If everything seems under control, you’re not going fast enough.
On February 17, a 21-car pileup interrupted the final laps of the Daytona 500. A mega-wreck like this one is known in NASCAR circles as “The Big One,” and this was a very big one. The massive pileup was triggered when one driver “tapped” another. Tapped. There isn’t much room for error when 20 or more cars traveling at average speeds of nearly 200 miles per hour are bunched together within inches of one another. NASCAR races are exciting because they push the limits of both human and machine. But that testing of limits means that they are not very resilient to error.
Resilience is the capacity of a system to maintain its stability when it is perturbed in some way. It is a limited resource, but it is also an invisible asset. NASCAR drivers trade resilience for speed. Companies often do the same thing, trading off the intangible benefits of resilience for the tangible benefits of efficiency. It usually works for the length of a NASCAR race; for companies, which need to survive across a much longer period, it can seem like a great trade—until something unanticipated happens and the business cannot respond.
Once growth stabilizes, almost all businesses seek to optimize. Every function strives to improve its performance year over year—to run the cars closer together and at higher speed, as it were. Technology is a key enabler of this optimization. Automation reduces manufacturing costs; sophisticated supply chains reduce materials costs; analytics and just-in-time inventory practices reduce working capital.
All of these changes look like good business decisions, but they can drain the resilience of a business design. Automation increases efficiency, but it often reduces the transparency of operations and eliminates the human element that is the last reservoir of resilience. Eliminating redundancy increases efficiency, but it also makes a system more brittle when a key system—operating with no backup—fails. Outsourcing can increase efficiency, as well, but the rigid interfaces between the company and the outsourcer can make the system less resilient. Innovations like just-in-time inventory management optimize asset utilization but they make it difficult for a company to adjust when any part of the supply chain is disrupted.
Resilience can be designed into a system. Safety systems often enforce some degree of redundancy; data centers are often distributed globally to ensure geographic diversity in case of regional disruption; supply chains are diversified to eliminate single points of failure; manufacturing systems are made more modular. But often, resilience is eaten up without anyone noticing, because more immediate goals are absorbing all of the attention.
Resilience is about more than surviving; it’s necessary to keep growing. It’s what allows a company to adapt as markets change and new technologies emerge. Resilience is essential for innovation because, almost by definition, innovation disrupts the business. When the resilience that saw the business through its growth phases has been traded for efficiency, change is almost impossible; the company loses the flexibility to grow.
Innovators have struggled with this issue and sought ways of working around the resistance, without disrupting daily operations. In essence, they try to add resilience back into the system. Each of the articles in this issue sheds light on one mechanism for building or rebuilding an organization’s resilience.
In “Intelligent Systems: The Big Picture,” Gayle Sheppard discusses how the technology itself can become inherently more resilient. She characterizes the history of artificial intelligence as occurring in three waves, each of which has led to an increased scope of application. Sheppard notes that the “first-wave expert systems had no capacity to learn from tacit knowledge that could not be expressed in the rule definition,” which implies an inherent brittleness. “The second wave [machine learning] has brought nuanced classification and prediction capabilities” but cannot reason or explain its results, another source of reduced resilience when something goes wrong. Sheppard sees the third wave, contextual adaptation, as enabling systems that are truly adaptable and collaborative with people.
Another approach to increasing resilience of the whole is discussed in a pair of papers, one concerning spin-outs and the other concerning acquisitions. As Henry Chesbrough has noted, companies often create innovative concepts that they do not have the organizational flexibility to take to market—in other words, opportunities that their business systems cannot stretch enough to exploit. In such cases, spin-outs are an option for capturing value. Mattias Axelson and Erik Bjurström discuss one such case in detail and offer a model for how to think about spinning out a new technology or product in “The Role of Timing in the Business Model Evolution of Spinoffs.”
On the other side of the transaction are acquisitions, which companies may pursue to acquire capabilities or technologies that it would take too long to build organically. In “Unmasking Smart Capital,” Tobias Gutmann, Jessica Schmeiss, and Stephan Stubner discuss how companies configure their corporate venture capital arms to attract and nurture startups that will complement corporate capabilities and support the company’s strategic direction. The internal venture capital function adds resilience by giving the company room to stretch and adapt.
Resilience can also be designed in. In “Managing Emerging (Mis)Alignments in Data-Driven Servitization,” Peter Altmann and Marcus Linder discuss how co-creation with ecosystem partners is essential to a successful shift from a product-led to a services-led business model. The key to a stable and resilient system in this context is alignment around the business logic and value allocation mechanisms that will carry the technology to market.
Finally, in this issue’s interview, Alex Osterwalder discusses business model design and the use of visual tools to foster innovation. He emphasizes that business model innovation is not just for startups but also for established companies seeking to improve their business models. Osterwalder believes that we have entered an era in which business models will have to change much more rapidly and that companies that succeed will be those that can continually reinvent themselves. This capability, if it can be established, is the ultimate in corporate resilience.
Resilience and innovation seem inextricably intertwined. New technologies can be used either to further optimize the existing system or to disrupt it. Optimization increases efficiency but also increases brittleness and limits flexibility. Disruption seeks to move the company to an entirely different plane, but it requires a degree of resilience to succeed. Mechanisms to add resilience back into the overall system become important, whether as an element of the technology itself (contextually adaptive AI), as part of a structural element of the corporation (spin-outs and acquisitions), as part of a business design, or as a new corporate capability (business model innovation). We may never get the balance just right, but we would do well to try, and to avoid the corporate equivalent of The Big One.