How much of this occupation’s work can be materially affected by current AI systems.
How likely exposure is to translate into reduced human demand.
Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured
Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.
What is driving the score?
Occupation scores are built from the task mix—not a single prediction about a job title.
Where AI can do more
Routine, digitized, and highly repeatable tasks face the greatest pressure.
- Perform grounds maintenance tasks, such as removing snow and mowing the lawn.74
- Advise managers, desk clerks, or admitting personnel of rooms ready for occupancy.73
- Maintain required records of work hours, budgets, payrolls, and other information.73
- Direct activities for stopping the spread of infections in facilities, such as hospitals.73
Where people still matter
These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.
- Supervise in-house services, such as laundries, maintenance and repair, dry cleaning, or valet services.01
- Select the most suitable cleaning materials for different types of linens, furniture, flooring, and surfaces.02
- Recommend or arrange for additional services, such as painting, repair work, renovations, and the replacement of furnishings and equipment.03
- Inspect work performed to ensure that it meets specifications and established standards.04
- Inspect and evaluate the physical condition of facilities to determine the type of work required.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
First-Line Supervisors of Office and Administrative Support Workers
Closely related work
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Compare these careers →Adoption and labour-market outlook
Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.
O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.
