Most AI pilots die before production. The post-mortems blame the model: it hallucinated, it was too slow, it was not accurate enough. That is rarely the real cause.
The real cause is that nobody agreed what success looked like before they started. So there was no bar to clear, no moment where someone could say "this is done," and no reason for a busy executive to push it into production over the objections of the people whose jobs it touches.
A demo is not a deliverable
A pilot that produces a good demo has proven one thing: the technology can do the task once, under supervision, on a friendly example. Production asks a different question. Can it do the task every day, unsupervised, on the ugly examples, at a cost and error rate the business will accept?
The gap between those two questions is where most pilots quietly stop. The demo gets praised in a meeting and then nothing happens, because moving it forward requires a decision nobody set up the criteria to make.
Define done first
The fix is unglamorous. Before any build, write down the success criteria as a checklist tied to a number the business already cares about. Error rate below a threshold. Cycle time under a target. Hours saved per week, measured against a baseline you capture now, not later.
Then the question at the end is not "is the AI good?" It is "did it clear the checklist?" That is a yes or a no, and a yes is permission to ship.
The boring discipline
This is step five of how we work, not an afterthought. We agree the criteria up front, in writing, with the people who own the metric. If the system does not clear the bar, it does not ship, and we would rather find that out in week three than in month six.
It is not exciting. It is the difference between a pilot that becomes a system and a pilot that becomes a story about the time you tried AI.