In 1850, the president of Harvard University remarked in surprise that over half of its graduates were going into a new profession, one that hadn’t even existed a few decades earlier.1 Although people were flocking to this new profession, no schools yet trained those wanting to join its ranks, and no programs existed to provide certifications. Yet this new profession would go on to become the largest professional class in history, spawning thousands of new colleges, programs, and schools.
This new profession, which today we call management, hadn’t previously existed because there had been no need for it before the industrial revolution. At that time, pre–industrial revolution, virtually all companies were small businesses, with thirty or fewer people employed in small workshops. But during the industrial revolution, the social order that had existed for centuries began to rapidly change. First, the formation of the Dutch East India Company in the 1600s laid the groundwork for the large corporation, with the creation of joint stock ownership. Then, technology, particularly the steam engine, changed the entire economic landscape, transforming it from an ecosystem of small workshops into a handful of giant organizations. Suddenly, the world faced new problems, namely, how to coordinate the exploding rail system so the trains ran on time, how to hire the armies of laborers now needed and how to pay them for their work, how to optimize production, and how to coordinate a huge enterprise. Business schools were founded to train the leaders who could solve these problems, primarily how to coordinate, optimize, and compete. Frederick Winslow Taylor, the father of modern management, was literally trying to answer the question of what size of shovel to use when a worker was shoveling iron ore, and at what pace, to optimize production. At the heart of these challenges was the question of how to capture value in the gold rush created by industrial manufacturing technology.
But in the last few decades, new forms of digital technology, starting with the transistor and all its digital ancestors, including microprocessors, sensors, and connectivity, have again transformed that landscape. Digital technologies have lowered the barriers to participate and create while magnifying the potential impact of such participation. So have geopolitical, social, and educational changes. As more and more individuals participate, the pace of invention, creation, and adoption has exploded while the dominance of big companies has fallen. For example, consider that the US patent application rate increased more than sevenfold.2 Over fifty million new businesses are created each year worldwide today.3 Meanwhile, from the 1930s until today, the number of years a Fortune 500 company stays on that list of titans has fallen from seventy-five to just eleven.4
In this more uncertain environment, the problems we face are less and less about how to capture value, which was the primary concern during the first industrial revolution, and more and more about how to iterate, explore, and innovate or, more generally, how to create value. But if the primary occupation of classical management has been capturing value in a world of relative certainty, what are the right frameworks for creating value in a world of uncertainty? Classical management has comparatively little of substance to say on this front. As the environment continues to become more uncertain and complex, leaders will require new perspectives and tools to solve new problems.
Fortunately, many new frameworks have emerged to fill this need. In computer science, agile methodologies emphasizing rapid sprints and iterative cycles were developed as a reaction to the failure of earlier waterfall planning methodologies (sequential approaches in which one step is not initiated until the preceding step is completed). Similarly, human-centered design that expresses empathy for customer problems was a reaction to more-traditional stage-gate product development. Likewise, the lean-startup methodology, focusing on rapid experimentation and minimum viable products, was created to countervail the armchair-quarterback approaches to business planning. Similarly, business model innovation approaches have become popular and highlight the possibilities of creating value with new business models.
Each of these frameworks has added immensely to our emerging understanding of how to manage in a world of uncertainty. But are they the complete answers? Does the lean startup offer all that we need in this complex world? Possibly not. Although each framework offers useful tools, they may offer only part of the eventual solution set for a world of uncertainty.5 Indeed, as a thought experiment, ask yourself, Is design thinking or the lean-startup approach likely to produce the next SpaceX, the next AI breakthrough, or the next figurative transistor? Or does making a “moonshot” require something different—imagination, commitment, and even denial of easily observed consumer reactions (consumers hated the Aeron chair, and Steve Jobs notoriously refused to listen to customer feedback on radical products). Just as it took almost two hundred years from the start of the industrial revolution to the formation of the first business school (in 1881) and then another century to refine the discipline of management, it may take more time to develop the mature discipline to navigate uncertainty and value creation.
Arguably, more radical innovations are characterized by both more uncertainty and situations with greater experimentation costs (the costs of experimenting with reusable rockets are orders of magnitude greater than those associated with app development) (see figure 2-1).6 As Nathan argues in his previous book, we should be looking more broadly for the theories and frameworks that are still missing, the theories that would help us better navigate and prosper from both uncertainty and opportunity.7
This book represents one of these frameworks, particularly in its attention to the behavioral limitations that keep us from seeing and capturing radical opportunities or transformational changes. But it does not represent the full spectrum of ideas, frameworks, or theories that can help us prosper in an uncertain world.
The Behavioral Innovation Opportunity
There are more opportunities, theories, and frameworks than just those described in this book. We face an opportunity for a behavioral revolution in innovation and transformation by understanding and addressing the human roadblocks to transformational change. Just as economics has been transformed by a behavioral perspective showing how cognitive factors (i.e., the human elements) reshape our understanding of rational decision making, so can a behavioral revolution in innovation and transformation revolutionize how we see these disciplines, primarily by uncovering and addressing the human limits to these activities. Since we have focused on the behavioral limits to transformation in this book, let us focus a moment on innovation (although the same observations apply to transformation).
Behavioral innovation can be defined as the application of cognitive sciences (e.g., psychology, applied neuroscience) to study the microprocesses of innovation (including the biases that limit innovation) and the mechanisms to address these biases. Thus, behavioral innovation is as much a scaffolding for a conversation between disciplines as it is a new discipline to examine how innovation and transformation happen. In suggesting such a behavioral revolution, we are not ignoring other people’s efforts to study the role of psychology, emotion, perception, and other forces on innovation. Clearly, many others have made valiant efforts. But the business world lacks a more systematic view of the problems or their proposed solutions—a view like that emerging in behavioral economics. A study of such problems and solutions may now be particularly promising, since the emerging application of neuroscience to innovation might take us a step beyond prior behavioral revolutions. Rather than just identifying that there are biases, as behavioral economics has done, neuroscience can define precisely where and when biases occur and then develop prescriptive tools to address these biases.
For example, the behavioral transformation process described in this book represents our attempt to contribute to this conversation by integrating psychological and neurological perspectives to help people overcome many common barriers to innovation and transformation. But the process we describe is simply one small component of the larger conversation; it neither describes the entire domain of behavioral innovation nor contains all the answers that may emerge from this new domain. By contrast, as a discipline, behavioral innovation, or behavioral transformation, can reshape how we think about innovation, change, and reaching our potential both as individuals and as organizations. Below we briefly describe some inspiration for the opportunities in such a conversation.
Behavioral Innovation Draws on Multiple Fields
As a domain, behavioral innovation draws on multiple fields, particularly the cognitive sciences, to understand the forces shaping innovation microprocesses and how to transform them. For example, although psychology has been seriously applied to economic decision making, far fewer have studied how psychology affects innovation. For example, psychological studies have shown that people tend toward incremental innovation. By contrast, we praise innovators who, like Elon Musk, seem to break free of incrementalism to revolutionize new domains like space travel, transportation, and energy. But we have paid far less attention to how people like Musk make long, imaginative leaps than we have to the importance of such leaps. Likewise, narrative and emotion are rarely discussed as serious elements of innovation, beyond their ability to persuade investors. But narratives are one of the oldest, most intuitive tools; they are wired deep in the brain, and their full potential remains to be understood and applied.
Similarly, our emotions aren’t entirely separate from our decision making. Yet the conversation about the emotional side of decision making rarely gets serious attention beyond crude descriptors like intuition or gut feel (for a notable exception, see the work of professor Quy Huy).8 Finally, beyond the kinds of neural indicators discussed here, neuroscience holds immense potential to reveal, through big data, the precise source of the biases that impede us and to suggest how to counteract them. Indeed, applied neuroscience might ultimately reshape human behavior and beliefs. In sum, these cognitively related fields—psychology, neuroscience, semiotics, and other fields—promise to help us more deeply understand and overcome the forces that hold us back from more-desirable futures.
Behavioral Innovation Provides Insights on Innovation Processes
These interdisciplinary lenses can provide insight on how to create real innovation and transformation. For example, the search for, commitment to, and development of innovations—at the heart of our story here—can be viewed in a new light, through a behavioral innovation lens. Rather than just asking people to innovate, which seems to be the current demand in most big organizations, if we could understand the psychological, cognitive, and identity biases that stymie innovation, we could better confront them.9 For example, studies Thomas conducted with his students reveal that simply explaining the elements of creativity to people can increase their creativity by 30 percent on average (range, 15 to 200 percent).10 We are currently studying how our natural predilections for being early adopters, for art or science, for certain careers, or for personality traits affects how we look for and respond to new ideas. In the future, we could match these predilections to gene types to understand the interplay between biology and choice in creativity. Alternatively, we could uncover the antecedents and obstacles to negative capability, or the ability to entertain uncertainty. Perhaps most importantly, we could design more tools like those described in this book to overcome the behavioral tendencies in the search for innovation or the execution of transformation. For example, we have described using science fiction as a tool to overcome the psychological tendency to search locally for new ideas, but what other tools could we use to imagine the adjacent possible?
Likewise, although some good research has been done on how the commitment to pursue uncertain innovations emerges and evolves (e.g., the critical role of interpretation and framing), there is still an incredible amount to understand.11 For example, what aspects of the structure, content, and channel of a narrative produce commitment? Why, for example, did comic books yield so much more commitment among readers than other approaches did? If everyone starts using comics, will the mere exposure dull their impact, or is the blend of visual and textual motivating in an enduring way? What is the role of reason versus emotion in commitment, and which comes first, or how are they intertwined? What impact do nonnarrative tools have on the suspension of disbelief and in creating commitment (e.g., tangible artifacts, analogies, metaphors)? How do we move beyond simplified tools, like archetypes, to understand the scales of behavioral tendencies that affect how people commit themselves or change their commitments? What are the biases and other limitations of our existing approaches? For example, what are the motivational alternatives to the “burning platform” approach that so many companies try to use for transformation (named after the now famous email from Nokia’s CEO trying to motivate a company transformation in response to the smartphone era set off by Apple, by describing their company as standing on a “burning platform.”) Do many our existing frameworks reinforce or counteract the tendency towards incrementalism (e.g., the tendency of the lean-startup approach or design thinking to reinforce local searching)?
Finally, in the development of innovation, neuroscience shows incredible promise to shape the direction, adaptation, and governance of innovation. For example, several once-radical innovations are now quite ordinary to us (consider that at the start of the twentieth century, flying in an airplane was the realm of fantasy and that, by the end of that century, most people only concerned themselves with complaining about a late flight departure). In studying neurological responses to new things, how do we accurately parse out which innovations consumers might adopt in the future, and which things are just too novel? And how will the responses evolve over time as the innovations become more familiar? Alternatively, how do different types of framing affect our commitment to, our development of, or our acceptance of an innovation (e.g., framing in terms of outcomes, aspirations, or analogies)? Despite some good work in both neuroscience and creativity, many questions remain unanswered.12 These questions represent important opportunities to understand innovation, transformation, and change through a more integrative behavioral lens.
Moving Forward
In this book, we have raised more questions than we answered. We have tried to suggest that behavioral innovation and transformation could be new disciplines for a world of increasing uncertainty. These disciplines have relevance to both practitioners and academics and to organizations and individuals. We don’t claim to have all the answers, but we are simply extending an invitation to join us in the search for them. If all of us are going to create the futures we desire, we need new tools, new approaches, and new inspiration. We are working toward creating the Transformation Lab, which could be a place to explore these ideas. Together, as uncommon partners, we can create that world—the world we want to live in together.