the innovator's manifesto by michael e. raynor - excerpt

8/6/2019 The Innovator's Manifesto by Michael E. Raynor - Excerpt

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Disclaimer: This work contains general information only and is not intended to beconstrued as rendering accounting, business, nancial investment, legal, tax, or other

professional advice or services. This work is not a substitute for such professionaladvice or services, nor should it be used as a basis for any decision or action that may

affect your business. The author and publisher disclaim any liability, loss, or risk that isincurred as a consequence of the use and application of any of the contents of this work.

Copyright 2011 by Michael E. RaynorForeword copyright 2011 by Clayton M. Christensen

All rights reserved.

Published in the United States by Crown Business, an imprint of theCrown Publishing Group, a division of Random House, Inc., New York.

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CROWN BUSINESS is a trademark and CROWN and the Rising Sun colophonare registered trademarks of Random House, Inc.

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Library of Congress Cataloging-in-Publication DataRaynor, Michael E.

The innovators manifesto : deliberate disruption for transformational growth /Michael E. Raynor.1st ed.

p. cm.Includes index.

1. Disruptive technologies. 2. Creative ability in business.3. Success in business. I. Title.

HD45.R296 2011658.4063dc22 2010052634

ISBN 978-0- 385-53166-5eISBN 978-0- 385-53167-2

Printed in the United States of America

Book design and illustrations by Bob Bull Jacket design by Jean Traina

10 9 8 7 6 5 4 3 2 1


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F O R E W O R D

FROM ARTTO SCIENCE

by Clayton M. Christensen

I n this fascinating book, Michael Raynor tells us that the world ofinvesting to create successful businesses is about to change. Justas theories in the world of biology or physics have allowed us to pre-dictably create desired outcomes in medicine or engineering, Raynorshows here that Disruption promises much greater predictability inthe realm of creating successful new businesses. Raynor shows usthat there are certain technologies and strategies that succeed much

more often than others. He shows us what they are, why they work,and how to apply them. Science at least in this one instance trulyis making a difference in the practice of management.

The ultimate signicance of The Innovators Manifesto will be re- vealed only over time. I, however, have high hopes for its longevity andimpact because Raynors work falls very neatly into a well- establishedpattern for the transformation of tacit, intuitive knowledge art, if

you willinto codied, well-understood, explicit rules in otherwords, science. I believe that Raynor is playing a central role in trans-forming the management of innovation from an art to a science. Thiswill truly be a landmark work.

To see the signicance of this contribution, consider that in theearly stages of any eld, our collective knowledge is little more thanan assortment of observations collected over many generations.
http://www.randomhouse.com/crown


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vi ii FOR EWORD

There are many unknowns, and so the work is complex and intuitive,and the outcomes are relatively unpredictable. Only skilled expertsare able to cobble together adequate solutions, and their work pro-ceeds through intuitive trial- and-error experimentation. This type ofproblem-solving process can be costly and time- consuming, but thereis little alternative when our knowledge is still in its infancy.

Creating new, successful innovations still looks very much likethis today. Investment decisions and strategic choices are typicallybased on intuition; learning, if it happens at all, is a very expensiveby-product of trial and error. Entrepreneurs and new venture investors

alike live a perpetual contradiction, convinced on a case- by-case basisthat the venture they have just launched will succeed, even as theycannot escape the fact that 90 percent of all new ventures includingtheirs ultimately fail. In such a world, we can make no clear con-nection among the attributes of the new business, the oversight pro-

vided by the investors, the management methods of the leadershipteam, and nal outcomes. That makes it very hard to learn how tosucceed at innovation.

In the face of this uncertainty, some widely accepted rules ofthumb have emerged. For example, a mantra for most venture cap-italists is that it is folly to make investment decisions based upon

the start- ups technology or strategy. The VCs have concluded fromtheir trials and errors that even they the best in the world cannotpredict in advance whether the technology or strategy described ina start-ups business plan will actually work. As a result, they typi-cally assessintuitivelywhether the management team has theintuition to succeed. If members of the team are experienced andperceptive, the VCs reason, they can develop the right technologyand the right strategy because they and only they will have the in-stinct to change direction when needed. As far as affecting outcomesin a meaningful and predictable way, however, this approach ranks upthere with feed a cold, starve a fever. It is little more than an apho-

rism based on selective memory, the force of repetition, and the hopethat at least it does no harm.

Getting beyond myth requires that we rst carefully documentpatterns that repeat over time. This does not provide any guarantee of


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FOREWORD ix

success, but it does provide at least some condence that there is acorrelation among factors of interest. Ultimately these patterns of cor-relation are supplemented with an understanding of causality, whichmakes the results of given actions much more predictable. Work thatwas once intuitive and complex becomes routine, and specic rulesare eventually developed to handle the steps in the process. Abilitiesthat previously resided in the intuition of a select group of experts ul-timately become so explicitly teachable that rules- based work can beperformed by people with much less experience and training.

To illustrate, consider the evolution of medical science. At its core,

the problem in medicine historically is that the human body has a very limited vocabulary from which it can draw when it needs to de-clare the presence of disease. Fever, for example, is one of the wordsthrough which the body declares that something inside isnt quiteright. The fever isnt the disease, of course. It is a symptomatic man-ifestation of a variety of possible underlying diseases, which couldrange from an ear infection to Hodgkins lymphoma. Medications thatameliorate the fever dont cure the disease. And a therapy that ad-dresses one of the diseases that has fever as a symptom (as ampicillincan cure an ear infection) may not adequately cure many of the otherdiseases that also happen to declare their presences with a fever.

As scientists work to decipher the bodys limited vocabulary, theyare teaching us that many of the things we thought were diseasesactually are not. Theyre symptoms. For example, we have learnedthat hypertension is like a fever it is a symptomatic expression ofa number of distinctly different diseases. There are many more dis-eases than the number of physical symptoms that are available, sothe diseases end up having to share symptoms. One reason why atherapy that effectively reduces the blood pressure of one patientis ineffective in another may be that they have different diseasesthat share the same symptom. When we cannot properly diagnosethe underlying disease, effective care generally can be provided only

through the intuition and experience of highly trained (and expen-sive) caregiversmedicines equivalent of Warren Buffett.

At the other end of the spectrum, we dene precision medicine asthe provision of care for diseases that can be precisely diagnosed and


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x FOR EWORD

for which the underlying causes are understood. This makes it possi-ble to develop a predictably effective therapy. In these circumstances,caregivers such as nurses and technicians can give effective care andat lower cost than is possible today by the best clinicians. Most infec-tious diseases live here: we have dispositive tests for their presenceand well-understood and highly effective treatments for their cure.

We can all but guarantee an outcome for an individual; exceptions arerare and noteworthy.

Not all of medicine falls into the intuitive or precision cate-gory, however. There is a broad domain in the middle called empiri-

cal medicine. The diagnosis and treatment of a pathology falls intothis third category when a eld has an incomplete but still very valu-able set of causal models and validated patterns. The connectionsbetween actions and outcomes are consistent enough that resultscan be usefully, if imperfectly, predicted. When we read statementslike 98 percent of patients whose hernias were repaired with thisprocedure expe rienced no recurrence within ve years, compared to90 percent for the other method, were in the realm of empiricalmedicine. Empirical medicine enables caregivers to follow the odds.They can generally guarantee the probabilistic outcome only for apopulation.

What makes The Innovators Manifestoso signicant is that it isperhaps the rst and in my view the most signicant and successfuleffort yet to move the eld of innovation from the intuitive stage intothe world of empirical management. Building upon groundbreakingresearch at Intel Corporation, Raynor has quantied the improve-ments in predictive accuracy and survival rates that are possiblethrough the careful application of Disruption to early- stage busi-nesses. He has elaborated upon particular elements of Disruption inways that make clear when and how the theory can be applied. Andhe has provided frameworks for its application that will enable mostany business to reap the benets that Disruption makes possible.

Achieving such an outcome means that this is not your typicalmanagement book. There are no just- so stories attributing the suc-cess of the latest bottle rocket to a new buzzword. Instead, you will


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FOREWORD xi

nd the careful collection of real data, considered and circumspectanalysis that recognizes shortcomings without being paralyzed bythem, a rigorous and reective treatment of some of the chestnutsof popular management thinking, and a genuine appreciation for thechallenges of applying real theory in the real world. You will haveto read this book carefully and reect upon it deeply. But it will beworth it.

As I have said elsewhere, my admiration for Michael Raynor hasno end. The integrity of Disruption theory has improved substan-tially since Michael and I coauthored The Innovators Solution, and

much of that improvement I attribute to my continued collaborationwith him. I love just to sit in his presence and listen as his magni-cent mind goes to work on the complicated puzzles of management.Though I have a busy life, for Michael Raynor I always have time. Ihope that you will enjoy being with him as you read this book.

Clayton M. Christensen is the Robert and Jane Cizik Professor ofBusiness Administration at the Harvard Business School in Bos-ton, Massachusetts.


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P R O L O G U E

THE FIVE-PERCENTAGE-

POINT SOLUTION

D isruption, used in a technical sense, is a theory ofinnovationof how particular types of new products andservices, or solutions, come to achieve success or dominance inmarkets, often at the expense of incumbent providers. Disruption wasdiscovered by Clayton Christensen, a professor at the Harvard Busi-ness School, in 1992 when he was a doctoral student there. (Whenusing disruption or its cognates in a technical sense I will use an

uppercase D.) Christensens 1997 best- selling book, The InnovatorsDilemma, was the rst popular expression of his ideas. Christensenand I collaborated on The Innovators Solution, published in 2003. Atleast seven more books and hundreds of articles have been publishedsince then exploring the theorys implications in different contexts. 1 It is in widespread use as an organizing principle for innovation atorganizations around the world. Many who have used it have creditedit with a signicant role in creating successful new businesses.

And yet, thanks to the confusing world of applied managementresearch, Disruption is still seen by many as just another theory.One new book after another cascades into the marketplace of ideas,

attempting to explain the latest success story or allegedly revolution-ary phenomenon with a newly coined term and a fresh set of casestudies as supporting evidence. How are practicing managers to de-


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PROLOGUE 3

heliocentric view of the solar system was a useful method for predict-ing the future locations of the planets. He got himself in trouble byclaiming that it explained why the planets moved as they did, namely,because the planets really do orbit the sun and not the earth.

Prediction and explanation require very different sorts of evidenceand rules of inference. Experiments to establish predictive poweradmit of sometimes signicant measurement and other sorts of error.Even under the most carefully controlled conditions there remains agreat deal that is, well, uncontrolled; indeed, experiments that comeout too close to perfect are often suspected of having been fudged.

We insist that the theory be specied in advance of the experiments,rather than creating our theory after the fact: our unconscious biasesmight lead us to create a theory that ts our data perfectly, and sincea data set is usually only a sample, this kind of interpolation under-mines a theorys broader application. Theories win based on the sta-tistical signicance of their results over a number of trials and their

parsimonytheir ability to explain the broadest range of outcomeswith the fewest and simplest theoretical constructs.

In contrast, explanatory frameworks address a xed and unchang-ing past. We cannot test a proposed explanation of what has alreadyhappened by turning back the clock and seeing if history plays out

the same way again. We must therefore decide what wins based onthe completeness of the explanation, the weight of circumstantial evi-dence, and wherever possible what Pigliucci calls a smoking gun:one or two critical facts that no other competing theory can plausiblyaccount for.

So, for example, how do we know that an asteroid impact explainsthe extinction of the dinosaurs sixty- ve million years ago? We canreasonably infer from what we know about asteroid impacts in gen-eral that an asteroid of sufcient size could trigger a mass extinction.

What we need to show is that there was an impact by an asteroid ofsufcient size at about the right time and that the pattern of extinc-

tions is consistent with the expected consequences. Over the yearsenough circumstantial evidence has accumulated to convince mostinformed observers that this was the case. For example, there is a cra-


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4 PROLOGUE

ter of the right size in the oor of the Gulf of Mexico (which was alsoan ocean back then), along with evidence of devastating tsunamisalong ancient coastlines. We also have a telltale layer of iridium oreof just the right concentration laid down at just the right time in rockstrata around the world. Finally, competing theories such as therise of egg-eating mammals or climate change due to eccentricitiesin the earths orbit cannot account for the fact that the dinosaurswere extirpated simultaneously with a great many plant and mammalspecies as well, nor for the rapidity with which the mass extinctionsoccurred.

Due to these differences in purpose and hence evidence, estab-lishing explanatory power says nothing about a theorys predictivepower. That the dinosaurs were wiped out by an asteroid implies littleabout what will cause the next mass extinction. It just turns out thatan asteroid strike caused that one.

Consider now the last management book you read. What kind ofevidence did it provide in support of its central claims? It very likelyrelied for evidence on an analysis of case studies, and out of that anal-

ysis emerged a framework purporting to explain why events turnedout as they did why a given company succeeded or failed or why agiven product was a hit or a op.

Very often, however, the explicit claim is that the principles thathave been extracted from an analysis of the past can be used to shapefuture outcomes in desired ways. Typically, authors seem to believethat case- study evidence alone supports prescriptive claims. In otherwords, most every management book I am familiar with and cer-tainly most of the best sellers makes predictiveclaims based on ex-

planatory power. Whether deliberate or not, it is a most unfortunateand potentially damaging form of conceptual bait and switch.

Is there any way to avoid this, though? After all, the subject mat-ter of management research actual organizations functioning in thereal worlddoes not lend itself to the kinds of carefully controlled

experiments that allow us to test predictive accuracy in the usualways. Perhaps we can do no better than simply to infer predictivepower on the basis of explanatory persuasiveness.


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PROLOGUE 5

THREE OBJECTIVES

I disagree. The rst objective of this book is to demonstrate that Dis-ruption has true predictive power. I hope to show this using what isfor many people the most persuasive evidence there is when it comesto prediction: controlled experiments. My hope is that you will ndthese data sufciently compelling to conclude that Disruption isunique in having evidence to support the claim that it is genuinelyuseful.

Second, I will make the case for Disruptions unique and superior

explanatory power. I will lay out a denition of Disruption preciseenough that Disruptive innovations can be accurately identied inadvance of knowing how they ultimately fare and their results in themarketplace explained more fully and parsimoniously than by anyother theory. To the extent I succeed, I hope you will conclude thatDisruption is far more than merely a useful perspective but is in facttrue.

Finally, I will offer some thoughts on how one can go about applying these concepts to greatest effect at the least expense. To theextent this third objective is achieved, I hope you will conclude thatDisruption is practical.

And if I can convince you that Disruption is useful, true, and prac-tical, I will go further and hope that you will want and be able to useit in support of your innovation efforts.

prediction: chapters 1 and 2

Chapters 1 and 2 describe the design and results of carefully con-trolled experiments testing the predictive power of Disruptions cen-tral claims: that an innovation has the best chance of success whenit has a very different performance prole and appeals to customersof relatively little interest to dominant incumbents, and the organiza-

tion commercializing it enjoys substantial strategic and operationalautonomy. In contrast, attempts to introduce better- performing solu-tions targeted at customers valued by successful incumbents will fail.


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6 PROLOGUE

To test these propositions I use a portfolio of forty- eight new businessproposals that received seed nancing from Intel Corporation.

To summarize the results, test subjects improved their predic-tive accuracy by as much as 50 percent when they applied Disrup-tion theory to make their choices. Specically, in the actual portfolioof funded businesses just over 10 percent survived. The portfoliochosen by MBA students who did not use Disruption theory had asimilar survival rate, while students using Disruption theory to pickwinners built a portfolio with a survival rate of up to slightly morethan 15 percent. That ve- percentage- point gain is a 50 percent im-

provement. (More recently, Intel reports that the survival rate of itsfunded businesses has increased, in part due to the application ofDisruption theory.)

Of course, neither the data nor the experimental design is perfect(and I will have more to say about the precise nature of the imperfec-tions of this work later on), but perfection is the wrong benchmark.In the mortal realm, all success is relative, and the most importantquestion is not What are the aws of this design and these data?but Are this design and these data better than what you have seenelsewhere?

Note also that I am not claiming that I have shown that Disrup-

tion theory is better than some other theory. Rather, I am claimingthat the evidence in support of Disruption theorys predictive poweris better than the evidence supporting any other relevant theoryspredictive power.

To see the difference between these two claims, consider tests forthe efcacy of new pharmaceutical drugs. Imagine that Disruption isa drug that purports to treat a given condition, and some other theoryis a different drug making the same claim. The evidence in theserst two chapters supports the claim that Disruption actually treatsthe condition: it improves predictive accuracy. I have not shown thatDisruption works better than any other drug; that requires comparing

the relative effectiveness of two drugs. At the same time, however, asfar as I know no one has shown that any other drug actually treats thecondition at all.


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PROLOGUE 7

What I hope to convince you of at the outset, then, is that Disrup-tion can claim more legitimately than any other theory to make youbetter than you are with respect to one critically important decision:assessing which businesses will live or die.

explanation: chapters 3 to 5

A common challenge in research of any kind, and certainly inthe eld of applied management, is determining the extent to whichone can generalize beyond the sample. For example, if someone does

a study on large public companies, do the ndings apply to small,privately held, family-run businesses?To extend our pharmaceutical drug testing analogy, consider clini-

cal trials on a drug that treats high blood pressure. Such trials typi-cally include thousands of people and years of observation in orderto determine whether a new drug is safe (does no harm) and effec-tive (actually helps in the desired way). Assume for the sake of argu-ment that the drug proved safe and effective, but it turned out thatthere were no subjects named Phil. Administering the drug to peoplenamed Phil with the expectation of safe and effective outcomes isgeneralizing beyond the sample. One is therefore open to the pos-

sibility that the drug could have a different effect on people namedPhil than it did on those observed in the study.

Thankfully, we can claim a credible understanding of what willhappen in circumstances we have not tested directly if we have acorrect understanding of why results turn out as they do. In thepharmaceutical example, if we understand the mechanisms of actionfor a particular drug and we know with a high degree of certaintythat being named Phil has no material impact on a drugs effect,then we are justied in generalizing beyond the sample. If, however,there are other attributes that we believe might affect the drugsefcacysay, a patients sex or age or being diabetic in ways that

we do not fully understand, then we are not justied in generalizingbeyond the sample.

In reality, as is often the case, such judgments are not binary: one


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8 PROLOGUE

is more or less justied in generalizing beyond the sample dependingon the sample, what one hopes to generalize, and how far beyond thesample one wishes to go. In the large public/small private companyexample, we might ask what the relationships are between behaviorsand outcomes being investigated and whether there are meaning-ful differences between these types of companies that might affectthe relationships we observe in our sample. A study about processesfor implementing a quality- management system might generalizeacross such diverse companies much better than a study on gover-nance processes, for example, since the public or private structure of

a company has a direct bearing on the relevant legal and regulatorygovernance requirements. With this in mind, the extent to which we can reasonably expect

the predictive power of Disruption to be evident in contexts that werenot directly tested in the experiments turns on whether Disruptioncan account for its predictive power by specifying when it should beapplied and providing sufciently powerful and compelling explana-tions for why it works. In other words, the generalizability of demon-strated predictive power is a function of explanatory power.

The experiments in chapters 1 and 2 test whether Disruption im-proved the ability of MBA students to predict the survival of very

early-stage business plans. Chapters 3 through 5 explore the extentto which other types of people in different circumstances can doanything with these ndings by making the case for Disruptions ex-planatory power. Unlike the tests of predictive power, this entails adirect comparison of the explanatory power of Disruption with the ex-planatory power of competing theories when accounting for specicoutcomes.

The test case, explored in chapter 3, is Southwest Airlines, for al-though Southwest has been analyzed seemingly ad nauseam, the sig-nal feature of Southwests performance its nearly twenty- year run ofslow growth and declining protability from the early seventies to the

early nineties, with a sharp turnaround and a decade of record- settinggrowth, increasing protability, and share- price appreciation hashad no parsimonious explanation. Disruption, however, explains not


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10 PROLOGUE

funding, and ultimately thriving. Since looking at a new venture from the

perspective of the entrepreneur is just the other end of the situation faced

by the investor, this is perhaps the most direct extension of Disruptions

applicability. In short, if you understand what makes a company successful

from an investors point of view, you have a better shot at building a busi-

ness with those characteristics.

If you are a manager trying to grow an existing business: to improvematerially your ability to identify or create opportunities to innovate suc-

cessfully. What makes Disruptive innovations successful is their trajec-

tory of performance improvement: the ways and rate at which a product

or service gets better. It is because Disruption allows you to assess and

determine these variables that it makes for better investment decisions.

Consequently, if you want to improve your chances of success in an exist-

ing business, Disruption prescribes that you guide your own innovation

efforts in ways that make you Disruptive to others whenever possible.

If you are in corporate M&A: to identify viable targets and manage them inways that are likelier to create value. Although materially different in impor-

tant ways from launching a new business from within an established com-

pany or piloting a going concern, acquiring an existing rm demands that you

think carefully about the strategy you hope to advance with the acquisition.

Disruption theory provides a way to think about this problem, with important

implications for how to manage the integration process in particular.

At the same time, Disruption is not a theory of everything. Thereare lots of other questions you will have to answer no matter whichof these roles you ll. For example, as an investor, you likely have toworry about the risk/return structure of your overall portfolio. If youare an entrepreneur you likely have to worry about how to raise capi-tal. If you are managing an existing business, you probably have toworry about organizational politics and the challenges of head- to-headcompetition in your core markets. If you are in corporate M&A, youlikely have to worry about how best to nance the deal and realize

cost synergies. These are important questions, but Disruption doesnot bear directly on them. What Disruption can do is materially andsignicantly contribute to your overall likelihood of success.


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PROLOGUE 11

applicat ion: chapters 6 to 8

Whatever scientic rigor and theoretical elegance might charac-terize Disruption, the proof of the pudding is in the eating. And sohow to apply Disruption successfully is addressed next.

Chapter 6 is an exploration of how Disruption can be used toshape a specic product innovation. We follow the evolution of whatis now Johnson & Johnsons SEDASYS automated sedation systemfrom an early-stage partial equity stake in a small start- up to a com-mercialized product aimed at revolutionizing a wide and increasingrange of surgical procedures the world over.

It is a fact that non- Disruptive innovations can succeed and thatbreakthroughs by new entrants sometimes revolutionize an industry something that Disruption theory cannot account for. Consequently,chapter 7 explores the implications of deliberately pursuing thissort of unexpected (to Disruption theory, at least) success for spe-cic management processes. Highlighting the key success factors,probability of success, magnitude of initial investment, time horizon,requisite autonomy, and connections to the established business foreach type of success should be helpful when deciding how muchto invest in different types of innovation. In other words, where

chapter 6 explores how to use Disruption to shape a single project,chapter 7 looks at how Disruption might t into a broader portfolioof innovations.

Finally, chapter 8 takes a process perspective on the application ofDisruption. Is Disruption a theory that can be plugged into existingways of thinking about and fostering innovation, or is a fundamentalshift in mind- set required to make the most of what Disruption im-plies? The claim here is that the existing paradigm of innovation isevolutionary (variation, selection, retention) and, despite the exhor-tation to fail fast, is unavoidably proigate. Disruption admits of adifferent tack: begin with a clear focus on areas ripe for Disruption;

shape ideas so that they are consistent with the prescriptions of thetheory; and persist in the pursuit of a Disruptive strategy, learning andadapting along the way.


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PROLOGUE 13

worth remembering that even physics so impressive in its predic-tive and explanatory poweris a long way from having everythinggured out. In addition to the long- standing difculties of reconcilingquantum mechanics and general relativity, current thinking is that weactually do not understand what the universe is made of.

Galaxies are rotating so fast that the gravitational force of the starswithin them is insufcient to keep those galaxies from ying apart.To account for their coherence, physicists have invoked the notionof dark matter, which is really just a label for whatever it is that isgenerating the additional gravitational force unaccounted for by the

mass of the stars. At the same time, the universe is expanding, notcontracting, which is what it should be doing thanks to all that darkmatter that is supposedly out there. So to counteract the effects ofthe dark matter, cosmologists have ginned up dark energy, whichis whatever is overcoming the dark matter and pushing the universeoutward.

When you put it all together, according to current estimates,the universe is made up of 24 percent dark matter (whatever thatis), 72 percent dark energy (whatever that is), and only 4 percentmatter the bit we actually think we understand, putting aside theschism between quantum theory and general relativity, of course. 4

And yet, with our arms barely around barely 4 percent of the universe, look what we have been able to accomplish.

Maybe ve percentage points is pretty good, after all.


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PART I

PREDICTION


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C H A P T E R O N E

A PROBLEMOF PREDICTION

If the purpose of a theory is to inform our choices today, we mustdemand more than compelling explanations of the past. For atheory to have a legitimate claim on our allegiance there must beevidence that it improves our ability to predict future outcomes.

C reating and backing winning businesses is by all accounts alow-probability endeavor. Far more new businesses fail, or atleast do little better than limp along mired in mediocrity, than actu-ally break away from the pack and create real wealth. There is moreto this statement than simply the necessary truth that only 10 percentof all businesses can be in the top 10 percent: the best businessestend to do fabulously well, while most of the rest, if they survive atall, generate returns that are embarrassingly small in comparison. 5

We have become collectively resigned, it seems, to the notion thatsuccessful innovation is unavoidably unpredictable.

Despite the challenges and the long odds, there is no shortageof players in this great game. Hedge funds and venture capital part-nerships channel capital into the businesses they feel will succeed.

Many corporations maintain internal venture functions for strategicpurposes, some seeking to create ecosystems around a core businessor to stake a claim to possible new growth opportunities in adjacent


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18 THE INNOVATORS MANIFESTO

markets or to establish a line of defense against possible usurp-ers of a valuable entrenched position, to name only three possibleobjectives.

Take, for example, Intel Corporation, best known for its signicantrole over the last thirty years in the global microprocessor industry.In 1998 Intel launched the New Business Group (NBG) in orderto coordinate and more effectively manage the companys attemptsto diversify beyond the microprocessor industry. 6 Within NBG, ap-proximately $20 million was earmarked for the New Business Initia-tives (NBI) group, which had the remit to identify, fund, and develop

new businesses that were especially far aeld, such as Internet- basedbusinesses and consumer products. NBIs mandate included explor-ing new technologies, new products, new markets, and new distribu-tion channels and had an investment horizon of ve to ten years.

NBI operated as a largely autonomous unit within NBG. Unlikethe relatively formal and structured annual planning and budgetingprocesses that drove sustained success in the microprocessor seg-ment, NBI typically committed only seed capital to new business

ventures, ramping up its level of commitment as various strategic andnancial mileposts were reached. In addition, leadership explicitlyaccepted the inherent unpredictability of incubating new businesses

along with an unavoidable implication of that uncertainty: that someand perhaps many of the ventures that were launched could fail.

Intel Optical Links (IOL) was one of NBIs investments. ThomasThurston, then an attorney in his midtwenties with an MBA and lawdegree, joined IOL in 2005, excited at the prospect of helping launcha new venture inside an established company. Although successfullyincubated, IOL was sold off following Intels broader divestiture ofoptical component and communications businesses. However, Thur-stons curiosity was piqued by this initial exposure to the internal ven-turing process: he wanted to understand better how Intel decidedwhich initiatives to support and why.

Something in excess of seventy business proposals are exploredby NBIs investment directors each year. They work with a rangeof people and sources, both inside and outside Intel, to determine


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A PROBLEM OF PREDICTION 19

the potential of a given idea. The constant challenge is to nd thediamonds in the roughthe concepts that have within them theseeds of sustainable success and perhaps greatness. It is an inher-ently risky undertaking, and the only way to avoid failure entirely is todo nothing, which of course reduces ones chance of success to zeroas well.

It is this unavoidable uncertainty that leads many observers to pre-scribe an investment strategy based on rapid failure: the willingnessto attempt as many different initiatives as possible with an eye tolearning what does not work as the inevitable prerequisite to discov-

ering what does. In Intels world, however, bone de initiatives thekinds of efforts that actually teach you something useful can get very expensive very quickly. NBI executives are therefore forced tomake difcult trade- offs between the need to husband their invest-ment capital and the risk of overlooking the next blockbuster productor service.

For present purposes, the salient features of NBIs investment pro-cess were the Seed Approval Meeting (SAM) and Business ApprovalMeeting (BAM). Proposals that were approved at the SAM receivedfunding of several hundred thousand dollars to typically less than$1 million, with an upper range that rarely exceeded $2 million. This

allowed a team to get beyond the idea stage and esh out a businessplan, perhaps by developing a prototype, collaborating with poten-tial customers, doing market research, and so on. BAM funding wascontingent on having demonstrated an increased level of viability andbrought with it investment capital that ranged from several milliondollars to in some cases as much as $20 million. Ultimately, NBIsgoal was to transition or graduate one new business opportunity per

year to an existing or new business unit within Intel. (Not every ven-ture had to pass through both stages of approval: some ventures weregraduated directly from SAM to an operating division in light of theirstrong performance.)

Intel takes a very rigorous approach to understanding competitors,technology, customers, market structure, and a host of other variableswhen analyzing opportunities for growth. Unfortunately for Intel, and


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everyone else who seeks to innovate in order to grow, there are nodata about the future, and so there often remained many importantbut unanswered questions. Consequently, well- informed, experi-enced executives could look at the same opportunity and come todifferent conclusions about that ventures challenges, nancial po-tential, and so on. Worse, only when a venture was funded could themerits of the decision- making process employed be assessed, since ifsomething was turned down, it rarely got funded via other channels,and so the opportunity cost of passing on what would have been awinner was almost always incalculable.

Thurston undertook a forced march through the popular manage-ment research into innovation in search of a more nearly rules- basedapproach in the belief that, given the importance of the subject andthe wealth at stake, any framework holding even a scintilla of advan-tage over the others would be readily identied. Yet Thurston dis-covered that instead of a vibrant marketplace of ideas populated bychallengers seeking to unseat the reigning champion, the agora wheretheoretical dominance is established is characterized by general dis-array. There were a great many frameworks supported by compellingevidence, yet when they conicted and counseled different coursesof action, there was little basis in the evidence to guide someone in

choosing one approach over the others. When different approachesdid not conict, it was difcult to treat them as cumulative and at-tempt to follow the sum total of their collective advice, since doing soresulted in a paralyzingly long to-do list.7

In light of this theoretical cacophony, in all likelihood NBI execu-tives made their choices in largely the same way most early- stage in-

vestors make their choices: do the best you can with the data youhave available, while necessarily relying on your experience and yourwits to ll in the sometimes signicant gaps. The very best practition-ers typically do all they can to create a solid fact base, but personal

judgment generally gures prominently in making the nal choice. 8

It is simply the nature of the beast that evaluation criteria differ fromperson to person and project to project. Thurston recounts that atNBI, this meant that sometimes the emphasis was on technology,


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sometimes on management expertise, sometimes on the promise ofthe market opportunity, sometimes on the strength of linkages withIntels core business. It is a process that seems to have served Intelwell, for there is no reason to think that its achievements are anythingother than representative of the very best efforts in this space.

The prevalence of this sort of approach is an understandable con-sequence of the reliance of popular management research into in-novation on post hoc case- study evidence to support its claims. WhatThurston was looking for was evidence supporting predictive accuracy in addition to the requisite explanatory power. And no theory he could

nd provided both.

CLOSE, BUT NO CIGAR

Christensens rst book, The Innovators Dilemma, introduced theworld to the notion of disruptive technology. Christensen describedhow large, successful incumbent organizations in all types of indus-tries were toppled by much smaller start- ups. Entrants typically suc-ceeded by developing solutions for relatively small and unattractivemarkets that were of essentially no interest to successful incumbents.

These constituted the entrants foothold markets. Sometimes cus-tomers in these foothold markets were quite happy with inferior butmuch less expensive solutions; sometimes they required solutionswith a vastly different performance prole. Either way, entrenchedplayers, focused on the needs of their established customers, provedsystemically unable to devote investment funds to those markets. Incontrast, driven by their desire to grow, the entrants were stronglymotivated to improve their initial offerings in ways that would allowthem to compete effectively for the larger, more lucrative mainstreammarkets. This was the entrants upmarket march, and entrants thatmarched upmarket successfully eventually captured the customers

that had been the incumbents lifeblood.Christensen observed that when entrants attacked successful in-

cumbents by adopting the incumbents models and technological so-


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lutions, they tended to fail. They tended to succeed by combining abusiness model suitable for a relatively less attractive market theentrants footholdwith an ability to improve their original solu-tions in ways that allowed them to provide superior performance in amanner incumbents were unable to replicate the upmarket march.Christensen called the union of these two elements a disruptivestrategy.

The archetypal illustration of this phenomenon is Christensensall-inclusive study of innovation and competition in the U.S. diskdrive industry from 1976 to 1994. In the midseventies, companies

such as Storage Tech and Control Data were making fourteen- inchdisk drives for mainframe computer makers. These companies,among them Amdahl and Unisys, wanted Storage Tech and ControlData to innovate: greater storage capacity, faster data- retrieval times,and lower costs per megabyte.

When minicomputers were rst brought to market by start- upssuch as Sun Microsystems and Hewlett- Packard, they required verydifferent disk drives: smaller, more modular, and less expensive. Toachieve these outcomes, disk- drive makers found they would haveto reduce storage capacity, increase data- retrieval times, and accepthigher costs per megabyte. The result, the eight- inch disk drive, was

close to the antithesis of what Storage Tech and Control Data wouldcountenance as an innovation; it was, if anything, a technologicalstep backward in the interest of serving a small and highly uncertainnew market. That opened the door for start- up drive makers such asMicropolis and Maxtor to develop something that was technologicallytrivial to Storage Tech and Control Data but strategically impossiblefor them to launch.

In the short run, no harm done: Storage Tech and ControlData went on printing money in the fourteen- inch disk-drive mar-ket while Micropolis and Maxtor eked out a living selling techni-cally inferior eight-inch disk drives to small minicomputer makers.

But then Kryders lawthe disk-drive equivalent of Moores law inmicroprocessors asserted itself: the areal density of disk- drive stor-age space was doubling annually thanks to improvements in record-


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ing media, software correction codes, and other key technologies.In addition, other dimensions of minicomputer performance wereimproving rapidly, fueled in large part by advances in microproces-sor technology and software design. As minicomputers began to en-croach on the mainframe market, and ultimately pushed mainframesinto decline, the fourteen- inch disk drive makers cast about for newmarkets but found only the minicomputer makers buying, and theywanted eight- inch drives. Thanks to their relative unfamiliarity withthe innovations rst commercialized by the eight- inch disk drive mak-ers (e.g., greater modularity and smaller size), the companies making

fourteen- inch disk drives were at an insuperable disadvantage. Mostwent out of business, and none was able to maintain its market domi-nance in the disk- drive industry.

The start- up eight- inch disk drive makers found a foothold by rstexploiting trade-offs among different dimensions of performance andappealing to the needs of an economically unattractive market. TheyDisrupted the fourteen- inch disk drive makers by ultimately break-ing those trade- offs and remaining the primary disk drive suppliersto the newly dominant minicomputer companies. In other words, asthe most lucrative and largest end customers for computers switchedfrom mainframes to minis, the fourteen- inch disk drive makers ended

up going down with their chip. (Sorry.)Accept for the moment that Disruption is a good explanation for

a specic phenomenon: the seemingly unlikely ability of entrantsto topple well- resourced and well- managed incumbents on theirhome turf. Still more remarkably, however, Christensen observedthat over the eighteen years of competition in disk drives that hedocumented, Disruptive strategies had a much higher frequency ofsuccess, and when successful were much more successful than sus-taining strategies.

On the strength of this, Thurston felt that Disruption wasamong the most promising of the frameworks he had studied. He

was particularly encouraged by the fact that Disruption lent itselfto fairly straightforward predictions of what would work and whatwould not.


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FIGURE 1: THE FREQU ENCY OF SUCCESS

OF DISRU PTIVE AND SUSTAINING STRATEGIES

100%

80%

60%

40%

20%

0%

TYPE OF INNOVATION TYPE OF INNOVATION

E N T

R A N T S S A L E S I N B I L L I O N S

F R E Q U E N C Y O F O U T C O M E S

SustainingDisruptive Disruptive Sustaining

Success: Disk drive companies that reached $100 million in sales in at least one year between 1976 and 1994 Failure: Disk drive companies that failed to reach $100 million during this period and subsequently

exited the industry N/A: No verdict as of 1994

$80

$60

$40

$20

$0

Sources: The Innovators Dilemma, p. 145; The Innovators Solution, p. 43

And then Thurston ran into a brick wall. There were no data tosupport any claims of predictive accuracy for Disruption. Chris-tensen and others had developed a robust library of literally hun-dreds of cases across dozens of industries that were explained byDisruption but the same was true of many other theories out there.

Worse, for just about every case study explained by Disruption therewere competing explanations that drew on entirely different sets ofconcepts. (Academic journals continue to debate whether Disruptionis the best explanation of the disk- drive industrys evolution.) Andeven if it were possible to win the battle for explanatory- power brag-

ging rights, until there was some evidence in support of Disruptionspredictive power it could not claim to be the right theory to use formaking decisions about the future. Thurston could have no more con-


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dence in the prescriptions of Disruption than he could in any othertheory.

EVERYONE COMPLAINS ABOUT THE WEATHER

Intel has worked with Christensen for some years, and the companyhas used Disruption theory in its own strategic planning processes.In fact, Christensen and former Intel CEO Andy Grove appeared to-gether on the cover of Forbes magazine in January 1999 under the

headline Andy Groves Big Thinker. Consequently, when Thurstonapproached NBIs leadership about exploring whether or not Disrup-tion might have predictive power when applied to NBIs portfolio ofinvestments, divisional leadership provided Thurston the latitude andsupport necessary to conduct some preliminary investigations.

Thurston began by stating Disruptions predictions. Specically,Disruptive innovations are dened as products or services that ap-peal to markets or market segments that are economically unattract-ive to incumbents, typically because the solution is worse from theperspective of mainstream, protable markets or market segments.Disruption predicts that leading incumbents with so- called sus-

taining innovations innovations targeted at their most importantcustomers typically succeed. New entrants with sustaining innova-tions typically fail.

Disruptions typically succeed, whether launched by incumbentsor entrants, but only when the ventures launching them are highlyautonomous and able to design strategic planning processes and con-trol systems and nancial metrics, among other characteristics, inde-pendently of systems built for incumbent organizations. This elementis important and hardly unique to Disruption: established, success-ful businesses can and should be held to very different measures ofperformance and expectations for future performance than start- up

organizations, and for at least two reasons. First, a start- up typicallyhas a trajectory of growth and protability that is very different fromthat of an established business. Second, start- ups typically must


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FIGURE 2: THURSTONS HYPOTHESES

In framing the predictions implied by Disruption in this way,Thurston was emphasizing two elements of Disruptors: they startout targeting markets or market segments that incumbents do not

value, and they have signicant autonomy. But he ignored oneother element that will prove crucial: Disruptors must improve in

ways that allow them to compete for mainstream markets from a position of structural advantage. That is, it is not enough simplyto appeal to a market or market segment that is unattractive toincumbents; that is a niche strategy. We will tie off this loose end

at the conclusion of chapter 4.For now, focus on what Thurston was trying to get done: he was

looking for actionable advice that would help him predict whether a start-up would succeed or fail, and Disruption as he inter- preted it provided the kinds of predictive, falsiable statementsthat he could test.


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Then came a detailed description of the value proposition. Thiswas the team making good on its claims of superiority, often includ-ing endorsements of prototypes by customers the team was target-ing as early adopters. This was followed by an implementation plan:which market segments would be targeted in what sequence, withspecic descriptions of how Intel would be successful in each, oftenaccompanied by a multigenerational product road map. Finally, nan-cial projections, complete with sensitivity analysis, described the an-ticipated economic value of the business to Intel, usually over threeto ve years.

To keep things as simple as possible, he dened success as sur- vivalthat is, the venture was still functioning as a going- concern venture, whether or not it was still controlled by Intel and failureas deadthat is, no longer a commercial going concern. Withoutknowing the actual outcomes for these ventures, if Thurston couldassess the relevant characteristics of the NBI- backed ventures andpredict subsequent success and failure more accurately thanchance alone, he would have solid evidence supporting Disruptionspredictive power.

Here is how it worked with Image Illusions, a disguised NBI- backed venture. Image- processing technologies, such as printers or

photocopiers, typically use a large number of application- specic in-tegrated circuits (ASICs) to handle different elements of image ma-nipulation, such as shrinking or rotating an image, prior to printing.ASICs are very efcient, but this efciency brings with it two draw-backs. First, because each ASIC is highly customized, manufacturingeconomies of scale are limited, which keeps costs up. Second, ASICsare not programmable, so changing the features of a product typicallyrequires designing and sourcing an entirely new chip, which is costlyand slows down development times.

Alternatives to ASICs, such as media processors, digital signalprocessors, and central processing units, provided vastly increased

economies of scale and programmability but sacriced performanceto such an extent that they were rarely viable. In other words, therewas a sharp trade- off among performance, exibility, and cost. Manu-


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facturers of image- processing technologyfor example, the folkswho make printers and photocopiers would nd it very valuable tobreak that trade- off, for then they could introduce a greater range ofmore powerful new products faster and at lower cost.

Intel is an incumbent in one of these three alternative technologiesmentioned above. Image Illusions sought to leverage this position tocreate a new solution that provided both efciency and exibility. Bycompeting with ASICs, Image Illusions would be leveraging one ofIntels core competencies to expand into a white space opportunityto generate new, innovation- driven growth.

In collaboration with a key potential customer a large, success-ful manufacturer of digital imaging technology the Image Illusionsteam developed a highly sophisticated and demonstrably superior so-lution based on proprietary intellectual capital. It cost almost twice asmuch per unit as ASICs, but the team felt (and the customer corrob-orated) that the higher price was more than offset by the increasedperformance and exibility. In other words, the team had broken thecritical trade- off that was limiting the performance, cost, and pace ofinnovation in image-processing technology.

There were, of course, challenges. The largest companies thatmade image processors including the one that Image Illusions had

collaborated with and all of the targeted early adopters had theirown in-house ASICs design staffs. Many of these people were alsoon the internal committees that assessed new technologies. To adopta non-ASICs solution was effectively to put themselves out of a job.That meant Image Illusions would likely have to be vastly superiorbefore customers would switch in volume, since the in- house ASICsdesign teams would be strongly motivated to show that they could uptheir game and match the new technology.

The Image Illusions team had reason for optimism. The image-processing market was ercely competitive, and the vast perfor-mance improvements Image Illusions could provide meant that all

the team needed was one major player to adopt its solution and therest would follow suit. The ability to leverage Intels strong brand andcustomer access made the odds of getting one domino to fall seem


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very favorable. The cash-ow projections for Image Illusions esti-mated a net present value (NPV) between $9 million and $100 mil-lion over ve years, a range that reected both the teams condenceand the unavoidable uncertainty that comes with launching a newbusiness.

Assessing the prospects of such a venture is reasonably seen asa complex and challenging task. Is the technology really that muchbetter? Is it better enough to overcome the entrenched interestsof the customers in- house design functions? Is the managementteam at Image Illusions up to the challenge of overcoming the in-

evitable and unforeseeable twists and turns on the road to success?Is Intel sufciently committed to this venture to support it for theone, two, or three years needed to make it to positive cash ow? Itwould appear that to predict with any condence what will hap-pen one must have deep experience and expertise in the relevanttechnologies and markets, strong familiarity with the managementprocesses at Intel, and an intuitive but accurate take on the abilitiesof the leadership team.

Not if you are Thomas Thurston trying to test the predictive ac-curacy of Disruption. For him, the only questions that mattered werethe following:

1. Is Intel an incumbent in this market; that is, does Intel alreadysell this sort of product to this sort of customer?

2. Is Intels innovation sustaining or Disruptive in nature? ADisruptive solution makes materially different trade- offs thanthe existing solutions purchased by mainstream customers; asustaining solution is straightforwardly better.

3. If the innovation is Disruptive, does the new business launch-ing it enjoy operational and strategic autonomy from Intelsestablished processes?

In the Image Illusions case the answers were pretty clear. Intelwas a new entrant: it did not sell image processors. The Image Il-


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lusions technology was sustaining: it promised better performancethan ASICs, as dened by the largest and most protable customers.According to Disruption, an entrant with a sustaining innovation canexpect to fail.

So that is what Thurston predicted.


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the innovator's manifesto by michael e. raynor - excerpt

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