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.
- Separate scrap waste and related materials for reuse, recycling, or disposal.80
- Dispose of scrap or waste material in accordance with company policies and environmental regulations.80
- Check work pieces to ensure that they are properly lubricated or cooled.80
- Confer with engineering, supervisory, or manufacturing personnel to exchange technical information.80
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.
- Set up or operate metalworking, brazing, heat-treating, welding, or cutting equipment.01
- Align and secure holding fixtures, cutting tools, attachments, accessories, or materials onto machines.02
- Lay out, measure, and mark metal stock to display placement of cuts.03
- Install repaired parts into equipment or install new equipment.04
- Measure, examine, or test completed units to check for defects and ensure conformance to specifications, using precision instruments, such as micrometers.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic
Closely related work
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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.
