In “New Bases of Competitive Advantage,” we argued that increased turbulence in the business environment has invalidated an implicit and critical assumption of classical business strategy: that competition is sufficiently stable and predictable for the basis of competitive advantage to be readily determined. Traditional approaches to strategic planning become futile in a world in which the key variables are constantly shifting and difficult to forecast.
We can distinguish three important dimensions of turbulence: volatility in market positions, unpredictability of outcomes, and the widening gap in performance between winners and losers. Most industries have experienced instability on at least one of these dimensions, but some—such as technology-driven industries and commercial banking—have been affected on all three. The hardest-hit industries are those that have been disproportionately affected by globalization, deregulation, digitalization, connectivity, deconstruction, and the shift from products to services.
Most companies, and especially those in industries characterized by both unpredictability and a high rate of change, need a more adaptive and dynamic approach to strategy—an approach that emphasizes iterative experimentation in order to overcome the limitations of deductive approaches and keep pace with incessant change.
With such an approach, organizations gain adaptive advantage: the ability to achieve superior outcomes in a turbulent environment by continuously reshaping the enterprise through a process of managed evolution. In this article, we explain how adaptive advantage can be harnessed in practice.
Three attributes are essential for survival in a changing environment: readiness, responsiveness, and resilience. They can be achieved by static measures such as improved forecasting, decentralized decision-making, and buffering with excess capacity, respectively. However, to gain a sustainable advantage in a turbulent environment, companies must employ a more dynamic, recursive approach in which better-fitting strategies continuously evolve in response to change. (See Exhibit 1.)