Emerald Publishing Limited
Copyright © 2017 Emerald Publishing Limited
The accelerated pace of knowledge development, well documented by scholars in management, sociology, history of science and other fields, gives rise to complexity and uncertainty in today’s business and scientific environments (Powell & Snellman, 2004). In most fields of expertise, the rate of new knowledge development requires people to invest considerable time just to stay current. In technical fields, the explosion of new knowledge leads inexorably to greater specialization. Specialized jargon proliferates and experts struggle to keep up with developments in even closely related fields.
On the one hand, the so-called knowledge explosion leads to narrower and deeper areas of specialization (Edmondson & Nembhard, 2009). Fields thus spawn subfields. For instance, as new medical discoveries proliferate, the number of subspecialties grew from about 40 in 1985 to 100 in 2000 (Donini-Lenhoff & Hedrick, 2000), with the promise of more to come. Internal medicine divides into subspecialties like cardiology, endocrinology, gastroenterology, hematology, infectious diseases, nephrology, oncology, pulmonary diseases, rheumatology, and so on. Radiology today encompasses angiography and intervention radiology, body imaging, diagnostic radiology, diagnostic roentgenology, diagnostic ultrasonography, neuroradiology, radiation oncology, radiation therapy, vascular-interventional radiology, nuclear radiology, and pediatric radiology. Each of these subfields faces continued change. Technologies that were barely used 10 years ago (e.g., virtual reality) are having a major impact in various medical subfields today (e.g., cybertherapy). The minimum number of years of study required to graduate in those fields and subfields has slowly but steadily increased, and beyond formal training, experts must invest ongoing effort in remaining current. The promise of specialization lies in the depth of understanding experts can offer.
On the other hand, concurrent with the rise of narrow and deep expertise, the problems facing organizations and society have not, of course, narrowed accordingly. Instead, they are increasingly complex and multifaceted. Addressing them requires multidisciplinary approaches. Consider what it takes to design, build, or maintain high-tech infrastructure projects, intelligent buildings, avionics systems, mobile telephone networks, or banking communication systems. Even products such as shoes that appear simple can become tremendously complex: for example, 50 biomechanical engineers, industrial designers, and electromechanical experts teamed up to make asymmetrical spikes for Olympic gold medalist Jeremy Wariner (Hochman, 2008). In healthcare, organizations such as Hacking Health show that varied professionals’ perspectives must be considered, in addition to the patients’ perspectives, to produce successful innovation (Dionne & Carlile, 2016). In short, dealing with today’s complex products and systems requires solving dozens, sometimes-even hundreds, of interconnected problems (Davies & Hobday, 2005).
Thus, to solve complex problems and innovate in ways that reflect the increasing rate of change, today’s organizations must take advantage of deep specialized knowledge and manage knowledge integration across these domains of expertise at the same time. These two opposing challenges create the need for organizations to master extreme teaming. This chapter thus builds a case for the importance of extreme teaming for a new and increasingly important type of initiative – one that brings together diverse areas of expertise to solve a particularly challenging societal or business problem.
In the pages that follow, we set the stage with a particularly compelling case study to illustrate the power of extreme teaming at its best. We then shift to discuss recent work on business ecosystems, to emphasize the utility of a new perspective on how organizations thrive and innovate as a result of their interactions with other organizations in highly interdependent systems. We finish the chapter by proposing essential features of innovation efforts employing an extreme teaming approach.
Extreme Teaming that Saved 33 Lives
The success of the now legendary mining rescue in Chile in 2010, while widely covered in the news and immortalized in a major Hollywood film, is not widely understood. We offer the rescue as an extraordinary example of extreme teaming, to illustrate the enormous potential of diverse experts coming together to innovate to overcome a nearly impossible challenge. The case illustrates the centrality of diverse perspectives in producing innovation, as well as the importance of leadership in making it happen.
An Unprecedented Challenge
Although mining accidents often present immense hurdles that make rescue unlikely, the situation at Chile’s San Jose copper mine that began on August 5, 2010 was unprecedented on several dimensions. The most daunting of these was the extraordinary depth – 700 meters below ground – at which the miners were trapped in the aftermath of an explosion that left half a million tons of rock blocking the mine's entrance of the mine. The number of miners trapped (33), the hardness of the rock, the instability of the land, and the complete inadequacy of provisions for the trapped men (enough food for two men for 10 days) combined to make the possibility of rescue appear all but impossible to consulted experts. A mining rescue in the United States just a few years earlier, in which nine men were trapped 240 feet below ground, had been considered at the time a remarkable feat (Robbins, 2007). In Chile, early estimates of the possibility of finding anyone alive – put at 10% – diminished sharply two days later when rescue workers narrowly escaped the secondary collapse of a ventilation shaft, taking away the initial best option for extracting the miners. At that point, no expert considered rescue of the 33 men a reasonable possibility. Nonetheless, within 70 days all of them would be alive and reunited with their families.
This outcome was the result of an extraordinary cross-industry teaming effort by hundreds of individuals spanning physical, organizational, cultural, geographic, and professional boundaries. Engineers, geologists, drilling specialists, and more came together from different organizations, sectors, and nations to work on the immensely challenging technical problem of locating, reaching, and extracting the trapped miners. Senior leaders in the Chilean government provided resources to support the on-site efforts.
How Senior Leadership Triggered Extreme Teaming
In Santiago, Chile’s capital city, President Piñera and Laurence Golborne, the Minister of Mining, met on the morning of August 6, 2010. Piñera then sent Golborne to the mine with a mandate to do whatever possible to bring the miners home, sparing no expense. Golborne and Piñera quickly reached out to their networks of colleagues around the world. As the president put it, “We were humble enough to ask for help” (Robbins, 2007). Michael Duncan, a deputy chief medical officer with the U.S. National Aeronautics and Space Administration (NASA), reported that the Chilean officials said, “Let’s try to identify who the experts are in the field – let’s get some consultants in here that can give us the best information possible.” Duncan brought experience with long space flights to help solve concerns related to the miners’ physical and psychological survival in their small quarters. NASA engineers played a crucial role in the design of the escape capsule that would be used in the final stage of the rescue to extract the miners from the refuge.
The Chilean Carabineros Special Operations Group – an elite Chilean police unit for rescue operations – had arrived a few hours after the first collapse. Yet their initial attempt at rescue had triggered that devastating secondary shaft collapse. As news of a mine cave-in spread, family members, emergency response teams, rescue workers, and reporters poured into the vicinity. Meanwhile, the Chilean mining community dispatched experts, drilling machines, and bulldozers. At the request of President Pinera, Codelco, the state-owned company, sent a senior mining engineer to lead the effort; Andre Sougarret, known for his engineering prowess, calm composure and ease with people, brought extraordinary technical and leadership competence to the project.
Parallel Teaming Efforts
Sougarret formed three teams to oversee different aspects of the operation. One searched for the men, poking drill holes deep into the earth in the hopes of hearing sounds to indicate that the men were alive. Another worked on how to keep them alive if found, and a third brainstormed solutions for how to extract them from the refuge. The first team came up with four possible rescue strategies. The most obvious through the ventilation shaft, was quickly rendered impossible. The second strategy, drilling a new mine ramp, also soon proved impossible as the instability of the rock was discovered. The third, tunneling from an adjacent mine a mile away, would have taken 8 months and was thus soon excluded. The only hope left was the last strategy – drilling a series of holes at various angles to try to locate the refuge.
The extreme depth of the refuge, along with its small size, made the problem of location staggeringly difficult. With the drills’ limited precision, the odds of hitting the refuge with each laborious drilling trial were about one in eighty. Even that was optimistic, because the location of the refuge was imprecisely known. Maps of the tunnels had not been updated in years. Additional technical challenges disallowed drilling straight down from the top of the mine, further exacerbating the drilling accuracy problem.
Rescuers soon divided into subteams to experiment with different strategies for drilling holes. More often than not, these teams failed to achieve their desired goals in any individual drilling attempt, but they soon learned to celebrate the valuable information each attempt provided, such as revealing features of the rock, to inform future action. For instance, the drillers and geologists discovered that fallen rock had trapped water and sedimentary rocks, increasing drill deviations and further exacerbating the odds of reaching the refuge in time. This was the kind of technical detail that engineers had to quickly incorporate into their plans, which shifted rapidly with each passing day. One dramatic change to procedure was the discovery and use of frequent, short action-assessment cycles. In normal drilling operations, precision was measured after a hole was completely drilled. Here, in contrast, drillers realized that to hit the refuge, they would have to make measurements every few hours and promptly abandon holes that deviated too much, discouraging as that might be. As they learned more about the search challenge, the odds of success diminished further, with one driller putting it at less than 1%.
In this extreme story, different clusters of experts came up with remarkably complementary pieces of an ultimately viable complex solution. Of course this didn’t happen by accident, but rather was enabled by a particular type of leadership. For example, a Chilean geologist named Felipe Matthews brought a unique technology for measuring drilling with high precision that he had recently developed. Matthews came to the site, and, working with several other strangers, discovered that his measurements were inconsistent with those of other groups; a rapidly improvised set of experiments showed that his equipment was most accurate. Matthews was then put in charge of measuring drilling efforts going forward. In this way, roles emerged and shifted as the teaming went on.
Leaders of different subgroupings met routinely every morning and called for additional quick meetings on an as-needed basis. They developed a protocol for transitioning between day and night drill shifts and for routine maintenance of machinery; “We structured, structured, structured all aspects of execution.” As drill attempts continued to fail, one after another, Sougarret communicated gracefully with the families. Despite these failures, Sougarret and his new colleagues persevered.
A NASA engineer who went to Chile in late August teamed up with engineers in the Chilean navy to design the rescue capsule, after first going back to the United States to pull together a group of 20 NASA engineers. The engineers developed a twelve-page list of requirements, used by the Chilean navy in the final design for the capsule, called the Fenix. The Fenix interior, just large enough to hold a person, was equipped with a microphone, oxygen, and spring-loaded wheels to roll smoothly against the rock walls.
On October 13, 2010, the Fenix started its life-saving runs to bring miners one by one through the 15-minute journey to safety. Over the next two days, miners were hauled up one by one in the 28-inch-wide escape capsule painted with the red, white, and blue of the Chilean flag. After a few minutes to hug relatives, each was taken for medical evaluation. The resulting fervor of the national – and even global – celebration cannot be underestimated.
How Leadership Enables Extreme Teaming
The Chilean rescue presents a superb example of teaming at its best. Reflecting on the situation, one readily comprehends that a top-down, command-and-control approach would have failed to achieve this stunning outcome. No one person, or even one leadership team, or one organization or agency, could have successfully innovated to solve this problem. It’s also clear that simply encouraging everyone to try anything they wanted could have led to chaos or harm. It required extreme teaming. Facing the unprecedented nature of the disaster, multiple temporary, constantly shifting groups of people working separately on different types of problems, and coordinating across groups, as needed, was the only viable approach. These separate efforts – managerial and technical – were intensely focused.
This approach necessitated progressive experimentation, a kind of rapid-cycle learning. Diverse technical experts worked collaboratively to design, test, modify, and abandon options, over and over again, until they got it right. They organized quickly to design and experiment with various solutions, and just as quickly admitted when these had failed. They willingly changed course based on feedback – some obvious (the collapse of the ventilation shaft), some subtle (being told that their measurements were inaccurate by Matthews intruding mid-process with a new technology). Perhaps most important, the engineers did not take repeated failure as evidence that a successful rescue was impossible. Unfortunately, extreme teaming involves risk. And risk necessarily brings both success and failure. Fortunately, there is nearly always much to be learned from the failures to inform next steps.
Finally, the support of senior leadership – not just the technical leadership on the front lines of innovation – was a critical input to the success of this extreme teaming process. Leadership’s commitment to the initiative gave others motivation and the protection they needed to take technical and interpersonal risks that are integral to extreme teaming. This turns out to be important in many business organizations where extreme teaming is employed by diverse technical experts to innovate.
As this example demonstrates, extreme teaming can produce awe-inspiring results. The problem is that its success can be too easily thwarted by communication failures at the boundaries between professions, organizations, and industries. As individuals bring diverse expertise, skills, perspectives, and goals together in unique configurations to accomplish challenging goals, they must overcome subtle and not-so-subtle challenges of communicating across boundaries. Some boundaries are obvious – being in different countries with different time zones, for example. Others are subtle, such as when two engineers working for the same company in different facilities unknowingly bring different taken-for-granted assumptions about how to carry out this or that technical procedure to collaboration. To understand the challenge, it’s helpful to review how industries operate in business ecosystems.
From Industries to Ecosystems
A new perspective on modern industries as interconnected networks of organizations, technologies, consumers, and products has led to a conceptualization of the activities and relationships taking place within industries as ecosystems (Iansiti & Levien, 2004). The computing industry is perhaps the most dramatic and well-documented example of this interconnectedness, exhibiting an astonishing degree of interaction between organizations bringing modular products and services together to produce value for consumers and business customers. Modularity allows interchangeability so long as relationships between parts are standardized and codified. The essential insight in this work is that the success of many companies is interdependent with (rather than at odds with) that of other firms in an ecosystem. In this way, organizations cannot thrive without a thriving ecosystem of suppliers, customers, and even competitors who spur learning and innovation. A company may thus experience a dramatic and unexpected loss due to failures occurring in suppliers or customers – rather than in their own operations. If the industrial economy was driven by economies of scale, the knowledge economy is increasingly driven by the economics of networks and platforms.
Historically, the way organizations structure activities to promote efficiency tended to encourage specialization. In a review of the historical development of the American legal profession, for example, Abbott (1988) showed that division of labor in large law firms came with a high degree of specialization and led to a dramatic increase in productivity. More generally, specialization occurs through an industrial organization approach that builds on the development of sophisticated contractual arrangements and provides organizations with external economies, that is a decrease in the average costs of doing business (Robertson & Langlois, 1995). Business and industrial activities become subject to more in-depth division and dispersion and organizations are thus prone to identify, cultivate, and exploit the core competencies that make their growth possible (Prahalad & Hamel, 1990). Organizations set out to be leaders in their field, focus on their strong points, and fine-tune them.
However, efficiency gains are not enough to undergird sustainable competitive advantage (Teece, 2014), and pressure is mounting for organizations to develop ways to deal with the increasingly complex problems that come with the constant demands for innovation. This is challenging. So much that executives from powerful multinational companies like GE claim that today’s problems are too big for them to solve alone and that to do so they need to collaborate like they never have before (Nagji & Walters, 2012). Successful organizations have become fast, timely expertise integrators for fragmented, scattered, and thinly spread knowledge to be available to the right people in the right place at the right time (David & Foray, 2002).
Thanks to advances in evolutionary economics (e.g., Nelson & Rosenberg, 1993; Nelson & Wright, 1992), our understanding of the economic activity has evolved alongside the phenomena described above. It is gradually moving from a view that focuses on industries and markets to study enterprise performance to one that considers organizations as part of innovation systems, which support the development of knowledge between several actors (e.g., for-profit and nonprofit organizations, governments, universities, research centers, and consumers). These actors are seen as diverse and the relationships between them as well as the institutions influencing them form innovation systems.
From this perspective, research on learning and innovation emphasizes the dynamics of learning by interacting and its co-regulation (e.g., Freeman, 1987; Lundvall, 1988). For instance, Lundvall and his colleagues (e.g., Jensen, Johnson, Lorenz, & Lundvall, 2007; Lundvall, 1992; Lundvall & Johnson, 1994; Lundvall, Johnson, Andersen, & Dalum, 2002) posit that innovation should be regarded as an interactive process where organizations do not learn and innovate in isolation, but in interaction with other actors. Such viewpoint goes beyond the contributions of Arrow’s analysis of learning by doing (1962) and of Rosenberg’s learning by using (1982) by taking account of several parties interacting together as they seek solutions to complex problems. As reflected in abundant scholarly works that call for more cross-sector interactions (Googins & Rochlin, 2000; Selsky & Parker, 2005; Senge, Smith, Kruschwitz, Laur, & Schley, 2008), three-time Pulitzer-Prize Winner Thomas L. Friedman noted the enormous potential of today’s innovation systems:
It is now possible for more people than ever to collaborate and compete in real time with more other people on more different kinds of work from more different corners of the planet and on more equal footing than at any previous time in the history of the world. (Friedman, 2006, p. 8)
The premises of learning by interacting within innovation systems are threefold: (1) organizations are not self-sufficient; (2) they cannot generate all the necessary resources internally, and (3) they must mobilize resources from other entities in their environment if they are to survive (Meeus, Oerlemans, & Hage, 2001). Firms, governments, universities, consumers, etc. can all contribute in a different and interactive way to the innovation process through the cross-fertilization of knowledge around complex problems and approaches to solve them. More or higher interactive learning capabilities enable organizations to reach higher degree of novelty in their ways to solve complex problems and innovate. Organizations therefore need to master complex learning processes that allow them to proactively integrate dispersed knowledge for skills to be continually developed, refined, and updated (Amin & Cohendet, 2004).
This need to go outside organizational boundaries to innovate is now general knowledge among managers, thanks to the pioneering work on open innovation (Chesbrough, 2003). Organizations have been exploring and continue to explore the full potential of “purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation” (Chesbrough, 2006b: 1). However, while this literature has developed rapidly in recent years, it has remained mostly focused on contracts and other strategic mechanisms through which organizations may accelerate the generation and commercialization of innovation (e.g., licensing out and licensing in, joint ventures, spin-offs and spin-ins, and acquisitions). Organizational challenges associated with open innovation have been studied extensively but there is a dearth of research at the individual- and team-level of analysis (Vanhaverbeke, Chesbrough, & West, 2014; West, Vanhaverbeke, & Chesbrough, 2006). Understanding factors associated with effective extreme teaming, the topic of this book, expands the possibilities for open innovation, and leverages research on teams and leadership to do so.
By gaining a better understanding of how interorganizational collaboration works at the group-level, we hope to provide much needed support to managers. These activities, for the most part, are extremely challenging (Majchrzak, Jarvenpaa, & Bagherzadeh, 2014). Most managers remain ill equipped to effectively lead extreme teaming endeavors because these collaborations pose different challenges than those that managers typically face when leading teams inside their organization. We argue that gaining a better understanding of team-level activities and leadership is key for organizations to take advantage of the innovation systems in which they find themselves.
Innovation in Cross-Boundary Teams
Cross-boundary teams allow organizations to leverage the potential of innovation systems. While a great variety of knowledge is accessible to organizations, it is the integration of knowledge that fuels innovative capabilities (Grant, 1996), just as happened in the Chilean mining rescue, and research shows that it is integration at the group-level that provides the most benefits (Van Den Bosch, Volberda, & De Boer, 1999). Teams have been described as “the basic building block of any intelligent organization” (Pinchot & Pinchot, 1993, p. 66), “the norm in a learning organization” (Senge, 1994, p. 355), and the unit of organizational learning (Edmondson, 2002). More recent work shows that teams are becoming increasingly common in the production of innovation (Jones, 2009; Wuchty, Jones, & Uzzi, 2007), and that teams are more likely than individuals to develop more innovative solutions (Singh & Fleming, 2010; Uzzi, Mukherjee, Stringer, & Jones, 2013). In a world where organizations are hard pressed to continuously produce innovation to maintain sustainable competitive advantage, the trend toward more dynamic and complex forms of teamwork will continue.
Recombining dispersed heterogeneous bits of knowledge is at the source of radical innovation. John Stuart Mill, two centuries ago, highlighted the creative friction that takes place at the interstices of knowledge domains when individuals interact together:
It is hardly possible to overrate the value… of placing human beings in contact with persons dissimilar to themselves, and with modes of thought and action unlike those with which they are familiar… Such communication has always been, and is peculiarly in the present age, one of the primary sources of progress. (Swedberg, 1990, p. 3)
This quote highlights what takes place at the interstices of knowledge domains. Burt (1992, 2000, 2002) calls these spaces “structural holes” – gap between two discrete groups with nonredundant knowledge. Structural holes act like insulators in an electric circuit: knowledge within each group is buffered and develops within a distinct logic. Innovation emerges from selection and synthesis across the structural holes between groups (Burt, 2004). As a result, the knowledge recombination perspective has become a significant stream of research both in economics (e.g., Boschma, 2005; Cohendet & Llerena, 1997; Nooteboom, 2000) and management (e.g., Fleming, Mingo, & Chen, 2007; Gruber, Harhoff, & Hoisl, 2013; Nerkar, 2003; Nerkar & Paruchuri, 2005).
Newly formed, temporary teams that assemble members from various backgrounds often set the stage for knowledge recombination and innovation. While research has shown benefits of team member familiarity (Edmondson, Bohmer, & Pisano, 2001; Huckman, Staats, & Upton, 2009), studies that focus on creativity and innovation show that when people have worked together many times previously, their projects exhibit less innovation than those with unfamiliar team members (Skilton & Dooley, 2010). Social network theory suggests that highly diverse teams can obtain valuable knowledge from interpersonal relations outside the team. For example, among others (e.g., Guimera, Uzzi, Spiro, & Amara, 2005; Reagans & Zuckerman, 2001), a study of over 2000 artists who worked on 474 creative teams developing new material for Broadway from 1945 to 1989 by Uzzi and Spiro (2005) showed that teams with highly familiar members were less effective than teams with less familiar members, in both artistic and financial terms. Other research shows that when teams have spent a long time working together, their members become cut off from new sources of information, and their thinking starts to converge (Katz, 1982). This blinds them to alternative perspectives that could improve performance (Janis, 1971). Moreover, the longer team members work together, the more likely it is that they will get “stuck in a rut” of habit or routine, and end up exhausting the idea permutations available (Gersick & Hackman, 1990). While routines have value in terms of boosting efficiency and reducing uncertainty over team working approaches, they ultimately impede innovative performance because they limit teams’ ability to generate new ideas to solve complex problems (Hirst, Van Knippenberg, Chen, & Sacramento, 2011).
From an interactionist perspective, it is the complex interaction between individuals and their work situations that drives creativity and innovation (Woodman, Sawyer, & Griffin, 1993). “By interacting and sharing tacit and explicit knowledge with others, the individual enhances the capacity to define a situation or problem, and apply his or her knowledge so as to act and specifically solve problems” (Nonaka, Von Krogh, & Voelpel, 2006, p. 1182). For instance, Hargadon and Sutton (1997) showed how designers managed to team across fields (mechanical, industrial, electrical, software engineering, as well as human factors and ergonomics experts) and made original connections between the old solutions of one industry and the new problems of another. As a group of people from eclectic backgrounds, such designers innovated by spanning boundaries and moving knowledge from one place to another – from energy to medical products, to financial services, to the public sector, and more.
In their study of open-ended problem solving, Hargadon and Bechky (2006) showed that management and design consultants relied on moments when people’s perspectives and experiences were brought together in work groups to bear on problems rather than relying on the cognitive skills of participating individuals. Kurtzberg and Amabile (2001) also moved away from individuals’ creative potential, emphasizing instead how creative minds interact in groups. Many other contributions in the creativity and innovation literature emphasize the benefits of social dynamics that occur during extreme teaming (e.g., Harvey, 2014; Paulus & Yang, 2000). Radical innovation thus occurs when cross-boundary teams make connections between heterogeneous knowledge.
Stories that portray the combinatorial nature of innovation in extreme teaming endeavors increasingly populate the news world. For example, to figure out how electronic devices can be adapted to the needs of people with mobility issues, Samsung brought together a team of people that was able to span a large variety of subfields of human-computer interaction and electrical engineering. That team has so far been able to develop a demo tablet that is controlled by a human brain (Young Rojahn, 2013). Spanning knowledge boundaries and successfully making connections between heterogeneous materials is also at the core of the work of teams in the healthcare and medical sectors: 3-D printing is now used to print teeth-straightening braces and expert knowledge in artificial intelligence and mobile technology is at the foundation of an application that serves as a diagnostic tool (Kotler, 2013).
Extreme teaming efforts are examples of what Nonaka and Konno (1998) termed “ba.” Meaning “space” in Japanese, the concept of ba denotes a context in which people enter a space of shared emotions that encourages interaction to create new knowledge (Nonaka, Toyama, & Konno, 2000; Von Krogh, Nonaka, & Rechsteiner, 2012). Consistent with a recent stream of literature that has identified the importance of collective forms in the process of invention and innovation (e.g., Drazin, Glynn, & Kazanjian, 1999), a ba gives the opportunity to individuals to become active participants in knowledge creation activities, and engage in what Tsoukas has termed “productive dialogue” (2009). Extreme teaming leverages the opportunity for active boundary-crossing dialogue and inquiry that allow people to adjust and reframe their own knowledge, to examine their own perceptions in a different light and reflect on experience to generate ideas and produce innovation (Edmondson, Dillon, & Roloff, 2007). In accordance with our efforts with this book, Nonaka et al. (2006) also posit that research needs to help identify ways in which leaders can develop Bas to foster knowledge processes in teams.
The so-called knowledge explosion has given rise to narrower and deeper areas of specialization. In this way, fields spawn subfields, and domain knowledge can quickly become obsolete, such that expertise is difficult to develop and to keep current.
Problems, in turn, do not narrow along with expertise; instead many problems, especially so-called “wicked problems” are multifaceted and complex. Complex problems require tackling many interconnected issues.
Integrating knowledge across domains is required for solving today’s most pressing and complex problems.
The 2010 Chilean mine rescue is an example of the power of collaboration across knowledge domains. Leadership played an important role in enabling the rescue against overwhelming odds.
The business world has become more complex, and the need for innovation ever greater. Organizations can rarely focus on a single industry to capture value, and instead must continually collaborate beyond their boundaries. Ecosystems have replaced industries, and organizations must learn to develop knowledge “in the open,” acquiring ideas and spinning others out in order to maintain and undergird their competitive advantage.
Innovation takes place in new extreme teams where heterogeneous knowledge inputs are integrated to produce new and useful products, services, processes or business models. Unfamiliar connections between entities with different perspectives fuel creativity and innovation.
- Part I Trends Giving Rise to Extreme Teaming
- 1 Why Extreme Teaming Matters
- 2 Leading Teams and Teaming
- 3 The Challenges of Extreme Teaming
- Part II Four Leadership Functions for Extreme Teaming
- 4 Build an Engaging Vision
- 5 Cultivate Psychological Safety
- 6 Develop Shared Mental Models
- 7 Empower Agile Execution
- Part III Looking Back and Moving Forward
- 8 A Model of Leadership for Extreme Teaming
- 9 Directions for Future Research and Practice