Why most AI pilots never ship

Every failed AI rollout we've seen shares the same shape: someone builds an impressive demo, leadership gets excited, a pilot runs for a few weeks, and then it quietly stops. Not because the model was wrong — because nobody owned what happened after the pilot ended.

The gap isn't technical. It's operational. A demo answers "can this work?" A production system has to answer "who fixes it when it breaks, who retrains it, and who's accountable when it makes a bad call?" Most pilots never answer those questions because nobody asked them before building.

The fix is boring but it works: before you write a line of code, decide who owns the system once it's live, what the escalation path looks like, and what "good enough to ship" actually means for this specific workflow. Everything else is downstream of that.