Introduction In today’s business environment, organizations must often make decisions under uncertainty. One widely used tool for exploring future possibilities is scenario analysis—a structured approach to imagining different potential futures. When used appropriately, scenario analysis can be a valuable tool. However, it has a critical weakness when applied directly to strategy development.
Some time ago, an executive asked me what I thought of their company’s overarching strategic goal: “A strategy that wins in every environment.” I told him it was a poor foundation for building a strategy. Needless to say, that wasn’t the response he expected.
I followed up with a question: “Would you build a refinery capable of processing every type of crude oil on the global market?” He replied, “Of course not—that would be prohibitively expensive, especially for crude oils that we were highly improbable to use.” Exactly.
Designing a strategy to succeed in every conceivable environment or in scenarios that are highly unlikely is like wearing a belt, suspenders, another belt, and a few more suspenders—redundant, costly, and inefficient. A much better objective is to craft a strategy that wins in a range of the most probable business environments. This points to a major limitation of traditional scenario analysis in developing strategies: it does not consider the likelihood of each scenario being evaluated.
Another related issue is bias. We humans do not think well in conjunctive probabilities. We tend to overestimate the likelihood when using several probabilities conjunctively. That tends to make our “worst case” scenarios – typically built using the worst cases for two or more uncertainties – highly unlikely.
While scenario analysis is rightly praised for encouraging strategic thinking, it has a notable limitation when used as a foundation for developing strategies. Consequently, it should not be relied upon as the primary basis for choosing among strategic options. However, once a strategy has been defined, scenario analysis becomes a powerful tool for testing a strategy’s resilience in different possible futures and for developing robust risk mitigation plans.
Absence of Probabilistic Guidance
The central weakness of scenario analysis lies in its inability to provide probabilistic estimates of how likely each scenario is to occur. Traditional scenario planning typically develops four, or sets of four, distinct futures—often derived from the permutations of two key uncertainties in their “high” and “low” states, however those are defined.
By contrast, probabilistic analysis engages subject matter experts to estimate a range of possible outcomes (usually between the P10 and P90 values) for each uncertainty. These ranges are then combined using a probabilistic model—commonly referred to as a Monte Carlo simulation (the term originates from the original sampling technique). In effect, this probabilistic engine generates thousands of scenarios that reflect the full spectrum of results given all the uncertainties. From this output, a distribution of potential outcomes for a given strategy can be created and visualized as a cumulative probability curve (S-curve) providing decision-makers with far richer insights.
Because it lacks this quantitative rigor, traditional scenario analysis is ill-suited for selecting among strategic options. Without an assessment of likelihoods, it risks encouraging misguided decisions (belt and multiple suspenders) or overemphasizing outlier scenarios.
For example, a manufacturing firm may create scenarios involving different levels of global demand or supply chain disruption. Without knowing the probability of each scenario, management cannot rationally determine whether to expand capacity, hedge supply contracts, or diversify markets. In such cases, quantitative approaches—like Monte Carlo simulation or decision-tree analysis—offer more actionable insights.
A Tool for Testing Strategic Resilience
While scenario analysis is not a good tool for developing strategies, it is highly effective in testing the resilience of existing ones. Once a lead strategy is defined, scenario analysis enables teams to visualize how that strategy might perform under diverse future conditions (worlds or scenarios). This can surface vulnerabilities, stress points, and areas of weakness that might require strategic agility.
Once a lead strategy has been identified, an AI SWARM analysis can be applied across the scenarios to assess which future state is most probable. Although not a full probabilistic quantification, this method can deliver meaningful insight that strengthens strategic foresight.
Armed with these insights, the team can design hybrid strategic options that can strengthen the lead strategy. A further application of AI SWARM analytics can assist in prioritizing and refining hybrid options—ultimately leading to an optimized, resilient strategy aligned with both current objectives and emerging realities.
Supporting Risk Mitigation and Contingency Planning
Scenario analysis also plays a crucial role in risk mitigation. By exploring extreme but plausible futures, organizations can identify key risk factors and design risk mitigation plans with signposts in advance. Additionally, scenario analysis can be used to identify uncertainties that may benefit from Value of Information or Value of Control exercises.
A practical example can be found in supply chain management. A company might develop scenarios involving geopolitical instability, climate-induced disruptions, or rapid shifts in consumer demand. Even without assigning probabilities, this exercise helps decision-makers pinpoint where risks concentrate and prepare appropriate responses—such as alternative sourcing or inventory strategies.
Conclusion
Scenario analysis is often misunderstood as a tool for choosing strategies. In reality, it lacks the probabilistic information necessary to make optimal strategic choices. However, when used appropriately—as a test of an already developed strategy—scenario analysis becomes highly valuable. It enables organizations to stress-test their plans against multiple plausible futures, uncover hidden vulnerabilities, and design effective risk mitigation plans. In this role, scenario analysis shifts from being a weak strategy development tool to a powerful instrument for building strategic resilience in an uncertain world.