Keep a clear record of what AI did at work

The inference

If an AI tool changes a document, drafts a customer response, updates a ticket, or takes an action, you should be able to answer five questions afterward: what did I ask it to do, what did it use, what did it change, what did I approve, and what did I check? A short, consistent record makes AI-assisted work easier to review and safer to hand off. It is not a transcript of every prompt.

That distinction matters as AI moves from generating text to taking actions. This week, Reuters reported that OpenAI shelved a planned model release after it failed internal standards for staying within scope and authorization and clearly communicating what it had done. NVIDIA introduced an agent-safety platform focused on enforcing boundaries around agent actions. These developments do not mean every AI task needs compliance paperwork. They do underline why users need a way to see what happened, not just the final answer. (Reuters, NVIDIA)

Record the work at the handoffs, not every token along the way.

A useful record is not a chat transcript

Conversation history can help reconstruct an interaction, but it often misses the facts a colleague needs: which file version changed, whether the AI used a web source or a company document, whether a person approved an external action, and what was checked before the work was shared. A long transcript can also preserve confidential information long after the task is over.

Instead, capture the smallest set of details that makes the result explainable and repeatable. NIST’s generative AI risk guidance emphasizes documenting context, measurement, and human oversight across the AI lifecycle. For everyday work, that can be a few fields in a ticket or document rather than a new governance system. (NIST)

Record this	What to note	Example
Task	The goal and boundaries	“Summarize these 12 support tickets. Do not send replies.”
Inputs	Source names, links, or version	“Tickets 410–421, exported 29 Sep”
AI contribution	What it drafted, changed, or suggested	“Grouped themes; drafted a response for ticket 417”
Human decision	What you approved, edited, or rejected	“Edited refund amount; approved draft only”
Verification	Checks performed and by whom	“Checked totals against source sheet; MJ”
Outcome	Where the final work lives	“Summary in Support weekly review, linked below”

The point is not to label every sentence as AI-generated. It is to preserve the decision trail that would matter if someone asks, “Where did this come from?” or “What exactly changed?”

Match the record to the consequence

Use a light touch for low-stakes work and more detail when AI affects customers, money, access, legal obligations, or safety. A useful rule is to log the task, inputs, changes, approval, and check every time; add deeper evidence when the consequences rise.

Work type	Minimum useful record	Extra check before completion
Brainstorming or private first draft	Task and tool, if you keep the draft	Read for unsupported claims and confidential details
Internal summary or analysis	Source links or file versions, AI contribution, reviewer	Spot-check important facts against the source
Code or spreadsheet edits	Changed files/cells, test results, reviewer	Inspect the diff; run tests or reconcile totals
Customer-facing message	Source of claims, final approver, sent status	Verify names, commitments, amounts, and recipient
Agent action in another system	Exact action, target, approval, result, timestamp	Confirm the action in the destination system

An “AI used” checkbox alone is weak evidence. It does not show what the tool did or what a person took responsibility for. Conversely, saving every prompt and response by default can create a new privacy and retention problem. Follow workplace policy, limit access to records, and avoid copying secrets or unnecessary personal data into a log.

Put it to work: add a handoff note

For a one-off task, add this short note to the document, ticket, pull request, or project record where the work will be reviewed:

AI work note
Task and boundary:
Tool / account:
Inputs (links or versions):
AI contribution:
Human changes or approval:
Checks performed:
Final location / status:

Fill in only the fields that apply. For code, link to the commit and tests. For research, link to the source material and mark any unresolved claims. For an agent, include the action and target, plus whether it actually completed the action or only prepared a draft.

Keep source material in its approved home and link to it instead of pasting it into a general-purpose log. If the AI operated on a live system, check the system itself. A reassuring completion message is not proof that a message was sent correctly or that a record was updated as requested. OWASP’s agent guidance likewise recommends logging tool use and monitoring actions, while restricting permissions and requiring human confirmation for sensitive operations. (OWASP)

Make review easy

A good record lets the next person continue the work without guessing. Use stable links, identify the file version, name the reviewer, and separate what the AI proposed from what the human approved. If a decision changes, add a note rather than silently overwriting the reason for the original choice.

For repeated workflows, save the note as a template in the place the work already happens: a pull-request checklist, ticket form, document heading, or CRM activity. Avoid creating a separate spreadsheet unless someone will maintain it. The best audit trail is the one that is present when the work needs to be checked.

The practical standard: record enough to explain the AI’s role, reproduce the important checks, and identify the person accountable for the final result. Keep it brief for routine work, make it more complete as the stakes rise, and never confuse a model’s report of its actions with independent confirmation.