// glossary
AI Pilot plain-English.
Operator-grade definition. Plain words, plus where the term shows up in real work.
What is a AI Pilot?
An AI pilot is a small, scoped, measured experiment built to prove whether an AI bet clears a defined bar — accuracy, cost, latency, safety, operator judgment — before the team commits to the production investment that would land it permanently.
The point of a pilot is to make a kill decision possible. A good pilot picks a narrow use case, defines the bar before it starts, ships an evaluation harness alongside the model, and runs against real (or realistic) data. At the end, the team can answer one question honestly: did this clear the bar.
Most pilots die at the wall between pilot and production because nobody designed the bar, nobody wrote the eval, and nobody modelled the production context — cost per call, p95 latency, integration surface, observability. The pilot looked good in a notebook and died when it had to ship.
A working pilot has a kill gate: if the bar is missed, the work is killed back to hypothesis, not killed back to a budget review. That discipline is what separates an honest pilot from a sunk-cost ramp into a project that should never have shipped.
Related questions
How long should an AI pilot last?
Two to six weeks for narrow use cases; longer if data acquisition is the bottleneck. If a pilot is taking more than a quarter, the scope is wrong.
What kills most AI pilots?
Missing eval harness, missing kill gate, production context never modelled, cost and latency only measured at the end. Diagram on /services/generative-ai-implementation.
Should a pilot use the production stack?
Get as close as you can without rebuilding everything. The further the pilot is from production, the more surprises it will hide.
Related work
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