Before committing significant budget to an AI strategy, one of the cheapest and most underused validation exercises is a structured pre-mortem: gathering the team involved and asking them to imagine, in specific detail, that the strategy failed a year from now, then working backward to identify the most plausible reasons why.
Why this exercise works better than standard planning review
Ordinary strategy review tends to invite refinement of an assumed-successful plan. Explicitly assuming failure and asking why removes the social pressure to defend the plan's viability, which tends to surface risks and weak assumptions that a standard optimistic planning session doesn't reach, simply because no one felt they had permission to argue the plan might not work.
What makes a pre-mortem genuinely useful rather than a formality
Specificity is what separates a useful pre-mortem from a token exercise - "it might not work" isn't a useful answer, while "the data quality assumption in section three turns out to be wrong once we look at our actual customer records" is something a team can actually act on before committing budget.
Why this is particularly valuable for AI specifically
AI strategies often rest on assumptions - about data readiness, about user adoption, about vendor capability - that are genuinely uncertain and rarely stress-tested before significant investment, making a structured pre-mortem a low-cost way to surface exactly the kind of hidden assumption that later turns into an expensive surprise.
What to do with the results
The most plausible failure modes identified in a pre-mortem should become specific, monitored checkpoints built into the strategy itself - not just a list generated once and filed away, but an active part of how the strategy's execution gets tracked and revisited over time.