Checklist

AI pilot readiness checklist

Interactive checklist to assess value hypothesis, data readiness, deployment pathways, governance, owners, and scaling readiness before launching an AI pilot. Capture evidence, owners, and a simple readiness score to support decisions and follow-up.

Interactive Tool

AI pilot readiness checklist

Use this checklist to quickly assess whether an AI pilot is likely to produce measurable value and to identify gaps you should address before starting. For each item, choose the current status, add brief evidence or notes, and assign owners when helpful. Save the form to record progress and to support later reviews or audits.

Is the pilot grounded in a clear business or operational value hypothesis with concrete metrics?
Briefly describe the hypothesis, the primary metric(s) you will measure, and what success looks like.
Do you have the data needed, at the right quality, and a traced lineage for provenance and compliance?
Note datasets, owners, known quality issues, and any remediation planned.
Is there a practical path to deploy the pilot output (API, batch job, human-in-loop), and has IT/the platform team signed off?
Describe the deployment pathway, required systems, and any technical blockers.
Has the pilot gone through a risk check that covers privacy, fairness, security, and regulatory/compliance concerns?
Summarize key risks identified and the planned mitigations or approvals required.
List the executive sponsor, the day-to-day owner, and the person responsible for measuring outcome metrics (name and role).
Are there clear criteria that will trigger scaling the pilot and a rollback plan if outcomes or risks require it?
Capture the primary thresholds, timeline, and operational changes needed to scale or rollback.
Quick composite judgment of pilot readiness based on the items above.
1.0 10.0
Anything else the team should track before launch (e.g., vendor dependencies, procurement, training).
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