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An AI pilot’s decision record should show what the team set out to learn, what the evidence revealed, and why the organization chose to stop, redesign, continue testing, or scale. The record does not replace the pilot’s operational learning or benefits; it preserves the reasoning so the decision can be reviewed, acted on, and revisited as the system changes.
Why the decision record matters
A pilot is useful when its evidence changes or confirms a decision. Without a clear record, later reviewers may see a result but not know what was tested, what counted as success, which risks remained, or why the team chose its next step. A record makes those connections visible to technical and nontechnical readers.
Official guidance supports documenting pilot design, findings, risks, ownership, and next steps. It does not establish that the record is always more valuable than the pilot itself, or that the record has a measurable return. Treat it as the durable explanation of a consequential decision, not as a substitute for testing.
What to put in an AI pilot decision record
The following structure synthesizes government guidance; it is a practical format, not a mandated official template.
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1. Decision at a glance
- Use case and workflow being considered.
- Pilot dates, scope, exclusions, decision date, and decision owner.
- Disposition: stop, redesign, continue testing, or scale.
2. Question and intended benefit
State the uncertainty the pilot was meant to resolve and the outcome that would matter to affected users or the organization. A question such as “Can the system reduce review time without increasing consequential errors?” is more actionable than “Does the AI work?”
3. Criteria set before testing
- Objectives and key performance indicators (KPIs), with thresholds appropriate to the use case’s risks.
- The baseline for comparison, such as the existing process, human performance, or an alternative system.
- How the team will handle borderline, uncertain, or conflicting results.
Setting criteria in advance helps prevent the team from redefining success after seeing the results. Australian Government guidance recommends defining objectives and measures and comparing outcomes with expectations. Its pilot guidance also addresses pilot scope and duration, participant selection and consent, risk mitigations, surprises, and adaptations.
4. Pilot design and boundaries
- System or model and version, where relevant; data used; and test environment.
- Participants, selection method, and consent arrangements.
- Workflow boundaries, human oversight, duration, and excluded cases.
- Risks anticipated before the test, safeguards, escalation route, and responsible owners.
Keep a clear boundary between a controlled pilot and live use. The UK National Audit Office advises organizations to use small, clearly defined, low-risk pilots with senior ownership and multidisciplinary oversight; it flags pilots drifting into live use without proper controls as a pitfall. The NAO’s guide also calls for clear, evidence-based stop, scale, or redesign decisions and clarity about how success will be judged.
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5. Results and supporting evidence
Record measured results alongside qualitative review, user feedback, errors, edge cases, and relevant limitations. Link or identify the underlying artifacts—such as evaluation results, review notes, or incident records—so readers can trace claims back to evidence. Separate observations from interpretation: “12 of 200 outputs required correction” is an observation; the conclusion about whether that rate is acceptable depends on the use case and the criteria.
Compare results with the pre-set expectations and explain material surprises or changes made during the pilot. The Australian guidance specifically recommends reviewing how outcomes compared with expectations, what issues arose, and how the use case changed in response.
6. Decision rationale and unresolved questions
Show how each material result relates to the criteria. Identify what passed, failed, or remains uncertain; explain important trade-offs; and record significant minority views rather than implying consensus where none existed. The disposition should follow from the evidence and stated criteria, not from the fact that the technology produced plausible outputs.
7. Owners, actions, and review triggers
- Changes or further evaluation required, with accountable owners and deadlines.
- Risk treatments and the person responsible for each.
- Measures, proposed monitoring intervals, and who responds to an alert or incident.
- Conditions that require a pause, reassessment, or reversal of the decision.
The UK government’s AI Risk Management Toolkit emphasizes that risk assessments and treatments need owners, must remain updateable, and continue across an AI system’s lifecycle. Its guidance is a general risk-management framework, not a replacement for jurisdiction-specific legal advice.
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Use evaluation methods suited to the task, not a single headline accuracy score. Depending on the use case, combine quantitative measures, manual review and error analysis, user feedback, relevant benchmarks, or comparison with human performance or another model. Accuracy targets should reflect context and risk; high-stakes decisions need very high accuracy and explicit handling of uncertain or borderline cases.
If the team is choosing among options, compare them using the same criteria. These are useful comparison dimensions synthesized from the Australian and UK guidance, not an official scoring rubric:
- Performance against pre-set criteria, reliability, and edge-case behavior.
- Fairness, usability, security, privacy, and legal fit.
- Human oversight, escalation, operational integration, and support needs.
- Total cost and expected benefit.
- Strength and limitations of the evidence behind each option.
A small, bounded pilot can inform a decision; a demonstration of plausible output alone cannot establish that deployment is safe, reliable, or beneficial. Document why evidence is sufficient for the proposed next step, and what uncertainty remains.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep the record active after the pilot
A scale decision is not the end of risk management. Connect the pilot’s conclusion to an operational monitoring plan: state what performance measures, anomalies, and incidents will be watched; how often they will be reviewed; and who must act. Review the plan after system upgrades, reports of errors, changes in input data, performance deviations, or stakeholder feedback.
The UK Information Commissioner’s Office recommends preserving documentation that explains the process from design to decision and provides an audit trail in language accessible across levels of technical knowledge. Its documentation guidance page says it is under review following the Data (Use and Access) Act; check its current version before relying on it for legal specifics.
What a documented evaluation can look like
NIST’s 2025 ARIA 0.1 pilot evaluation report describes work involving five participating organizations and seven submitted AI applications. The evaluation included model testing, red teaming, field testing, dialogue annotation, tester questionnaires, and measurement trees. This is an example of documenting a multifaceted evaluation design, not a recommended participant count or benchmark for organizational pilots. Read the NIST ARIA pilot evaluation report.
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