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Decide what AI should do one task at a time—not by labeling an entire job “automatable.” Let AI handle bounded, repeatable work when someone can check it; keep people in the lead when errors are consequential, hard to detect, or impossible to review in time. In every case, a capable person must retain responsibility for how the output is used.
Start by dividing the workflow into tasks
A single workflow can combine routine steps with decisions that require context, judgment, or accountability. Map its steps separately, and mark where a result becomes a decision, commitment, or communication to someone outside the team. Microsoft’s guidance on choosing between Copilot, an agent, and human work recommends assessing the work at this task level rather than treating a whole role or project as one unit: Microsoft’s task framework.
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Assess each task with four questions
These criteria are a practical screen for judgment, not a numerical score or a promise that AI will be correct. Consider them together: repeatability alone does not make a task appropriate to automate.
- Repeatability: Does the same pattern recur, or is the work novel and exploratory?
- Impact: What would happen if the output were wrong?
- Error detectability: Can a qualified person check it against reliable facts, or could a mistake remain hidden?
- Time sensitivity: Is there enough time for effective review before the result is used?
These four criteria are set out in Microsoft’s guidance. Add one operational question before assigning review: does the named reviewer have the expertise, time, and authority to challenge the output?
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Choose who owns the work
Automate a bounded step, with review
This is the best fit for repeatable work with limited consequences and outputs that are straightforward to check. For example, AI can draft a routine internal update or summarise meeting notes; a person should verify the result before it is shared or relied on. Microsoft lists these as examples of work where AI can help, not as a universal guarantee of suitability: Microsoft’s examples.
Use AI support while a person leads
When a task needs interpretation or judgment, AI may help with drafting, summarising, or analysis, while a person frames the question, evaluates the reasoning, and owns the result. Spreadsheet formulas and research summaries deserve particular scrutiny: a plausible-looking error may be subtle, so check formulas against source data and summaries against primary sources.
Keep the critical step human-led
When a mistake could have serious consequences, is difficult to detect, or cannot be reviewed before use, do not hand the decisive step to AI. A person can still use AI for preparation if there is time and expertise to verify that support. Microsoft identifies customer-facing proposals, budget approvals, and external communications as examples where human-led ownership matters; these illustrations are not a universal legal classification. See Microsoft’s task guidance.
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Make human review real, not nominal
Assign a specific reviewer who understands the task, knows they are responsible for reviewing the output, has time to examine it, and can reject, correct, or escalate it. A person who merely clicks approve without relevant expertise or authority is not meaningful oversight. UK Government resources on scaling and de-risking AI and its “Hidden” AI Risks Toolkit emphasise the need for effective human oversight, including the conditions that let people challenge outputs: scaling and de-risking guidance and the toolkit.
Responsibility does not transfer to the model. The UK Government’s Data and AI Ethics Framework says people should be able to monitor and influence how systems work and remain responsible for decisions supported or informed by AI. The U.S. Intelligence Community’s AI Ethics Framework likewise links the degree and point of human involvement to assessed risk and calls for clear accountability. These are frameworks, not general workplace laws.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Apply extra care to consequential decisions
The UK Government Data and AI Ethics Framework says fully automated decisions should be avoided when outcomes could significantly affect individuals or groups, with a person making the final decision. The UK Government’s Generative AI framework for UK Government says legal, health, and care uses are likely to always require human involvement. These are UK government guidance statements, not globally exhaustive legal rules. Check the law and sector requirements that apply to your location and work before treating them as compliance advice.
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Revisit the boundary when conditions change
A work boundary is not a one-time decision. Reassess when the model changes, new data or users are introduced, the potential impact rises, or the time available for review shrinks. UK Government organisational guidance treats rollout as continuing work involving training, support, risk management, and monitoring, rather than a single approval: see its scaling and de-risking guidance and the generative AI framework.
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