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AI Pilot Evaluation Scorecard

A practical, repeatable evaluation form and rubric to score industrial AI pilots across business impact, model performance, operational fit, data readiness, integration feasibility, operator acceptance, and cost/scalability — with clear thresholds for go / further work / no-go.

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AI Pilot Evaluation Scorecard

This scorecard helps teams evaluate industrial AI pilots objectively and consistently. Use it to capture key evidence, compute a weighted score, and produce a clear recommendation. The form focuses on shop-floor outcomes: downtime reduction, yield improvement, scheduling gains, and inspection automation.

How to use: complete the fields below, score each dimension on a 1–5 scale (higher is better), calculate the weighted score using the weights shown in the help text, then enter the weighted score (0–100) and select a recommendation.

Decision thresholds: Go >= 70 (clear path to production), Further work 50–69 (needs additional data, refinement, or operator trials), No-Go < 50 (insufficient value or too risky/costly).

Descriptive name or identifier for the pilot (e.g., 'Line 2 Vibration Anomaly - Mar 2026')
Where the pilot ran (site, line, cell)
Person completing this evaluation
YYYY-MM-DD or preferred local format
Potential measurable impact on key KPIs (e.g., downtime, throughput, first-pass yield). 1 = negligible, 5 = transformational. Weight: 25%
Model performance relative to operational needs (false positives/negatives matter). 1 = poor, 5 = excellent. Weight: 15%
How quickly predictions produce an actionable window for operators or systems. 1 = too slow, 5 = immediate. Weight: 15%
Volume, labeling, completeness, and drift risk. 1 = insufficient, 5 = production-ready. Weight: 15%
Ease of connecting outputs to MES/SCADA/controls and embedding into workflows. 1 = high friction, 5 = seamless. Weight: 10%
Operator trust, explainability, and ergonomics of suggested actions. 1 = unlikely to be used, 5 = likely to be adopted. Weight: 10%
Estimated total cost of ownership and ability to scale across lines or sites. 1 = costly/limited, 5 = low cost/highly scalable. Weight: 10%
Compute weighted score using: BusinessImpact*25 + Accuracy*15 + LeadTime*15 + DataSufficiency*15 + Integration*10 + Operator*10 + Cost*10. Each dimension score is 1–5; compute weighted average and multiply to scale 0–100. Example: if weighted average = 3.6 then score = 3.6/5 * 100 = 72. Enter the final number.
Use thresholds: Go >= 70, Further work 50–69, No-Go < 50. Add rationale in comments.
Summarize key evidence (metrics, sample confusion matrices, operational observations), risks, required next steps, and stakeholders to involve.
Location of model artifacts, code repo, data diagnostics, dashboards, integration plan, SOPs, or test logs.
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