Checklist

AI Pilot Validation & Governance Checklist

Interactive checklist to validate AI pilots in manufacturing. Captures pilot metadata, concrete validation criteria, monitoring and rollback plans, human-in-the-loop controls, compliance checks, and governance signoffs so teams can run safe, measurable pilots and record decisions.

Interactive Tool

AI Pilot Validation & Governance Checklist

This interactive checklist helps teams run AI pilots that deliver measurable operational value and maintain safety, transparency, and operator trust. Complete each item, add evidence or notes where helpful, and capture governance signoffs. Save this form to record the pilot state and decisions.

Short descriptive name for the pilot (e.g., 'Press Line 2 Vibration Anomaly Pilot')
Model name, version, or registry ID (if available)
Date of this checklist completion (YYYY-MM-DD)
Person responsible for the pilot results and actions
Clear short statement of the operational outcome this pilot aims to achieve (e.g., reduce unplanned downtime by X% or detect defects earlier)
Metric to measure operational impact (OEE, downtime minutes, scrap rate, throughput, cost per part)
Current measured value for the business metric before pilot (use same units)
Target improvement to consider the pilot a success (use same units)
Concrete acceptance criteria for the pilot (statistical thresholds, minimum uplift, operator acceptance level, runtime duration)
Is training and validation data representative of production, sufficiently labeled, and free of major biases?
Where is the data from, sample sizes, known gaps, and issues? Provide links or locations for datasets.
Select the metrics used to evaluate model performance on the validation dataset
Is a held-out validation dataset defined and frozen before evaluation?
How was the validation set created? Dates, sampling, stratification, and size.
Has performance been compared against existing rule, human, or historical baseline?
Quantify comparison results or attach evidence.
Are the operator touchpoints, override options, and hand-off rules specified?
Describe how operators will see, act on, or override model outputs. Include UI examples if available.
Have intended users validated the model's outputs in realistic conditions?
Summarize operator feedback, required fixes, and acceptance criteria.
Is there a plan that defines metrics, detection methods, thresholds, and frequency?
Describe which signals will be monitored (data distribution, feature drift, performance decay) and how.
How often will monitoring run?
Concrete thresholds that will trigger investigation, who is notified, and how.
Are clear rollback triggers, automated steps, and escalation contacts documented?
Step-by-step rollback actions and names/roles to contact during a rollback.
Has the pilot been reviewed for privacy, IP, and data access controls?
Is there documentation covering model inputs, preprocessing, training procedure, performance, limitations, and known failure modes?
URL or repository path for model docs and datasets.
Have potential operational harms, safety implications, and mitigation plans been assessed?
List mitigations in place for highest-risk failure modes.
Has the model been tested end-to-end with live data flows, interfaces, and downstream consumers?
Have operators and supervisors received training on model outputs, limitations, and override procedures?
Links or brief summary of training materials and attendance.
Current governance decision after completing this checklist
Person approving the pilot decision
Date of approval (YYYY-MM-DD)
List unresolved items, owners, and target completion dates.
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