Introduction

Design thinking has emerged as the next frontier in the competitive landscape of many industries and firms. Design thinking has been defined as combining empathy for the context of a problem, creativity in the generation of insights and solutions, and rationality in analyzing and fitting various solutions to the problem context (Kelley & Kelley, 2013). Its principles and practices are directed toward intractable human issues, so-called wicked problems for which an optimal solution or even a knowable solution may not exist (Buchanan, 1992). It is a method of creative action and experimentation, focused on solving complex problems.

How to best handle knowledge for large intractable problems for which optimal solutions are not knowable is a tantalizing arena for purveyors of knowledge management experts to explore. Typically, organizations structure their knowledge management systems to cumulate knowledge, experience, and expertise in certain market and technology domains, and leverage that knowledge repeatedly to enhance their efficiency and effectiveness, thereby outpacing competitors and maximizing profitability for their shareholders. Partly as a consequence, knowledge management (KM) approaches have mostly been applied to these routine facets of an organization's operations, including product development, market orientation, customer relationship management, and others, where known markets and known technologies are leveraged for success, and the challenge is being more efficient and effective than competitors. But the application of KM principles to design thinking is also a fruitful arena for consideration.

KM had its origins in information technology (IT), but recognizing that decisions involve information and knowledge, not merely data, the KM that many organizations institutionalized came to be viewed as a function dealing with acquisition, utilization, and dissemination of knowledge. Indeed, in some facets of product or business development, KM tools have been valuable. However, the “fuzzy front end of innovation” did not and indeed could not benefit from traditional KM tools. Some suggest that this situation is beginning to change.

This chapter will focus on the perspectives, principles, and practices required in KM to address the world of breakthrough innovations—innovations that demand design thinking because they are characterized by high levels of uncertainty and address big, complex, and sometimes intractable problems. We will discuss the shift in perspective from intelligence leveraging to intelligence amplification as a key characteristic of KM to address the arena of breakthrough innovations. This shift emphasizes the role of insight development over leveraging available knowledge.

The chapter will deal with the tools for embedding KM in design, with examples from several large but leading-edge companies and other organizations such as incubators, idea labs, and consulting organizations. The tools will be described within the framework of discovery, incubation, and acceleration, three capabilities necessary for breakthrough innovations (O'Connor & DeMartino, 2006; O'Connor, Leifer, Paulson, & Peters, 2008). The material in this chapter is targeted at people in corporations and other organizations (such as incubators) who engage in innovation of a nonincremental nature, and who find that the more they leverage what is known, the further removed they are from the possibilities that their opportunities enable. We hope this chapter provides a frame of reference to help readers recognize that they are engaged in a different sort of innovation altogether, and that the use of knowledge must occur in a different way than typifies most organizational practices today.

13.1 Designing Amidst Uncertainty

Much of the world of design for new product development (NPD) has been fitted within the traditional Stage-Gate process (Cooper, 2001). Ideas are generated, screened, and approved by a gate review board, and the project follows a sequence of steps designed to ensure cross-functional involvement through project scoping, building the business case, detailed design and development, testing and validation, and launch. While the team may not have access to the necessary information, it is easily accessible using traditional tools. This approach works well for incrementally new products that leverage past designs, technologies, and customer loyalties.

But firms also introduce breakthrough innovations. These opportunities arise in a couple of ways. One way is to engage customers directly to understand a deep-seated problem, find solutions, develop really new products and services, and get to market first. A second approach is based on identifying applications for advanced, emerging technologies that enable new solutions to interesting problems. Companies that invest heavily in research and development (R&D) develop deep technical expertise and can gauge shifts in technology to meet known and unknown needs in the marketplace. Firms that adopt the first way are sometimes labeled Need Seekers, and those following the second approach Technology Drivers (Jaruzelski, Loehr & Holman, 2013). Both of these approaches lack complete technical or market expertise, respectively, in the domain in which they are innovating, and so they operate in domains of uncertainty. But they do produce breakthroughs. Regardless of which approach, both can benefit from a KM framework and tool set that helps managers expand beyond current knowledge base. Technology Drivers do not use their current customer base as a referent for future innovation. And those engaging deeply in the market to identify unarticulated needs do not necessarily draw on known solutions as they forge a new product for a deep seated need they've uncovered. In fact, they may work with customer partners to co-develop solutions through experimentation.

These groups' innovation experiences compare with those using the Stage-Gate® process for incremental innovation as more ambiguous; forecasts, business cases, operating models, market reactions, and production systems are all unknown. Following O'Connor and DeMartino (2006), we define three basic stages required for breakthrough innovations (BIs): discovery, incubation, and acceleration:

1. Discovery: Involves creating, recognizing, elaborating, and articulating potentially breakthrough opportunities. Discovery activities can include invention and lab research, hunting inside and outside the company for ideas and opportunities, partnering with universities and licensing technologies or placing equity investments in small firms that hold promise.
2. Incubation: Matures breakthrough opportunities into business proposals. A business proposal is a working hypothesis about a technology platform, potential market space, and a business model. Incubation is not complete until that proposal—or, more likely, a number of proposals, based on the initial discovery—has been tested in the market, with a working prototype. The skills needed for incubation are experimentation skills. Experiments are conducted not only on the technical front but also for market learning, market creation, and testing the business proposal's match with the company's strategic intent.
3. Acceleration: Activities ramp up the fledgling business to a point where it can stand on its own relative to other business platforms in the ultimate organizational unit (SBU) in which it will reside. Whereas incubation reduces market and technical uncertainty through experimentation and learning, acceleration focuses on building a business to a level of some predictability in terms of sales and operations. Acceleration activities include investing to build the business's physical infrastructure, focusing and responding to market leads and opportunities, and developing repeatable processes for typical business functions such as manufacturing scheduling, order delivery, and customer relationship management. Scaling a business involves uncertainty on a variety of dimensions as the opportunity is faced with many degrees of freedom and the company's and market's reactions to choices made are extremely malleable.

Discovery, incubation, and acceleration differ markedly from the conventional Stage-Gate process for NPD, where markets and solutions are drawn from the existing stock of knowledge and expertise held within the company. For the breakthrough innovation stages, the firm cannot rely on its past storehouse of knowledge. It must instead rely on its ability to amplify what it knows. The company will be engaged in creating new knowledge together with market agents as the opportunity is enlarged in discovery, and then incubated and accelerated into a full-fledged new business.

Table 13.1 provides a summary comparison of the differences between incremental and breakthrough innovation and the challenges of KM for each. We take this theme up next.

13.2 Knowledge Management Tasks for Breakthrough Innovation: From Intelligence Leveraging to Intelligence Amplification

KM as practiced in organizations has relied on a number of tools that enabled intelligence leveraging: garnering existing data and knowledge to create efficiencies in organizational processes. Broadly, these tools enabled codification and dissemination of information, and identification or establishment of social networks. These tools (e.g., FAQs, data mining), organizational mechanisms (e.g., communities of practice), and analytic approaches (e.g., social network analysis) have come to represent the technical core of KM; they enable embedding best practices in many organizational processes. During the past two decades, spurred by the IT revolution, many corporations have institutionalized a KM function. Recognizing that decisions involve information and knowledge, not merely data, KM came to be viewed as a function dealing with acquisition, utilization, and dissemination of knowledge. Over time, KM scholars and managers have developed a number of tools currently in use in organizations. These tools enabled intelligence leveraging: garnering existing data and knowledge to create efficiencies in organizational processes. Table 13.2 provides a select set of KM terms and tools in use today.

As noted in the previous section, in the case of incremental innovation, relatively low levels of ambiguity characterize the discovery, incubation, and acceleration stages. Thus, the individuals or teams that are involved in the discovery are familiar with the markets, customers, and the dominant designs (and the technologies undergirding them). Their tacit knowledge and attendant interpretive frames are robust enough to wade through the databases and knowledge depositories. Similarly, incubation can be modeled as a structured process (e.g., Stage-Gate process) using well-understood tools to gather information about customer preferences and business models, and acceleration has some chance of success because reasonable predictions can be made about cash flows and returns on investment. In incremental innovations, KM, as intelligence leveraging, can enhance the efficiency of the process by enabling the search of market and customer data through knowledge depositories, deriving best practices though the creation of communities of practice, whereby product development teams are brought together for sharing their experiences, and building social networks for the transfer of personal knowledge.

Breakthrough innovation, however, requires that KM functions—acquiring, disseminating, and utilizing information and knowledge—take on a hue that is different from the case of incremental innovation because BI stages, especially discovery and incubation, are characterized by high levels of ambiguity. Very often, the engineers involved in the discovery stage may be familiar with the technical details of the proposed solution, but neither they nor their marketing colleagues understand the markets and customers (who may not yet exist). Indeed, in the case of Technology Drivers, companies typically misjudge how the application markets unfold for the opportunity, where the products are likely to earn the highest returns, and how to meet the cost of capital objectives. In the case of Need Seekers, the individuals who are close to the markets and customers often are blind to the more effective solutions to the customer needs (see Christensen, 2000). Incubation requires market engagement more intensely than can be obtained through traditional market research techniques (Leonard-Barton, 1995), and both prototype and business model development involve “out of the box” thinking increasingly discussed in the literature. Even acceleration, with its focus on scaling the business, is fraught with unknowns regarding new processes, scaling issues, and market inquiries about new applications that require different tools than those that presume a grounding in what is known to be an adequate basis for judgment and decision making.

Breakthrough innovations can be facilitated by KM in the sense of intelligence amplification. By intelligence amplification we refer to the processes of discovery, imagination, and experimentation by which individuals, teams, and organizations expand their base knowledge, perspectives, and practices beyond what is currently available to them. Here, the focus is not on the efficiency of the process but on developing insights, unleashing imagination, and enabling experimentation—activities that are the key to success during discovery, incubation, and acceleration. This in turn requires a focus on interdisciplinary, peripheral, and sometimes speculative information and exceptions (Ruggles, 1997). For example, to develop insights, the individuals (or teams) involved in BI must gain exposure to information about potential markets or applications not in their areas of expertise; they may need to engage the customer more intensely, may need tools to unleash their imagination, and may have to be coaxed to experiment more frequently than in the case of incremental innovation. To unpack the enabling activities in insight development, imagination, and experimentation, we highlight five key functions that are part of knowledge amplification.

1. Information arbitrage

   An information arbitrage function involves the deliberate movement of data and knowledge from one location to another to create value. In the context of breakthrough innovation, where problem–solution links are not obvious, this function allows for creative links of disparate elements of information. Information arbitrage addresses questions such as: How might we couple “problems” and “solutions” in a manner that yields highest returns? In other words, how might we move technologies to their most productive uses and/or identify the most effective solutions to customer problems? Examples of information arbitrage at work in companies include technology fairs, for example, which 3M and others hold for internal personnel. Exposing members of business units or other global regions to the technological capabilities and discoveries that the company has allows 3M to increase the likelihood that someone in the company will articulate a new business opportunity because of their recognition of what is possible in the technology solution space. Another interesting version of information arbitrage stems from an HR policy. At Air Products, the director of New Business Development encouraged his staff members to attend multiple conferences each year to expose themselves to new markets, with the caveat that they could not attend the same conference two years in a row. They were required to learn about new markets each time. Each of these practices of information arbitrage unleashes imagination and enables insight development through specific managerial actions.

2. Customer engagement

   This function involves deep engagement of the customers in the design and use of product. How might we engage the customer in the design process in a meaningful and productive fashion to conceptualize and prototype products? IBM's earliest attempts at developing an electronic book in the mid-1990s took place in partnership with Boeing. The first perceived application was a way to simplify the cumbersome manuals used by aircraft maintenance crews. So the IBM team loaded those manuals into an e-format and asked the maintenance crews to go about their work with the new devices. Three months later the IBM team engaged them to understand their reactions to this novel technology, and a number of iterations regarding the display, battery life, and storage capacity of the first e-book resulted from that experiment. This managerial practice of engaging an innovation team along with a customer partner in experimental learning enabled insight far beyond what any traditional KM technique might have surfaced.

3. Visualization

   This function involves creating graphical (two- or three-dimensional) representations of products and business models that enable individuals (designers and customers) to get a nuanced understanding of the aesthetics and functions of products or the coherence, completeness, and value potential of business models. How might we enable individuals and teams to visualize their products and business models, both of which may be hazy in the beginning in the case of breakthrough products? The first presentation of a concept within Kodak for satellite-based photographs of earth to aid farming were simply dummied photographs that showed possible ways to analyze crop and field size and shape to maximize crop yield. The dummy photos sold Kodak's Venture Board on the idea by helping them imagine the possible.

4. Fail fast

   This principal underscores the need for fast experimentation whereby an idea can be tested without much investment, and the unsuccessful ideas can be weeded out quickly. How might we devise a process to (a) allow individuals and teams to fail fast, (b) learn from the process so that early-stage investment of resources is low, the process is kept iterative and recursive, and thereby (c) enhance the probability of success?

5. Application migration

   This is the phenomenon observed in technology push and breakthrough innovation environments in which a product is launched for one purpose but ends up being used for many others. How might we leverage breakthrough technologies into markets that were never imagined or planned? What are the best ways to speed the diffusion of a breakthrough innovation into niche applications? Analog Devices' commercialization of its accelerometer-enabling computer chip started with a killer app dream of replacing the detonation system for airbags, but in reality started with satellites, gyroscopes, niche video games, and many other smaller market opportunities before the killer app was realized. None of those first markets were predicted or planned, yet market experiments led to new insights.

Implicit in this set of five activities is the recognition that in order to be useful for BI, KM has to assist individuals and teams to “invent” rather than “embed best practices” identified. Each of these KM practices is a managerially controllable action that can be taken to enhance insight development, enable imagination, and encourage experimentation, all of which lead to the outcome of knowledge amplification.

KM can accomplish these through a variety of organizational mechanisms and technology enablers. Organizational mechanisms include individual roles such as knowledge brokers, facilitators, and transition managers and institutional mechanisms such as communities of practice (Wenger, 1988). Knowledge brokers serve as bridges of information by connecting people or teams who have different pockets of information and knowledge. Facilitators are individuals who are trained to guide a discussion group with specific goals. Transition specialists understand the challenges of transitioning between stages and help make the group or organization movement smooth. Technology enablers include physical resources, IT augmented tools, and knowledge depositories.

In what follows, we will illustrate how several knowledge amplification tools facilitate the discovery, incubation, and acceleration stages of BI. Table 13.3 provides a summary for reference.

13.3 KM and Selected Tools for Breakthrough Innovation

13.4 Organizational Implications

In large firms, where new business growth is generated through breakthrough innovations, a KM amplification function is most useful. Especially in the early stages of a breakthrough innovation project, KM has the potential to influence the evolution of the project in value creating ways. To accomplish this, a firm has to develop an appropriate organizational architecture. As shown in Table 13.4, the organizational architecture may include setting up idea labs or knowledge broker roles. Since all these require commitment of resources, different firms may adopt different mechanisms.

A second requirement for implementation is the presence of a supporting culture. As also shown in Table 13.4, the organization and team leaders require a healthy attitude toward multidisciplinary thinking, ideas from outside, courage to undertake low-cost experiments without fear of reprisals for failure, and, finally, the willingness to undertake disciplined inquiry beyond intellectual domains familiar to the firm. This means new business creation teams, their team leaders, and those to whom they report must be willing to admit they do not have the answer and that, in some cases, the answer is unknowable for now. To nurture this culture is the single most important leadership act in the implementation of intelligence amplification.

These firms typically encounter two pitfalls. The first is imposing intelligence leveraging as a KM approach for breakthrough innovations. Intelligence leverage relies on what is already known rather than on learning the new, and is an approach, as we have argued, that is appropriate for incremental innovations. Yet we see this occur all the time in companies, where admitting one does not know is viewed as weak and can result in a career misstep. As mentioned, this is where leaders must step up to enact the appropriate culture changes. A second pitfall is the use of intelligence amplification tools as silver bullets. These tools are to be viewed as facilitating a form of inquiry that is different from the ones typically conducted in organizations, and hence the shift in perspective, rather than the tools themselves, is the critical end point.

13.5 Appendices

Appendix 2: Technology Market Mind Maps

References

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About the Authors

V. K. Narayanan is the Associate Dean for Research, Director of the Center for Research Excellence, and the Deloitte Touche Stubbs Professor of Strategy and Entrepreneurship in Drexel University, Philadelphia, Pennsylvania. His research focuses on three themes: innovation, corporate entrepreneurship, and strategy during industry emergence; political and cognitive process in strategy formulation; and the epistemological foundations of strategy. His work has spanned biopharmaceutical, aerospace, and information industries, and educational and innovation-focused governmental agencies. His consulting assignments have been with large pharmaceutical and high-technology companies primarily in strategy implementation and corporate innovation. Direct correspondence related to this article to Le Bow College of Business, Drexel University, [email protected].

Gina Colarelli O'Connor is Professor of Marketing & Innovation Management and Associate Dean for Academic Affairs at Rensselaer Polytechnic Institute's Lally School of Management. Her research examines how established companies link advanced technology development to market opportunities and how they build capabilities for breakthrough innovation. She has published numerous articles in refereed journals including the Journal of Product Innovation Management, Journal of Marketing, Organization Science, and R&D Management, and is co-author of several books about breakthrough innovation in mature industrial firms. Gina teaches and consults on the topic of organizational change for breakthrough innovation. Direct correspondence related to this article to Gina Colarelli O'Connor Lally, School of Management, Rensselaer Polytechnic Institute or [email protected].