How to Evaluate AI Pilots: A Practical Framework

AI pilots fail when they start with a tool, not a problem. This framework helps you validate value early, reduce risk, and get buy-in before scaling.

1) Define a narrow, high‑impact use case

• Target a single workflow with measurable friction (time, cost, error rate).
• Keep scope to a 2–8 week pilot that a small team can own.

2) Data and access reality check

• Inventory inputs/outputs, sample 50–100 records, confirm quality and permissions.
• Decide redaction or synthetic data for pilot if needed.

3) Success metrics before you start

• Pick 2–3 metrics tied to outcomes (e.g., 40% faster intake, 30% fewer rework loops).
• Set baselines now; define what “good enough” looks like.

4) Pilot guardrails

• Document failure modes and fallback steps (human-in-the-loop).
• Track cost drivers: tokens, API calls, and manual review time.

5) Stakeholders and change management

• Identify pilot owner, decision maker, IT/security, and 2–3 end users.
• Schedule weekly demos; collect structured feedback.

6) Scale decision

• If metrics meet targets and risk is managed, plan phase 2 (integration, training, SLA).
• If not, capture lessons learned and pivot—pilot success is learning, not always deployment.

Artifacts to produce

• 1‑page problem statement and KPIs
• Data checklist and access plan
• Pilot plan with timeline, roles, guardrails
• End‑of‑pilot decision memo