From AI Prototype to Production: What Business Teams Often Miss
An AI prototype can look impressive in a demo. Turning it into a reliable business solution takes clear goals, good data, workflow integration, testing, and ongoing ownership. Building an AI prototype
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The five areas in your summary, clear goals, good data, workflow integration, testing and ongoing ownership, match what I see when a demo stalls. The model is rarely the problem. The gap is everything around it that the demo quietly assumed.
Ownership is the one I would move to the top. A prototype usually belongs to the person who built it, and when it moves into a real workflow nobody has been named to answer for it when the output is wrong or a data source changes. Naming that person before the pilot starts tends to settle several of the other items, because they will ask for the tests and the data checks themselves.
It also helps to write the goal as one specific problem with a measurable finish, for example drafting orders from incoming emails with fewer than two corrections in twenty, rather than "use AI for order processing". Which of the five do business teams underestimate most often in your experience?