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.
- Record production data such as numbers and types of bobbins wound.71
- Unwind lengths of yarn, thread, or twine from spools and wind onto bobbins.70
- Tend spinning frames that draw out and twist roving or sliver into yarn.70
- Thread yarn, thread, or fabric through guides, needles, and rollers of machines.69
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.
- Install, level, and align machine components such as gears, chains, guides, dies, cutters, or needles to set up machinery for operation.01
- Observe bobbins as they are winding and cut threads to remove loaded bobbins, using knives.02
- Repair or replace worn or defective parts or components, using hand tools.03
- Clean, oil, and lubricate machines, using air hoses, cleaning solutions, rags, oilcans, and grease guns.04
- Observe operations to detect defects, malfunctions, or supply shortages.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Paper Goods Machine Setters, Operators, and Tenders
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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.
