Decide what AI should do one task at a time—not by labeling an entire job or project “automatable.” Let AI handle bounded work when a person can reliably check it; keep a person in the lead when mistakes could be consequential, hard to spot, or impossible to review in time. In every case, a capable human must remain accountable for how the output is used.
Start by breaking the workflow into tasks
A single workflow can mix routine steps with decisions that require judgment. Separate the work into concrete tasks, then note where an output becomes a decision, a commitment, or a message to someone outside your team. Assigning a boundary at this level makes it possible to use AI for a useful part of a process without handing over the whole process.
Microsoft’s guidance on choosing Copilot or an agent recommends evaluating work by task because risk, ambiguity, and the amount of judgment can differ from one step to the next. Microsoft’s task-level framework is a practical screening aid, not a guarantee of accuracy or a numerical risk score.
Assess each task on four criteria
For each task, consider the following questions together. Repeatability alone does not establish that a task is appropriate to automate.
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- Repeatability: Does the same pattern recur, or is the work unique, exploratory, or dependent on context that changes each time?
- Impact: What could happen if the result is wrong? Consider who might be affected and whether the output could trigger a consequential decision or commitment.
- Error detectability: Can a qualified person compare the output with reliable facts or source material, or might a plausible-looking error be difficult to notice?
- Time sensitivity: Is there enough time for a meaningful review before anyone relies on the output?
These are the criteria in Microsoft’s framework. Use them to guide judgment rather than adding them into a made-up score: the sources do not establish a universal threshold that makes a task safe to automate.
Choose who owns the work
Automate a bounded step, with human review
This is the best fit for repeatable work with limited consequences and errors a person can check. For example, AI can prepare a first draft of a routine internal update or summarize meeting notes; a team member should check accuracy and context before the draft is used. Microsoft’s examples are illustrative, not a guarantee that every internal draft or summary is low-risk.
Use AI as support while a person leads
When work involves ambiguity or judgment, AI may still help with drafting, summarizing, or analysis. The person leading the task should frame the question, assess the reasoning, verify relevant facts, and own the result rather than accepting an output as a decision.
For a spreadsheet formula, for instance, check the formula against the underlying data and expected results. For a research summary, follow the claims back to reliable source material. These are cases where an answer can look convincing while containing an error that is not immediately obvious.
Keep the critical step human-led
Keep a person in the lead when an error could have serious consequences, could be hard to detect, or might reach someone before it can be reviewed. Microsoft gives customer-facing proposals, budget approvals, and external communications as examples where human ownership matters. AI may assist with preparation, but a person should make or approve the consequential decision and take responsibility for the communication.
For decisions that could significantly affect individuals or groups, the UK Government’s Data and AI Ethics Framework says to avoid fully automated decisions and ensure a person makes the final decision. The UK Government’s Generative AI framework says legal, health, and care uses are likely to always require human involvement. These are UK government framework guidance, not universal legal classifications; check applicable law and sector rules before making a compliance decision.
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Make human review effective
A human approval step is not meaningful if the reviewer cannot tell whether the output is sound or has no real power to stop it. Name the reviewer and make sure they have:
- Relevant knowledge of the task and enough expertise to assess the output.
- Time to check it before it is used, not merely an approval prompt at the last moment.
- Access to the facts or source material needed to verify important claims.
- Authority to reject or correct the output, or to escalate the matter when they cannot verify it.
- A clear understanding that they are accountable for the decision to use the output.
UK Government organisational guidance warns that oversight can fail when people lack expertise, time, or authority to challenge AI outputs. Its Mitigating ‘Hidden’ AI Risks Toolkit and guidance on a human-centred approach to scaling and de-risking AI tools also frame safe use as an organisational practice involving support and risk management, not a checkbox attached to a tool.
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A task that was manageable with review can become riskier if the model changes, new data or users are involved, consequences grow, or reviewers have less time. Reassess the task and its review arrangements when those conditions shift. UK Government guidance on organisational AI adoption treats training, support, risk management, and monitoring as ongoing work; its Generative AI framework provides a government context for that approach.
The U.S. Intelligence Community’s Artificial Intelligence Ethics Framework likewise ties the appropriate degree of human involvement to assessed risk and calls for clarity about accountability and when review occurs. It is a corroborating framework, not workplace law for general readers.
Quick Recap
A quick decision checklist
- Map the workflow: list its tasks and identify where outputs become decisions, commitments, or external communications.
- Assess each task: consider repeatability, impact if wrong, how detectable errors are, and the time available for review.
- Set the ownership mode: use AI with review for bounded, checkable steps; use it as support under human leadership when judgment is needed; keep consequential or unreviewable steps human-led.
- Assign a real reviewer: confirm that the person has expertise, time, source access, and authority to intervene.
- Reassess after changes: revisit the boundary when the model, data, users, stakes, or review conditions change.
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