A job title can contain a wide variety of tasks, from routine information handling to negotiation and physical work. The ILO’s research on generative AI and occupational exposure examines that distinction. Exposure is an analytical concept, not a guarantee that a job will disappear. The practical business question is which tasks could change and how the surrounding role would be reorganized.
Map the workflow before the forecast
A hypothetical administrative role might include drafting, checking records, resolving exceptions and coordinating with people. AI may affect those activities differently. A tool that accelerates one stage can change the volume or complexity of work arriving at another stage, rather than simply removing the same proportion of the whole job.
The mapping exercise should involve the people doing the work. They often know which exceptions consume time and which judgments depend on context missing from formal process documents. A task list written only from a job advertisement can miss that operational knowledge.
Separate capability from adoption
A system’s ability to produce an output does not establish that an organization can deploy it reliably or economically. Integration, data access, review and customer expectations influence the decision. Organizational choices also determine whether saved time becomes capacity for better service, a different workload or another outcome.
Workers need clear information about what is being tested and how their feedback will be used. Ambiguity can make it harder to learn from a pilot, especially if people believe that reporting a problem or an improvement will have unexplained consequences.
Build a transition around evidence
A useful plan identifies the affected tasks, the support required and the measures used to evaluate the change. The future of a role becomes easier to discuss when the organization can describe a concrete workflow rather than a sweeping prediction about a job title.
