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Before you accept or share AI-assisted work, check whether the task is appropriate for AI, verify the claims that matter, and decide whether the result is complete and safe to use. The person approving the final work—not the AI—remains accountable for it.
Decide whether AI belongs in the task
Choose the level of AI involvement by weighing four factors: how repeatable the task is, the impact of an error, how easily mistakes can be detected and corrected, and whether saving time matters without removing necessary oversight. Microsoft recommends these considerations when deciding whether work should be automated, supported by AI, or kept human-led. Microsoft’s guidance on choosing Copilot or an agent also asks: “Who will review or validate the output before it’s used or shared?” and “Can you easily verify the result before it’s used or shared?”
| Approach | When it may fit | Key trade-off |
|---|---|---|
| Human-led | The consequences of error are serious, or mistakes are difficult to detect and correct. | People retain direct control, but the work may take longer. |
| AI-supported | AI can help draft, summarize, analyze, or prepare material, while a person makes the substantive decisions. | May save preparation time while keeping judgment with the reviewer; the output still needs checking. |
| Automated with review | The task is repeatable, errors can be detected and corrected, and a reviewer can validate the result before use. | Can reduce routine effort, but depends on effective review and clear responsibility. |
If you cannot explain how an error would be caught, avoid handing the task over end to end. Microsoft suggests partial automation or using AI for drafting and preparation while a person leads the work when verification is difficult.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteVerify claims, figures, and instructions
Review the output before accepting it. Compare important claims, numbers, and instructions with reliable source material or the original record. Check the details most consequential to the task, including content types that the particular AI system may handle less reliably.
- For a summary, compare key statements with the source document and confirm that qualifications or exceptions have not been lost.
- For an analysis, trace important conclusions back to the underlying records and check the assumptions they rely on.
- For instructions, confirm that each step is accurate and appropriate for the intended situation.
- For figures, verify them against an authoritative source or original data when appropriate.
Microsoft’s Responsible AI practices overview recommends surfacing potential inaccuracies and encouraging verification against external sources. Its example of targeted checks for numbers is conditional: such checks may be useful where measurements show lower accuracy for that content type. It does not establish that every number, system, or task has the same risk.
Check whether the answer is complete and on task
A response can sound convincing and still answer the wrong question, omit an important condition, or introduce a claim unsupported by the source material. Ask whether it addresses the actual task, preserves the context needed to interpret it, and includes the information required for someone to act on it.
- Does it answer the request rather than a nearby or easier question?
- Are relevant constraints, exceptions, and dependencies included?
- Can each material conclusion be traced to evidence?
- Would someone using the output understand what remains uncertain or unverified?
Microsoft’s overreliance framework highlights that harder-to-detect mistakes increase the risk of overreliance and recommends making it easier to identify errors and verify correctness and completeness. A citation, explanation, or confident tone is not proof that an answer is correct; verification aids can also be unreliable.
Make the decision and keep ownership clear
After checking the work, choose to accept it, edit it, reject it, or escalate it to someone with the right expertise or authority. Do not let the presence of AI obscure who approved the final version. UNESCO’s Recommendation on the Ethics of Artificial Intelligence says that AI systems should not displace ultimate human responsibility and accountability. That principle supports keeping a person responsible for the final content; it does not prescribe one universal review procedure for every workplace task.
Use stronger checkpoints when AI can take action
When an AI system can access data, execute tasks, or drive decisions through connected tools, reviewing its written response alone may not be enough. Consider safeguards that match the workflow’s permissions and potential consequences:
- Auditable activity: Keep records that let appropriate reviewers inspect what the system did.
- Role-based access: Limit what the system can access or change to what the task requires.
- A circuit breaker: Provide a way to stop activity if the system behaves unexpectedly or the risk changes.
These are safeguards to consider, not universal legal requirements. Microsoft’s Responsible AI in Azure Workloads discusses them in the context of agentic systems, so the appropriate controls depend on what a system can do and the consequences of those actions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Apply guidance within its stated scope
Guidance does not automatically apply across every setting. UNESCO’s 2023 guidance for generative AI is specifically about education and research; it is useful for background on generative AI and those contexts, not as a general workplace rule. ISO lists ISO/IEC FDIS 42105 as a 2026 Edition 1 final draft in the approval phase. Its abstract describes guidance on human control and monitoring across the AI system life cycle; it should be described as a draft, not as a finalized standard.
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