What Changes in a Job When AI Takes on a Task

A job description may make a role look like one coherent activity. The working day usually contains different kinds of effort: gathering information, making judgements, coordinating with colleagues, recording decisions and handling cases that do not fit the usual process.

AI may assist some of these tasks while leaving others largely unchanged. Grouping them under a single label such as administration makes it difficult to see where assistance could be useful and where responsibility still sits.

The International Labour Organization's 2025 assessment of generative AI and jobs examines potential exposure at task level. Exposure is not a forecast of redundancies in a particular business.

For an SME, begin with one role and observe the work it actually involves. Ask the employee to describe an ordinary case, a difficult case and the interruptions between them. Identify the information used, the decisions made and the people affected by an error.

Avoid turning the exercise into a minute-by-minute surveillance programme. The purpose is to understand how the work is organised and where it could improve. Explain that purpose and how the findings will be used.

The resulting task map should make distinctions that matter. Drafting a response is different from authorising a commitment. Summarising a record is different from deciding whether it is complete. The design of AI assistance depends on those boundaries.

Consider a hypothetical purchasing coordinator who compares supplier quotations. An AI tool might extract proposed prices, delivery dates and stated conditions into a comparison.

That could be useful, but the comparison is only an intermediate result. Someone still needs to check that the offers cover the same items, identify missing conditions and decide which differences matter. A neatly formatted table does not establish that the quotations are commercially equivalent.

The redesigned workflow should identify where the source information remains available. The coordinator needs a practical way to verify a questionable entry without repeating the entire exercise from scratch. Missing or uncertain information should remain visible rather than being filled with a plausible answer.

Then consider the next step. If the tool prepares a recommendation, who may authorise the order? If it drafts a supplier message, who checks that the wording reflects the business's position? Those permissions should follow the actual consequence of the action.

This example also exposes work that can move between roles. A coordinator may save preparation time while a manager receives more recommendations to review. The business has changed the queue, not necessarily increased the capacity of the whole process.

Evaluate the complete passage from incoming quotation to an authorised, recorded decision. That is the unit of useful work. A faster first draft matters only in relation to what happens before and after it.

A requirement for human review is incomplete until the business defines what the reviewer is expected to establish.

The reviewer might check factual accuracy, contractual conditions or whether the proposed action is appropriate in the circumstances. Different checks require different knowledge. A colleague who can spot a spelling error may not be able to verify a supplier's commercial terms.

NIST's AI Risk Management Framework core calls for clear responsibilities and defined human oversight. The practical work is to make those responsibilities achievable in the workflow being introduced.

Specify what should be accepted, corrected, rejected or escalated. Give reviewers access to relevant source information and a way to record a recurring problem. They should be able to stop an action that exceeds the agreed boundaries.

Review capacity must also be part of the plan. If the system produces output faster than people can assess it, the business may create pressure to approve work superficially. Adding an approval button does not solve that operating problem.

Measure the effort of review during a trial. Include the time spent investigating uncertainty and communicating corrections. If the result requires extensive rewriting, understand why before describing the task as automated.

A useful redesign also preserves a fallback. Staff should know what to do if the tool is unavailable or unsuitable for a particular case. That route should be credible enough to use, rather than a paragraph in a document nobody has practised.

An introductory session can show employees how to enter instructions and obtain an answer. It does not establish that they know when the answer is suitable for the work.

Training should use the cases the role encounters. Include incomplete information, conflicting records and outputs that appear convincing but need correction. Let employees practise deciding when assistance is useful and when they should proceed another way.

The ILO's 2026 report on changing workplace skills identifies AI literacy and human agency as important alongside changing skill requirements. For a business, that means keeping employees able to understand and influence the work, rather than merely operate a new interface.

Explain the permitted use of information in the chosen tool. Staff need clear boundaries about what they may submit and how output may be used. An ambiguous rule leaves each person making a policy decision under time pressure.

Protect time for learning. If normal workload expectations remain unchanged throughout a trial, employees may have little opportunity to test the new process carefully or report what they discover.

Consider how newer colleagues will develop judgement. If a system takes over every routine preparation task, the organisation may need another way to teach the underlying business knowledge. Reviewing outputs requires expertise that has to be acquired somewhere.

The training plan should therefore describe both immediate tool use and the capability the team needs to retain over time.

Employees can often identify exceptions that do not appear in a process diagram. A seemingly repetitive task may involve recognising an unusual customer request, noticing missing information or remembering a commitment made elsewhere.

Bring that knowledge into the design before assigning the task to an automated workflow. Ask where assistance would reduce unnecessary effort and where it could make work harder to understand.

Set up a route for feedback that leads to decisions. A trial participant should be able to report that checking an output takes longer than doing the task directly, without the observation being treated as reluctance to adopt technology.

Be clear about the intended outcome of the change. The business may want shorter waiting times, more consistent records or capacity to serve additional customers. Avoid implying that every saved minute has already been committed to another target before the working arrangement has been assessed.

Look at the distribution of work. A task removed from one role may reappear as exception handling in another. A specialist may inherit a larger volume of difficult cases while routine work disappears. That shift deserves discussion even if the overall task count falls.

The quality of the redesign depends on whether people can perform the responsibilities assigned to them. Participation helps uncover that question early enough to change the proposal.

Define a small set of outcomes before the trial. They should describe the task and the people operating it, rather than the number of generated outputs.

Useful measures may include completed cases, correction effort, waiting time at the next decision and the quality of the final result. Ask employees about workload and clarity as well. Those observations can reveal problems a throughput measure misses.

Compare like with like. A trial made entirely of simple cases cannot establish how the new arrangement will handle the role's difficult work. Keep track of the cases excluded and the reason for excluding them.

Count the work the system creates before claiming the capacity it releases. A faster activity may free useful time, but the business must decide how that time can actually be used. Small fragments across a day are different from a dependable block of capacity.

Review the arrangement after changes to the tool, the task or the people using it. AI work redesign remains an operating responsibility after the initial implementation.

A well-scoped AI and automation project should make those responsibilities clearer. Begin with one role, one useful outcome and enough evidence to judge whether the new way of working serves both the business and the people expected to make it succeed.