The strategy palette has three dichotomous variables – predictability, malleability, and harshness. Predictability is defined as the extent to which the organization can predict the key strategy variables in its environment which impact the focal strategic issues for the organization. It also relates to how far into the future the organization can make such predictions. Malleability is defined as the extent to which the organization and its competitors could influence these key strategy variables. Harshness refers to the ability to survive a harsh environment.

The shaping strategy approach is most suitable for environments which are unpredictable but malleable. These environments usually exist in new industries where there are no established leaders or rules of competition. Many companies can enter these low barrier industries and introduce innovative business models, products, and services. Mature markets may also be ready to be disrupted if they are overserving major customer segments or not serving customers. The disruption is usually through business model innovation.

The classical strategy approach is most suitable for stable environments which are predictable, where the rules of competition or conduct are well-established, making them non-malleable. These predictable and non-malleable environments are continuations of the past. Hence, the bases for achieving sustainable competitive advantage are known and can be achieved through competitive positioning using differentiation or cost leadership through scale.

First, the use of dichotomous variables (predictability, malleability, and harshness) has resulted in the creation of a limited, coarse-grained strategy space. This is problematic because it means that fewer strategy approaches are identified to cover the strategy possibilities space, and these approaches are broad (i.e., umbrella approaches). This has resulted in a loss of precision in guiding the selection of the appropriate strategy. Take for example the umbrella strategy approach of adaptation, which has been operationalized primarily through continuous experimentation. Within this umbrella approach, there are several approaches such as static and dynamic robust strategy approaches [], which would only be revealed if the predictability dimension incorporated a greater number of states. This would help create a richer, more textured, and nuanced strategy space which differentiates between the varying levels of uncertainty [].

The adaptive strategy approach is evolutionary, necessitating the creation of solution hypotheses and testing them through experiments, then selecting promising options and scaling them up. The emphasis here lies in creating strategic flexibility. BCG research has identified strategy tools that can be used with the adaptive approach. These strategy tools include time-based competition, first mover advantage, dynamic capabilities, strategy as simple rules, adaptive advantage, and transient competitive advantage.