Most AI pilots fail for a predictable reason: they start with a model, not a decision. A team proves that a forecast can be generated or a document can be summarized, and then the pilot ends, because nobody identified the recurring business decision the output was supposed to change.
The programs that reach production invert the order. They begin with a decision that happens often enough to matter: which supplier to award, how much to stock, whether to escalate an exception. The decision has an owner, a cost of being wrong, and a measurable outcome.
From there the architecture questions get easier. What data does the decision actually need? What confidence threshold justifies a recommendation? Who approves, and what must they see? These are operational questions, and they are what separate an AI demo from an AI system.
A practical test for any proposed AI initiative: name the decision, the decider, and the metric. If any of the three is missing, the project is still a science experiment.