Automate repeatable work when mistakes are easy to catch; keep a named person responsible for ambiguous or consequential decisions. The practical balance is set task by task—not by choosing a blanket percentage of work for AI. Decide what a system may draft, analyze, or complete, and build meaningful review into the workflow before anyone acts on its output.
How to decide which work AI can handle
Break a workflow into individual steps rather than labeling an entire job “automated” or “human-led.” For each step, discuss four questions Microsoft recommends considering. They are a decision aid, not a validated universal scoring system.
- How repeatable is the task? Stable, standardized work may be easier to automate. Novel, exploratory, or highly variable work generally calls for more human-led execution.
- What is the impact of an error? A draft internal update is different from approving a budget, sending a customer proposal, or making a consequential decision. The greater the impact, the more human ownership the task needs.
- How easy is an error to detect? If a reviewer can compare the output with source records or known facts, checking may be straightforward. Subtle or hidden errors call for stronger validation or manual handling.
- Is there enough time for a real review? If the workflow leaves no opportunity to inspect and correct the result, human-led work may be safer than relying on a nominal approval step.
Apply these questions in your domain and account for customers, workers, and legal obligations. Microsoft’s task guidance explains the criteria in its recommendations on when Copilot or an agent is the right tool.
Choose the AI role for each workflow step
AI delegation is not all-or-nothing. A system can perform a task, defer to a person, or inform a human decision. NIST recommends defining and differentiating human roles and responsibilities in human-AI interaction. Make the system’s role explicit in the workflow:
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- Draft or organize: Let AI prepare a first draft, sort information, or summarize records when a person will check the work before use.
- Analyze or recommend: Use AI to surface patterns or options, while a responsible person evaluates the evidence and decides what to do.
- Complete a bounded, repeatable task: Permit automation where the task is stable and errors are readily detectable, with a defined process for exceptions and correction.
- Defer or pause: Route ambiguous cases, missing information, or out-of-scope requests to a person rather than forcing an automated answer.
NIST cautions that human-AI performance depends on context: some interactions can amplify human bias, while well-designed teams can also complement one another. Its guidance is a risk-management framework, not a rule for how much work must receive human review. See NIST’s appendix on AI risk management and human-AI interaction.
Make human review meaningful
A reviewer’s approval matters only if that person can evaluate the result and change what happens next. Put review before an output is sent, published, or acted on when consequences justify it. Microsoft’s guidance puts it plainly: “Agents expand what you can do, not what you are responsible for.”
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- Give reviewers access to the relevant source information and enough context to check the output.
- Set aside time for review, and ensure the reviewer has the competence and authority to question, correct, or reject a recommendation.
- Define what happens when information is missing, an answer is uncertain, or the output conflicts with a source record.
- Make override and pause authority clear, along with how an error is reported and corrected.
A click-through without time, information, or authority is not meaningful oversight. Increase human ownership when errors are hard to detect or the consequences of a mistake are greater. Microsoft’s recommendations are available in its Copilot and agent task guidance.
Keep accountability clear as work changes
Name the person or role accountable for the outcome, even when AI supplies a draft or recommendation. Specify who can override or pause the workflow and where errors go for correction. That clarity addresses a concern reported by managers using algorithmic management tools: in an OECD study, 28% cited unclear accountability when a decision is wrong.
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The OECD findings are about algorithmic management broadly, not generative AI specifically. More than 6,000 mid-level managers in France, Germany, Italy, Japan, Spain, and the United States were surveyed for the OECD’s 2025 research. Nearly two-thirds of managers using these tools reported at least one concern; 27% cited difficulty following decision logic, and 27% cited inadequate protection of workers’ physical or mental health. These figures should not be read as estimates of generative-AI use. The findings and discussion appear in the OECD report on algorithmic management in workplaces.
Account for workers, skills, and changing workflows
When AI changes how work is allocated, monitored, or evaluated, involve affected workers and consider transparency, fairness, autonomy, and health. The OECD identifies worker consultation as a recommended practice, while noting that more research is needed to measure the effectiveness of governance measures.
Review how delegation affects people’s ability to learn and maintain skills, too. In Microsoft’s company-sponsored Work Trend Index survey, 50% of AI-using knowledge workers said quality control of AI output was a human skill gaining importance, and 46% named critical thinking. Edelman Data x Intelligence surveyed 20,000 AI-using knowledge workers across 10 markets from February 18 through April 7, 2026. These are self-reported survey responses, not proof that a particular training or oversight practice improves outcomes. Microsoft also reports that some advanced AI users intentionally do some work without AI to keep their skills sharp; that is self-reported evidence, not proof of a general intervention effect. See the 2026 Work Trend Index.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Revisit the boundary as evidence accumulates
Track errors, exceptions, and how well reviewers can identify problems. Look at worker impacts as well as output quality. If mistakes are difficult to catch, review is rushed, or accountability is unclear, narrow the automated role or add a human checkpoint. If a task proves stable, low-impact, and easy to verify, its automation boundary may be reconsidered. Treat the decision as an ongoing governance choice, not a one-time declaration that a tool is safe for an entire job.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchRequirements vary by jurisdiction, sector, and use; these general practices are not a legal assessment. The available OECD discussion notes differing policy approaches and the need to comply with existing law.
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