A 90-Day Enterprise AI Pilot: From Use Case to Review
A staged enterprise AI pilot that selects a measurable use case, tests real data, releases to a small group, and makes an evidence-based scale decision.
A pilot must prove a business loop
A demo proves technical possibility. A pilot proves that a real user can complete a real task, safely and repeatedly, and that the organization can measure and operate the result. Ninety days is a useful scope boundary, not a universal promise.
Days 1–15: choose and measure
Score candidate use cases by value, frequency, data readiness, feasibility, risk tolerance, and measurability. Prefer a clear owner, reviewable errors, and an observable result. Record baseline volume, cycle time, waiting, rework, quality, and cost before development.
Days 16–45: build with real controlled data
Map sources, versions, permissions, tool actions, review, and write-back. Create an evaluation set with normal, edge, failure, and malicious cases. Test evidence fidelity, refusal, parameter validation, idempotency, timeout, and rollback. Involve users weekly and record why they distrust or abandon results.
Days 46–75: release to a small group
Use limits and a rollback switch. Monitor adoption, success, escalation, latency, unit cost, feedback, and business metrics. Give users a short SOP covering scope, verification, prohibited data, and support. Classify failures as knowledge, data, prompt, model, system, or process issues.
Days 76–90: scale, adjust, or stop
Review business improvement, guardrails, adoption, TCO, and operational ownership. Valid outcomes include scaling, observing longer, narrowing the scope, replacing AI with rules, or stopping. Before replication, package the workflow, evaluation set, permissions, training, and review method.
Based on the local “AI Project Implementation Method” and “Three AI Production Gaps” notes.