What record will prove a human reviewed AI-assisted work?
A matter-linked review note can show what AI-assisted output a named person checked, against which sources, and what they accepted or changed. Match detail and retention to the work's risk.
Use a decision log, review checklist, matter note or workflow audit entry to record the review before the output is used. Keep it close to the work and detailed enough to show what was checked.
The safest record is a decision log, not a vague sign-off
Record the work reviewed, the AI tool or workflow used, the reviewer, the checks performed and the final judgement. Explain changes made after review where the output affected client advice, financial analysis, insurance wording, regulatory communication or another material decision.
A simple entry might say: Reviewed by Priya Shah on 30 June 2026. Source documents checked against the AI summary. Two unsupported statements removed. Final wording approved for internal planning only, not client advice.
The entry gives context for the decision, including the source checks, changes and internal-use restriction. A generic approval tick would leave those details out.
Check where AI review evidence is missing
What the record should include
Use the following fields as a starting point for the review record:
- Workflow or matter reference: the client file, project, task, ticket, document or business process.
- AI involvement: the tool, model, embedded feature or vendor used, plus the broad task it performed.
- Reviewer identity: the named person accountable for checking the work.
- Review date and version: enough information to reconstruct which output was reviewed.
- Checks completed: accuracy, source alignment, confidentiality, bias, regulatory constraints, client instructions and quality standards where relevant.
- Decision made: accepted, amended, rejected, escalated or restricted to internal use.
- Reason for the decision: a short explanation, especially where the work affects clients or regulated outcomes.
How detailed does the evidence need to be?
Match the detail to the use case. A low-risk internal brainstorm may need a brief note; a client advice draft, regulated communication, due diligence summary or insurance claims assessment calls for closer consideration of the audit trail.
Check whether the record lets you explain the review to a client, regulator, insurer, partner or board member without relying on memory.
Identify the actual engagement and decision before choosing the review record. Ask the relevant professional which duties apply, then specify the evidence, reviewer and escalation needed for that activity. A sector-wide list is not the applicability decision.
Where should the review record live?
Keep the record in the document management system, CRM, case management platform, GRC tool, ticketing system or workflow software used for the work. If a separate spreadsheet is the practical starting point, decide who will maintain it.
The record should be searchable by matter, client, workflow, tool and reviewer. It should also preserve enough context to show what changed between AI output and final work product.
Set up practical AI governance records
What does good human review look like in practice?
The reviewer should compare the output with trusted source material, check assumptions, remove unsupported claims and test edge cases before deciding whether it is suitable for the intended use.
Firms should define review standards by workflow. For example:
- AI summaries of client documents need source checks and confidentiality controls.
- AI-assisted advice drafts need qualified professional review before client use.
- AI-generated financial or operational analysis needs data provenance and calculation checks.
- AI vendor outputs need documented limits, retention settings and escalation routes.
Give the reviewer a way to change, reject or escalate the work. Pressure to approve quickly without evidence can prevent those checks from happening.
Common weak records to avoid
The following records leave gaps in the account of the review:
- A generic policy statement saying all AI work is reviewed.
- An approval tick box with no reviewer name or review criteria.
- A chat transcript saved without the final decision.
- A file note saying checked without explaining what was checked.
- A review record stored outside the matter or workflow, so nobody can connect it to the work.
The record should show the checks performed and who took responsibility for the final judgement.
Implementation checklist
Start with work where AI output could affect clients, regulatory duties, commercial decisions or personal data. Define the record, reviewer role, checks and evidence storage for each workflow before adding the fields to your system.
- List the AI-assisted workflows that create client-facing or decision-support outputs.
- Set a review standard for each workflow based on risk.
- Add required fields to the existing matter, ticket or workflow system.
- Train reviewers on what counts as evidence rather than bare approval.
- Choose a sampling schedule for the workflow's risk; monthly sampling is an option to assess, not a compliance guarantee.
Build AI review logs into your workflows
Conclusion
Connect the specific AI-assisted output to the named reviewer, checks and final decision. Keep the record close to the work and easy to retrieve, with detail appropriate to the risk. The record supports an explanation of the judgement; it does not establish compliance by itself.
Need a safer AI route for your firm?
If this article reflects a live decision in your practice, the useful next step is to map the workflows, confidentiality risks, supplier controls and governance gaps before tools spread informally.