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AI Pilot Evaluation Scorecard

A practical, interactive scorecard to evaluate industrial AI pilots across data readiness, model performance, business impact, operator acceptance, integration complexity, safety/regulatory risk, and scaling cost. Includes adjustable weights, a place to record a weighted score, a clear recommendation field (continue / iterate / stop), and space for key next steps and evidence links.

Interactive Tool

AI Pilot Evaluation Scorecard

Use this scorecard immediately after an AI pilot to make an objective, documented scale decision. Rate each dimension from 0 (weak) to 5 (strong). Adjust the default weights if your organization values some dimensions more than others. Calculate a weighted score using the formula below and use the suggested thresholds to guide the recommendation.

Weighted score formula: WeightedScore = sum(score_i * weight_i) / sum(weights). Suggested thresholds: ≥4.0 = Continue; 3.0–3.99 = Iterate (refine & re-run); <3.0 = Stop.

Name or short description of the pilot (model, production line, process).
Person completing this scorecard (name and role).
Evaluation date (YYYY-MM-DD).
Is the data complete, labeled, representative, and accessible? 0 = poor; 5 = production-quality data.
1.0 10.0
Precision/recall or domain-relevant metrics. Focus on actionable performance for the use case rather than only aggregate accuracy.
1.0 10.0
Quantified expected benefit (e.g., €/hr saved, quality escapes prevented, throughput gains).
1.0 10.0
Likelihood frontline staff will trust and use the model in daily work; consider explainability and workflow fit.
1.0 10.0
Effort and time to integrate into MES, control systems, UIs, and workflows. 5 = low complexity / easy integration.
1.0 10.0
Lower scores mean higher risk. Consider failure modes, HIPAA/GxP/other regs, and required controls.
1.0 10.0
Operational and infra cost to productionize and maintain the model. 5 = low cost to scale.
1.0 10.0
Default weight (percent points). Adjust if needed.
Default weight.
Default weight.
Default weight.
Default weight.
Default weight.
Default weight.
Calculate using the formula: sum(score_i * weight_i) / sum(weights). Example: if DataReadiness=4 and DataReadinessWeight=20, contribution = 4*20. After summing contributions, divide by sum of weights. Suggested thresholds: ≥4.0 Continue; 3.0–3.99 Iterate; &lt;3.0 Stop.
Choose the recommendation based on the weighted score, discussion, and risk tolerance.
Actionable next steps, owners, timeline, and acceptance criteria if you choose Continue or Iterate.
Links to dashboards, validation results, datasets, code, model artifacts, or meeting notes.
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