AI strategypilots
How to Scope an AI Pilot That Actually Reaches Production
· AgenticLabs India
Most AI pilots die after the demo. Here's the scoping discipline that gets pilots into production: one workflow, measurable outcomes, fixed scope, honest evaluation.
Define done before you start
A pilot without success metrics is a science project. Before writing a line of code, agree on numbers: 'triage 200 emails a day with 95%+ correct routing' or 'cut invoice processing time from 4 hours to 20 minutes'.
Write these into the pilot agreement. If the pilot hits them, it graduates to production. If it doesn't, you have an honest answer — not a sunk cost.
Timebox it to 2–4 weeks
A pilot that drags for three months isn't a pilot, it's a project without a plan. Constrain the scope ruthlessly: one workflow, one team, one integration surface.
Short timeboxes force clarity. They also cap your downside — a fixed-scope pilot is a cheap experiment with bounded risk.
Plan for the boring parts from day one
Accuracy monitoring, cost guardrails, fallback to humans, audit logs — these aren't phase-two nice-to-haves, they're what make a pilot trustworthy enough to run unsupervised.
We scope every pilot with its production hardening included, so 'it works in the demo' and 'it works at 9am on a Monday' are the same thing.
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