The post-mortem every AI project needs, win or lose

A post-mortem after a failed AI project is common practice, if uncomfortable: what went wrong, what would we do differently. A post-mortem after a successful one is much rarer, on the reasonable-sounding logic that if it worked, there's nothing urgent to examine, and that logic quietly prevents an organization from learning what actually made the difference.

A successful project's post-mortem asks a different, more useful question: was this success because of a repeatable process, or because of a specific person's judgment call, a lucky data quality situation, or a stakeholder relationship that won't necessarily be there for the next project. Distinguishing those matters enormously for whether the success is a template or a one-off.

The organizations that build genuine AI capability over time, not just a string of individually successful projects, are the ones running this examination after wins as rigorously as after losses, because a string of successes nobody understood the mechanism behind isn't capability, it's a streak, and streaks end without warning.

Running a post-mortem on a win feels unnecessary in the moment. It's the only way to find out whether what just happened is something the organization can reliably do again, or whether it was this specific team, this specific data, this specific stakeholder, working together in a way nobody can guarantee will recur.