Read enough published AI strategy documents and a common gap emerges: they describe what a company plans to do with AI, in some detail, but rarely specify what evidence would cause the company to change that plan. A strategy that commits to a direction without defining the conditions under which it should be abandoned isn't really a strategy-it's a bet dressed up as a plan, with no mechanism for recognizing when the bet has failed.
Why this gap matters especially now
AI capability and cost curves are moving quickly enough that a reasonable strategic bet made eighteen months ago can look clearly wrong today, and the companies best positioned to adapt are the ones that built review triggers into their strategy from the start: specific metrics, checked on a specific cadence, with a predefined threshold for reconsidering the approach. Without that structure, sunk cost tends to keep a failing strategy running well past the point where the evidence against it is clear.
What a sunset clause looks like in practice
A well-built AI strategy specifies, in advance, something like: if adoption of this tool across the target workflow hasn't reached a defined threshold within a defined period, or if the measured error rate in production exceeds an acceptable bound, the initiative gets reevaluated rather than simply continuing on inertia. This is a discipline borrowed from good product management and good investing, and it's conspicuously rare in how companies talk about their AI strategy publicly.
Why companies avoid stating this publicly
Committing publicly to specific failure conditions creates accountability that a vaguer commitment to "exploring AI opportunities" doesn't. It's understandable that companies are reluctant to set a bar they might visibly fail to clear. But internally, even if not disclosed publicly, that discipline is what separates AI strategies that adapt from ones that persist well past their useful life because no one defined what "not working" would look like.
What to ask when evaluating a company's AI strategy
Beyond what a company says it's doing with AI, it's worth asking what specific evidence would change that plan, and on what timeline they expect to have that evidence. A leadership team that can answer this concretely is demonstrating a materially more rigorous strategy than one that can only describe its current direction.