Checklist

AI Pilot Selection & Readiness Checklist

Interactive checklist and rubric to select, assess, and record readiness for AI pilots across value hypothesis, data readiness, risk, governance, operational feasibility, and scaling criteria.

Interactive Tool

AI Pilot Selection & Readiness Checklist

Use this structured checklist to evaluate AI pilot candidates consistently. Capture the value hypothesis, data readiness, risks, operational dependencies, and clear success and scaling criteria. Save responses so teams can compare pilots, avoid vendor-driven shiny-object experiments, and build documented evidence for go/no-go decisions and scaling.

Concise name that identifies the pilot (team, use case, or feature).
Person accountable for the pilot's outcomes.
Short statement: who benefits, what will change, and how we'll measure it (metric/KPI). Example: 'Reduce average handle time by 15% for Tier 1 support measured by AHT within 6 months').
Order-of-magnitude estimate of annualized benefit if pilot succeeds. Leave blank if unknown.
List 1–3 measurable indicators used to judge pilot success. Be specific about baseline and target.
Estimate how much customers (internal or external) will be affected.
Be honest about access, completeness, and legal constraints.
Enter approximate number of usable records or examples. Useful for ML feasibility.
If ML requires supervision, note whether labels exist or must be created.
If labeling is needed, estimate person-hours/cost or a plan to acquire labels.
Sets deployment and engineering complexity.
Important for safety, compliance, and staffing.
Who monitors outputs, how will errors be handled, and who has final authority? Describe roles and cadence.
Identify regulatory domains that require special controls or approvals.
Helps determine model choices and monitoring obligations.
Consider integration points, security, latency, and ops readiness.
List systems, APIs, or teams required for pilot to operate.
What will you monitor (data drift, performance), who receives alerts, and what thresholds trigger action?
How will you disable or revert the model if it misbehaves? Include timelines and owners.
Specific, measurable conditions (e.g., sustained KPI improvement for X weeks, cost per inference below $Y).
Include development, labeling, cloud compute, and operating costs.
Typical pilots run 6–16 weeks depending on scope.
Clear pass/fail criteria the governance committee will use to decide next steps.
Judgment call from assessment of above factors. Use consistently across pilots.
Governance or the pilot owner should record a short rationale.
Any additional context, risks, or recommended next steps.
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