an example of a small set of disruption vectors and their potential impact in a study prepared for the research firm Gigaom on hardware design.

Figure 12.2: Companies best placed to take advantage of different disruptions

The disruption vectors sit along the top of the diagram. They show that a group of analysts reached some kind of view that these six vectors were significant to the future of hardware design as a business process. The scores at the tip of the vectors is an average of each person’s views on the relative weight of the vector out of a total of 100. The group of companies to the right is a short list of companies with a dependency and capability on hardware design. It shows a crowdsourced view that Nike is the most capable of taking advantage of the emerging disruption vectors with Intel a close second and Disney third.

Disruption mapping can give leadership groups fresh ways of thinking about high-level propositions: how we create value and for whom, how we will relate to customers in the future, the competitive position available to us, how we assess and measure value, the potential for disruption and the processes (and hence people) we need in order to make change an integrated competitive advantage. The most significant ethos, though, is to accept that people can think in terms of possibilities without having to offer a probability outcome. Possibilities open up options. And they make us focus on potential rather than on benchmarks. The sense of possibility needs to be explicit in the exercise—you are setting out to see what might possibly happen rather than describing the (false) probability of success.

MAKING UNCERTAINTY A VIRTUE

In the previous chapter we looked at how to build options. The essence of optionality is being able to deal with uncertainty in some formal way. As an approach to this, Mikael Collan and I devised a small exercise in uncertainty scoring. We wanted to put data on how uncertain a company felt about its capabilities in relation to its competitors. We explored this in the context of the company’s decision on whether to be a platform company, or not.

Many people concur with the assumption that companies make rational decisions if they have the right information about the challenges they face. We might apply that more generally—don’t we all make rational decisions if we are fully informed? Not at all.

There are deep structural and behavioral biases against rationality. The field of behavioral economics studies those, as do economic psychologists like Daniel Kahneman. But bias is misleading. Although many biases exist in decision making, the problem of enacting change actually goes much deeper. There are many times when there is no right decision. The course of action we take is dependent on many aspects of faith, and the distortions of experience, that participants bring to a decision process.

Good executive teams try to bring discipline into this by setting out their corporate decision priorities—for example, Ericsson’s desire to leverage its global footprint and to work in areas where competition has yet to emerge. But not even these criteria eliminate the truth that many of our decisions are based on the confused currents of faith that make up our lives.

In researching decision making in innovation projects, I interviewed thirty executives about how they made decisions about or within new projects. Bear in mind that all companies interviewed for this research were undertaking some kind of transformation program. That is to say, they had already acknowledged the need for change and had allocated resources to it. But decision making was by no means a straightforward, rational affair.

One respondent conceded that her company arbitrarily limited its ability to deal with all major decisions. The company had a rolling roster of five crisis issues it would deal with at any one time. The best hope of securing executive attention was therefore to get a decision onto the crisis list. That encouraged middle management constantly to avoid decision points or to magnify their potential impact on the company.

In another example, an executive lamented that after he supplied ideas and plans into a pipeline, he was told that a major strategic decision could be up to ten years in the making. Another executive admitted his firm had invested resources in transformational thinking but was avoiding decisions until it had no alternatives. He found himself hoping for a crisis that would force action on the leadership.

These behaviors are really symptomatic of cultures that are confused about the idea of probability. They have processes that strangle change at its roots. Having said that, in both of these two cases the companies were very capable at doctrinal innovation.

Neither company was displaying a bias in their processes. The best way to describe their actions is to say they couldn’t really cope. Their processes were adapted to their limitations. In the crossfire that typifies many group activities, these two cultures had worked out a survival mechanism. Clearly they need opening up to different ways of thinking, not because the executives are not smart. A company might well be smart under conditions of severe constraint (constrained cash, talent, opportunity) to manage by crisis. Opening up thinking brings options onto the table and assuming people have the mental bandwidth to cope with options, then the exercise can lead to change.

In conditions of uncertainty, as opposed to constraint, we lack knowledge. By definition, uncertain circumstances are full of unknowns. That is precisely what uncertainty means.

What might be useful to know is how much certainty or uncertainty we are dealing with. That same calculation affects our personal lives, too. Often we act in biased, emotional and prejudicial ways and academics of late have lionized that emotional engine in decision making.8 If we can measure doubt, then at the very least we have a basis for understanding when we are acting from faith and when from some form of reason (with all the compromises that go with it). When I say acting from faith I don’t mean to imply a religious faith, though there is plenty of that in decision making. I mean the spiritual container that we wrap around our actions. It can range from justifications such as, “I am going to act now, and hope it pans out,” to a bold insistence that we are damn right and won’t brook any more argument.

If we look at the challenge of innovation as an exercise in quantifying uncertainty it can be fun. It gives us a method for creating a more numerical approach to doubt. If we can quantify uncertainty, we can support options’ building with the type of rough and ready risk calls that good leaders have to make.

Collan suggests companies should consider possibility analysis. There are also other options—such as priority estimation tools. Rita Gunther McGrath has written about decision support in uncertain conditions as a discovery process, where we experiment with metrics and tools until we learn what works.9 Another possibility is to engage the ecosystem, or crowd, more deeply in strategic learning.

I undertook this type of crowd experiment in a major financial institution. The company had a linear business plan to create one new service to offset declining revenues in its core. I used possibility analysis to help create and assess options.

The first step was to create a list of attributes necessary for the adoption of a platform-based approach to the company’s overall business.

The second step was to give people an opportunity to calibrate their sense of data. People are poor at scoring in the abstract. If you say, tell me how good x or y is on a scale of 1 – 10, it is meaningless unless it has a point of comparison, such as, in comparison with Liverpool, how would you rate the defense of Manchester United? So a comparative question needs setting up. It should be something like: Against the background of economic change in our industry, which of the following scenarios do you think are fully possible (using a scale of 1–100 percent to represent fullness).

The third phase consisted of asking people to make comparisons between themselves and competitors, but it could equally be done to compare options. Ideally in the third phase, the capability assessment would be done by people inside and outside the business, but in this particular case, it was limited to an internal scoring.

Table 12.3, anonymized for confidentiality, summarizes a part of that exercise, as it relates to ecosystem development skills.

The overall exercise had some fifty questions. The table includes a few samples. In each case, I asked the client to score the degree to which each attribute is fully possible as a future scenario for itself and three competitors. The important intellectual exercise is to avoid questions of probability—these are not probability scores. The question is different in spirit from a probability test. It asks people to think in two phases. The first is to imagine what is fully possible if the firm were to realize its potential; the second is to put a comparative metric on that.

Table 12.3: A sample uncertainty analysis

What each score does is reveal the uncertainty level and interval (the gap) that the crowd attributes to each company in this sector.

Imagine we have scored a total of twenty-five attributes. That gives us access to a crowdsourced measure of capability, with a rough idea of likely outcomes, across the industry.

In each score, there is a lower and upper level of competency. The average of these scores yields an industry standard to judge different companies against. It expresses the degree of uncertainty people feel about the chances of different companies enjoying success—relative to the competition.

It also yields a crowd-scored view of every competency across our main competitor groups, so a savvy executive team will know where to place their dollars if they want to close the gap with competitors. This is all “rough and ready” but real information, and in fast-moving, uncertain environments, it can be an invaluable aid to strategy setting and the adaptation process.

In our application of uncertainty analysis, it became clear the client had high levels of uncertainty surrounding the attributes it needed to deliver on its strategy. It had developed the strategy in consultation with a major advisory firm and the strategy was sound on paper. However, when tested with this simple analysis, it was very clear that the company’s uncertainty about its own attributes was very high in many of the skill areas that were critical to success.

The five methods are simple ways that companies can experiment with new ways of using data and new ways of thinking. The future is there to be mapped and companies like Autodesk have their own language for how to do this. For them it is about “finding the fact pattern” or figuring out what the case for change really consists of, over a long period of time. Every long journey of course starts with the first step, and the first step is to stop pretending that business plans are works of probability analysis; the second step is to experiment with new ways of plotting out options. These are big steps away from false certainties to positive doubt. It’s discomfiting but necessary. There are consequences of avoiding it not just for an enterprise but for society as a whole.

CHAPTER 13

Future Shift

Societies are doctrinal by nature. It is impossible for us to live with the degrees of injustice we generate or to orient ourselves toward the demands of reputation and success without a doctrine that confirms for us that the sacrifices we make are worthwhile or that the people we leave behind had their chance. The doctrines of Western society are, in my view, wearing thin. That is not to say there are better doctrines in the East or in other spiritual traditions. Rather, my view is that we have doctrines in areas like the open-source movement and in the common pool that better represent what skilled, creative people believe—that much of what we need to make an economy work is better achieved in meritocratic systems that combine cash, reputation, interest and collegial work in a reward system that more or less reflecting the scale and importance of contribution.

This doctrine continues to be incredibly effective at keeping the Western economy functioning. We should be thankful that the open-source software movement exists, and that it has spawned the creative commons, open engineering, open design and so on. Yet our doctrinaire approach to the economic system, insists that there are two sides only to this economy - the market and governmental intervention. This wilfully neglects the human dimension and is surely a reason why many enterprises neglect the human side of investment.

However, there is no denying that deep antagonisms exist, even in the economic sphere. Alternative or new economic models like the common pool or like crowdfunding are used to create extraordinary wealth in organizations like Facebook or Apple or Google. There is also antagonism between enterprises that create work and governments that don’t.

The shift comprises many conflicting currents of change:

1. Perhaps the most potent in the long term will be the shift to commons-defined currencies that usurp the existing financial system.

2. That in turn is going to feed the new economy’s need for instant transactions and rapid cash flows in place of capital investment.

3. This though is connected closely with changing business models enabled by technologies like Cloud, which in turn is at the centre of platform and ecosystem business processes.

4. All these are combining in ways that create more disruption, the pulling apart of vertical industries and their re-establishment as broad-based horizontal ecosystems.

5. Yet those ecosystems are unequal places that force people to create finely balanced portfolios of non-paid work and paid, reputation and networks, priced and transaction-free leisure; and that oblige companies to create options to arm themselves against disruption.

What comes next, though, is a backlash against the excessive enclosure of common assets. The post-Google generation appears to be less patient with the existing institutional structures and hence is creating new ways to do business, through BitCoin, Ripple and a newer candidate for disruption, Ethereum. Ethereum’s founders believe that many of the assets and contracts that we conclude in the business world can be better processed as code. Put another way, code can represent value, and the transfer of value, better than legal contracts can.

In the context of a post-Google generation of entrepreneurs whose aim is to change the economy in fundamental ways, what are the shifts that matter next?

THE SHIFT IN THE PERSONAL ECONOMY

Already over 70 percent of Americans spend time in the freelance labor pool, and the amount of time is increasing. The likelihood is they will spend more time doing non-earning work, as well as time out of work. They are participating in small-scale economic opportunities such as Airbnb and Uber, Facebook markets, eBay, Etsy; and in common pool initiatives where there is little financial reward. The challenge for them is to extend activities like these into a stable career and to do that they need to learn how better to create and prioritize options over time. People need to unlearn “work” and figure out optionality.

Traditionally, people have been educated to be part of an organization. There has been some pressure to switch this emphasis to educating people to work in teams. In practice, though, they need to be educated into self-sufficiency and start-up culture. They need to learn how to gain from the emotional disruption of repeated failures and under-achievement as well as how to capitalize on good times. Their lives will be a mix of all of these. Self-sufficiency and self-determination are important because welfare systems cannot cover the future needs of the workforce.

These emotional currents are sometimes stronger than the financial disruption that we all tend to focus on. People, however, need to understand what it takes to manage their lives as options with good calls and bad. At present they are encouraged to follow dreams without the emotional support they need when they get a rude wake-up call. People need to be taught hedging strategies so they can participate in new projects without ruining their future prospects or indebting themselves in ways that bring psychological damage.

The big shift will be toward things like domestic-level engagement in economic activity, as people seek out economic advantage in a non-corporate environment. The pursuit of non-transaction lifestyles will revolutionize economic activity, perhaps without a blip on the GDP data that countries collect because much of it will be cashless or in new currencies that governments do not control. Another learning curve for people will be how to transact without money, how to engage with new currencies and with the new organizational forms that cryptographic currencies are now spawning.

As governments fail to create jobs and preside over less business dynamism, government credibility will continue to diminish.

Governments need to build their relevance to individual lifestyles rather than corporate. They must create tax incentives that reward individual risk. At the moment, they preside over tax systems that reward risk avoidance by large enterprises. In contrast, most self-employed regimes are currently punitive for people outside the formal workplace.

A policy maker rarely sees the individual as an economic agent. The individual is not a constituency. He or she is not a part of that larger macroeconomic debate between Keynesian and libertarian views of the economy (ironically libertarian philosophy leads to more monopolistic power for large private enterprises as distinct from large public organizations).

There needs to be a step change in the number of individuals and small companies that have an astute understanding of change, companies that we might call “transformation agents,” who are out there to create something new. Right now the rewards that go to disruptors are huge, but society needs to look at the other side of this development and reward rather than punish people for being self-sufficient.

THE SHIFT IN THE BALANCE OF THE ECONOMY

The economy has always been characterized by concentrations of activity in small and large companies. In economies like Germany, there has been a concentration of wealth creation around middle-sized, family-run companies. Today, however, the economy depends more than ever on the new relationships between large companies, micro-companies (under ten people), individuals, and the commons. Large enterprises that have the capacity to build and manage relationships with micro-companies, at scale, are clearly gaining economic power; a second dependency lies between large companies in company-to-company ecosystems; but perhaps the most important shift is the one that has more large companies depending on the common pool. The basis of capitalism—the use of capital to invest in productive resources—is undermined by the increasing value of the commons, which is now growing faster than Moore’s Law. Capital is increasingly irrelevant to the workings of the new economy and even cash flow and payments, as traditionally understood, are at risk from new currencies emerging from the commons.

From the beginnings of open innovation at Procter & Gamble in 2001, to the huge ecosystem of Apple, to the activity of GE on Quirky and in Healthymagination (GE’s investment in a new health diagnostics’ ecosystem), companies are creating mechanisms to build a new economic structure. The platform and ecosystem element is in a significant sense a beginning. It is the Google Generation’s contribution. But the next generation has different ideas about how the commons can be beneficially used.

In parallel, large enterprises are increasingly dependent on open-source communities. To an increasing extent, they organize open source as a company-to-company activity and bypass the commons, but regardless of that, the commons is now the source of many major elements of business infrastructure. It is a source of unprecedented skill, creativity, and devolved organization, and it is a source of disruption.

Open-source initiatives like Bitcoin, Ripple, alongside telco-IT convergence, will create a new type of global economy. But something else is now afoot. Deep in the recesses of start-up culture, young developers now see a way to revolutionize how all transactions of wealth take place. In effect they have found ways to make centralized control of the economy irrelevant. The exploration of the repercussions of decentralized economics have not even begun. We simply do not know what it means to have economies functioning in ways that do not defer back to a banking system with a controlling central bank. But that open-ended future is underway, inspired by BitCoin, but gradually feeding into a new generation, the post-Google disruptors.

In the new global economy, knowledge workers are competing with each other on price, often through start-ups, in advanced and underdeveloped economies. Competition in the start-up community is creating more wild card innovations like BitCoin and Ethereum. The medium of exchange (money) is itself being transformed, producing new, unregulated ways to accelerate business growth around the world. Meanwhile, a small number of enterprises are becoming vast new utilities with huge concentrations of resources, bringing them on a par with many governments in key areas of policy.

High-profile exits from community-like projects like the sale of The Huffington Post, Forbes, and WhatsApp to Facebook have stirred emotions about the issue of commons’ enclosure by larger firms. With the sale of The Huffington Post, writers who had created much of the value were not rewarded, and one group launched a lawsuit against the company. In the case of the WhatsApp sale, WhatsApp leaders had promised not to sell their company, as they built a loyal following.1

Companies need to find ways to reward the ecosystem better than they do. Google’s use of its ecosystem with Google Glass, getting people to pay to participate, is a case in point. It devolved all innovation and marketing risk on the ecosystem and came up with very little in the way of rewards. The ecosystem can be a tough place, but it is responsible for innovation, for moving large enterprises into exciting new adjacency moves, and for helping them to scale quickly. The refusal of platform leaders to share value equitably with the ecosystem will have repercussions. It seems to fuel more wild card innovation like crypto-currency and has created deeper unknowns about the immediate future of the economy.

THE SHIFT IN FINANCIAL VALUE AND POWER

What will really shift the axis of business is the fully realized development of the new utility businesses in finance. It has already started. As banks struggle to come to terms with deeper layers of regulation, the financial domain looks ripe for platform-based businesses to organize, as far as feasible, outside the limits of regulation. In keeping with the changes described so far, the vision emerging for finance from the commons is for a decentralized, digital banking infrastructure outside the control of central banks.

After a few false starts with P2P lending and crowdfunding, the idea of an alternative financial system is now starting to fly, based around Bitcoin, Ethereum and Ripple, and the ability of existing platform companies to extend their activities into finance.

Bitcoin is essentially an open-source currency that acts as both platform and ecosystem. At the same time, it is an idea about value. Bitcoin describes itself as “a consensus network that enables a new payment system and completely digital money. It is the first decentralized peer-to-peer payment network that is powered by its users with no central authority or middlemen. From a user perspective, Bitcoin is pretty much like cash for the Internet.”2

The fascination of Bitcoin is that it is also highly speculative. Its value is driven by expectation rather than by the volume of use or scarcity. It is a growing experiment in trust, because all of its value relies on it being a trusted network. The fact that it works probably resides in it being open source. The idea of the commons substantiates Bitcoin, in the sense that people now have forty years of experience of open source, its safety mechanisms, and the strength of its methods. They trust that its mechanisms will create the desired goal. As Bitcoin.org acknowledges, “Bitcoin can only work correctly with a complete consensus among all users. Therefore, all users and developers have a strong incentive to protect this consensus.” This is an extraordinary contrast to the established financial system, where trust runs very low.

In reviewing BitCoin, the Bank of England makes the point that it is an ideologically driven attempt to create a parallel value system. The Bank notes that the first part of BitCoin’s ledger, what is known as the genesis block, refers to the second round of bank bailouts that occurred in 2009. That event seems to be at the root of BitCoin, a daring attempt to circumvent existing and broken financial institutions.3

What Bitcoin has done is substantiate the idea that trust and community can create a viable measure of value, even in the abstract sense of a new form of money. But leave the idea of money aside, BitCoin, like the dollar or sterling, is simply a powerful idea. By forcing us to see fiat currencies as ideas, it undermines the canonical, singular authority of governmental control. Once that idea is destroyed, governments will struggle to reassert authority, especially against a background of indebtedness, and impotence in job creation. We are surely witnessing the erosion of governance, even as capitalism becomes less of an investment allocation mechanism and more of a cash management one.

Crypto-currency systems create real-time payments, is currency agnostic, and charges next to no fees. Bitcoin might evolve in a number of ways, possibly by becoming a reference currency for new ventures and new coins. It could help solve many of the asymmetries in our polarized economy.

As an example, take the possibility of a new start-up, one with high potential and the need to attract a broad audience of participants early on, both developers and customers. At present, it is difficult to create an organizational form that can reward participation in this. Stock in a company is one possibility, but the practice of giving away stock in return for participation already has a name—a cooperative.

What if a company could form or create a subsidiary with its own currency? If it could distribute its company’s crypto-currency, it could potentially bring in participants by sharing value from the outset in the form of digital currencies. In fact, the situation would be better than that. Users or customers would be participating in creating value and reaping the reward. The valuation of companies would not just be the founders’ estimation or the market, in any abstract sense. Valuation would have to hit the right amount for people to support the project, and over time, people’s support would elevate the value in ways that reflect usefulness.

Imagine, for example, if Facebook had created a Facebook crypto-currency that it had gifted to people who had joined. What if the first million members got a Facebook coin each; the next million half a coin, and so on? Or what if Facebook gifted people Facebook coins in exchange for sharing their personal information on the site? Even without affecting its CEO’s status as a billionaire, wealth could have been better distributed across the Facebook community.

It might sound far-fetched, but those principles are close to what the company Ripple has done with its currency management and exchange platform. In the Ripple system, everybody has to buy into the Ripple currency system in order to use it. And the system is dependent on building trust between participants in order for it to be effective. The algorithm is built for efficiency (finding the best exchange rates for a transaction) and for trust (ensuring everybody in the system is honoring contracts). And it is essentially a crypto-currency. Ripple describes itself in much the same way that Bitcoin does:

“Ripple is an open-source, person-to-person payment network—a simple way for anyone in the world to send money to anyone else at practically no cost.”4

A number of American banks already support Ripple deposits—Wells Fargo, Chase, USAA, TD Bank, and Bank of America among them. Gold Bullion International has also provided a gateway between Ripple and gold. Ripple, the company, expects to limit the number of Ripple currency units (XRP) to one hundred billion with 20 percent of those remaining with the company.

Ethereum has done something similar by inviting people to invest in its start-up activity in BitCoins that are then translated into its own currency “ether.” The start-up raised $14 million this way. It brings people into to the project with a sense of commitment to its goals. It also means that “capital”—the reserve of value that banks and venture capitalists invest as part of the global resource allocation advantage of capitalism—is being bypassed in favor of code.

In the future, there will be a currency value attached to commons activities based on these new currency developments. Naval Ravikant runs the AngelList of affluent investors. In his view, the way forward for open-source projects is to launch distributed companies. In this scenario, a project could launch with a token currency attached to it, say one million crypto-coins. Members of the open-source community, or for that matter, business partners or consumers of the service, could be allocated crypto-coins based on a community’s view of their contribution value.

This appears on the surface like an equity split or a form of crowdfunding. The differences, though, are many. There is no need in this system to seek out accredited investors. People are investing skills as they would in any open-source project. But unlike so many open-source projects, they earn a stake in addition to reputational gains. That stake will make them more fairly rewarded and stronger advocates of the service. Lead customers could also be enrolled in the same value system, plugging a gap where customers who invest time in early stage projects get very little added benefit. In almost all cases crypto-currencies are pegged to a dollar value, so are convertible, but over time dollar convertibility might not matter as a new ecosystem of currencies grows.

In the future, we are likely to see systems evolve with similar distributed governance. Its effects will be two-fold, at least. First, more people will be earning more monetary value outside the conventional economic system. And second, the likelihood is that more incomes will be made up of many small assets in a variety of crypto-currencies.

Could there be many crypto-currencies? There are already more than two hundred. It is likely that crypto-currencies will proliferate before the phenomenon settles into an established pattern. Those that retain trust, though, will inevitably create substantial value for themselves. Right now, a Ripple is worth $.006. That exchange rate will vary with the trust placed in the system. There is no reason to believe it won’t grow. The value of a Bitcoin was worth close to zero in 2012 and $480 by mid-2014. If Ripple enjoys a similar level of trust or speculative attraction, then in theory, OpenCoin, the company behind Ripple, could eventually have a value of $20 billion x $480, or close to $1 trillion, in only five years’ time. That is only theoretical, but it is easy to see a position in the near future when currency platforms have more assets at their disposal than do indebted governments. At the very least, Ripple ought to cover the cost of its own development from its own currency.

Ripple happens to be backed by major venture capital firms, whereas Bitcoin is a true commons. Once people begin to understand it better, they may choose to express their social values by using Bitcoin in place of a Visa Card. But we are also likely to see more instances of commons-based projects having a value attached to them, bringing easier monetary value to people’s “free” activity. Ripple is an example of what might be achieved in the wider business community, but Ethereum is the real wild card, because its founders are intent on applying crypto-currency techniques to all forms of contract.

The future enterprise might be funded by the crowd or by a crypto-currency, supplied by a growing commons, animated by the activities of burgeoning ecosystems that have access to cheap resources in computing and various branches of science, design, and materials. Many people have also made the point that they will have easy access to manufacturing technology.

There will be an uneasy truce between people who create and update the commons and those who commercialize it, but this tension will find expression in the development of new ways to fund start-ups and growth. Because of the history of overexploitation and the concentration of resources, the future enterprise will incorporate all participants into the initial value proposition—owner, developer, commons, and customer, especially customer groups that have strong personal data management agents working for them, a concept we will touch on below (The Shift in Consumer Power).

THE SHIFT TOWARD A NEW UTILITY

One of the most important shifts at the enterprise level is the growth of a new class of utilities with quasi-monopolistic power. In the previous chapters, we have looked at how platforms help companies create a utility position. The modern utility is a platform that enables business activity in the way that Apple enables apps developers to develop applications and to reach customers.

Achieving a utility position provides a company with significant advantages, and it will continue to do so as long as it keeps clear of commoditization, or price competition. We’re also beginning to see a shift toward utilities that provide advantages to the consumer, mobilized in large part by one of the most successful utility companies: Apple.

Following the launch of the iPad in 2010, Apple looked as though it could go on and conquer new heights with its stock price and enter whatever new markets it chose. However, the stock price stalled and Apple did little to suggest it knew how to handle the scale of success it had created. It did not push on. It began to hire new people, including two highly successful CEOs from fashion brands Yves Saint Laurent and Burberry. It began to widen the base of its activities and appeared to create a less charismatic form of innovation with less dependence on its own CEO.

There is no question that the platform and ecosystem approach that enabled its utility position provided Apple with superior market power. There is also an argument, however, that says Apple’s new focus on shareholder value will diminish its innovation capability, because under these conditions, it will be unable to reward significant innovations in the company. Companies that create disruptive products like the iPhone and iPad inevitably pass into a period of sustaining innovation where little disruption takes place and they farm the benefits of their earlier inventions.

In actuality, what’s happening is that Apple is increasing the complexity of its business by entering the enterprise, auto, health, and home automation markets. In doing so, it might also shift the current data paradigm (more about this in the next section).

Utilities are the rule in smartphones and are growing in finance, manufacture, travel and hospitality, retail, media, and more. Companies that don’t make the utility position are stuck with partial and fragmented innovations to keep up with the utility’s software and services stack. But utilities can also emerge for consumers, especially around personal data management.

Where utilities can draw on the strongest commons, they are becoming formidable, sucking in profitability and denying resources to competitors. They are becoming conglomerates reminiscent of the great Asian business houses that straddle multiple sectors and rival governments for economic power. The financial stresses this causes and the general addiction to a stakeholder value philosophy mean that competitors often lack resources for human resource development or cannot escape NPV/ROI calculations long enough to truly innovate.

The lesson for companies is to take the commons more seriously, to strive for a new utility position, and to make stronger use of non-NPV investment calculations. They need to start thinking in options, how to build options portfolios and even how to value them differently than in the past, including redefining shareholder value.

THE SHIFT IN CONSUMER POWER

The smartphone sector has set the pace in systemic innovation, changing not just the product but the way business is done. Apple could be taking the model a step further with its Health Kit and Home Kit services, two Apple platforms for the health industry and home automation, respectively.

Greg Lloyd, CEO at the collaborative work platform Traction Software, puts it like this: “Google, Yahoo, and others gather, correlate, analyze, and use personal identity metadata including your location, search history, browsing history to monetize for their own purposes or to sell to others. I believe Apple is trying to build a counter story on security using identity and services encapsulated in devices you own.”5

At the heart of this lies the management and exchange of data.

When you do business with Google and Facebook, as a consumer, you strike a deal. In return for free search or social utility, you receive ads. For those ads to be contextual (or relevant to you), you agree to your data being collected, stored, and sold. Currently, consumers are forced to accept this deal without any transparency on the part of the companies; this is considered the price of using these utility services. Yet if Apple’s health services continue developing in their current direction, when you use these services, Apple will be managing data for you on your terms. If it happens, it will be a revolution, and there is certainly pressure in the system to make it happen.6

During 2013 and 2014, a start-up ecosystem began to spring up around the personal data economy. These companies offered to manage data on behalf of users. For many years, the personal data economy seemed to be a fringe activity. It went by the name of vendor relationship management (or VRM). In VRM systems, the rationale for data collection is to provide consumers with an opportunity to let vendors know when they intend to make purchases. Look at it like this—if your personal data is in a store with Personal Data Store (PDS) Supplier X, and PDS Supplier X is in contact with a range of vendors, you have an opportunity to let those vendors know that you want to purchase—say, a vacation. Instead of vacation vendors wasting money on Google Ads or Facebook Ads to attract you, they can ask to be kept informed by PDS Supplier X of any intent to buy vacations among its clients or owners.

In the VRM or PDS system, consumers become a much more powerful force, advertising declines in importance as marketers and consumer groups interact directly, and the role of news media and social media as carriers of advertising diminishes (and with it their revenues).

Right now the bulk of content production (news, blogs, YouTube) relies to some extent on contextual advertising. The content industry is ad-dependent. Yet, Facebook has acknowledged that traditional advertising techniques don’t cut it anymore. Writing in Advertising Age, Facebook’s Brian Boland acknowledged: “It’s imperative that we move toward people-based measurement—and soon. People-based measurement gives marketers both accuracy and transparency and has the added benefit of eliminating wasteful spending.”7

By people-based measurement, Boland is implying ways to relate directly with people rather than inferring their intentions the way that advertising currently does. That is really a recipe for greater use of PDS. One example of such a personal data store in action illustrates the possibilities: In the United Kingdom, Moneysupermarket.com runs a service called Cheap Energy Club. A PDS company, Allfiled, currently provides the infrastructure for it. The Cheap Energy Club is one million consumers strong. It alerts its users to any price reductions among the United Kingdom’s twelve energy suppliers, allowing customers to switch to new tariffs at a short notice. This kind of notice-giving is only a step away from automatic account switching, a step that would wholly commoditize energy selling. But it is only one more step away from the consumer being in total control. Under a PDS scenario, users would dictate the price at which they will become loyal. Complicating this scenario further, consumers will increasingly become energy producers, through facilities like solar power generation. Their current, latent, bargaining power will become real ecosystem power when they have energy to sell into the grid. There is plenty of room for disruption here.

Moneysupermarket.com currently has no plans to become a true PDS. The PDS movement will seriously impede current marketing strategies, in fact it will reverse today’s marketing paradigms. It will lead to vendors becoming captive to consumer groups rather than the other way round, in the way that Allfiled could demand energy price reductions on behalf of its members. It will create the same demand-led disruption we see in microprocessor design.

And the reverse data community is growing. “Using personal data to provide utility to individuals is a new growth market,” argues Alan Mitchell of personal data specialists Ctrl-Shift. “But it will take time to mature because so many ‘moving parts’ need to be put in place. Our latest research, conducted over the last two weeks with 50 players in the market, shows most of them thinking the market will begin to gain critical mass in 2–3 years’ time.”8

The potential is obvious but so, too, is the level of disruption likely when data is put in the hands of consumer groups. When data is no longer the preserve of (increasingly data-centric) large companies, there will be clear risks to all those business models built on an asymmetrical view of the web. Apple, as a platform, is beginning to show a way out of this, but other large companies are also beginning to realize that they have to build their businesses on a more positive view of customer relationships, one where customers are an integral part of the ecosystem.

The empowered customer is an important new disruption vector. In the 1990s and 2000, customers were often described by IT-led vendors as “exceptions.” Systems like enterprise resource planning were designed as a standard way of conducting business. Vendors conceived the standard as a route to greater scale, especially as companies grew through better supply chain management. For customers who didn’t fit the standard, there was always the call center to deal with their complaints. Sadly, most call centers became overloaded and ineffective.

The essential weakness of supply chain management and customer relationship management in these years lay in treating the customer as a source of controllable demand with “exception handling” as the fallback. The central role of the leader today is to help the enterprise transform, and the purpose of transformation is to build relationships around a new customer contract. As mentioned earlier, that contract is not clear-cut. Traditional customer relationship management, a more social form of customer interaction, and a more customer-led paradigm will need to operate alongside each other. This will be profoundly difficult for companies to manage.

THE SHIFT IN THE ECOSYSTEM

To be effective, ecosystems require a well-established self-learning community. However, outside open-source and developer ecosystems, the self-learning community is generally weak. Currently only open-source software has an ecosystem deep-rooted enough to create its own economic objectives, like transformation in finance. Policy makers need to learn how to build self-learning communities so that ecosystems can thrive. Large companies need more ecosystem dynamism, in the absence of good start-up activity. The ecosystem itself, in health, biology, manufacturing, and many more sectors, has to become the object of good policy.

GE is an example of a company taking this role seriously. In the area of breast cancer diagnosis, GE has invested $1 billion in new projects to help speed up and improve cancer diagnostics.9 GE’s aim is to bring together bright, innovative people from different areas of data analytics with diagnostics specialists to see if it can create a new paradigm in cancer diagnosis. This is a good move in that it somewhat decentralizes research away from the current incumbents.

But GE’s program is not necessarily designed to break down existing cancer paradigms. In that sense, it is not disruptive in the way that crypto-currencies are in finance. By the same token, medicine in general is adrift from people’s sentient needs because it focuses on their biological condition rather than their sensibilities.10

A challenge to the cancer diagnostics paradigm would demystify cancer research and make it accessible to broader ecosystems. One credible theory of cancer development points to highly heterogeneous bacteria or viruses interacting in novel ways in many different groups of people. It suggests that cancer is a highly differentiated disease, and there may be many thousands of cures for it. It looks like a problem for economies of scope or an instance of mass differentiation, a problem without a mass-market cure. But opening up that logic means the pharmaceutical industry would have relatively small markets for each treatment they might develop. That could raise the cost of drugs to unacceptably high levels, as well as push the various cures far down the road, or it could mean a different type of pharmaceutical company.

A more customer- and ecosystem-centric cancer platform and ecosystem model would almost certainly involve disruption. It would redefine priorities away from conventional oncology. It might lead to a far more decentralized approach to the disease, pushing research dollars outside of the genomics and oncology communities. It would bring multiple alternative theories into the mainstream (cancer specialists acknowledge that significant changes have arisen when outside agencies look in).11 But we are a long way from creating a customer-responsive medical system that shapes up around platforms and ecosystems and economies of scale and scope. The problem isn’t just confined to health. Financial services companies are also grappling with it, as well as consumer electronics and retail.

The way customer-centricity has flourished in smartphones is through the decentralization of innovation, essentially through app stores. That model has proved to be very potent. Its main strength is the existence of a stable, open-source community, not only to provide the infrastructure for change but also to provide a self-managed learning environment for programmers. An additional advantage is its ability to provide structure and change in one model.

Could this customer-centric model apply to the health and pharmaceutical industries? It is the basis for innovation strategy at Japanese pharmaceutical Otsuka, a leader in psychological and psychosis drug treatments and in treatments for solid tumor cancers. Otsuka’s primary objective in the 2014–2018 period is to significantly expand its business options. It intends to shift its portfolio toward unmet consumer needs as well as unimagined needs. For the most part, Otsuka’s business strategy looks quite orthodox. It will expand into neutraceuticals and well-being products. But privately, Otsuka acknowledged that they will need an ecosystem to make it work.12 GlaxoSmithKline has tried the ecosystem route by placing its malaria compounds research into the public domain. And GE acknowledges that many of the problems faced by the health sector cannot be solved by any one company.

The route to an ecosystem future looks clear, but companies in this sector have little history of community management. They lack an open-learning environment, which makes them dependent on the foundational educational system and its orthodoxies. These are the capabilities they need to work on and there are signs of change.

In the area of biological design, Autodesk is already providing design tools for future biological or programmable matter products. The number of independent biological laboratories is also increasing as the open-source movement evolves rapidly in citizen science. Forums such as the Open Science Summit and DIYbio are a focal point for people wanting to access experimental facilities at low cost. Already in 2011, John Brunstein put together a home lab for just $500. An ecosystem to rival open-source software is many years away, but an unpaid, free approach to science will free it from the constraints of orthodoxy.13 Good policy would accelerate the opportunities for learning and it would power ecosystem development. To achieve that, policy makers need to address the issue of education in general.

THE SHIFT IN EDUCATION

Neither second nor third level educational systems currently train students for a platform- and ecosystem-dependent economy, but they need to. The economy is being defined by the computational culture that underlies the new economic structure. The economy is driven forward by computational thinking often because there are no countercurrents staking a claim on our attention. The need for education is vast, to give people a better chance of prospering in an options-rich world and to provide a counterweight to computational mindset. However, the mechanisms to educate at scale do exist.

Every day, hundreds of thousands of people engage in open-source communities that are, in effect, self-help training. It could be that web-based learning platforms like Khan Academy, which provide access to educational modules for self-motivated people to follow, may fill some of the training gap, but they’re unlikely to provide a comprehensive solution because the fundamental need is to recreate education as a commons-based activity. That seems easiest to develop around computational thinking but the commons is broadly based and includes literature, engineering, biology and more.

When policy makers think of computational culture, their reaction is to teach people computer languages or to qualify them as developers.

Ajit Jaokar, who founded Feynlabs and put it on Kickstarter in 2013, senses a different approach is needed. According to Jaokar, the real deficit in education lies in the absence of a computational mind-set among the majority of people. Just like we all need a sense of history, we also need a sense of what computers make possible.14 With this in mind, the Feynlabs’ mission is to help people understand and develop a computational mind-set before they learn computer languages. The curriculum was developed with the help of pilot inner-city schools in London. It would surely pay to develop more concepts like Feynlabs, because their goal is to improve the individual’s learning capability. Teaching people how to learn appears to be a big gap. It is one that ecosystems help fill but as I stated earlier, few ecosystems are as well developed as open-source software’s. We need people to learn from this example.

But governments need to find mechanisms that promote educational initiatives like this at speed. It is a singular characteristic of government that it lags behind the rapid evolution of economic innovation. Today, we live in a personal, sentient economy with new values based on the commons. The ability to grasp the implications of change needs to be developed in as many people as possible. The scale is on the lines of—can we educate 1,000,000 people a month over the next two years? Few governments realize that they are free to think that way.

The real win here lies in extending the principle of commons-based education—reaching out to communities like those that are evolving in innovation, social media, biological research, alternative medicine and helping to encourage new objectives, posit new economic gains, and develop truly critical reasoning mechanisms and reward systems.

THE SHIFT IN POLICY

Policy makers in the United States and Europe hope they can participate in the transformations being unleashed by platform companies. To further that aim, they have opened up governmental data and favored smart city policies, both of which could stimulate new local businesses in the apps economy. The hope of stimulating an apps economy harks back to other policies, particularly cluster policy. For about thirty years now, governments have lived in the hope that creating a critical mass of activity (say of artists in a run-down area of a city) will create more dynamism and lift everybody else’s boat with it. These cluster-type ideas have a grain of truth. Artists and creative types do help renovate urban areas. But the urban experience is still immersed in poverty and alienation. This wider context should force governments to ask if any real breakthrough in economic development, particularly in large scale innovation (such as the introduction of electricity or the invention of moving images) is now possible.

One line of argument is that we actually have an abundance of ideas for constructive change, and innovation can become more pervasive if enough eyeballs are directed at enough problems. The commons is an example of eyeballs at work. Governments are putting their faith in this eyeballs argument by placing more of their assets in the public domain through initiatives like open data. It’s like saying we don’t quite know if this will work but here is some data, please create business from it.

Over the past thirty years, geographical entities like cities and regions have increased expenditure on initiatives that raise the profile of their locality to attract inward investment. In other words, cities have been marketing-led. Marketing a city or locality based on a brand idea is cheaper than substantive investments like new educational systems and a vibrant innovation culture. Over time, the brand has proved to be powerful (particularly as the web makes commerce very place agnostic—i.e., companies have no strong need to be located anywhere in particular).

The growth of place branding went hand-in-glove with the growth of economic cluster strategy. Clusters are concentrations of specific skills and business activity. There are natural clusters—the north of Sweden is a cluster for forestry activity; clusters that emerge from the predominance of one type of production—Detroit is a cluster for auto technology, as are Stuttgart and Munich; knowledge clusters such as the one around Cambridge University in England; and innovation clusters like Silicon Valley.

Governments are hoping to stimulate new clusters in the elastic economy through opening up data and stimulating start-up activity in technology applications. Lately this has been accompanied also by significant tax-breaks to companies that create intellectual property.

The problem policy makers face is one of scale. Start-up initiatives might help launch a hundred new companies when most economies really need 10,000 of them—yet business dynamism is actually in decline. Policy makers are currently filling a bath where the plug is missing.

Cluster theory owes its origins to the work of Michael Porter, who pointed out in The Competitive Advantage of Nations the intense accumulation of skills and business relationships in locations like Hollywood and Silicon Valley.15 Porter describes clusters as “geographic concentrations of interconnected companies and institutions in a particular field.” He argued that they “encompass an array of linked industries and other entities important to competition. They include, for example, suppliers of specialized inputs such as components, machinery, and services, and providers of specialized infrastructure. Clusters also often extend downstream to channels and customers and laterally to manufacturers of complementary products and to companies in industries related by skills, technologies, or common inputs. Finally, many clusters include governmental and other institutions.”16

Although Porter drew attention to a range of clusters, the concept was already in circulation. It just went by a different name—industrial districts. The best-known industrial district in the 1980s, before Porter published on the topic, existed in Emilia Romagna, Italy, home to the Italian shoe and tailoring industry.

At the time Porter wrote about clusters, a burgeoning new clothes retailer, Benetton, had emerged on the international retail scene and seemed to offer a new model for growth, based on the use of a highly distributed, but local, network of suppliers. Benetton began life as a local apparel company in 1965; by the mid-1990s, it had the world’s most widely distributed retail network in apparel with about $1.65 billion in turnover. The Benetton family still ran the business. Businessweek once described it as the McDonald’s of fashion.

Benetton initially grew in scale due to its ability to draw on the talents of a wide range of companies and designers nearby who were experts in color, fashion trends, and clothing, but also because it did not have to invest in its own substantial manufacturing facilities. Compared to other regions, Emilia Romagna presented Benetton with a lower capital requirement, just like Cloud computing does to software-driven enterprises today. The reason was the substantial local network of competing companies.

Here is how one observer described Emilia Romagna: “There are 90,000 manufacturing enterprises in the region, surely one of the highest densities per capita in the world! Small, medium enterprises (SMEs) predominate. One person in twelve is self-employed or owns a small business. In recent years the region has produced the highest GDP per capita in the country, and it now ranks with the ten best in Europe…2/3 of the citizens of Bologna belong to a co-op…45 percent of the GDP is produced by co-ops…(and) 85 percent of the social services in Bologna are delivered by co-ops.”17

Benetton was able to grow rapidly because it externalized much of its core activity to these groups. Emilia Romagna was home to the most intense, yet distributed, production system of its day. A few years after Porter wrote about clusters, Benetton was in decline because of rapid diversification by the Benetton family. Yet Benetton and Emilia Romagna were the prototype for today’s platforms and ecosystems, and indeed for tightly coupled ecosystems like ARM chip design. The ecosystem is, in essence, a virtual industrial district.

Cluster policy has become the default economic development tool for local and regional authorities. In a 2008 report, a group of researchers concluded that “European cluster policy is to a great extent aimed at promoting innovation in the private sector.”18 In fact, cluster policy is more widely distributed in the United States.

The challenge for goverments, though, is that the platform and ecosystem model has no natural home. It is free of geography. It is almost futile to talk about developing local ecosystems. It would be productive to talk about heightening developer skills, and extending ecosystem skills outside of software, but the reality is these ecosystems are not clusters and are not easy to program into policy. The impact of any investment would be difficult to contain within a city or region.

The right policy instruments for governments are the most difficult to imagine in practice: acknowledging that the city might not have the mechanisms to create sufficient economic activity, seeking economic diversity by shifting investment away from technology to education, forgoing high-profile projects in favor of developing self-sufficiency mechanisms, seeking ways to promote self-learning and self-teaching communities, and engaging with the wider world of self-sustaining knowledge workers. These are all difficult for policy makers to project as significant instruments of change even though they might be the most effective ones.

THE SHIFT IN INTERVENTION

Can governments continue to operate at their current levels of intervention? It seems unlikely. In fact, government and politics are, in a sense, being replaced by a new commons that is generating huge economic value, often without reward, and is now reacting against centralized authority.

The commons is spreading and needs no intervention mechanism other than facilitating participation through education. Unfortunately, governments at present distort economic activity by providing too much support for large companies and by biasing economic development toward clusters. Their best course of action is no action other than to present themselves as peers and to invest in our education—everybody’s education—fast.

Governments can and should intervene in areas that can reduce average living costs in order for people to construct their own option portfolios of activity. When Charles Handy first began writing about portfolios, they seemed to be a very attractive alternative to corporate life. Today they are essential, yet few people are educated to understand how to play out personal business options. Much of the wealth they create leaks into house- and auto-related spending. Traditionally, national economic policy has favored big businesses like the auto industry with grants and with tax policies, and financial companies that provide mortgage. Policy makers now need to shift toward favoring individuals.

Policies that curb house price increases and radically reduce transportation costs would give families and single people far more flexibility to construct viable incomes at different stages of their lives. Government for the majority in this case would favor individual and household viability before favoring large enterprises. It would shift ideology to a different type of prosperity, built on viable household incomes and participation across a variety of economic vectors, the ecosystem around large enterprises, self-learning and self-teaching commons, and participation in microeconomic platforms, allowing, indeed educating, people to assemble the lives they want from a variety of mechanisms.

In addition to policy problems, governments also face fading revenues and increasingly large debt as a distributed economy emerges outside of regulation and as large companies increasingly position themselves outside the tax net. How can they respond?

Platforms and ecosystems simultaneously build and exacerbate wealth problems. The solutions are not comfortable ones to discuss because they primarily involve investing in individuals’ capabilities. Governments are generally antagonistic toward micro-businesses or the self-employed. Unless policy makers wish to remain ineffectual bystanders in the face of continued economic crisis, they will need to undergo a series of shifts. Policy makers have to face up to a declining level of revenue in real terms, with a larger revenue portion continuing to be directed toward debt repayments.

Against this background, they need to focus initiatives on devolving responsibility for personal income and welfare. As it is, people are gravitating toward self-sufficiency and self-determination and domestic scale incomes, or small income streams from activities on sites like eBay and Etsy or from Uber and Airbnb. People need support in developing their personal economies and mitigating their exposure to global forces by reinforcing domestic-level entrepreneurism.

The idea of policy for individual economic actors might sound like too big a stretch for governments. It might even diminish the self-regard of politicians to realize they have to rebuild the economy by helping people augment their incomes in many small ways. But the reality is spending on major line items like health, welfare, pension, and education will continue to decline. The future is inevitably going to revolve around non-state welfare and pensions, by default, regardless of society’s ideological preferences.

It is beginning to happen in the sense that people are earning money in the shadow economy via Airbnb or Facebook markets, but it needs to be reflected in policy to stimulate further growth in the domestic-level (i.e., home) economy. It might sound counterintuitive in the age of global markets, but people need to be more buffered by local and domestic options. By planning these outside of tax collection (but not outside of tax reporting), governments can gauge the potential of different layers of renewal.

THE SHIFT IN THE ENTERPRISE

Many organizations across the public and private sector share similar problems when trying to create mechanisms for change. Most companies, in fact most entities, are assessed only on their outputs, particularly their profits, but also ratios like return on assets. It is becoming more widely accepted that these metrics really inhibit positive change. In addition, enterprises need to grapple with global cash flow issues and new commons-based currencies. How we judge enterprises in future might shift from return on asset rations to their ability to demonstrate an enduring ability to fund themselves and their diverse options portfolios. In other words we will seek metrics of longevity.

There is a bigger shock in store as people turn toward non-transacted activities. As new digital currencies emerge, individuals will have less need to trade in the conventional economy. As more of their activity becomes virtual, they can assign virtual value to it, if they wish to have any value placed on their sentient needs.

Enterprises need to follow people into the price-free economy. They need mechanisms to change their own internal structures (more to platform and process innovation) and their external relationships (more to long-term value propositions and cooperation with customers). This shift is impossible to do in the context of the value metrics that companies typically use.

To marshal company behavior behind their outputs, management teams use KPIs—key performance indicators. KPIs help managers to align behavior and incentivize performance around very specific revenue goals.

This works very well until an organization has to change. Management then finds itself in the unlucky position of incentivizing counterproductive behavior. As employees continue their focus on execution, the organization becomes captive to its KPI program.

To counter this, I suggested (in Chapter 12) a new set of metrics that I call KCIs, or key capability indicators. The point about a KCI is that it measures an organization’s capability to change at a structural level rather than its ability to create new outputs. It is not an output metric. It does not provide an indicator of new products created or improved asset utilization. Those types of metrics are already in place and can be adapted readily. What most organizations lack is a KCI set, a scorecard of its evolving key capabilities. These are measures that leaders can use to promote better skill sets, skill investment, and to acquire change-promoting assets.

In a 2013 study involving leaders in thirty organizations undergoing some form of transformation, I found that most relied on old investment metrics (NPV) for spending decisions, and the overwhelming majority (over two-thirds) found themselves unable to invest in new foundational capabilities because these did not fall into an NPV scenario. Put that another way—most organizations that want to change don’t invest in the capabilities needed to change, and worse, feel that they cannot do so because of the metrics in place. In contrast, KCIs provide metrics that make investment in change easier. KCIs can:

• help leaders understand the skills they need to have in place in order to effect change

• provide a model for change because capabilities map directly to a future, desired organizational competency

• use the capability metrics to benchmark their organization against others

• apply KCIs to make investment decisions

• use them as a barometer of capability development

KCIs have been developed through interviews with leaders in all sectors and an analysis of over four hundred companies across technology, finance, media, pharma and health care, and more. The metrics are a work in progress, of course. Each study yields new lessons and new possibilities for measurement, and the scorecard itself remains open-ended as it is adapted for each sector, while remaining true to a core capability scorecard that allows cross-sector benchmarking.

The indicators are based on a five-year-long analysis of changing organizational structures undertaken in a variety of studies on the interaction of technology, human factors, and organizational change. Put simply, most organizations have been created on a product-centric model and use product-centric accounting principles. KCIs help companies to switch to a relationship-centric model with different measures of operational value.

Complex change often needs a simple statement to make it easy to digest. The reality I have tried to paint here is one where a talented group of entrepreneurs helped each other to exploit a new phenomenon called The World Wide Web. Although the Web originated in a lab in Switzerland in 1989, it was only after the dot-com bust in 2001 that entrepreneurs were able to assess its true, long-term value. At the same time enterprises were bogged down with legacy software that made them inflexible and unpleasant, hierarchical places to work. The Google Generation changed much of this, introducing better software techniques that have vastly accelerated change, and a value system based around openness that is still transforming how enterprises and governments think. The Google Generation also produced the platform and ecosystem model of enterprise and in doing so transformed the global economy in ways that the web itself could not. But those changes are in the bank. Something else is now happening. In a new economic structure, the commons is more powerful and it is the wild card. Its latest black swan is the crypto-currency. But there will be more of them. The post-Google Generation is no less motivated by the high rewards of disruption, but it is based around a more genuine loyalty to the idea of global collaboration. The disruption it can unleash has only just begun.

Afterword

There are two views of the state of the Western economy. On one side there is angst over the way large firms behave—their reluctance to invest, bloated CEO salaries, and the ever-present risk of being eclipsed, either by start-ups or through misreading changes in technology and the competitive landscape. On the other side, there is some optimism that a new commons-based economy, or collaborative economy (or sharing economy) is rewriting the rules of wealth creation and distribution. Both sides might bemoan the concentration of wealth in fewer hands but neither has a way to deal with the extent of inequality in society.

In Shift, I have put the underlying changes in a different context. An important part of the shift is a rebalancing of power across the economy, as investment capital loses its historical role at the center of economic development. There is a much higher degree of dependency between large and small firms, and between both and the common pool of resources. There is, also, growing consumer power but the full extent of spontaneously organized consumers has yet to have the impact it might. It is easy to envisage consumer groups owning their own data and becoming serious bargaining partners that force large enterprises to create products in new ways.

These structures have emerged as new relationship technologies have made it easier for global groups to form in any segment of the economy. They could be new mobile apps communities, open source developer communities, open engineering, open biology, new construction application suppliers, new health platforms, or incursions of the fragmented economic model into environment industries, manufacturing, power and retail.

It is important to recognize the fact of this structural change. In Europe and a lesser extent the United States, a major plank of policy has been to create conditions where concentration of wealth and opportunity can be redistributed. Policy makers have assumed that capital is an important magnet in concentrating wealth but capital today is significantly less important in fostering opportunity. A lack of capital does not inhibit people from starting new businesses, and the capital requirement to scale a business is less than ever before. Capital concentration is therefore far less significant than the concentration of productive assets—platforms that control vast ecosystems of activity, mini-nations of wealth creating relationships.

Concentration is partly a result of historical accident (being in the right place with the right tools), partly a result of technological mastery (being able to define the technologies in the new business infrastructure), and partly due to a lack of competition. We see new oligopolies emerging with frightening speed (Apple, Samsung and Google in mobile; Box and DropBox in distributed asset sharing; PayPal, Visa and MasterCard in payments; Skype and Google in Voice over IP; Verizon, AT&T and Sprint in mobile networks; Disney in animation), to set alongside old oligopolies like Procter & Gamble and Unilever in fast-moving consumer goods.1

The concentration of power is offset to some degree by the common pool and by ecosystems. The commons is a source of wild card horizontal disruption with initiatives like BitCoin that threaten the notion of economic stability. There will undoubtedly be more wild cards as oligopoly practices incite opposition.

Meanwhile, people seek refuge in non-transaction behavior. They are allowing more of their sentient needs to drive their economic activities like free music and photo-sharing, perpetual social interaction; they are engaging in more low-level economic activity—renting out rooms in social networks-like environments, creating smaller maker businesses that sell on Etsy, Facebook and eBay; free labor—unpaid internships, reputation building social activity like reviews, personal platform building activity on social media and in blog posts. These contribute in unexplored ways to the general drift toward deflation.

This is a very different economy from the one most of us grew up in. It will be dominated by platforms that have the capacity and desire to organize highly fragmented business activity. Those platforms will accrue extraordinary returns while the members of ecosystems will have to learn significant options’ planning skills in order to carve out interesting, durable careers and businesses.

For many small to medium-sized companies, the future will be dominated by the need to speed up change, seek people who are comfortable with ambiguity and uncertainty, and who are smart enough to deal with multiple options, making the right calls in a faster business environment with a development methodology that begins to resemble game-play.

On the more abstract level of how democracy evolves, we already see highly-indebted governments forced to make major tax concessions to global companies (through double-Irish-type schemes and the patent-box idea) and to individuals (through intellectual property royalties and income tax concessions).

Government is losing relevance as a job creator and many of its advisers and the practitioners of modern politics simply do not understand the shift. They do not understand the technologies or the structural shifts and therefore miss the policy areas where they can be effective, rapidly, at scale. It would be no surprise to see this toxic mix of affable ineptitude and wealth concentration lead to anti-democratic movements similar to those that led to national socialism in the 1930s. Already the concentration of economic power resembles that of the late 19th century and early 20th century cartels. On the other hand, the levers of power exist elsewhere, in the open source movement. This leaves open a question about the future role of government in economic policy. The implications of that question go largely unstated. But so too does the new power of the commons. If we don’t begin to recognize these realities, then the future looks set for conflict. The world is badly in need of smarter politics.

Endnotes

PREFACE

1. The Bank of England reported in September 2014 that “left to grow unchecked, BitCoin or an equivalent crypto-currency could create an inability in the money supply to vary in response to demand and would likely cause welfare, destroying volatility in prices and real activity.” Quoted in Izabella Kaminska, “The BoE on the Potential of the Block Chain,” FT Alphaville (blog), Financial Times, Sept 11, 2014 http://ftalphaville.ft.com/2014/09/11/1967942/boe-on-the-potential-of-the-blockchain/., See also Robleh Ali, John Barrdear, Roger Clews and James Southgate “Innovations In Payment Technologies and The Emergence of Digital Currencies”, Bank of England Quarterly Bulletin , Q3 2014; and “Robleh Ali, John Barrdear, Roger Clews and James Southgate The Economics of Digital Currencies”, Bank of England Quarterly Bulletin, Q3 2014 At the 2014 bankers’ conference SIBOS in Boston, BitCoin sessions were the best attended of all banking sessions.

2. Haydn Shaughnessy and Nick Vitalari, The Stages of Corporate Evolution: How Innovation Method Becomes Transformation Process, nGenera Think Tank, July 2011; Haydn Shaughnessy, “Bluefin Elastic Innovation Index, 2014,” Bluefin, January 13, 2014, http://www.bluefinsolutions.com/Bluefin/media/Bluefin/PDFS/Bluefin-Solutions-Elastic-Innovation-Index-2014-Global-top-50-most-innovative-companies-(2).pdf; Haydn Shaughnessy, “Innovation Capability in the Financial Services Sector,” Paper presented at SIBOS, Boston, MA, October 2014; and an unpublished study of the Finnish software industry.

SHIFT 1 INTRODUCTION

1. Mark Mulligan and David Card, “Sizing the EU App Economy,” Gigaom, February 13, 2014, http://research.gigaom.com/reportsizing-the-eu-app-

economy/.

2. “Usage Statistics and Market Share of WordPress for Websites,” accessed June 15, 2014, http://w3techs.com/technologies/details/cm-wordpress/all/all.

3. Haydn Shaughnessy, Rethinking the Value of a Brand (IntentHQ, May 2014). All data supplied by StatSocial.

4. Barry Lynn, Cornered: The New Monopoly Capitalism and the Economics of Destruction (Hoboken, NJ: John Wiley and Sons, 2010).

5. Steve Denning, “Has Capitalism Reached a Turning Point?,” Forbes, September 26, 2014, http://www.forbes.com/sites/stevedenning

/2014/09/26/has-capitalism-reached-a-turning-point/.

6. This has been described in a wide range of management books. The types of problems companies face as they grow are a lack of internal communications, an overfocus on sales at the expense of profit, management as firefighting, and an inability to innovate for fear of cannibalizing existing revenues. But once they are established, these problems can become exacerbated by predatory behavior as large companies buy up competing companies and become less willing to function competitively themselves. On the issue of scaling a business, see Robert I. Sutton, “Eight Essentials for Scaling Up without Screwing Up,” HBR Blogs Network, Harvard Business Review, February 10, 2014, http://blogs.hbr.org/2014/02/eight-essentials-for-scaling-up-without-screwing-up/.

7. On GE’s healthymagination initiative, see http://www.healthymagination.com/; on its relationship with Quirky, see Joshua Brustein, “Why GE Sees Big Things in Quirky’s Little Inventions,” Bloomberg Businessweek, November 13, 2013, http://www.businessweek.com/articles/2013-11-13/why-ge-sees-big-things-in-quirkys-little-inventions.

8. Adam Ludwig and Adrienne Jane Burke, “Ge-Quirky Deal Opens Tech Patents to Almost Everybody,” Techonomy, April 10, 2013, http://techonomy.com/2013/04/ge-quirky-deal-opens-tech-patents-to-almost-everybody/.

9. Interview with the author, April 2014.

CHAPTER 1

1. Interview with the author, August 5, 2014.

2. Both are members of Facebook’s Open Compute Project.

3. I have relied heavily on discussions with Ulf Ewaldsson and Hans Verbek, respectively CTO and CEO at Ericsson, to understand the direction and implications of IT/telco convergence.

4. “Watch Out Banks, Ailbaba Is After Your Business,” Finextra, October 16, 2014, http://www.finextra.com/news/fullstory.aspx?newsitemid=26588.

5. Paul Muller, Vice President of HP, e-mail correspondence with the author, April 22, 2014.

6. Ian Hathaway and Robert E. Litan, “Declining Business Dynamism in the United States: A Look at States and Metros,” Brookings Institute (May 5, 2014).

7. John Haltiwanger, Ian Hathaway, and Javier Miranda, Declining Business Dynamism in the U.S. High-Technology Sector, Kauffman Foundation, February 2014, http://www.kauffman.org/~/media/kauffman_org/research%20reports%20and%20covers/2014/02/declining_business_dynamism_in_us_high_tech_sector.pdf.

8. Gregory V. Milano, “Too Big to Succeed?,” CFO Magazine, April 29, 2011, http://www.cfo.com/printable/article.cfm/14572986.

9. For a literature review, see Petter Gottschalk and Nolan J. Taylor, “Strategic Management of IS/IT Functions: The Role of the CIO” in System Sciences, 2000. Proceedings of the 33rd Annual Hawaii International Conference on System Sciences (Hawaii, January 4–7, 2000).

10. Shaughnessy, “Innovation Capability.”

11. Eric D. Beinhocker, Diana Farrell, and Adil S. Zainulbhai, “Tracking The Growth of India’s Middle Class,” McKinsey Quarterly, August 2007.

12. For example, Hans Rosling (see http://www.youtube.com/watch?v=jbkSRLYSojo), a noted authority on global health and development and statistician, also sees continued global economic growth. However, he, like others such as Dani Rodrik (see http://rodrik.typepad.com/dani_rodriks_weblog/2007/11/doing-growth-di.html), also sees a variety of impediments that must be solved for long-term sustainability, such as health, education, marketable skill development, new forms of energy, sustainable population growth, and, most important, long-term level of investment. See also the UN’s Millennium Development Goals at www.un.org/millenniumgoals/reports.shtml

13. Chelsey Dulaney, “Apple to Roll Out iPhone 6 in 36 New Markets by End of October,” Wall Street Journal, October 13, 2014, http://online.wsj.com/articles/apple-to-roll-out-iphone-6-in-36-new-markets-by-end-of-october-1413205294.

14. The advantages of the economics of scale and its relationship to productivity and growth are well understood, having formed the foundations of the industrial business model. However, an opposing and vexing issue, often not discussed, is the problem of diseconomies of scale.

15. For more on “everything as a service” see “Why Enterprises Are Embracing ‘Everything as a Service,’” http://www.cognizant.com/latest-thinking/perspectives/why-enterprises-are-embracing-everything-as-a-service

16. For a description of how supply chain changed during the 1990s, see Huan Neng Chiu, “The Integrated Logistics Management System: A Framework and Case Study,” International Journal of Physical Distribution and Logistics Management 25, no. 6 (1995): 4–22.

17. At this stage Cloud is widely accepted as the future infrastructure, though P2P may still make an impression as the common pool resource grows in significance.

18. Dean Evans, “What Is BYOD and Why Is It Important?,” TechRadar Pro, August 23, 2013, http://www.techradar.com/us/news/computing/what-is-byod-and-why-is-it-important--1175088.

19. Jeff Loucks, Richard Medcalf, Lauren Buckalew, and Fabio Faria, The Financial Impact of BYOD: Top 10 Global Insights from the Cisco IBSG Horizons Study, Cisco, 2013, https://www.cisco.com/web/about/ac79/docs/re/byod/BYOD-Economics_Top10.pdf.

20. Eric Lai, “With Latest Features, Box.Net Claims Edge over SharePoint, Google Docs.” Computerworld, January 21, 2010, http://www.computerworld.com/article/2523042/cloud-computing/with-latest-features--box-net-claims-edge-over-sharepoint--google-docs.html.

21. The Ericsson Mobility Report, November 2014

22. C. K. Prahalad and G. Hamel, “The Core Competence of the Corporation,” Harvard Business Review 68, no. 3 (1990): pp. 79–91.

23. Clayton Christensen, The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail (Boston, MA: Harvard Business School Press, 1997); Michael E. Porter, Competitive Advantage: Creating and Sustaining Superior Performance (New York: Free Press, 1985).

24. Giussepi Berlingieri, “Outsourcing and the Shift from Manufacturing to Services,” CentrePiece (Winter 2013/2014): http://cep.lse.ac.uk/pubs/download/cp413.pdf.

25. Porter, Competitive Advantage.

CHAPTER 2

1. AlixPartners, A Tale of Two Tiers: The 2014 Global Consumer Electronics Outlook, November 2013, http://www.alixpartners.com/en/Publications/AllArticles/tabid/635/articleType/ArticleView/articleId/822/A-Tale-of-Two-Tiers.aspx#sthash.RxHRB05A.dpbs.

2. Uncovered jobs are those that have no unemployment insurance coverage; they are on the rise in every sector of the US economy. See Joshua Wright, “Data Spotlight: Independent Contractors on the Rise,” EMSI, April 29, 2011, http://www.economicmodeling.com/2011/04/29/independent-contractors-other-noncovered-workers-on-the-rise/.

3. The idea is associated with the economist Ronald Coase and originated in his article, “The Nature of the Firm,” Economica 4, no. 16 (1937): 386–405.

4. Oliver E. Williamson, “Transaction Cost Economics: An Introduction,” Economics (March 2007).

5. Porter, Competitive Advantage.

6. Chris Anderson, Free: The Future of a Radical Price (New York: Hyperion, 2009).

7. Drucker is widely quoted in this regard. See Stefan Stern, “The Importance of Creating and Keeping a Customer,” Financial Times, October 10, 2011, http://www.ft.com/intl/cms/s/2/88803a36-f108-11e0-b56f-00144feab49a.html#axzz3HZ6O08r8.

8. Nathalie Quizon, The Porous Enterprise (San Francisco: Orange Labs, 2010).

9. Haydn Shaughnessy, “A Semantic Analysis of 20 Years of Writing in the Harvard Business Review” (excerpts published in “The HBR Approach to Failure,” HBR Blog Network, March 28, 2011), http://blogs.hbr.org/2011/03/the-hbr-approach-to-failure/.

10. Rita McGrath, The End of Competitive Advantage: How to Keep Your Strategy Moving As Fast As Your Business (Boston, MA: Harvard Business Review Press, 2014).

11. Steve Denning, “The Dumbest Idea in the World: Maximizing Shareholder Value,” Forbes, November 28, 2011, http://www.forbes.com/sites/stevedenning/2011/11/28/maximizing-shareholder-value-the-dumbest-idea-in-the-world/; Steve Denning, “The Unanticipated Risks of Maximizing Shareholder Value,” Forbes, October 14, 2014, http://www.forbes.com/sites/stevedenning/2014/10/14/the-unanticipated-risks

-of-maximizing-shareholder-value/.

12. “The Repurchase Revolution,” The Economist, (September 13, 2014).

CHAPTER 3

1. Stephanie Overby, “Cloud Computing to the Max at Bechtel,” CIO, October 7, 2008, http://www.cio.com/article/2433165/infrastructure/cloud-computing-to-the-max-at-bechtel.html.

2. Gagan Biyani, “Explained: The Actual Difference between Growth

Hacking and Marketing,” (blog), TNW, May 5, 2013, http://thenextweb.

com/insider/2013/05/05/the-actual-difference-between-growth-hacking

-and-marketing-explained/.

3. Esmeralda Swartz (CMO, MetraTech), provided GRU information in an interview with the author, June 3, 2014.

4. Chris Zook, Beyond the Core: Expand Your Market without Abandoning Your Roots (Boston, MA: Harvard Business School Press, 2004).

5. Haydn Shaughnessy, “Growing Adaptive Innovation through an Innovation Maturity Model,” Hype, January 2014, http://i.hypeinnovation.com/innovation-maturity-model.

6. Alberto Prado (Vice President Digital Innovation at Philips Consumer Lifestyle), in an interview with the author, November 20, 2013, and February 2014.

7. Matt O’Sullivan, Qantas, “Frequent Flyers Program Changes Put Loyalty at Risk,” Sydney Morning Herald, March 28, 2014, http://www.smh.com.au/business/aviation/qantas-frequent-flyers-program-changes-put-loyalty-at-risk-20140328-35n22.html; Justin Bachman, “Value of Frequent-Flyer Miles Will Soon Drop for Delta and United Travelers,” Bloomberg Businessweek, December 19, 2013, http://www.businessweek.com/articles/2013-12-19/value-of-frequent-flier-miles-will-soon-drop-for-delta-and-united-travelers.

8. On Tesco and its profits, see Alessandro Pasetti, “How Tesco PLC Could Drop to 100p!,” Motley Fool, October 22, 2014, http://www.fool.co.uk/investing/2014/10/22/how-tesco-plc-could-drop-to-100p/.

9. Jamie Doward, “Silk Road Website Did Roaring Trade in Tesco Clubcard Vouchers,” Observer, March 8, 2014, http://theguardian1481.rssing.com/browser.php?indx=25167829&item=25.

10. This is sometimes called Vendor Relationship Management (VRM) or the personal data store movement. See respectnetwork.com. Doc Searls, The Intention Economy: When Customers Take Charge (Boston, MA: Harvard Business Review Press, April 2012).

SHIFT 2 INTRODUCTION

1. IBM Institute for Business Value, Reinventing the Rules of Engagement: CEO Insights from the Global C-Suite Study, 2014: http://public.dhe.ibm.com/common/ssi/ecm/en/gbe03579usen/GBE03579USEN.PDF.

2. Mark P. McDonald, De-capitalizing Digital Disruption, Digital Business (blog), Accenture, January 8, 2014, http://www.accenture.com/us-en/blogs/digital-business/archive/2014/01/08/de-capitalizing-digital-disruption.aspx. In contrast, Jill Lepore has argued that we are now generating disruption fear in order to sell new services. Jill Lepore, “The Disruption Machine: What the Gospel of Innovation Gets Wrong,” New Yorker, June 23, 2014, http://www.newyorker.com/magazine/2014/06/23/the-disruption-machine.

3. Haydn Shaughnessy, “Research Study on Innovation Capability in 60 Financial Institutions,” (unpublished manuscript, October 2014).

4. Google developed many similar devolution techniques through Project Oxygen, designed over the period 2002–2010 with the expressed intention of keeping innovation alive. David A. Garvin, “How Google Sold Its Engineers on Management,” Harvard Business Review, December 2013, http://hbr.org/2013/12/how-google-sold-its-engineers-on-management/ar/1.

5. For a summary of the literature on supply chain management, see Ronald M. Salazar, The Effect of Supply Chain Management Processes on Competitive Advantage and Organizational Performance, (master’s thesis, Air Force Institute of Technology, March 2012).

6. For a rundown on supply chain management, its development phases, and its impact, see Tim Laseter and Keith Oliver, “When Will Supply Chain Management Grow Up?,” Strategy and Business 32 (Fall 2003).

CHAPTER 4

1. For an excellent analysis of early platform-based competition in the high-technology industry, see Annabelle Gawer and Michael A. Cusumano, Platform Leadership: How Intel, Microsoft, and Cisco Drive Industry Innovation (Boston, MA: Harvard Business School Press, 2002). See also Caroline Calkins and John Sviokla’s work on the advantages of business platforms in M & A, Mergers and Acquisitions in the Age of Business Platforms, Insight Series (Chicago, IL: Diamond Management & Technology Consultants, 2006). Nick Vitalari discussed the evolution of platforms in business: see Nicholas Vitalari and Laura Carrillo, Building a Platform for Business Growth: Enabling Connection, Collaboration and Innovation, nGenera Research Report, 2009. A more recent contribution can be found at Phil Simon, The Age of the Platform (Las Vegas, NV: Motion Publishing, 2011).

2. Geoffrey Moore, “Systems of Engagement and the Future of Enterprise IT,” AAIM, 2011, http://www.aiim.org/futurehistory; see also Josh Bersin, “The Move from Systems of Record to Systems of Engagement,” Forbes, August 16, 2012, http://www.forbes.com/sites/joshbersin/2012/08/16/the-move-from-systems-of-record-to-systems-of-engagement/, and Dion Hinchcliffe, “Moving Beyond Systems of Record to Systems of Engagement,” Sprinklr, June 8, 2011, http://www.sprinklr.com/social-scale-blog/moving-beyond-systems-of-record-to-systems-of-engagement/.

3. Michael A. Cusumano and Annabelle Gawer, “The Elements of Platform Leadership,” MIT Sloan Management Review (Spring 2002): http://sloanreview.mit.edu/article/the-elements-of-platform-leadership/.

4. A summary of the field can be found in Andrei Hagiu and Julian Wright, “Multi-Sided Platforms” (working paper, Harvard Business School, Boston, MA, October 12, 2011).

5. Apple announced a partnership with IBM for enterprise systems in June 2014. It had previously announced partnerships with Honda/Acura, Mercedes-Benz, Nissan/Infiniti, Ferrari, Chevrolet/Opel, Kia, Hyundai, Volvo and Jaguar, BMW, LandRover, Audi, Toyota, and Chrysler in cars; it announced a partnership with Epic in hospital medical records in July 2014.

6. Hagiu and Wright, “Multi-Sided Platforms.”

7. Christensen, The Innovator’s Dilemma.

8. Larry Downes and Paul F. Nunes, “Big-Bang Disruption,” Harvard Business Review, March 2013, http://hbr.org/2013/03/big-bang-disruption/.

9. Steven Klepper and Kenneth L. Simons, “Technological Extinctions of Industrial Firms: An Inquiry into Their Nature and Causes,” Industrial and Corporate Change 6, no. 2 (June 1997): 379–460.

10. C. Scott Hemphill and Tim Wu, “Parallel Exclusion,” Yale Law Journal 122, no. 5 (March 2013).

11. Gillian Tett, “Hubris and the City,” Financial Times, October 3, 2014, http://www.ft.com/intl/cms/s/0/5d430638-49bf-11e4-80fb-00144feab7de.html#axzz3HZ6O08r8.

12. Shaughnessy, “Bluefin Elastic Innovation.”

13. Charles Arthur, “Nokia’s Revenue and Profit Trend Points to Key Problem Commoditisation,” Guardian, September 10, 2010, http://www.theguardian.com/technology/blog/2010/sep/10/nokia-profits-falling-explanation.

14. James Niccolai, “Nokia Lays Plan for More Internet Services,” New York Times, December 4, 2007, http://www.nytimes.com/idg/IDG_002570DE00740E18002573A70046F2EF.html?ref=technology.

15. Charles Arthur, “Nokia’s Revenue.”

16. Correspondence with the author, September 13, 2013, and October 14, 2013.

17. Robin Wauters, “Ovi Store Launch Is a Complete Disaster,” Techcrunch, May 26, 2009, http://techcrunch.com/2009/05/26/nokia-ovi-store-launch-

is-a-complete-disaster/.

18. Cristine Gonzalez, “Mobile App Developers Fire Back: Nokia Sucks,” VentureBeat, April 12, 2009, http://venturebeat.com/2009/04/12/mobile-app-developers-fire-back-nokia-sucks/.

19. Chris Thomas and Haydn Shaughnessy, “Surviving in iPhone Territory: An Analysis of Social Media Coverage of the HTC G1 Launch,” The Conversation Group, February 2010.

20. Eileen Yu, “Former CEO Admits Errors Led to Nokia Downfall,” ZD Net, October 18, 2013, http://www.zdnet.com/former-ceo-admits-

errors-led-to-nokia-downfall-7000022107/.

21. Julian Skan, Richard Lumb, Samad Masood, and Sean Conway, “The Boom in Global Fintech Investing,” Accenture, 2014, http://www.accenture.com/Microsites/fsinsights/capital-markets-uk/Documents/Accenture-Global-Boom-in-Fintech-Investment.pdf.

22. Quoted in Neil Hughes, “iTunes and App Sales Projected to Grow to 20% of Apple Profit by 2020,” Apple Insider, March 24, 2014, http://appleinsider.com/articles/14/03/24/itunes-and-app-sales-projected-to-grow-to-20-of-apple-profit-by-2020; also see Chuck Jones, Apple May Have Generated $60 Billion of Revenue in the December Quarter, Forbes, January 2014, http://www.forbes.com/sites/chuckjones/2014/01/17/apple-may-have-generated-60-billion-of-revenue-in-the-december-quarter/.

23. Shaughnessy, Rethinking the Value.

24. OECD, New Sources of Growth: Intangible Assets, updated September 2011, http://www.oecd.org/sti/inno/46349020.pdf; The OECD also reported that incorporating intangible assets (IA) into accounting is difficult in high IA countries but also that companies often fail to collect data on IA and hence also fail to report it. “In KPMG’s survey of non-executive directors (2003), more than 60% noted that they were not very knowledgeable about non-financial performance indicators due to the fact that information provided by executives is mainly financial; this situation does not appear to have evolved significantly.” OECD, Corporate Reporting of Intangible Assets: A Progress Report, April 2012, http://www.oecd.org/daf/ca/Intangible%20Assets.pdf.

25. Jon Udell, “Being Observable,” Slideshare.com, October 2010, http://www.slideshare.net/grlloyd/tug2010-being-observable-j-udell-13-oct2010; Jim McGee, “Doing and Managing Knowledge Work: TUG2010 Keynote Reflections,” (keynote speaker, Traction User Group, Newport, RI, October 2010), http://mcgeesmusings.net/2010/10/20/doing-and-managing-knowledge-work-tug2010-keynote-reflections/.

CHAPTER 5

1. Danny Sullivan, interview with History of Search Engine Optimization project, http://www.thehistoryofseo.com/seo-interviews/danny-sullivan/interview/.

2. “Do you need an SEO?,” Google.com, 2014, https://support.google.com/webmasters/answer/35291?hl=en.

3. Jeremy Liew, “Alltheweb and Alta Vista Better for General Web Search?,” Lightspeed Venture Partners, April 26, 2007, http://lsvp.wideumbrella.com/2007/04/26/alltheweb-and-altavista-better-than-google-for-general-search/.

4. Apple.com, “Apple Reports Fourth Quarter Results,” press release, October 28, 2013, http://www.apple.com/pr/library/2013/10/28Apple-Reports-Fourth-Quarter-Results.html.

5. “Apple Admits It Can’t Produce Enough of the iPhone 3Gs,” Digital Trends, July 23, 2009, http://www.digitaltrends.com/mobile/apple-admits-

it-cant-produce-enough-of-the-iphone-3gs/.

6. Quoted in Haydn Shaughnessy, “The Fluid Core,” 2014, http://haydnshaughnessy.com/the-fluid-core/.

7. In this account of Autodesk, I have relied on a discussion with Jordan Brandt, technology futurist at Autodesk, April 24, 2014, and Jeff Kowalski, CTO, November 4, 2013.

8. Interview with the author, September 10, 2014.

9. Interview with the author, September 26, 2014.

10. Information provided by Expedia. Interview with Ariana Gorin, June 24, 2014.

11. Reproduced with permission from Conor O’Neill: http://conoroneill.net/some-thoughts-on-twitter-rebuilding-a-developer-ecosystem-and-how-they-might-succeed?utm_source=dlvr.it&utm_medium=twitter

CHAPTER 6

1. Adam J. Brandenburger and Barry J. Nalebuff, Co-opetition (London: HarperCollinsBusiness, 1996).

2. Amit Deshpande and Dirk Riehle, “The Total Growth of Open Source,” (proceedings of the Fourth Conference on Open Source Systems [OSS 2008], Springer Verlag, 2008), http://dirkriehle.com/publications/2008-2/the-total-growth-of-open-source/.

3. See North Bridge Ventures and Black Duck, The Eighth Annual Future of Open Source Survey, 2014, https://www.blackducksoftware.com/future-

of-open-source.

4. Ibid.

5. PwC Cities of Opportunity 6; the remarks of the city Deputy mayor are quoted in Simon Phipps, “It’s Official: Open Source Is an Engine for Growth,” InfoWorld, October 2012.

6. Steven Poole, “What Does the Oculus Rift Backlash Tell Us? Facebook Just Isn’t Cool,” Guardian, March 27, 2014, http://www.theguardian.com/commentisfree/2014/mar/27/oculus-rift-facebook-buy-out-kickstarter.

7. James F. Moore, The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems (New York: HarperBusiness, 1996).

8. Marco Iansiti and Roy Levien, The Keystone Advantage: What the New Dynamics of Business Ecosystems Mean for Strategy, Innovation, and Sustainability (Boston, MA: Harvard Business School Press, 2004).

9. James F. Moore, Shared Purpose: A Thousand Business Ecosystems, A Connected Community, and the Future (CreateSpace Independent Publishing Platform: 2013).

10. Moore, Death of Competition, p. 26.

11. There are many accounts of the Internet Explorer/Firefox rivalry. Here is one contemporaneous news account: Penelope Patsurus, “Internet Explorer Vs. Firefox,” Forbes, October 4, 2004, http://www.forbes.com/2004/10/04/cx_pp_1004mondaymatchup.html.

12. Ostrom wrote about creative commons, along with Charlotte Hess, as the open-source and open-science movements began picking up momentum. Charlotte Hess and Elinor Ostrom, “Ideas, Artifacts, and Facilities: Information as a Common-Pool Resource,” Law and Contemporary Problems 66, no. 1 (Winter 2003): 111–146.

13. W3Techs, Usage Statistics and Market Share of WordPress for Websites, November 1, 2014, http://w3techs.com/technologies/details/cm-wordpress/all/all.

14. Matt Burns, “Google Finally Shows Off Google Glass UI, Announces #ifihadglass Purchase Campaign,” Techcrunch, February 2013, http://techcrunch.com/2013/02/20/google-finally-shows-of-google-glass-ui-announces-ifihadglass-purchase-campaign/.

15. David Pogue, “Epson Moverio BT 2000: Actually Useful Smart Glasses,” Yahoo! Tech, August 21, 2014, https://www.yahoo.com/tech/a-lot-of-people-make-fun-of-google-glass-some-of-95295566229.html.

16. Larry Dignan, “Apple’s App Store Economics: Average Paid App Sale Price Goes for $1.44,” ZD Net, July 11, 2011, http://www.zdnet.com/blog/btl/apples-app-store-economics-average-paid-app-sale-price-goes-for-1-44/52154; Erica Ogg, “The Average Price of an iPhone App Is 19 Cents—and It’ll Probably Keep Shrinking,” Gigaom, July 18, 2013, https://gigaom.com/2013/07/18/the-average-iphone-app-price-is-now-0-19-and-itll-probably-keep-shrinking/; Tristan Louis, “How Much Do Average Apps Make?,” Forbes, August 10, 2013, http://www.forbes.com/sites/tristanlouis/2013/08/10/how-much-do-average-apps-make/; Quoted in Neil Hughes, “iTunes and App Sales.”

17. Interview with the author, November 2011.

18. James Pearce, “Introducing TODO: Working Together to Make Open Source Easier,” (blog) Facebook, September 15, 2014, https://code.facebook.com/posts/1477886975799647/introducing-todo-working-together-to-make-open-source-easier/; Derrick Harris, “Facebook Launches an Open Source Organization and Releases New Routing Tech,” Gigaom, September, 15, 2014, https://gigaom.com/2014/09/15/facebook-launches-an-open-source-organization-and-releases-new-router-tech/.

19. Jon Brodkin, “How Red Hat Killed Its Core Product—and Became a Billion-Dollar Business,” Ars Technica February 28, 2012,

http://arstechnica.com/business/2012/02/how-red-hat-killed-its-core-

productand-became-a-billion-dollar-business/.

20. This section relies on Haydn Shaughnessy, “Recognizing the Ecosystem Phase-Change: A Guide to Four Types,” Strategy and Leadership 42, no. 1 (January 2014): 17–23.

21. Interviews with Amar Hanspal, Senior Vice President Platforms at Autodesk, September 26, 2013 and April 3, 2014.

22. Moore, Shared Purpose, p. 11.

23. Interview with Fabian Schlage, Head of Innovation Tools, Nokia Solutions and Networks, September 2013.

24. Paul Graham, “Do Things That Don’t Scale,” Paul Graham (blog), July 2013, http://paulgraham.com/ds.html.

25. David Zax, “How Amazon’s Mechanical Turk Fails Low-Income Workers, and How It Can Succeed,” Fast Company, December 30, 2010, http://www.fastcompany.com/1713300/how-amazon-mechanical-turk-fails-low-income-workers-and-how-it-can-succeed.

26. Jeff Bertolucci, “GE Contests Shoot for Innovation Via Crowdsourcing,” Information Week, April 3, 2013, http://www.informationweek.com

/big-data/big-data-analytics/ge-contests-shoot-for-innovation-via-crowdsourcing/d/d-id/1109369?.

27. Eric Von Hippel, Susumu Ogawa, and Jeroen P. J. de Jong, “The Age of the Consumer–Innovator,” MIT Sloan Management Review (Fall 2011): http://sloanreview.mit.edu/article/the-age-of-the-consumer-innovator/.

CHAPTER 7

1. Mark Sweney, “Guardian Publisher Cuts Annual Losses as Digital Revenues Grow by Nearly 30%,” Guardian, July 16, 2013, http://www.theguardian.com/media/2013/jul/16/guardian-cuts-annual-losses-digital-revenues. The Guardian lost over £30 million in 2013, which it considered a success after its previous year’s losses.

2. Lewis Dvorkin, “Inside Forbes: The Role of Pay and Traffic in Our Search for a New Media Equation,” Forbes, April 1, 2014, http://www.forbes.com/sites/lewisdvorkin/2014/04/01/inside-forbes-the-role-of-pay-and-traffic-in-our-search-for-a-new-media-equation/.

3. Craig Burton, “Is API Growth in a Stall?,” Kuppinger Cole Analysts (blog), May 17, 2012, http://blogs.kuppingercole.com/burton/2012/05/17/is-api-growth-in-a-stall; Chart by Craig Burton and Phil Windley, Projected Open API Growth Rate.

4. Interview with the author, April 25, 2014.

5. Interview with the author, November 2012 and May 2014.

6. These issues are now being discussed in the context of personal data violations by large companies, security breaches, and consumer rights but also the declining effectiveness of online ads and contextual advertising. See “Personal Information Management Services—An Analysis of an Emerging Market,” Ctrl Shift, June 16, 2014, https://www.ctrl-shift.co.uk/research/product/90, and Haydn Shaughnessy, “Google and Facebook Hedge against the Data Backlash,” Forbes, March 30, 2014, http://www.forbes.com/sites/haydnshaughnessy/2014/03/30/google-and-facebook-hedge-against-the-data-backlash/.

CHAPTER 8

1. Figures quoted in David P. Anderson, Jeff Cobb, Eric Korpela, Matt Lebofsky, and Dan Werthimer, “SETI@home: An Experiment in Public-Resource Computing,” Communications of the ACM 45, no. 11: pp. 56–61.

2. Amazon began using the figure in its press releases and online collateral in 2007. See http://affiliate-blog.amazon.co.uk/2007/02/post.html.

3. See Tony Kontzer, “Amazon’s E-Commerce Technology Is On Target,” InformationWeek, August 16, 2002, http://www.informationweek.com/amazons-e-commerce-technology-is-on-target/d/d-id/1015979?.

4. Nikolaj Nielsen, “Snowden Scandal to Cost US Cloud Companies Billions,” EU Observer, January 20, 2014, http://euobserver.com/justice/122774.

5. E-mail interview with the author, November 2013.

6. Jennifer Van Grove, “Entrepreneurs Take Over at Live Nation, Launch New Fund to Finance Innovation,” VentureBeat, March 14, 2012, http://venturebeat.com/2012/03/14/live-nation-labs/.

7. See as an example, Rob Spieldenner, “Continuous Delivery at Netflix: Speed at Scale,” SlideShare presentation, December 23, 2013, http://www.slideshare.net/robspieldenner/continuous-delivery-at-netflix.

8. Paul Muller, Vice President of HP, e-mail correspondence with the author, April 22, 2014.

9. Alberto Prado, Vice President Digital Innovation at Philips Consumer Lifestyle, talked with us about these difficulties at consumer electronics giant Philips, interview with the author, November 20, 2013.

10. Shaughnessy, “Growing Adaptive Innovation.”

11. Prado, interview with the author.

SHIFT 3 INTRODUCTION

1. Michael E. Porter and Mark R. Kramer, “Creating Shared Value,” Harvard Business Review, January 2011, http://hbr.org/2011/01/the-big-idea-creating-shared-value.

2. Clayton M. Christensen and Derek van Bever, “The Capitalist’s Dilemma,” Harvard Business Review, June 2014, http://hbr.org/2014/06/the-capitalists-dilemma.

3. Steve Denning, “The Dumbest Idea in the World: Maximizing Shareholder Value,” Forbes, November 28, 2011, http://www.forbes.com

/sites/stevedenning/2011/11/28/maximizing-shareholder-value-the-

dumbest-idea-in-the-world/.

4. As a general rule wealth grows faster than economic output, he explains, a concept he captures in the expression r > g (where r is the rate of return to wealth and g is the economic growth rate). Other things being equal, faster economic growth will diminish the importance of wealth in a society, whereas slower growth will increase it (and demographic change that slows global growth will make capital more dominant). But there are no natural forces pushing against the steady concentration of wealth. The Economist explains Thomas Piketty’s “Capital,” summarised in four paragraphs May 4, 2014, http://www.economist.com/blogs/economist-explains/2014/05/economist-explains.

5. An excellent account of cartels and competition can be found in Jeffrey Fear, “Cartels and Competition, Neither Markets Nor Hierarchies,” (working paper, Harvard Business Review, 2006); Fear points out: “Cartels are not necessarily the opposite of liberalism and competition, but a variation on them. For better or for worse, they shaped economic and business history since the late 19th century. From the company perspective, joining, managing, or combatting cartels was a major entrepreneurial act.” See also Peter Thiel, “Competition Is for Losers,” Wall Street Journal, September 12, 2014, http://online.wsj.com/articles/peter-thiel-competition-is-for-losers-1410535536. Thiel was a co-founder of PayPal and backed Facebook from its early days.

6. For a summary of this argument, see Mai Fujita, “The Japanese and Chinese Models of Industrial Organization: Fighting for Supremacy in the Vietnamese Motorcycle Industry,” (discussion paper, IDE-JETRO, June 2013), http://www.ide.go.jp/English/Publish/Download/Dp/420.html.

7. For the background on this movement, see “Business Process Re-engineering,” Economist, February 16, 2009, http://www.economist.com/node/13130298 ; see also “Michael Hammer,” Economist, February 20, 2009, http://www.economist.com/node/13130340. Hammer was the father of BPO. The Economist says, “So popular was re-engineering that one survey in the 1990s showed it to have been adopted by almost 80% of Fortune 500 companies. It was often blamed for the widespread lay-offs that became part of almost every company’s radical redesign at that time.”

8. William Lazonick, Mariana Mazzucato, and Öner Tulum, “Apple’s Changing Business Model: What Should the World’s Richest Company Do with All Those Profits?” (working paper, The Academic Industry Research Network #13, Cambridge, MA, July 1, 2013).

CHAPTER 9

1. Jeremy Rifkin, The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism (New York: Palgrave Macmillan, 2014).

2. Cooper Smith, “Facebook Users Are Uploading 350 Million Photos Each Day,” Business Insider, September 18, 2013, http://www.businessinsider.com/facebook-350-million-photos-each-day-2013-9; Photo stock library Shutterstock employs less than 300 people, is valued at over $2 billion, and supplies photos to websites around the world. “What began ten years ago with one $1,000 Canon Rebel and a 600-square-foot office has exploded into a platform that adds 20,000 new photos a day from 40,000 contributors in more than 100 nations.” Steven Bertoni, “Silicon Alley’s First Billionaire Aims to Dominate Images on the Web,” Forbes, October 9, 2013, http://www.forbes.com/sites/stevenbertoni/2013/10/09/silicon-alleys-first-billionaire-aims-to-dominate-images-on-web/.

3. Michael Goldharber, “The Attention Economy and the Net,” First Monday 2, no. 4 (April 7, 1997): http://firstmonday.org/article/view/519/440#dep13.

4. Don Tapscott, Anthony D. Williams, and Alan Sklar, Wikinomics: How Mass Collaboration Changes Everything (Old Saybrook, CT: Tantor, 2007); Rachel Botsman and Roo Rogers, What’s Mine Is Yours: The Rise of Collaborative Consumption (New York: Harper Business, 2010); Lisa Gansky, The Mesh: Why the Future of Business Is Sharing (New York: Portfolio Penguin, 2012).

5. Michael Bauwens, Nicolas Mendoza, and Franco Iacomella et al., “Synthetic Overview of the Collaborative Economy,” Orange Labs and P2P Foundation, 2012, http://p2pfoundation.net/Synthetic_Overview_of_the_Collaborative_Economy.

6. Courtney Boyd Meyers, “How Much Does It Cost to Build the World’s Hottest Startups?,” Next Web, December 2, 2013, http://thenextweb.com/dd/2013/12/02/much-cost-build-worlds-hottest-startups/.

7. Erik Brynjolfsson and Andrew McAfee, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (New York: W. W. Norton and Company, 2014).

8. Mark Scott, “Real Estate Losses Weigh on Santander,” New York Times, January 31, 2013, http://dealbook.nytimes.com/2013/01/31/santanders-profit-hit-by-real-estate-concerns/?_php=true&_type=blogs&_r=0. The bank has also been surrounded by legal problems (Mark Scott and Raphael Minder, “Confronted by Renewed Legal Problems, Chief of Santander Resigns,” New York Times, April 29, 2013, http://dealbook.nytimes.com/2013/04/29/santanders-chief-executive-resigns/) and had problems in managing its widely dispersed assets (Paul Murphy, 46 hours later, Financial Times, September 26, 2014, http://ftalphaville.ft.com/2014/09/26/1984512/46-hours-later/); customer relations have also suffered (Patrick Collinson, “Is Santander Britain’s Worst Bank?,” Guardian, February 25, 2011, http://www.theguardian.com/money/2011/feb/26/santander-britains-worst-bank).

9. Both of these points are made by Robert Fabricant, Why Is an App Worth as Much as a Small Oil Field?, Harvard Business Review, February 24, 2014, http://blogs.hbr.org/2014/02/why-is-an-app-worth-as-much-as-a-small-oil-field/.

10. Peter K., “Xiaomi Joins Apple and Samsung in the List of Best-Selling Devices for February 2014,” PhoneArena.com, April 4, 2014, http://www.phonearena.com/news/Xiaomi-joins-Apple-and-Samsung-in-the-list-of-best-selling-devices-for-February-2014_id54779. Xiaomi overtook Samsung in sales in China in Q2 2014.

11. In the smartphone market, iOS and Android phones dominate sales and profitability, but Xiaomi and Lenovo are becoming high-growth device manufacturers because they create high-quality phones at low cost. Microsoft’s Windows 8 was growing during 2012 and 2013 but suffered a setback in Q2 2014 as Xiaomi and Lenovo spurted. Tom Warren, “Windows Phone Sales Decline as Share Shrinks to Just 2.5 Percent,” The Verge, August 14, 2014, http://www.theverge.com/2014/8/14/6003427/windows-phone-sales-decline-q2-2014-idc.

12. Kasia Moreno, “Why Smart CEOs—Whatever Their Industry—Compete

with Uber and Amazon,” Forbes, August 13, 2014, http://www.forbes.com

/sites/forbesinsights/2014/08/13/why-smart-ceos-whatever-their-industry

-compete-with-uber-and-amazon/.

13. Nielsen, The U.S. Digital Consumer Report, February 10, 2014, http://www.nielsen.com/content/corporate/us/en/insights/reports/2014/the-us-digital-consumer-report.html.

CHAPTER 10

1. OECD, Withholding and Information Reporting Regimes for Small/Medium-Sized Businesses and Self-Employed Taxpayers, August 2009, http://www.oecd.org/tax/administration/48449751.pdf.

2. Brynjolfsson and McAfee, The Second Machine Age.

3. The Linux open-source ecosystem is an exception to the open-source culture with 75% of its participants now on salary.

4. Justin Fox, “The Freelance Economy Still Runs on Word of Mouth,” (blog), Harvard Business Review, October 2014, http://blogs.hbr.org/2014/10/the-freelance-economy-still-runs-on-word-of-mouth/.

5. Sarah O’Connor, “UK Evolving into Self-Employment Capital of Western Europe,” Financial Times, August 12, 2014, http://www.ft.com/intl/cms/s/0/dc0c1d2e-216f-11e4-b145-00144feabdc0.html#axzz3HZ6O08r8.

6. Businessweek suggests they also work harder, earn less, and are more stressed than their employed counterparts. Scott Shane, “The Job Satisfaction Paradox for the Self-Employed,” Bloomberg Businessweek, April 1, 2010, http://www.businessweek.com/smallbiz/content/apr2010/sb2010041_151187.htm.

7. Stephan J. Goetz, David A. Fleming, and Anil Rupasingha, “The Economic Impacts of Self-Employment,” Journal of Agricultural and Applied Economics 44, no. 3 (August 2012): 315–321.

8. Katie Allen, “Bank of England Chief Economist ‘Gloomier’ about UK Prospects,” Guardian, October 17, 2014, http://www.theguardian.com/business/2014/oct/17/bank-england-chief-economist-gloomier-interest-rates-andrew-haldane.

9. David Leonhardt and Kevin Quealy, “The American Middle Class Is No Longer the World’s Richest,” New York Times, April 22, 2014, http://www.nytimes.com/2014/04/23/upshot/the-american-middle-class-is-no-longer-the-worlds-richest.html?abt=0002&abg=1; America’s middle class has “endured a lost decade for economic well-being. Since 2000, the middle class has shrunk in size, fallen backward in income and wealth, and shed some—but by no means all—of its characteristic faith in the future.” Pew Research Center, The Lost Decade of the Middle Class: Fewer, Poorer, Gloomier, August 22, 2012, http://www.pewsocialtrends.org/2012/08/22/the-lost-decade-of-the-middle-class/.

10. MBO Partners, 2013 MBO Partners State of Independence Report, September 2013, http://www.mbopartners.com/state-of-independence/index.html.

11. Mapping the State of the Crowdfunding Nation, The Crowdfunding Centre, May 2014, http://thecrowdfundingcentre.com/?page=datacenter#report|page/report/?.

12. Marc McDonald, “Which Social Media Platforms Drive the Most Sales?,” Shopify.com, March 10, 2014, http://www.shopify.com/blog/12731545-which-social-media-platforms-drive-the-most-sales-infographic; Facebook announced its figures at Dreamforce 2013, http://www.salesforce.com/dreamforce/DF14/.

13. Eurostat News Release, “Government Debt Increased to 93.9% of GDP in Euro Area and to 88.0% in EU28,” news release, July 22, 2014, http://epp.eurostat.ec.europa.eu/cache/ITY_PUBLIC/2-22072014-AP/EN/2-22072014-AP-EN.PDF.

14. Matthew Lawrence, “Red Ink Rising: How Worrying Is the UK Economy’s Total Debt Burden?” UK Economic Outlook (blog), PwC, November, 2010, http://pwc.blogs.com/files/economic-outlook_report-november-2010.pdf. To quote, “Our analysis has shown that the total burden of debt on the UK economy rose from around twice national income in 1987 to around three times national income in 2000, but then accelerated much more rapidly to around 5.4 times national income by 2009.”

15. OECD, OECD Reviews of Innovation Policy: Netherlands 2014 (Paris: OECD Publishing, 2014), p. 201.

16. Bureau of Labor Statistics, United States Department of Labor, Labor Force Projections to 2022: The Labor Force Participation Rate Continues to Fall, December 2013, http://www.bls.gov/opub/mlr/2013/article/labor-force-projections-to-2022-the-labor-force-participation-rate-continues-to-fall.htm.

17. Jeremy Ashkenas and Alicia Parlapiano, “How the Recession Reshaped the Economy, in 255 Charts,” New York Times, June 6, 2014, http://www.nytimes.com/interactive/2014/06/05/upshot/how-the-recession-reshaped-the-economy-in-255-charts.html?_r=0&abt=0002&abg=1.

18. Hal Salzman, B. Lindsay Lowell, and Daniel Kuehn, “The Bogus High-Tech Worker Shortage: How Guest Workers Lower US Wages,” PBS Newshour, July 24, 2013, http://www.pbs.org/newshour/making-sense/the-bogus-high-tech-worker-sho/.

19. Interview with the author, October 2012.

20. Richard Waters, “Mounting Cash Piles an Embarrassment of Riches for Tech Companies,” Financial Times, January 21, 2014, http://www.ft.com/intl/cms/s/0/63e60612-827c-11e3-8119-00144feab7de.html#axzz3HZ6O08r8.

21. Gartner, “Gartner Says Worldwide IT Spending on Pace to Grow 2.1 Percent in 2014,” news release, June 30, 2014, http://www.gartner.com/newsroom/id/2783517; Gartner, “Gartner Says Middle East IT Spending to Reach $211 Billion in 2014,” news release, April 1, 2014, http://www.gartner.com/newsroom/id/2696917.

CHAPTER 11

1. Steven Levy, “A Master Class on Google,” Backchannel (blog), October 9, 2014, https://medium.com/backchannel/google-motorola-was-a-win-we-couldve-won-with-groupon-wed-never-do-what-amazon-is-doing-a17f27b3aec0. “We don’t expect all the acquisitions to be successful. We do expect the acquisitions to give us options and some of those options really paid off very, very well, like Double Click and YouTube. If the consequence of the Motorola buy-sell is that Lenovo ends up being a global Android platform and brings prices down, you could argue that on a strategic basis it was a very smart move. I believe financially it was a good move independent of any other fact, right?”

2. Charles Handy, The Empty Raincoat: Making Sense of the Future (London: Hutchinson, 1994).

3. Mikael Collan, The Pay-Off Method, Re-Inventing Investment Analysis (CreateSpace Independent Publishing Platform, July 2012).

4. For a more detailed examination of real options and their use in enterprises, see Johnathan Mun, Real Options Analysis: Tools and Techniques for Valuing Strategic Investment and Decisions, 2nd ed. (New Jersey: John Wiley & Sons, Inc., 2006). For an illustrative case study, see Tom Arnold and Richard L. Shockley, Jr., “Value Creation at Anheuser-Busch: A Real Options Example,” Journal of Applied Corporate Finance 14, no. 2 (Summer 2001): 52–61.

5. Dan Graziano, “Samsung Successfully Tests 5G Wireless Data Service at Gigabit Speeds,” Yahoo! News, May 13, 2013, http://news.yahoo.com/samsung-successfully-tests-5g-wireless-data-gigabit-speeds-224516822.html.

6. Yu, “Former CEO Admits Errors.” Rifkin, Zero Marginal Cost; Botsman and Rogers, What’s Mine Is Yours.

7. Nick Vitalari and Haydn Shaughnessy, The Elastic Enterprise: The New Manifesto for Business Revolution (Longboat Key, FL: Telemachus Press, 2012). Nick Vitalari and I discussed the active strategy component in creating and managing options.

8. Laura Locke, “The Future of Facebook,” Time, July 17, 2007, http://content.time.com/time/business/article/0,8599,1644040,00.html.

9. Examples are taken from Netflix Conference: “Women in Technology Meetup at Netflix!,” Netflix Tech Blog, Netflix, March 27, 2014, http://techblog.netflix.com/2014/04/women-in-technology-meetup-at-netflix.html.

10. Shaughnessy, “Growing Adaptive Innovation.”

11. Interview with Colin Nelson, Director of Strategy, Hype Innovation, April 11, 2014. Hype has a global customer base and regularly analyzes the use of its platform among them.

12. Interview with Michael Harries and Devon Crews, Citrix Start-up Accelerator; see also http://citrixstartupaccelerator.com/.

CHAPTER 12

1. Cusumano and Gawer, “Elements of Platform Leadership.”

2. Interview with the author at f.ounders.com, October 2011.

3. Porter and Kramer, “Creating Shared Value.”

4. Philip E. Tetlock, Expert Political Judgment: How Good Is It? How Can We Know? (Princeton, NJ: Princeton University Press, 2006).

5. Pierre Levy, Collective Intelligence: Mankind’s Emerging World in Cyberspace (Cambridge, MA: Perseus Books, 1999).

6. The Pay-Off Method: Re-Inventing Investment Analysis – With numerical application examples from different industries by Mikael Collan, 2012; See also, http://payoffmethod.com/

7. The preceding paragraphs rely heavily on discussions with Professor Mikael Collan.

8. The work on emotional decision making can be traced to neuroscientist Antonio D’Amasio, Descartes’ Error: Emotion, Reason, and the Human Brain (New York: Putnam, 1994), and was later picked up by David Brooks, The Social Animal: The Hidden Sources of Love, Character, and Achievement (New York: Random House, 2011).

9. Discovery-based planning was proposed nearly twenty years ago but more as a set of personal management disciplines than a methodology. See Rita Gunther McGrath and Ian C. MacMillan, Discovery Driven Planning: Turning Conventional Planning on its Head (Philadelphia, PA: Snider Entrepreneurial Center, Wharton School, 1999).

CHAPTER 13

1. Dan Sabbagh, “Bloggers Take Legal Action over Huffington Post Sale,” Guardian, April 14, 2011, http://www.theguardian.com/media/2011/apr/12/arianna-huffington-post-sale; Todd Wasserman, “1 Month Ago, the WhatsApp CEO Promised He Wouldn’t Sell,” Mashable, February 21, 2014, http://mashable.com/2014/02/21/whatapp-jan-koum-not-sell/.

2. “Frequently Asked Questions,” Bitcoin, accessed November 3, 2014, https://bitcoin.org/en/faq.

3. The first block in Bitcoin’s block chain (the ‘genesis block’) includes the text: The Times 03/Jan/2009 Chancellor on brink of second bailout for banks in reference to a newspaper article from that day (Duncan and Elliott (2009)), presumably in order both to demonstrate that it could not have been created before that date and to highlight the conceptual distinction between Bitcoin and the structure of modern monetary economies. Robleh Ali, John Barrdear, Roger Clews and James Southgate Innovations In Payment Technologies and The Emergence of Digital Currencies, Bank of England Quarterly Bulletin , Q3 2014

4. “What is Ripple?,” Ripple, accessed November 3, 2014, https://ripple.com/about-ripple.

5. Haydn Shaughnessy, “The Revolution Hidden in the Apple Health Kit,” Forbes, June 17, 2014, http://www.forbes.com

/sites/haydnshaughnessy/2014/06/17the-revolution-hidden-in-the-apple-health-kit/.

6. Haydn Shaughnessy, “The Future Business Platform—You,” Forbes, June 30, 2014; Shaughnessy, “Facebook and Google Hedge”; Even the World Economic Forum has a personal data economy initiative: http://www.weforum.org/issues/rethinking-personal-data.

7. Randall Stross, “Smartphone Ads and Their Drawbacks,” New York Times, September 15, 2012, http://www.nytimes.com/2012/09/16/technology/smartphone-ads-and-their-drawbacks-digital-domain.html?_r=0. Brian Boland, Facebook Exec, “Cookies Don’t Cut It Anymore for Online Ad Measurement,” Advertising Age, March 21, 2014, http://adage.com/article/digitalnext/cookies-cut-anymore-online-ad-measurement/292225/.

8. Shaughnessy, “Facebook and Google Hedge.”

9. Under its healthymagination program, a program that tries to do in health what GE achieved in environmental industries with ecomagination.

10. Patients routinely have priorities that surprise professionals in health care, according to Helen Bevan, Director of the NHS Horizons program in the UK. Interview with the author, November 13, 2013.

11. See Cancer Research, UK, http://scienceblog.cancerresearchuk

.org/2014/05/23/big-data-media-buzzword-or-breakthrough-in-waiting/.

12. Speaking off the record, a senior Otsuka executive acknowledged that it will take the development of new ecosystems to make this work.

13. See Aaron Saenz, “Making the Modern Do It Yourself Biology Laboratory,” SingularityHub, August 3, 2010, http://singularityhub.com/2010/08/03/making-the-modern-do-it-yourself-biology-laboratory-video/; John Brunstein, “The Quest for the $500 Home Molecular Biology Lab,” MLO, December 2011, http://www.mlo-online.com/articles/201112/the-quest-for-the-500-home-molecular-biology-laboratory.php.

14. Various interviews with the author, 2014.

15. Porter, Competitive Advantage.

16. Michael E. Porter, “Clusters and the New Economics of Competition,” Harvard Business Review, November 1998, http://hbr.org/1998/11/clusters-and-the-new-economics-of-competition/.

17. “The Faded Colors of Benetton,” Businessweek, April 9, 1995, http://www.businessweek.com/stories/1995-04-09/the-faded-

colors-of-benetton.

18. Oxford Research, Cluster Policy in Europe: A Brief Summary of Cluster Policies in 31 European Countries, January 2008, http://www.clusterobservatory.eu/system/modules/com.gridnine.opencms.modules.eco/providers/getpdf.jsp?uid=100146.

AFTERWORD

1. See Tim Wu, “The Oligopoly Problem,” New Yorker, April 15, 2013, http://www.newyorker.com/tech/elements/the-oligopoly-problem.

Acknowledgments

My co-author on The Elastic Enterprise, Nick Vitalari, has supplied his thirty-plus years of experience in business research and analysis at many points in my work since we got to know each other. Nick is the foremost expert in business platforms. Special mention also goes to Peter Vander Auwera, at SWIFT, and additional thanks to Fabian Vandenreydt. SWIFT’s support has been an invaluable source of research resources, and Peter is a source of personal support. Stacy Ennis has organized the team behind the book and made it happen in practical terms, through the whole editing process and publishing. Robin Bethel’s help on structure was invaluable as was Kim Foster’s copy editing. Kathy McIntosh at AWellPlacedWord.com provided great support with final copy. Rachel Langaker and Shiloh Schroeder at Fusion Creative Works did some phenomenal work to make the text look good and read well. All errors are of course mine. Thanks also Sarah Tregay at DesignWorksCreative.com for cover design and extreme patience, and to Kevin Mullani, my publishing partner.

The senior management at Ericsson gave me an opportunity to listen in on their media strategy and talked to me afterward about their vision of the future. Thanks to Hans Vestberg, Ulf Ewaldsson, and Daisy Dunkley-Clark for arranging. Steve Denning has been a great champion, encouraging my ideas in a way that really kept me at the desk, writing. Sean Belka at Fidelity has been generous with his time and insights, and thanks also to Steve Austin at Fidelity. Thanks go to Ben Pring and Paul Roehrig, Co-Directors of the Center for the Future of Work, at Cognizant who kindly provided funding for thirty of the interviews undertaken in the preparation of this book. Both engaged in invaluable dialogue. Michael Hussey has provided mountains of data from his StatSocial analytics database. Kudos, too, to Bryan Kirschner, Director at the Apigee Institute, another important collaborator and funder. David Card at Gigaom Research has been a source of wisdom, ideas, and … funding, too. Mona Vernon of Thomson-Reuters provided invaluable encouragement via Twitter. Lee Williams has been a particularly valuable interviewee and, in the past, colleague. Conor O’Neill gave content generously and has always supplied insights in my various discussions with him.

Thanks also, among others, for interview time to Jon Bidwell, SVP at Chubb Insurance; Rick Kreifeldt, VP of Research and Innovation at Harman; Andre Durand, CEO at Ping Identity; Matt Flanagan, FAMA PR; Patrick Reynolds, Chief Strategy Officer at Triton Digital; Declan Moore, President at National Geographic Digital; Yuvi Kochar, at the time, CIO of The Washington Post Company; Lewis DVorkin, Chief Product Officer at Forbes; Martin Ertl, Head of Innovation at Bombardier Transportation; Aaron Levie, CEO at Box; John Donovan, Senior Executive Vice President at AT&T; Fabian Schlage, Head of Idea and Innovation Management at Nokia Networks and Systems; Magnus Karlsson, Head of Innovation at Ericsson; Jaspar Roos, formerly of ABN AMRO Bank; Subroto Gupta, VP of Digital Marketing & Unified Collaboration at Genpact; Amar Hanspal, SVP at Autodesk; and Jeff Kowalski, CTO at Autodesk.

About the Author

Haydn Shaughnessy trained as an economic historian at the London School of Economics and Oxford University. He is a research fellow at the University of California, Irvine, and a former fellow of the Society for New Communications Research. He was a visiting fellow at Genera Insight, a senior research associate of the European Institute for the Media, and the Centre for Employment Research, at Manchester Victoria University and Manchester Metropolitan University respectively. He worked as a program manager for advanced communications projects at the European Union in the early 1990s where he wrote about 3G, Fiber to the Home and Advanced Electronic learning environments. He was a partner at The Conversation Group, the first social agency, and has advised major companies and government departments in the Netherlands, Sweden and the European Union. He wrote the “Re: Thinking Innovation” column at Forbes and has written on innovation for the Irish Times. He has also written for Gigaom, the Wall St. Journal, the Times, Radio Times, and many others.