Most enterprise AI pilots succeed on their own terms. They demonstrate that a model can do something useful with a sample of data. What they rarely demonstrate is that the organization is prepared to change a decision because of it.
Production readiness requires three things that pilots typically defer: an owner accountable for the decision, a data foundation that survives contact with real operating conditions, and a workflow that has been redesigned around the new capability rather than decorated with it.
The practical implication is that the pilot should be scoped to test adoption risk, not just technical feasibility. If the hardest question is whether a business leader will act on the output, the pilot should answer that question first.