1. Introduction

“The race is not always to the swift, nor the battle to the strong, but that’s the way to bet.”

—Damon Runyon

Placing Bets

The proclivity to bet on the “swift” might serve you well, especially when it comes to placing bets at the racetrack. But in a new and complex world, few people actually know what “swift” or “strong” means in every context. For example, an executive may know what a “strong” market analyst or a “strong” manufacturing department head looks like when it comes to hiring someone. Yet that same executive might find it hard to identify a “strong” pharmacokineticist, which is a new, highly specialized branch of pharmacology. Indeed, it’s because of this difficulty that universities issue PhDs in pharmacokinetics. You can assume that if people have a PhD, they are certified as “strong,” even if you can’t make that assessment on your own.

But in today’s heavily connected world, what if, instead of relying on handicappers like universities to help you improve your odds, you could sometimes place your bets after the race is run? Wouldn’t this render measurements of “swift and strong” utterly moot? After all, you would know who WON. Not who might have won or even should have won but who actually won. This contrarian notion is one of several at the heart of this book and will be further developed as the numerous present-day channels, or modalities, of innovation are elaborated and how you can more effectively make these choices and manage them.

Who is this book for? It is directed to the decision makers who are ultimately accountable for driving business performance and innovation in their organizations. These leaders have many different titles and specific roles. They are CEOs, company founders, chief scientific officers, foundation directors, government agency heads, and policy makers, to name a few. They run an equally broad range of entities, from a not-for-profit foundation seeking cures for orphan diseases, to a corporate business unit bringing a new product to market, to a government agency seeking breakthroughs to better provide security for its citizens.

Innovation processes and approaches are undergoing significant change—in business, in philanthropy, and in government. A new lexicon is emerging to describe these changes: terms such as, open innovation, crowdsourcing, prize philanthropy, public-private partnerships, broadcast search, and so on. Through years of practitioner experience with real-world clients, the authors have developed an approach that we call Challenge Driven Innovation (CDI) which is the focus of Part I. CDI brings an approach and rigor. It reframes the innovation and business process in light of the many new channels and partnerships available. And, it helps guide innovation leaders at all levels in the selection of those channels. This is an approach with enough precision to drive innovation, but agile enough to create value everywhere in the organization. We believe that organizations that adopt CDI pervasively essentially virtualize the enterprise and will have an even greater opportunity to drive business performance and market leadership in the twenty-first century. We call that vision the Challenge Driven Enterprise (CDE), the focus of Part II.

What can a business expect from the full adoption and strategic practice of Challenge Driven Innovation? Nothing less than the following:

• More cost effective problem solving

• A greater diversity of approaches to innovation

• Better management of risk

• Not reinventing the wheel

• Accelerated innovation

• Ability to pay for results and not just efforts

Managing the Innovation Process

How business, philanthropy, and government leaders direct the research and development (R&D) resources of their organizations to foster innovation is a critical part of their overall leadership. And, it is crucial to the market performance of many institutions. Ultimately, this leadership and its consequential innovations play a bigger role in society: These innovations underpin the quality of life of every person on earth. Think about it: It was innovation, defined as creativity and implementation, that created efficient farm practices to better feed people, water purification to minimize disease, educational advances to better the understanding of the world, the printing press, the steam engine, the Internet—a near endless list of human advances.

Within the walls of various corporate, nonprofit, and governmental departments charged with innovation are “the geese that have laid the golden eggs.” These “eggs” are the products and ideas on which the company was built and on which it subsequently thrived. And the “geese?” Well, they’re the scientists, designers, technologists, artists—the “creatives” that produced those eggs in the past and are counted on to produce them in the future. Organizational culture and lore often suggest that, “many leaders don’t understand the mysterious innovation process and are as likely to kill those geese as to nourish them,” or so the thinking goes. Given this reality, it certainly seems risky for a non-R&D leader to tinker with that part of the organization—“let’s just leave it to others,” is a too often accepted groupthink.

But if leaders had this attitude toward operating divisions such as marketing or manufacturing, it would rarely be tolerated. And yet, for the sanctum sanctorum, areas like R&D and product development, somehow it seems OK. It’s not. Some would simply call this hands-off behavior a dereliction of duty. Whatever you call it, truth be known, the scientists and technical leaders in these innovation departments like this status quo and resist change.

We also want to assure you that we are ever mindful that innovation is hardly a strictly R&D phenomenon. Innovation spans across all areas of a business. Innovative solutions are, of course, needed for new product lines and product improvements. But, we can also talk about innovative marketing campaigns, innovative business strategies, innovative manufacturing processes, and innovative sales approaches. None of that is even a stretch. We must therefore ask that this broad notion of innovation be kept in mind. We sometimes use terms specific to technology or engineering, but that is to allow the use of specific examples, concrete language, and clarity. We sometimes, but not always, use broader language or multiple role descriptors as a reminder of our intention and awareness. Most importantly, the principles we discuss will have application in the broader sense, and that breadth is a key to understanding the CDE, central to Part II of this book.

This book uses a fairly well-established definition of innovation: an event characterized by an act of creation or invention followed by successful implementation and deployment so that the benefits of that creation may be widely enjoyed. By defining innovation as “realized invention,” you can create two distinct subevents which, in practice, have their own separate set of properties, conditions, and approaches. Of the two events, creation/invention and implementation/realization, the second act of implementation, or realization, is the one most amenable to processes, structure, and what you would classically think of as managerial intervention.

The first, the creation or invention part, has always been and remains a bit murkier. When you imagine your own personal experience with innovation, it is always much easier to describe to others the implementation part. Just how you went about inventing something isn’t perfectly clear even to you. How can you manage the invention process when it just seems to happen? Surely the “conditions” were right. And so, much effort is given to the managerial duties of creating the right environment. How else to explain all those beanbag chairs on corporate invoices in the 1990s that were supposed to kick-start out-of-the-box ideas?

So, on the one hand, invention seems to be a tricky thing to manage and best left to the inventors. At the same time, we openly accuse general managers of dereliction of duty for having anything less than a robust management strategy for their innovation functions. How can managers resolve this contradiction?

Balancing a Portfolio

The answer isn’t all that complicated as an oversimplistic metaphor can demonstrate. A billboard recently seen in Nevada, where gambling is legal, advertised prime rib dinners with the price of $7.77, showing as three winning numbers on a slot machine. Yet think about slot machines and the casino business. You never know each time you pull the handle or press the button of a slot machine whether you are a winner or a loser. Yet, in spite of the unpredictability of any given play or even any given machine, few casino owners worry about losing money across the casino. The methods for managing systems of probabilities and unpredictable processes have been around for a long time; however, they need to be applied with greater rigor to the innovation processes, on which we survive.

Now, of course, you can fully appreciate that the notion of managing innovation as a portfolio of opportunities is hardly novel. But we can and should explore more deeply the relationship between innovation portfolio management and the historical growth of “open innovation,” which is the use of invention sources independent of the organization charged with delivering the innovation to the marketplace. This phenomenon was defined, and its substantial business impact analyzed in the seminal work Open Innovation¹ by Professor Henry Chesbrough and to which we refer the reader for a deeper grounding. What are the strategic opportunities created by a more deliberate integration of the role of “open innovation” into the overall portfolio? When you use the term portfolio, you must, of necessity, mean a balanced portfolio. And actually, since a portfolio would be nothing more than a collection in the absence of that adjective, “balanced” is always implied. And so what is it that “unbalances” your portfolio? In later chapters, we spend far more time on the topic of diversity, so all those arguments aren’t replicated here. But internally generated projects are bound to possess a certain sameness. Thus, balancing is likely to demand an openness to external ideas, external projects, and external products—namely, ones that originate outside the organization.

Sourcing of more projects from outside the organization is not just a numbers game. Do not trivialize these arguments as merely the admonition to “take more shots on goal.” Plenty of analyses from the sports world suggests that the higher scoring team makes a higher percentage of attempted shots as well. The processes used to manage a portfolio of innovative ideas must be well designed and rigorously applied, but the tools and specifics—that is, pareto diagrams, decision-trees, and option theories—deliberately remain beyond the scope of this book. The focus of this book is not “how” to manage a portfolio. It is rather to show that new innovation channels offer a portfolio balancing capability, and therefore a desirable outcome, unavailable with internal projects alone. This notion that a diverse portfolio of assets predictably outperforms a more correlated one is an important concept and one which is approached from multiple angles.

Beyond the factor of diversity, a second factor linking open innovation and innovation portfolio management is risk. There are many complex and valuable ways you can address the topic of risk and far more is said and written about risk than is done to contain it. When advancing an innovation portfolio, you need to worry about three kinds of risk: financial, technical, and execution. Closed innovation systems, where all the invention and creation takes place within the walls of a single institution, compel the innovator to load all this risk into one organizational basket. And that cumulative risk too often results in a “bet the farm” scenario. This risky strategy has resulted in the disappearance of many fine companies across numerous sectors. The pharmaceutical sector stands as a notable example. Think of the various medicines you and your family have taken. Many of the original producers, of well-known products like Motrin, no longer exist as individual entities, including SmithKline, Beecham, Ciba-Geigy, Roussel, Hoechst, Marion, and Merrel. What happened to Parke-Davis, Upjohn, Burroughs, and a host of others? These brands no longer exist. The executive teams no longer exist. The stock in those companies no longer exists. And although the answer is complex and has many elements idiosyncratic to those specific institutions, it is also a generally true statement that their demise could be traced to an over-accumulation of risk within their business entity.

The simple point to be drawn is that open innovation provides an invaluable means to balance an innovation portfolio and share risks. The consequences are so significant that all business leaders should be actively charged to attend to the innovation process and its strategic role.

Now recognize that the implementation of an actively managed innovation strategy won’t be without obstacles. Scientists generally prefer to be ignored by process-mongers, portfolio managers, and others who might not be there with an agenda that is fully aligned with theirs, which is, first and foremost, peeling away nature’s layers of obscurity. Similar comments could be made about the resistance of technologists or artists. Although there might be plenty of opportunity to talk about the underlying reasons that these creative types, as a whole, have a tenuous relationship with authority, we would rather focus on how the overall systems tend to be biased toward flawed portfolio management. We will look next at how corporate culture and organizational myths distort attempts to effectively manage a portfolio of innovative projects with uncertain outcomes and a frequently low probability of success.

False Positives Versus False Negatives

An actively managed portfolio demands judgment calls. The judgments may well be based on quantitative values and careful measurements. But unless you have nearly inexhaustible resources and can see every risky project through to its final conclusion, imperfect judgments will have to be made, running the risk of being wrong. Two simple criteria for effective portfolio management are to make judgments as early as possible and to make as few errors as possible. When speaking of errors in this context, you need to classify two types of error, often referred to as alpha errors and beta errors or, in other contexts, false positives and false negatives, and sometimes just as simply as type I and type II errors.

In a portfolio, a false positive is a project deemed to be “successful” and that gets resources, and advances, but that ultimately fails. A false negative is a project terminated on the assumption that it will fail and then ultimately proves successful. Although each type of error is easy enough to make, it is harder to track false negatives because after a project is terminated, it is only occasionally reincarnated to prove its ultimate worth. A typical false-negative scenario is one in which the project is terminated, with regard to the expenditure of resources, but is licensed elsewhere, and the licensee ultimately succeeds. When good judgments are made under conditions of incomplete and imperfect knowledge, both these types of error must occur. Logically, any attempt to eliminate one error type results in a greater number of instances of the other type. So if you never want to make the error of a false positive, you need to ruthlessly terminate projects with any hint of potentially failing—to avoid unnecessarily committing resources to them. Thus, you create many more false negatives in the process.

Well-managed portfolios result in both types of error. But what are the cultural pressures that might result in an overcommitment of one error type and consequently the commission of too many errors overall? Naturally, no innovator, whether scientist, technologist, or artist, wants to see their project terminated. So, not surprisingly, there is pressure to commit the error of falsely identifying a project as positive when it ultimately will fail. Consistent with this pressure—and serving the interest of individual project leaders and team members—most organizations have generated highly adverse stories about “the one that got away.”

This doesn’t mean that false negatives are somehow good. All errors are costly: The false positive error consumes resources and capital that, if deployed elsewhere, could have benefited the organization and its customers; and false negatives represent the very project in which the application of additional resources and capital would have served the organization and its customers. Remember that a bias toward one type of error will increase the total error population, and because both types of error represent cost without return, the goal clearly has to be to keep the sum of all errors as low as possible. Errors are a natural part of decision making under uncertainty, but they can be managed well or poorly, and good decision processes are often the difference between your ultimate success versus a competitor’s.

Rationalizing Innovation Failure

How this error type preference works in real organizational life is that the story about the “one that got away” is relayed with such anxiety that every project with an uncertain outcome is identified as yet another example of one which you may “let get away.” This vigilance to avoid any future embarrassment of the false negative results in a host of projects kept alive well beyond their time.

To rationalize this portfolio-inefficient mentality, a variety of other behaviors surface throughout the organization. Researchers wanting to avoid their pet projects being terminated identify closely with this tale, and research leaders adopt the mantra, “we can’t afford to at least not try.” Leaders outside the research departments are drafted to get on board by having it patiently explained to them that waste is a completely natural part of the research process and can’t be avoided if one is to do great things. All parties can count on pithy snippets of history to aid and abet them in this effort. Even the venerable “wizard of Menlo Park,” Thomas Edison, dismissed his critics by insisting that his failures were an integral part of his success by proudly declaring that shareholder investment had absolutely not been wasted because he now knew “10,000 ways not to make a light bulb.”

None of this is to say that there is some magic formula by which research will just progress from one success to another, or that at some high standard of portfolio management, payment for failure magically disappears. We challenge these institutionalized versions of R&D simply because they have become so entrenched that they are all but invisible and leaders are too quick to accept the “nature of the beast” as part of institutional lore. Making these myths apparent is only a tiny first step to more effectively addressing them. And what should now be clear is that the addressing of these issues is the responsibility of all organizational leadership and not just those directly charged with executing the innovation projects upon which the future of the organization inevitably rests.

Portfolio Management and Open Innovation

After promising to tie this issue of portfolio management to open innovation, it may appear that this promise was sidetracked. Not so. One important component of open innovation is that it creates an opportunity to share risks and expenses with external parties. The adverse consequences of the false positive are effectively neutralized when someone else is underwriting some or all of the costs. Details of how you can manage this risk-sharing, and the organizational structures that support it, will be covered in subsequent chapters. But, for now, error minimization, portfolio management, and open innovation need to be integrated into a total innovation management system that copes effectively with risk and probability, and that manages to a desirable economic outcome.

Many companies—and even whole industry sectors—compete primarily on the basis of innovation, for example, pharmaceutical companies that must routinely invent new medicines or the advertising industry that constantly must come up with snappy original taglines. Innovation is what enables these companies to maintain a competitive “edge” as opposed to competing on price, convenience, added services, or some other aspect of business. Even as the argument is made for other modes of competing, one cannot help but be reminded that convenience, services, and low-pricing is often the opportunity presented by an innovation of some type. No, the reality in the twenty-first century is that virtually all businesses are months away from a wave of novel competitors. Innovative companies survive.

Historically, innovation competition has revolved around each company’s capability to assemble creative departments—and most important, teams of exceptional talent that strive to out-innovate their competition. This was accomplished by smart people, with excellent equipment and facilities, inventing new products—and even new technologies—and often making fundamental advances in science. Think Bell Labs as a prototype. Of course, even though Bell Labs continues as a distinct entity, it has not fully survived in the form that characterized it in its heyday because it has been altered by spinoffs, layoffs, mergers, and mission changes.

No doubt many factors contributed to the transformation of the central lab, with a broad remit for science. It is not the intent to thoroughly analyze Bell Labs or even to propose a scholarly hypothesis to explain its mutation. Surely some of those factors must include the broader access to knowledge because of the “information age.” Business, also, has become more sophisticated in its capability to locate and license ideas. This decreased the need to invent it all in-house. The adage that “none of us is as smart as all of us” has been scaled up and globalized.

Even so, an enormous percentage of the applied science and technology, and ultimately, “reduction to practice” remained an internal skill. Responding to this reality, a significant number of graduating scientists, engineers, and technologists historically went to work for large corporations—as did designers, graphic artists and draftsmen. The shift to “distributed innovation” has taken place slowly—over decades—until today, when many sectors can point to significant fractions of their new product introductions and underlying technologies as originating outside of their corporate labs. Distributed innovation is a gathering of ideas and solutions from many quarters and the integration of the pieces, by a central organization into what would be considered the final innovation. Some, such as Procter & Gamble, have even declared this as a strategic intent, one they call “Connect and Develop,” or C+D. They have set quantitative goals to increase licensing as the primary mode of innovation growth while maintaining a more constant level of internal R&D resources. This initiative is one you learn more about in the case study at the conclusion of Chapter 6, “The Challenge Driven Enterprise.”

Now is the era of “Open Innovation.” The shift to contract labs and licensed technologies is currently the major part of the open innovation movement. But recent increases in broadband Internet access and other leaps in communication enable you to imagine a future in which technical problem solving, on the spot invention, and on-demand innovation can be realized—maybe even predominantly—through open communities of scientists. Examples of these open innovation communities are InnoCentive, the authors’ company, and TopCoder. These enabling platforms, and their attendant business entities, in which the network is managed on behalf of other institutions, have been named innomediators by Professor Mohanbir Sawhney at Northwestern University’s Kellogg School of Management.

Later chapters discuss how the various innovation channels are selected, how they play off against one another, and how they are ultimately integrated for innovation. Putting all this together ushers in new organizational and partnering realities: Marketplaces in which intellectual property—with or without its legal appendages—is exchanged as readily as Hummels on eBay. Maybe that’s a bit of an exaggeration in 2011, but stay tuned.

Just as the random soot patterns created the ability for the Naskapi to explore unknown regions, so too, do the various modes of open innovation enable organizations to explore unknown and unbiased, or at least differently biased, regions of technology, design, or policy, in ways previously too costly or too difficult.

Meta-Innovation

In examining the broad topic of open innovation a little more deeply, you find that some innovation modalities or innovation channels have been around a long time. One example would be the specialty labs that have long been used for customized testing or analyses or to which selected operations may be outsourced. On the other hand, some open innovation modalities are newer, with only limited examples of historical use: for instance, tech-scouting, crowdsourcing, and public-private partnerships. Taken altogether, the introduction of these new modalities, and even more important the integration of all modalities, into an innovation effort is a new approach to innovation strategy. It is what creates an Open Innovation Marketplace—a collection of channels and exchanges, an innovation bazaar, where creativity and ideas can be contracted, openly sourced, or globally brainstormed. It represents what The Economist’s Tom Standage cleverly termed “meta-innovation: innovating on how we innovate.”²

The 1990s saw many varieties of open innovation emerge or increase. There was also a growing movement in open source software. Although open source software development was indeed “open to the source of the solutions,” the term referred to the underlying code, the “source code,” and its intent to be “open to the public,” meaning not copyrighted but placed in the public domain. Thus, the novel development practice of being “open to the source of ideas” didn’t actually have a name of its own. There were a few examples near the turn of the century, such as Hello Brain, InnoCentive, TopCoder, BountyQuest, and X-Prize; although, X-Prize is a not-for-profit foundation that also fits the category of prize philanthropy.

As this approach was replicated at varying levels of complexity, rapid-fire, problem solving and consulting appeared in models such as e-Lance: a website that matches freelancers and work assignments; Gerson-Lehrman, a website that says it “connects the world’s leading institutions with the world’s leading experts”; and later Amazon’s Mechanical Turk, a website that matches software developers with businesses and entrepreneurs who want mechanical tasks done; and Google Answers, an “online knowledge market” offered by Google that enabled users to post bounties for well-researched answers to their queries.

These examples are hardly exhaustive, but you get the idea. In this climate, Jeff Howe’s Wired article in 2006³ introduced the term crowdsourcing—a descriptor that has gained considerable traction. The term has been comfortably applied to both the quick response “answers” systems and more complex endeavors, such as InnoCentive.

Prize Philanthropy

On the not-for-profit side of the spectrum, the phrase Prize Philanthropy is defined as the use of donated prizes to incentivize breakthroughs perceived as having some sort of broad social or philanthropic intent. In this expanding sector, you see diversification of the widely familiar X-prize efforts—the addition of the Virgin Earth Challenge, to find means for scrubbing atmospheric carbon dioxide; the Prize4Life, a foundation focused on treatment and detection of Lou Gehrig’s disease; and other examples as well. What characterizes this end of the spectrum is that the qualifying submissions are often heroic in execution and require considerable investment and likely a coalition of talents and disciplines to pull off. However, Prize Philanthropy is rapidly moving “down-scale” to seek modular, turnkey solutions that are part of a bigger ecology in global problem solving. These would include many independent efforts, and collaborative efforts in which existing open innovation platforms serve the needs of foundations and obviate the need for massive duplication of efforts in platform construction.

Problem Solving Versus Question Asking

When all the world becomes “the innovation lab,” how do innovation competitors compete? As pointed out by Michael Raynor and Jill Panetta in Harvard Business Publishing’s higher education newsletter, the new basis for innovation competition shifts from controlling the prior “limiting resource” of problem solvers to the new limiting resource of question-askers.⁴ In open innovation, in which resources well beyond any imaginable corporate lab are available to solve problems, solvers often respond to the innovation challenges because they perceive a commitment to manufacture, market, and make their invention available to the public. In the two parts of innovation—invention and realization—this new openness, and the ease with which it can be accessed, represents a leap forward for the “invention” part, with the commitment to “realization” being a form of currency to the inventor.

Essentially, you pay people by selling what they invent. That is, the resources an idea seeker will use to distribute and commercialize an invention is beyond the scope of many would-be inventors who will engage with an expectation of some monetary return, but also with the prospect of being “paid” by having their invention marketed or even freely distributed and thus, the ability to make a difference.

This question-based competition also redefines the role for internal corporate staff, a topic addressed in much more detail in Chapter 3, “A New Innovation Framework.”

In summary, innovation strategy is not an oxymoron. Failure by an organization to own that at the highest levels is a dereliction of duty.