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.
- Mark items with details, such as grade or acceptance-rejection status.74
- Notify supervisors or other personnel of production problems.74
- Check arriving materials to ensure that they match purchase orders, submitting discrepancy reports as necessary.74
- Grade, classify, or sort products according to sizes, weights, colors, or other specifications.74
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.
- Fabricate, install, position, or connect components, parts, finished products, or instruments for testing or operational purposes.01
- Remove defects, such as chips, burrs, or lap corroded or pitted surfaces.02
- Clean, maintain, calibrate, or repair measuring instruments or test equipment, such as dial indicators, fixed gauges, or height gauges.03
- Adjust, clean, or repair products or processing equipment to correct defects found during inspections.04
- Compare colors, shapes, textures, or grades of products or materials with color charts, templates, or samples to verify conformance to standards.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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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.
