How to scope an AI project so it doesn't drift
AI projects fail at roughly twice the rate of ordinary IT projects, and they rarely fail loudly — they drift, because a language model makes every adjacent feature look one prompt away. The fix is a scoping discipline you apply before a line of code is written: one workflow, one metric, one month.
Most AI projects do not fail in a dramatic way. They drift. The brief was a document-processing workflow; three months in, the team is debating chat interfaces, the demo has sprouted four features nobody asked for, and the original workflow still is not live. This is not a rare misfortune: RAND's research into why AI projects fail found they collapse at roughly twice the rate of ordinary IT projects, and the cause it ranks first is not technology — it is misunderstanding about what the project was for. If you are about to commission AI work, drift is the failure mode most likely to eat your budget, and it is preventable at the scoping stage.
Have an AI feature stuck between demo and production?
The gap — reliability, evals, cost control, the plumbing that keeps it running unattended — is exactly the work I do. If that sounds familiar, a short conversation is usually enough to point you the right way.
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