An AI use-case decision log should record the user’s problem, the proposed solution as a hypothesis, the relevant workflow, decision points, supporting evidence, and unresolved questions. It should keep the need separate from the proposed answer and show where automated sign-off requests are combined with human decision-making.
Public-service guidance recommends focusing on the user’s problem rather than possible solutions. Workflow documentation also describes combining automated sign-off requests with human decision-making. These principles prevent a preferred technology from being mistaken for a validated user need.
What the decision log should contain
A practical minimum record includes the following fields:
| Field | What it should make clear |
|---|---|
| User problem | Who has the problem, what needs to be accomplished, and why it matters |
| Evidence | What research, observations, or other evidence supports the problem statement |
| Solution hypothesis | What proposed AI capability or process might address the problem |
| Workflow | The steps, inputs, outputs, dependencies, and handoffs involved |
| Sign-off requests | Which requests may be automated and which actions they initiate |
| Human decisions | Where judgment is required, which decision is being made, and which role holds authority |
| Assumptions | Which parts of the problem, workflow, or proposed response remain unverified |
| Open questions | What must be checked before the use case advances |
| Decision and rationale | Whether the team is proceeding, revising, pausing, or stopping, and why |
| Next validation step | The specific check, evidence gap, or unresolved decision to address next |
The distinction between the problem and the solution is essential. A statement such as “users need an AI assistant” describes a proposed response, not yet the underlying user problem. The log should first establish what the user is trying to do, what difficulty they encounter, and how that need was identified.
Where automation meets human judgment
A workflow entry should not treat automation and approval as interchangeable. It should distinguish an automated sign-off request from the human decision that follows or accompanies it.
For each relevant step, the log should identify:
- the action that triggers a request;
- what, if anything, can be automated;
- where a person must assess information and make the decision;
- the role responsible for that decision;
- what information the person receives; and
- which exceptions or unresolved conditions require further review.
This makes the control points visible without claiming that a particular workflow is appropriate before it has been checked.
How to check that the entry is decision-ready
A useful review asks:
- Can the user problem be stated without referring to a model, vendor, or feature?
- Is the proposed solution clearly labelled as a hypothesis?
- Are evidence, assumptions, and open questions distinguishable?
- Are automated requests and human decisions shown as separate parts of the workflow?
- Does the record explain the current decision and the reason for it?
- Is the next validation step specific enough to resolve an important uncertainty?
If any answer is unclear, the entry may describe an idea rather than a decision-ready use case.
What the team must still confirm
The cited sources do not establish a universal decision-log template, determine whether a particular use case is feasible or effective, or confirm its legal, regulatory, security, data, or operational requirements.
Before approval, the team must still confirm the actual user need, the real workflow, sign-off authority, applicable constraints, success measures, and failure handling with the relevant stakeholders. Neither source substitutes for that context-specific validation.