AI can help a client team prepare a reply, summarise a conversation or draft a project brief. These tasks can look low stakes until a draft invents a date, leaves out a client condition or turns an internal suggestion into a promise.
Build review around the type of work and the consequence of an error. The checklist here is for everyday client communications and project preparation. It is not a certification of an AI system, and it does not replace specialist review where the subject requires it.
The useful idea
Review the output against the source and the decision it could influence. A fluent draft is not evidence that its facts or commitments are correct.
Choose what AI may prepare and what a person must decide
Start with preparation tasks whose source material and purpose are clear. A draft follow-up based on an identified conversation is easier to review than an unrestricted instruction to manage a client relationship. Name the person who will check and approve the result.
Keep approval separate from generation. Writing an email, choosing its recipients and sending it are different actions. The same applies to proposing a task change and committing that change. A workflow should make the boundary visible instead of relying on the reviewer to remember it.
Check facts against identifiable sources
Compare names, dates, amounts, deliverables and decisions with the relevant records. Open the source when a detail matters. A summary can omit a condition or merge two conversations while remaining perfectly readable.
Ask the reviewer to distinguish confirmed information from inference. If the source does not support a claim, remove it, qualify it or ask the responsible person. Do not accept a statement simply because another generated answer repeats it.
- Does each important fact have a source we can inspect?
- Are dates current and tied to the correct project?
- Does the draft preserve conditions and uncertainty?
- Are names and references associated with the right client?
Look for commitments hidden in natural language
Read words such as ‘will’, ‘confirmed’, ‘included’ and ‘approved’ carefully. They may turn a suggestion into a commitment. Check whether the person named has agreed and whether the team can deliver what the draft promises.
For an illustrative example, ‘We’ll add the new page by Friday’ may be wrong if the team has only agreed to assess the request. The safer draft reflects the actual next action: ‘We’ll review the new page request and come back with its effect on scope and timing.’ The wording should follow the record, not manufacture certainty.
Check who can see the information
Verify the recipient and remove internal notes, unrelated client details and information the recipient does not need. Review attachments and linked documents as well as the body of the message. Correct wording does not make the wrong audience appropriate.
Use the actual access and data-handling rules for your organisation and the tools involved. Do not assume a system inherits the right permissions merely because it can retrieve a record. If you cannot establish that the intended use is allowed, stop and ask the responsible person before using the material.
Match review depth to the consequence
A suggested internal agenda may need a quick source check. A client proposal, a sensitive message or a statement affecting contractual commitments needs the responsible people to review it. Some subjects need qualified advice rather than a general workflow checklist.
NIST’s Generative AI Profile discusses risks and context-dependent oversight. Our process recommendation is to make the reviewer, the sources and the approval boundary explicit. This does not mean that a human check guarantees correctness; the check itself needs relevant context and authority.
| Output | Review focus |
|---|---|
| Conversation summary | Source fidelity, missing conditions and correct client |
| Client email draft | Facts, tone, recipients and promised next actions |
| Project brief draft | Agreed scope, dependencies and unresolved decisions |
| Proposed record change | Affected work, authorised reviewer and confirmation before commit |
Record mistakes so the workflow can improve
When a review catches an error, record its type without spreading unnecessary client information: invented fact, missing condition, wrong recipient, unsupported commitment or irrelevant context. Look for patterns in the task instructions and source selection.
Keep a final human owner even as the workflow becomes familiar. In Tamaam’s product direction, Nefer prepares and proposes while people review and decide. The product is in development; explore the current demonstrations without assuming every workflow is live or that a generated answer is ready to send.
AI-assisted work review checklist
Run this before sharing a draft or approving a proposed change.
AI WORK REVIEW Purpose and intended audience: [summary] Source records checked: [links] Facts: [names / dates / amounts / deliverables verified] Conditions: [uncertainty and exceptions preserved] Commitments: [supported by the current agreement] Access: [audience, attachments and links appropriate] Tone and next action: [clear and suitable] Unresolved questions: [questions / owners] Reviewer: [responsible person] Decision: [revise / approve / escalate] Action after approval: [explicit next action]
Sources and further reading
Primary background on generative AI risks and oversight; no compliance or certification claim is made.
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