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.
- Establish zero reference points on workpieces, such as at the intersections of two edges or over hole locations.72
- Position and secure workpieces on tables, using bolts, jigs, clamps, shims, or other holding devices.72
- Lift workpieces onto work tables either manually or with hoists or direct crane operators to lift and position workpieces.72
- Verify that workpiece reference lines are parallel to the axis of table rotation, using dial indicators mounted in spindles.72
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.
- Turn valves and direct flow of coolants or cutting oil over cutting areas.01
- Select and set cutting speeds, feed rates, depths of cuts, and cutting tools, according to machining instructions or knowledge of metal properties.02
- Perform minor assembly, such as fastening parts with nuts, bolts, or screws, using power tools or hand tools.03
- Move machine controls to lower tools to workpieces and to engage automatic feeds.04
- Observe drilling or boring machine operations to detect any problems.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
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Related work
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.
