Put human review at four explicit boundaries: before a draft sets direction, before a factual claim is relied upon, before the exact final item is approved, and before ongoing AI-assisted work drifts beyond what the team intended.
Review should not be a generic final click after unchecked automation. A named person should be able to stop, change, or reject the output at each boundary. The depth of review can vary with the consequence, reversibility, and detectability of a mistake, but responsibility must remain explicit.
NIST’s official Artificial Intelligence Risk Management Framework 1.0 organizes risk management into GOVERN, MAP, MEASURE, and MANAGE; its MANAGE guidance addresses human oversight and assigned responsibilities. The four review boundaries below are editorial advice for small teams, not a NIST rule or universal compliance checklist.
Write the boundary before selecting the add-on
For each proposed use, complete this sentence before evaluating a product:
The add-on may perform a task until a named person reviews the output and decides whether it can proceed.
Fill it in separately for drafting, fact checking, exact approval, and monitoring. Name both the responsible role and the person currently holding it. If the team cannot complete the sentence, the proposed use is not yet specific enough for responsible adoption.
1. Drafting: review direction before development
Use the add-on to produce options, variations, transformations, or an initial structure. The human boundary comes before the team spends time refining prose or acting on the draft.
The reviewer should confirm the purpose, audience, source material, constraints, and intended next step. The pass condition is an approved direction—not merely a draft that sounds polished. The reviewer decides whether the outline solves the assigned problem, whether anything important is missing, and whether the draft should be revised or abandoned.
Keep unverified statements labeled as such. Drafting approval should not also imply factual approval: that creates a separate review gate with a different purpose and evidence standard.
2. Fact checking: verify every material claim before reliance
Create a claim list from the draft. “Material” means a statement that could change the reader’s understanding, decision, or action. Check quoted wording, numbers, dates, names, and consequential inferences against a source the reviewer can inspect.
For each material claim, record one of four outcomes: supported, corrected, removed, or unresolved. Attach the source used. A generated summary is a pointer for further review, not evidence for itself. If the underlying source cannot be accessed, the claim remains unverified and cannot pass this boundary.
This gate should happen after drafting is stable and before exact approval. Checking facts inside a still-changing draft wastes effort because later edits may create new claims or alter their meaning.
3. Exact approval: attach responsibility to the final version
Approval must apply to the exact artifact the team will use or send—not to the original request, prompt, outline, or an earlier draft.
An approver with the appropriate authority should compare the final wording, figures, and attachments with the approved brief, verified evidence, and operating constraints. Record the approver, approval time, and a version identifier. If the add-on regenerates or materially edits the artifact after approval, repeat the review before use.
Changes to facts, meaning, commitments, dates, recipients, or instructions should reopen the relevant earlier gate. Nobody should be able to say that a final item was approved when they actually reviewed a different version.
4. Monitoring: review what happens after release
Human oversight should not end when the workflow goes live. Before rollout, assign an owner and define what the team will inspect. Relevant signals include user corrections, reviewer overrides, source mismatches, escalations, complaints, and missed handoffs.
Review the first outputs, a defined sample of later outputs, every flagged exception, and outputs following changes to the add-on, its configuration, the workflow, or its source material. The owner should also decide in advance what would trigger a pause, who has authority to pause the workflow, and how work can continue without the add-on.
Monitoring is active review, not passive log collection. If no one owns the signals and the response, the deployment has no complete human-review boundary.
Use procurement questions to test the design
Ask vendors to demonstrate—not merely promise—the controls your proposed boundaries require:
- How can a reviewer inspect and retain the source behind a factual claim?
- How can the team identify the exact version that was approved?
- How can reviewers edit, reject, stop, or escalate an item?
- How can the team inspect later outputs and receive notice of relevant changes or failures?
Do not assume an add-on supports these controls because it supports generation. If a vendor cannot demonstrate a necessary control, narrow the proposed use or decline it.
Scale effort, not accountability
Exploratory internal drafting may justify lighter review than work that is consequential, hard to reverse, or difficult to detect. You can sample some low-consequence checking while inspecting every material claim in higher-consequence work. Exact approval and monitoring still need named owners.
The practical test is simple: can a person understand the relevant output well to accept, change, or reject it? If not, placing that person’s name in the workflow provides accountability without providing meaningful review.
Use AI to work faster inside explicit boundaries: review direction during drafting, verify material facts before relying on them, approve the exact final version, and monitor the workflow after release.