Why AI pilots stall, and how to get them into production
Most generative AI pilots never reach real users. The causes are usually data, security and ownership, not the model.
A generative AI demo can be built in an afternoon. Getting the same idea in front of real users, with real data and real accountability, is where most projects stall. In our experience the model is rarely the problem. The blockers are almost always the same four.
1. The data isn't ready
An assistant that answers from company knowledge is only as good as the documents and tables it can reach. If policies live in five versions across shared drives, or customer data is split between systems that disagree, the AI will confidently repeat the confusion. Before building, decide which sources are authoritative, clean them, and set up a pipeline that keeps them current.
2. Security was an afterthought
Pilots often run on a copy of data with broad access. Production cannot. Users should only get answers from information they are already allowed to see, and every question and answer should be logged. Build role-based retrieval and logging into the first version, not the last.
3. Nobody measured accuracy
"It looked good in the demo" is not a quality bar. Assemble a set of real questions with known correct answers, score the system against them, and re-run that test every time you change prompts, models or data. This turns arguments about whether the AI is good enough into a number everyone can track.
4. There is no owner after launch
AI systems drift as documents change, models are updated and users find new questions. Someone has to own monitoring, feedback and fixes. Treat the assistant like any other production application, with support hours, release notes and a backlog.
A practical path
- Pick one workflow with a clear user and a measurable outcome.
- Fix the data sources for that workflow first.
- Build with access control, logging and an evaluation set from day one.
- Launch to a small group, measure, then expand.
Done this way, the second and third use cases arrive much faster, because the data foundation, security model and evaluation approach are already in place.
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