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.
- Provide groundskeeping services, such as landscaping or snow removal.68
- Position, attach, or blow insulating materials to prevent energy losses from buildings, pipes, or other structures or objects.67
- Adjust functional parts of devices or control instruments, using hand tools, levels, plumb bobs, or straightedges.65
- Order parts, supplies, or equipment from catalogs or suppliers.65
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.
- Operate cutting torches or welding equipment to cut or join metal parts.01
- Perform routine maintenance on boilers, such as replacing burners or hoses, installing replacement parts, or reinforcing structural weaknesses to ensure optimal boiler efficiency.02
- Assemble, install, or repair wiring, electrical or electronic components, pipe systems, plumbing, machinery, or equipment.03
- Paint or repair roofs, windows, doors, floors, woodwork, plaster, drywall, or other parts of building structures.04
- Fabricate or repair counters, benches, partitions, or other wooden structures, such as sheds or outbuildings.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Millwrights
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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.
