Quick answer: Most AI pilots stall because they are built for a demo, not for production: no business metric, curated data, no integration plan and no owner. Pilots that ship start from one workflow a
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The decision table at the end is the part I would copy. Continue, change or stop, with the criteria written before the pilot starts, is what keeps a stalled pilot from drifting for months because nobody wants to make the call. Your note that two rounds of "change" on the same check usually mean "stop" is honest and rarely said.
The order of the steps matters too. One workflow and one number first, real production data early, the integration and the human hand off planned before picking a model. Most pilots I have seen reversed that order and chose the model in the first meeting.
The questions to answer in writing before starting are a good test of whether a project is ready to be priced. If the answer to who runs it after launch is "we will figure it out later", the build is not yet one defined problem, and any estimate will move.
Has a client ever pushed back on writing the stop criteria first?
Emma Parker
Great insights! Moving AI from a successful pilot to production requires much more than a working model. Real-world data validation, seamless system integration, measurable business goals, and ongoing monitoring are essential for long-term success. At IT Path Solutions, we recognize the importance of aligning AI solutions with actual business workflows to ensure they deliver practical value, scalability, and reliability. Starting with a clearly defined use case and planning for production from day one can make a significant difference in turning AI experiments into sustainable business solutions.