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Automate or delegate tasks that are repetitive, clearly specified, digital, and inexpensive to check. Keep a person responsible when work depends on context, consequential judgment, exceptions, or relationships. For many tasks, the strongest option is a human-plus-AI workflow: let a tool prepare or process the work, then have someone review it before it matters.
Decide task by task, not job by job
Whether to automate work is best judged at the level of individual tasks, not by labeling an entire occupation “automatable.” A job can include routine work that software can assist with and other responsibilities that rely on a person’s experience, judgment, or interaction with colleagues and customers. Automating one task therefore does not automatically mean replacing the person who does it.
The distinction matters because exposure to generative AI is not the same as certain job loss. The International Labour Organization’s 2025 update estimates that one in four workers worldwide are in occupations with some degree of GenAI exposure. It says most jobs are more likely to be transformed than made redundant, in part because occupations still contain tasks requiring human input. The ILO’s 2025 update and its refined global exposure index measure potential exposure, not a prediction that a particular worker or job will be displaced.
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Compare the three ways to handle a task
The right arrangement depends on how predictable the work is and what happens when the output is wrong. The table is a practical decision framework, not a scoring test validated by the ILO, OECD, or WEF.
| Approach | Best fit | Main trade-off | Example pattern |
|---|---|---|---|
| Human-led | The work needs context, negotiation, empathy, discretion, or accountable judgment. | People may spend time on work that could be made more consistent or less repetitive with a tool. | A manager weighs an employee’s circumstances before deciding how to respond to a sensitive request. |
| Tool-assisted, human-reviewed | A tool can draft, classify, extract, or prepare material, but a person must verify meaning or decide what happens next. | Review takes time and must be designed into the workflow; the tool’s output is not automatically reliable. | A person checks a generated summary against the original document before sharing it. |
| Automated with limited routine review | The task is repeatable, inputs and outputs are clearly defined, exceptions are rare, and errors are low-impact and detectable. | Exceptions, changing inputs, or a hidden failure can undermine a workflow that once seemed reliable. | A system routes a correctly formatted internal request to a standard queue, with a way to catch failures. |
These examples illustrate the framework; they are not claims that a particular tool can perform those tasks safely or accurately. A workflow’s suitability depends on its actual inputs, rules, error consequences, and review arrangements.
Run a four-question delegation check
For each candidate task, answer the same four questions. Record the answers together with the decision and the person accountable for the result. This makes it easier to revisit the choice when the task, tools, or stakes change.
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- Is the task repeatable, with structured inputs? Describe what starts the task, what information it may use, what output is expected, and which rules govern the result. Automation is a stronger candidate when these conditions are stable and explicit. If the work depends on missing context, ambiguous requests, or one-off exceptions, resolve those issues or keep a person in the loop.
- Does the task require context, judgment, or interaction? Consider whether the worker must interpret intent, handle an unusual case, weigh competing interests, communicate a sensitive decision, or build trust. The more a result depends on those capabilities, the more important it is for a person to lead the decision rather than simply accept a generated answer.
- What is the impact of an error, and what would review cost? A wrong internal label may be easy to correct; an incorrect decision that affects a customer, employee, or important commitment may be much harder to undo. Estimate both the likely consequences and the effort needed to detect mistakes. Cheap, effective checks make assistance more practical; severe consequences or hard-to-detect errors call for stronger human control.
- Will the tool augment a worker or substitute for this task? State what changes: Does the tool prepare material for a person to use, or does it take over a defined task? Identify any new checking, exception handling, or follow-up that moves to the worker. A tool can reduce one kind of work while creating another, so assess the whole workflow rather than just the automated step.
There is no universal score or threshold established by the cited studies for combining these answers. Use them to make a local decision that reflects the workflow and its stakes, not to give an occupation a permanent “safe” or “replaceable” label.
Match common task patterns to the right level of oversight
Use these patterns as starting points for analysis, not blanket permissions. The label “AI task” does not establish that a particular system is appropriate, and even a well-defined task can become riskier when its output affects a consequential decision.
| Task pattern | A reasonable starting arrangement | What the person should check or retain |
|---|---|---|
| Repeated formatting or conversion under fixed rules | Consider automation when inputs and required outputs are defined. | Check that unusual files, missing fields, and failed conversions are visible rather than silently passed through. |
| Drafting a standard internal message from supplied facts | Consider tool-assisted drafting with a human review before sending. | Verify facts, tone, audience, and whether the draft implies a promise or policy that was not intended. |
| Summarizing documents or requests | Use a tool to prepare a summary only when a person can consult the underlying material. | Check that important qualifications, exceptions, and conflicting information have not been lost. |
| Classifying routine requests into known categories | Consider automation or assisted classification if categories are clear and misrouting is manageable. | Set a route for uncertain cases and periodically look for mistakes that are not being corrected. |
| Handling complaints, sensitive conversations, or individual circumstances | Keep a person responsible; a tool may assist with preparation if its role is bounded. | Retain the human relationship, interpretation, and authority to respond to context the workflow did not anticipate. |
| Making decisions with significant consequences for a person or organization | Do not treat a generated recommendation as the decision. Define human authority and appropriate review. | Establish who checks the relevant evidence, handles exceptions, communicates the outcome, and corrects errors. |
Put a human-plus-tool workflow into practice
A useful pilot is small enough to inspect and important enough to represent real work. Before changing a process, document how it currently works and what a correct result looks like. Without that baseline, it is difficult to tell whether a tool has saved effort, shifted effort into checking, or introduced new errors.
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- Choose one bounded task. Define the start and end of the task, its users, and what it does not include. Prefer a workflow with identifiable inputs and a clear way to compare the output with the source or an established rule.
- Write the task specification. List permitted inputs, required output, rules, known exceptions, and what to do when information is missing. If workers currently rely on undocumented judgment, capture the relevant decision points before trying to automate them.
- Set the human role before the tool role. Name the person or team accountable for the result. Specify which outputs need review, what evidence the reviewer should inspect, and who may resolve exceptions. “A human is in the loop” is not enough unless that person has time, information, and authority to intervene.
- Try the workflow on a limited set of work. Compare its outputs with source material or the existing process. Include ordinary cases and the exceptions that the specification identifies. Note incorrect results, missed cases, review time, and the effort required to correct problems.
- Decide whether to continue, change, or stop. Consider the full process: work avoided, new review and exception-handling work, quality, and error consequences. Tighten the task boundary or review if problems are fixable; keep the task human-led or stop if important failures cannot be detected or managed.
- Recheck after changes. Reassess when the tool, input format, task rules, or consequences change. A workflow that worked for a stable, narrow task should not automatically inherit approval for a broader one.
Design review around the cost of mistakes
Review should be proportionate to risk, not treated as a ceremonial final click. Reviewers need access to the source material, a clear standard for correctness, and an easy way to reject, edit, or escalate an output. If the reviewer cannot tell whether a result is wrong, the presence of review does not by itself make the workflow safe.
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- For low-impact, readily reversible outputs, a workflow may use sampling or exception checks if errors are visible and can be corrected in time. Define how failures surface and who responds to them.
- For outputs that shape a consequential action, assign a qualified person to check the relevant evidence and retain authority over the decision. The higher the impact or the harder an error is to discover, the less appropriate it is to rely on an unchecked output.
- For uncertain or exceptional cases, route work to a person instead of forcing it through a standard automated path. An explicit “cannot determine” outcome is often safer than making the workflow appear complete.
- For repeated mistakes, pause or narrow the workflow while the cause is investigated. More review is not a durable fix if the task is poorly specified or the output cannot be verified.
Keep enough information to understand how a result was produced and how it was checked, in line with the organization’s needs. The aim is not to create paperwork for its own sake; it is to make responsibility, corrections, and recurring failure patterns visible.
What the workforce figures do—and do not—tell you
Global studies help explain why organizations are considering automation, but they cannot decide whether a particular task in your workplace should be delegated. Their measures describe exposure, survey intentions, or skill patterns at broader levels than an individual workflow.
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| Finding | What it supports | What it does not establish |
|---|---|---|
| The ILO’s 2025 update estimates that one in four workers worldwide are in occupations with some degree of GenAI exposure. | GenAI may affect work across a substantial share of occupations. | That one in four workers will lose a job or have every task automated. |
| The ILO’s refined 2025 index places 3.3% of global employment in its highest exposure gradient. | Potential exposure is concentrated differently across task combinations and occupations. | A probability that a worker in that gradient will be displaced. |
| The ILO index reports overall exposure of 11% of employment in low-income countries and 34% in high-income countries. | Exposure varies with country income as well as across occupations. | That every person or job in either group faces the same task-level prospects. |
| For its 2025 index, the ILO reports a mean automation score of 0.29, compared with 0.30 in 2023, and lower score variability in 2025. | The index’s estimates changed modestly between those editions and became less variable. | A 29% chance of job loss, or an individual worker’s personal automation risk. The index score is not a probability of losing a job. |
| In the World Economic Forum’s 2025 survey, 73% of surveyed employers planned to accelerate process and task automation, while 63% intended to complement or augment workers with technology over 2025–2030. | Employers reported intentions to pursue both automation and augmentation in that forecast period. | Observed outcomes, a guarantee that plans will be implemented, or a prediction for every employer. These are survey expectations, not measured results. See the WEF workforce strategies chapter. |
The ILO’s refined index combines task-level information, worker perceptions, expert input, and AI predictions. Its exposure gradients and employment figures are useful context for workforce planning, but the ILO does not turn them into a decision rule for delegating an individual task. Its announcement quotes Senior Economist Janine Berg describing the tool as helping countries assess potential exposure and prepare labour markets for a fairer digital future. The ILO announcement was published on 20 May 2025.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Account for skills and changing work
Automation changes the mix of work people do, so a delegation decision should include the skills needed to operate and review the workflow. The OECD’s April 2024 analysis of occupations that do not require specialized AI skills identifies management and business skills as important in highly AI-exposed occupations. Its November 2024 policy brief reports a relatively small decline in demand for management, business, and digital skills in workplaces most exposed to AI. Those findings describe different aspects of changing skill demand; they do not show that all human skills are universally rising or falling.
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For a workplace, translate those broad patterns into concrete responsibilities: who specifies the task, supplies suitable information, checks an output, handles an exception, and communicates a decision? If a tool removes routine steps but leaves workers with more oversight or coordination, make sure the workflow recognizes those responsibilities rather than assuming the human contribution has disappeared.
Revisit delegation as the work changes
A task’s fit for automation is not permanent. Inputs may become less consistent, rules may change, or a process may start influencing decisions with greater consequences. A workflow that once handled a narrow, routine step may gradually expand into interpretation or decision-making without anyone formally reassessing it.
- Review a workflow when its input sources or task rules change.
- Look for recurring corrections, exceptions, and cases sent to the wrong path.
- Check whether people have enough time and authority to perform the review assigned to them.
- Reconsider the arrangement if the output begins to affect a higher-stakes decision or a new group of people.
- Keep the task human-led if reliable verification is unavailable or the human responsibility cannot be clearly assigned.
The aim is not to maximize the amount of work handed to a tool. It is to remove avoidable repetition where the task can be specified and checked, while preserving human responsibility wherever context, consequences, or relationships make that responsibility necessary.
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