From pilot to production: why most AI projects stall and how to avoid it
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Gartner-style statistics about AI projects failing all point to the same gap: the distance between a compelling proof of concept and a system the business can actually rely on. Here is how we close it.
Treat evaluation as a first-class feature
Before scaling, build an evaluation set that reflects real usage. It tells you whether a change actually improves quality and gives stakeholders the confidence to ship.
Engineer for the unhappy paths
Production means rate limits, timeouts, bad inputs, and edge cases. The pilot proves value; hardening these paths is what makes it trustworthy at scale.
The goal is not a clever demo. It is a system your team owns, understands, and can improve after we leave.
Transfer ownership as you go
The most durable transformations leave the client able to run and extend the system themselves. Documentation, playbooks, and pairing during the build matter as much as the model itself.
