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An AI pilot is useful when its evidence helps the organization decide what to do next. A decision record preserves that evidence, explains why the team stopped, redesigned, tested further, or scaled the use case, and assigns responsibility for what follows. It is a management principle, not a claim that documentation is always more valuable than the pilot’s operational learning or benefits.
What an AI pilot decision record needs to do
The record should let a reader who was not in the pilot understand the question, the evidence, the decision, and the follow-up. It should distinguish what the team observed from what it inferred, and show how the result relates to criteria set before testing began.
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There is no single official template established by the guidance cited here. The structure below is a practical synthesis of Australian, UK, and US guidance, not a mandated standard.
Set the decision question and criteria before testing
Start with the uncertainty the pilot is meant to resolve. For example: can this system assist a particular task within an acceptable level of error, with workable human oversight and a worthwhile benefit? Define what success, failure, and an inconclusive result would mean before seeing the outputs.
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- Scope and duration: identify the workflow, intended users, test period, boundaries, and exclusions.
- Objectives and measures: define the intended benefit, key performance indicators, thresholds, and comparison baseline. Explain how borderline or uncertain cases will be handled.
- Participants and data: document selection, consent where relevant, data sources, and the test environment.
- Safeguards: specify human oversight, risk mitigations, and how incidents or unexpected outputs will be escalated.
Australian Government AI assurance guidance recommends documenting pilot scope and duration, objectives and measures, participant selection and consent, and risk mitigations. It also advises comparing results with expectations and recording surprises and adaptations: Guidance 5: Reliability and safety.
Record the pilot design and evidence
Describe enough of the setup for someone to interpret the results: the system and model version where relevant, data and environment, workflow boundaries, oversight arrangements, evaluation methods, and any significant changes made during the pilot. Note deviations from the original plan rather than silently treating the final setup as if it were the one originally tested.
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Evidence can combine quantitative measures with manual review, error and edge-case analysis, user feedback, relevant benchmarks, or comparison with human performance or another system. Choose methods and targets that fit the use case. For high-stakes decisions, accuracy expectations should be very high, and the team should define what happens in uncertain or borderline cases. A result without its context—such as the data, task, and evaluation method—can be misleading.
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Present observations separately from interpretation. Record measured outcomes, qualitative findings, limitations, unexpected behavior, and how results compared with the pre-set criteria. Link or identify supporting artifacts where the organization can preserve them. NIST’s 2025 ARIA 0.1 pilot evaluation report illustrates a documented evaluation design: five participating organizations submitted seven AI applications, and the report describes model testing, red teaming, field testing, dialogue annotation, tester questionnaires, and measurement trees. Those figures describe that NIST pilot; they are not a recommended size or benchmark for an organizational pilot. NIST, Assessing Risks and Impacts of AI (ARIA): Pilot Evaluation Report.
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Make the disposition explicit
State the decision plainly: stop, redesign, continue testing, or scale. Then show how the evidence and success criteria led to it. Identify what passed, what failed, and what remains uncertain; explain material trade-offs and record substantial dissent where it affected the discussion.
A demonstration that produces plausible outputs is not enough to justify expansion. The UK National Audit Office recommends small, clearly defined, low-risk pilots, senior ownership, multidisciplinary oversight, and clear criteria for decisions to stop, scale, or redesign. It flags poor data quality, unmanaged bias or errors, and pilots drifting into live use without proper controls as pitfalls. Its guidance is aimed at organizations using AI, especially in the public sector, rather than a universal legal standard. UK National Audit Office, Good practice guide for organisations using AI.
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When comparing options
If the team is choosing among systems or approaches, compare them on the same dimensions rather than relying on a single headline score. The following are useful synthesized dimensions, not an official scoring rubric:
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- Fairness and usability for affected users.
- Security, privacy, and legal fit for the intended context.
- Human oversight, escalation, and the ability to correct or contest outputs.
- Operational integration, support needs, cost, and expected benefit.
- Quality and limits of the evidence behind each comparison.
Use a decision-record structure people can follow
- Decision at a glance: use case, pilot dates and scope, decision owner, decision date, and disposition.
- Question and intended benefit: the uncertainty tested and the outcome that would matter to users or the organization.
- Pre-set criteria: objectives, measures, thresholds, baseline, and treatment of uncertain results.
- Pilot design: system and version where relevant, data, environment, participants and consent, workflow boundaries, oversight, duration, and exclusions.
- Risks and safeguards: key risks, mitigations, owners, unresolved exposures, and incident or escalation route.
- Results and evidence: quantitative results, qualitative review, user feedback, errors and edge cases, limitations, and supporting artifacts. Separate observations from interpretation.
- Decision rationale: comparison with criteria, what passed or failed, remaining uncertainty, trade-offs, and material minority views.
- Next actions: required changes, accountable owners, deadlines, further evaluation, monitoring measures and intervals, and triggers for pausing or reconsidering.
Write for both technical and nontechnical readers. The UK Information Commissioner’s Office says documentation should make it possible to explain the process from design to decision and support an audit trail. Its guidance page notes that it is under review following the Data (Use and Access) Act; check the current version before relying on it for legal specifics. ICO, Documentation: Explaining decisions made with artificial intelligence.
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Carry ownership and monitoring beyond the pilot
A pilot sign-off does not complete AI risk management. If testing continues or the use case moves toward deployment, name the people responsible for each open risk and treatment. Specify what will be monitored, how often it will be reviewed, who responds to a concerning result, and what conditions trigger a pause or renewed decision.
Monitoring plans can cover acceptance criteria, performance measures, anomalies, and incidents. Set review triggers for system upgrades, error reports, changes in input data, performance deviations, and stakeholder feedback. UK DSIT’s AI Risk Management Toolkit emphasizes lifecycle risk management, including updateable assessments and treatments with owners, and questions of suitability, robustness, deployment, scaling, and response to discovered issues. It is UK government guidance, not a substitute for jurisdiction-specific legal advice. UK DSIT, AI Risk Management Toolkit: guidance.
Why preserve the rationale
People reviewing the decision later need more than a pass/fail label. They need to know what was tested, what evidence supported the conclusion, which limits were recognized, and who accepted the remaining risks. The ICO’s documentation guidance captures the purpose: “It is essential to document each stage of the process behind the design and deployment of an AI decision-support system in order to provide a full explanation for how you made a decision.” The page is under review, so treat it as guidance rather than current legal advice.
The Australian guidance likewise describes a pilot as a way to assess the real-world performance and impact of an AI use before full deployment. Neither source establishes a universal statistic for the value of a decision record or a single required template. The practical value is in making the decision traceable and the next steps accountable.
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