A discipline increasingly showing up in more mature AI investment governance is a requirement, built into the approval process itself, that any significant new AI investment include a defined exit plan - specifically, what the organization will do, and by when, if the investment isn't delivering as expected.
Why this requirement is emerging now
Enough organizations have experienced the sunk cost pattern with earlier AI investments - a pilot that underperformed but kept receiving incremental additional funding because no one wanted to be the one to call it a loss - that more disciplined investment committees have started requiring the exit conditions to be specified upfront, before enthusiasm and momentum make an honest reassessment harder.
What a real exit plan requires
Specific, measurable conditions defined at approval time - not vague language about "monitoring progress," but concrete criteria like a defined adoption threshold by a defined date, below which the investment gets seriously reconsidered rather than simply extended on the strength of continued optimism.
Why this is uncomfortable to build in upfront
Requiring an exit plan at the moment of approval means confronting the possibility of failure before the investment has even started, which cuts against the optimism bias that tends to accompany a new initiative's launch. This discomfort is exactly why the discipline is valuable - it forces honest engagement with failure conditions before enthusiasm makes that engagement harder.
What this means for how AI investments get pitched now
A pitch that can't specify clear failure conditions and a plan for responding to them is increasingly viewed as less credible by investment committees that have adopted this discipline, regardless of how compelling the upside case sounds on its own.