An AI-assisted decision log records what an authorized human decided, which evidence they checked and what remains uncertain. It should not simply store a model’s answer and call that an audit trail. Generated reasoning can be incomplete or unsupported; the record needs to distinguish the suggestion from the human’s actual judgment.
Use this illustrative template for an appropriate internal operating decision. It is not a validated audit system, a legal record-retention policy or permission to use AI for a high-impact decision. Sensitive or regulated work requires qualified owners and established organizational controls.
Identify the decision, not the conversation
Write a specific decision question and identify who is authorized to answer it. Record the scope and limits of that authority. A useful example might be choosing which of two approved, non-sensitive internal summary formats to test. It is not deciding someone’s employment prospects or evaluating private customer records.
The decision rights matrix helps clarify owner, input and escalation boundaries. A generated recommendation does not expand anyone’s authority.
Keep proposal, evidence and acceptance separate
Use three short sections. First, state the AI’s contribution: drafting options, summarizing an approved document or suggesting questions. Second, list the actual evidence the human checked. Third, state which reasoning the human accepted and why. Identify rejected or unverified claims where they matter to the decision.
Do not imply that the model inspected material it did not receive or that a reviewer checked a source they did not open. A reference produced by AI is not verification. Use the organization’s approved tools and access arrangements, and do not copy unnecessary sensitive prompts into a broadly shared log.
Copy this decision record
Question and scope: What is being decided, and what is outside this decision?
Authorized human owner: Who decides, and whose approval or input is required?
AI contribution: What assistance was actually used?
Checked evidence: Which approved sources or work examples were examined?
Accepted choice and reason: What did the human decide?
Uncertainty and limits: What was not verified or remains unresolved?
Action and review: Who does what next, and what condition would reopen the decision?
Record tool or version information when useful and available under company policy. Do not invent a precise version because the record has a field for it.
Work through a low-risk example
For the fictional summary-format decision, AI suggests organizing updates by completed work. The reviewer compares that option with an approved decision-first format and finds that the team mainly needs unresolved approvals. The human chooses a bounded test of the decision-first format, names the receiving team and records what would count as a useful update.
The record preserves the choice without claiming that AI established a business benefit. The team will examine accepted updates and recipient feedback at the review point.
Keep the record proportionate
NIST’s AI RMF core includes risk documentation and human oversight responsibilities. This short log is an editorial application, not evidence of compliance with the framework.
Retain and share records according to approved policy, with an identified owner. The AI review-capacity article explains the leadership question behind the paperwork. If your team cannot explain who accepts AI-assisted work, discuss the workflow with Shannon before increasing output.
About this resource: Prepared with AI-assisted drafting. Examples and templates are illustrative, not reported client outcomes, a validated assessment or a substitute for required policies and qualified supervision.