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.
- Adjust sizes or positioning of timepiece parts to achieve specified fit or function, using calipers, fixtures, and loupes.81
- Change timing weights on balance wheels to correct deficient timing.81
- Mount hairsprings and balance wheel assemblies between jaws of truing calipers.81
- Bend parts, such as hairsprings, pallets, barrel covers, and bridges, to correct deficiencies in truing or endshake, using tweezers.81
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.
- Disassemble timepieces such as watches, clocks, and chronometers so that repairs can be made.01
- Assemble and install components of timepieces to complete mechanisms, using watchmakers' tools and loupes.02
- Replace specified parts to repair malfunctioning timepieces, using watchmakers' tools, loupes, and holding fixtures.03
- Clean and lubricate timepiece parts and assemblies, using solvents, buff sticks, and oil.04
- Examine components of timepieces such as watches, clocks, or chronometers for defects, using loupes or microscopes.05
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Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
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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.
