The conventional apparatus of product strategy assumes a future stable enough to forecast, which is why it performs so poorly precisely when strategy matters most. Roadmaps that extend confident lines into quarters the organization cannot actually predict are not strategy but the appearance of it, and Courtney, Kirkland, and Viguerie (1997) warned in their foundational treatment that traditional strategic planning under genuine uncertainty is, in their words, at best marginally helpful and at worst downright dangerous, because it induces executives to commit to single bold forecasts or to retreat into paralysis, when the situation demands neither. This module develops the discipline of strategy under uncertainty through three instruments, namely assumption mapping that exposes what a strategy depends on, scenario planning that prepares for futures rather than predicting one, and optionality thinking that structures commitments to preserve flexibility, with the AI transition serving as the defining contemporary case of a future that refuses to hold still.
Calibrating to the Level of Uncertainty
The first discipline, which Courtney et al. (1997) contribute and which most teams skip, is to diagnose how much uncertainty actually obtains before choosing how to respond, because the appropriate strategic posture differs by level. They distinguish a spectrum running from a future clear enough for a confident forecast, through a future with a small set of discrete possible outcomes, to a future with a bounded range of outcomes, and finally to genuine ambiguity in which even the range cannot be specified. The strategic error is to treat all uncertainty as though it were the first level, applying point forecasts to situations that demand scenarios or options, and the corrective is to match the instrument to the level, reserving confident forecasting for the rare cases that warrant it and deploying scenario and option methods where the future is genuinely plural. Owing to the pace of the AI transition, most product decisions now sit at the third or fourth level, where a bounded range or genuine ambiguity prevails, which is why the instruments that follow have become essential rather than optional.
Assumption Mapping: Exposing What a Strategy Depends On
Every strategy rests on assumptions, and the difference between a robust strategy and a fragile one is largely whether those assumptions have been made explicit and tested. Assumption mapping, developed as a core practice by Bland and Osterwalder (2019), is the discipline of surfacing the beliefs a strategy depends upon and arranging them by two dimensions, namely how important each assumption is to the strategy’s success and how much evidence currently supports it. The assumptions that are simultaneously most important and least evidenced are the ones that should be tested first, because they carry the greatest risk of invalidating the entire strategy and the team currently knows the least about them. The strategic value of this practice is that it redirects effort from building on unexamined beliefs to testing the beliefs on which everything else rests, which is the most efficient possible allocation of learning, since it resolves the largest uncertainty at the lowest cost before significant resources are committed.
The discipline this imposes on a product organization is to treat a strategy not as a plan to execute but as a set of hypotheses to validate, identifying the load-bearing assumptions, designing the cheapest experiments that would disconfirm them, and committing resources in proportion to the evidence accumulated rather than to the confidence asserted. In the context of AI, where strategies routinely rest on assumptions about how model capabilities will advance, how users will trust autonomous systems, and how quickly costs will fall, assumption mapping is the instrument that prevents an organization from building an elaborate strategy on a capability forecast it has never examined.
Scenario Planning: Preparing for Futures Rather Than Predicting One
Where assumption mapping tests the beliefs underlying a single strategy, scenario planning prepares an organization for multiple plausible futures, and its purpose is frequently misunderstood. The objective of scenario planning, as developed by Schoemaker (1995) from the practice pioneered at Shell, is not to predict which future will occur but to construct a small set of distinct, internally coherent narratives about how the future could unfold, so that the organization develops the perceptual range to recognize each as it emerges and the prepared responses to act when it does. The value lies in the process as much as the product, because the discipline of imagining several genuinely different futures breaks the organization’s habit of planning for a single extrapolation of the present, which is the habit that leaves it blindsided when the future diverges from the forecast.
The practice constructs typically three or four scenarios around the most important and most uncertain driving forces, develops each into a coherent story, and then stress-tests the strategy against all of them, asking which choices succeed across multiple futures and which depend on a single future obtaining. The strategic payoff is the identification of robust moves that perform acceptably across scenarios and the early-warning indicators that signal which scenario is materializing, which together convert an unpredictable future from a threat into a set of conditions the organization has already rehearsed. The AI transition is the canonical subject for this method, since its trajectory admits genuinely different futures regarding the pace of capability advance, the structure of the model-provider market, and the regulatory environment, and a product strategy that is robust across these scenarios is far more valuable than one optimized for the single future its authors happen to expect.
Optionality Thinking: Structuring Commitments to Preserve Flexibility
The third instrument reframes how commitments themselves are structured, drawing on the logic of real options and on Taleb’s (2012) argument that some strategies benefit from volatility rather than merely surviving it. Optionality thinking treats an investment not as a binary commitment to a forecast outcome but as the purchase of the right, without the obligation, to pursue an opportunity once uncertainty resolves, which means that under high uncertainty the most valuable moves are frequently those that buy information and preserve the ability to scale up or abandon cheaply rather than those that commit fully in advance. A strategy structured as a sequence of staged options, in which small investments resolve key uncertainties and earn the right to larger investments, dominates a strategy structured as a single large commitment when the future is genuinely plural, because it limits the downside of being wrong while preserving the upside of being right.
Taleb’s (2012) contribution is to distinguish strategies that are merely robust, surviving a range of futures unchanged, from those that are antifragile, gaining from the disorder, and to observe that a portfolio of small bounded-loss bets with unbounded upside is positioned to benefit from precisely the volatility that destroys strategies built on point forecasts. In the context of AI, optionality thinking counsels the product leader to make many small, staged, cheaply reversible bets on emerging capabilities rather than a single large bet on a predicted trajectory, since the trajectory is genuinely uncertain and the value of preserving the ability to pivot as it resolves is high.
The synthesis for the product leader is that strategy under uncertainty is not a weaker form of strategy but a more honest and ultimately more powerful one. The agenda is to first diagnose the level of uncertainty rather than defaulting to confident forecasting, to map and test the load-bearing assumptions on which the strategy depends before committing to it, to prepare for a small set of distinct futures through scenario planning rather than betting on a single extrapolation, and to structure commitments as staged options that preserve flexibility and limit downside while retaining upside. A product organization that adopts these instruments will make fewer confident pronouncements about the future and far better decisions within it, which, in a regime defined by a future that refuses to hold still, is the only durable form of strategic advantage.
References
Bland, D. J., & Osterwalder, A. (2019). Testing business ideas: A field guide for rapid experimentation. Wiley.
Courtney, H., Kirkland, J., & Viguerie, P. (1997). Strategy under uncertainty. Harvard Business Review, 75(6), 67–79. https://hbr.org/1997/11/strategy-under-uncertainty
Schoemaker, P. J. H. (1995). Scenario planning: A tool for strategic thinking. Sloan Management Review, 36(2), 25–40.
Taleb, N. N. (2012). Antifragile: Things that gain from disorder. Random House.