What record proves a human reviewed AI-assisted work?

What record will prove a human reviewed AI-assisted work?

A professional services firm needs more than a policy saying that people stay in control. It needs a record that connects the AI-assisted work to a named reviewer, a real client or internal matter, and a clear decision.

That record does not have to be a long compliance essay. The useful version is short, repeatable and close to the work: a decision log, review checklist, matter note or workflow audit entry that proves a competent person checked the output before it was used.

The safest record is a decision log, not a vague sign-off

The best evidence is a human review record that captures five things: the work reviewed, the AI tool or workflow used, the reviewer, the checks performed, and the final judgement. If the output affected client advice, financial analysis, insurance wording, regulatory communication or another material decision, the record should also explain any changes made after review.

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.

This proves control in a way that a generic approval tick box cannot. It shows accountability, quality review and the boundary between machine assistance and professional judgement.

Check where AI review evidence is missing

What the record should include

For most firms, the review record should include:

• 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?

The level of detail should match the risk of the use case. A low-risk internal brainstorm may only need a lightweight note. A client-facing advice draft, regulated communication, due diligence summary or insurance claims assessment needs a stronger audit trail.

The test is simple: if a client, regulator, insurer, partner or board member asked how the firm knew the AI-assisted work was checked, could you answer from the record without relying on memory?

For legal services, that may include confidentiality, privilege, professional judgement and supervision. For financial services, it may include Consumer Duty, SM&CR accountability, model risk, recordkeeping and quality assurance. For insurance, it may include fair treatment, underwriting rationale, claims handling evidence and escalation rules.

Where should the review record live?

Keep the record where the work already happens. That might be the document management system, CRM, case management platform, GRC tool, ticketing system or workflow software. Avoid creating a separate spreadsheet that nobody maintains unless it is the only practical starting point.

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?

Good review means more than reading the AI output and clicking approve. The reviewer should compare the output against trusted source material, check assumptions, remove unsupported claims, test edge cases and decide whether the output 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.

The reviewer should be able to change, reject or escalate the work. If the process pressures people to approve quickly without evidence, the firm has automation theatre rather than human oversight.

Common weak records to avoid

These records usually fail when challenged:

• 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 aim is not bureaucracy. The aim is evidence that the firm used AI responsibly and that a human remained accountable for the final judgement.

Implementation checklist

To make this operational, start with the workflows where AI output could affect clients, regulatory duties, commercial decisions or personal data. For each workflow, define the minimum record, the reviewer role, the checks required and where evidence is stored.

1. List the AI-assisted workflows that create client-facing or decision-support outputs.
2. Set a review standard for each workflow based on risk.
3. Add required fields to the existing matter, ticket or workflow system.
4. Train reviewers on what counts as evidence rather than bare approval.
5. Sample records monthly and fix gaps before they become audit findings.

Build AI review logs into your workflows

Conclusion

The record that matters is the one that proves a named person reviewed a specific AI-assisted output, applied the right checks and made the final decision. Keep it close to the work, scale the detail to the risk, and make it easy to retrieve when a client, board or regulator asks how the judgement was made.