John Seely Brown
As a student of innovation for some twenty odd years, I still find it amazing just how hard innovation continues to be. But today, we are faced with the extra problem that our ideas of innovation have themselves gone stale. So we need to be innovative in the area of innovation itself, which is what this book will help us do and what I mean by calling this introduction “innovating innovation.” Some more definitions: by innovation I mean something quite different from invention. Innovation to me means invention implemented and taken to market. And beyond innovation lies “disruptive innovation.” By this I mean something that actually changes social practices — the way we live, work and learn. Really substantive innovation — the telephone, the copier, the automobile, the personal computer or the Internet — is quite disruptive, drastically altering social practices.
Disruptive innovation presents some major challenges. First, although it may be relatively easy to predict the potential capabilities of a technological breakthrough in terms of the products it enables, it is nearly impossible to predict the way that these products or offerings will shape social practices. The surprising rise of email is but one example. It is not technology per se that matters, but technology-in-use, and that is what is so hard to predict ahead of time. Nevertheless, technological breakthroughs that do end up shaping our social practices can produce huge payoffs, both to the innovator and to society.
A second major challenge is that a successful innovation often demands an innovative business model at least as much as it involves an innovative product offering. This is a hard lesson for research departments of large corporations to learn. It is why so many great sounding innovations in the research lab fail to see the light of day. In the lab, we have devised many ways to rapid prototype an idea, explore its capabilities and even test lead customers' reactions to it. But innovations that intrigue the customer don’t necessarily support serious business models — as the dot.com boom and bust showed again and again — and even those that do may support a model that threatens to cannibalize the sponsoring corporation's existing business models. So, as one aspect of innovating innovation, we need to find ways to experiment not only with the product innovation itself, but also with novel business models. Rapid business model prototyping is thus of critical importance to the future of technological innovation, and Henry Chesbrough makes it a centerpiece of the “open innovation” scheme of this book.
are additional reasons to innovate innovation. Most prior models turned
on the creativity within the firm. In today’s world we are faced
with two new realities. The first is that there are now powerful ways
to reach beyond the conventional boundaries of the firm and tap the
ideas of customers and users. Indeed, the networked world allows us
essentially to bring customers into the lab as co-producers. We can
tap not only their explicit knowledge, but also their tacit knowledge
made manifest as they start to use a prototype. Prototypes used by
real customers at work on their own problems afford a kind of reflection
in practice that help to flush out serious flaws, misleading instructions
and missing functionality before the product is brought to market.
New technology offers us new tools to help in this meta type of innovation. I have already mentioned the use of the Net to bring customer’s practices (rather than just the customer’s voice) into shaping a prototype. Even cars are first rendered in software and as such can be directly experienced (and driven in some cases) as a soft, highly malleable prototype and can thus evoke tacitly held opinions of a customer. With the power of today’s computers to simulate massively complex and nonlinear systems coupled to phenomenal visualization techniques, the customer can be brought ever closer to the design process.
There is another set of tools that can deeply shape the practices of innovation, and in particular to work on the business models that innovation also needs. These are financial tools that build bridges between the flexibilities of the venture world and the predictable financial constraints of a public company. They use real options theory for managing the staging, gating and scaling of cash flow decisions in an innovation process. Unlike the use of net present value (NPV) calculations, which inherently view the innovation process as static, compound real options honor its inherently dynamic nature. They provide a way to fold into the decision processes two sources of learning, one involving learning by doing — what is learned about the technology while developing the product; the other involving learning while waiting — what is discovered from the market as the product is being developed. At each stage for potentially scaling or exiting the project (option), both sources of information come into play. Furthermore, by using options theory one moves from viewing the pursuit of an innovation as making a bet to viewing it as buying a possibility for the future while still delaying substantial cash flow. Although this quickly becomes a complex topic in its own, I mention it here because it provides a rich set of tools for modeling many of the ideas of open innovation as well as providing a way to capture, but now inside of the corporation, some of the better properties of the venture capital world.
John Wolpert’s Alphaworks and his new efforts at IBM called “Extreme Blue” — an incubator for talent, technology and business innovation — provides another way to combine learning by doing and learning while waiting. In John's scheme (and in our own implementation called AlphaAve at PARC that follows his lead), a web site allows users to download already patented experimental software and explore ways they might find it useful. When thousands of users download and use alpha stage software that had been deemed of marginal interest by marketing departments, its unnoticed potential comes back to the attention of both research and marketing, who realize that there is a need to explore the software more fully. According to Wolpert’s article in Harvard Business Review (August 2002), although most have initially been passed up as targets of opportunity, 40 percent of the new technologies on the Alphaworks site eventually make it to market as new offerings or as new standards.
The open innovation model that Chesbrough describes in this book shows the necessity of both letting ideas flow out of the corporation in order to find better sites for their monetization and also to flow into the corporation as new offerings and new business models. Finding the right balance and mechanisms for this is critical. If a rising corporate star brings forth a risky innovation that ends up failing, his career is apt to be damaged considerably more than that of the executive who squelches an innovation that could have been a winner. An open innovation model diminishes both the error of squelching a winner (a false negative) and of backing a loser (a false positive). With luck, protective moves or face saving mechanisms will no longer cause potentially great innovations to be shelved. Innovation is too important to let either corporate politics or outmoded assumptions carry the day. Although wisdom is usually worshiped, misplaced wisdom can always find reasons or examples from the past to prove that any particular innovation is foolhardy. Let us, instead, all engage in the process of innovating innovation. This book is a timely, carefully researched and thoughtfully articulated effort towards that end.