The VUCA model in CI

The COVID-19 pandemic is arguably the greatest global challenge we’ve faced in nearly a century. All four characteristics of VUCA events are present during this coronavirus pandemic: Volatility, Uncertainty, Complexity, and Ambiguity. There is no “best practice” to follow these challenges. However, the competitive landscape is still there, and a framework exists to help navigate these situations.

VUCA and Competitive Intelligence

The term was initially cited in the US Army War College primer in and around the 1980s. The original curriculum was and is about leadership, not CI. However, chapter 2 introduces the concept of VUCA to describe the environment where a leader will find him or herself.

VUCA: stands for VolatilityUncertaintyComplexityAmbiguity.

Ultimately, the VUCA model allows us to characterize situations and events in the environment in which we operate, whether introduced by a competitor action or not.

Before I define the situation types, it's also essential to understand the challenges. First, VUCA events can be misinterpreted as anything out of our control that cannot be explained. Second, they are not necessarily singular, but each element within VUCA is unique and requires different interventions. There may even be situations and events of all 4 types.


In this context, volatility refers to the degree and speed of change in the environment or conditions that can affect decision-making processes. It implies that the environment is unpredictable, unstable, and subject to rapid and significant changes that can make it difficult to anticipate. In other words, volatility suggests that the domain is highly dynamic.

Characteristics of Volatility

The recommended approach to this situation is to build a buffer of resources to gain reaction time and allow for tolerance in volatility.

Examples include long lines in shipping ports in California, introduced by unexpected longer-than-usual stock limitations.


Uncertainty is the inability to know everything about a situation and the difficulty of predicting the nature and effect of change (i.e., the bond of uncertainty and volatility.) Uncertainty often delays decision-making processes and increases the likelihood of vastly divergent opinions about the future. It drives the need for intelligent risk management and hedging strategies.

Characteristics of Uncertainty

A world-class competitive and market intelligence unit collects, analyzes, and recommends outcomes. This is the everyday life of a CI practitioner, and we tend to ignore the other 3 situations.

An example of an uncertain situation is when a competitor launches a new product that threatens your core offer.


Complexity happens when understanding the interactions of multiple parts or factors is difficult and predicting the primary and subsequent effects of changing one or more elements in a highly interdependent system. Complexity differs from uncertainty; though it may be possible to predict immediate outcomes of single interactions within a network, the non-linear branches and sequels multiply quickly, which overwhelms most assessment processes.

Characteristics of Complexity

The way to address complexity is by increasing Subject Matter Expertise, which can help break the challenge down. A Technical Competitive Intelligence function might serve such a role.

An example of a complex situation is when your company is looking to enter a new geographical market that has complex and unique regulations.


Describes a specific type of uncertainty that results from differences in interpretation when contextual clues are insufficient to clarify meaning. It refers to the difficulty of interpreting meaning when context is blurred.

Characteristics of ambiguity

Experimentation is the best approach to overcome ambiguity because hypothesis and testing for validation go hand in hand. “Fail Often, Fail Fast” is an often successful approach.

A good example is when launching a new product in a very immature or emerging market that is outside of your core competency.

VUCA 2x2 analysis framework

We can link the four VUCA elements as they are interrelated by two orthogonal factors: (1) How much do I know about the situation, and (2) how well can I predict the results.

Knowledge Depth represents how much you (think you) know about the situation. Prediction Accuracy is how well you can predict the results of your actions.

You can ask two simple questions about the event to identify the VUCA quadrant and later determine the respective best actions and potential implications. The definitions above can guide you there. 

Happy navigating situations.