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.
- Line slots with sheet insulation, and insert coils into slots.71
- Review work orders and specifications to determine materials needed and types of parts to be processed.70
- Attach, alter, and trim materials such as wire, insulation, and coils, using hand tools.68
- Apply solutions or paints to wired electrical components, using hand tools, and bake components.68
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.
- Cut, strip, and bend wire leads at ends of coils, using pliers and wire scrapers.01
- Disassemble and assemble motors, and repair and maintain electrical components and machinery parts, using hand tools.02
- Stop machines to remove completed components, using hand tools.03
- Examine and test wired electrical components such as motors, armatures, and stators, using measuring devices, and record test results.04
- Review work orders and specifications to determine materials needed and types of parts to be processed.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Electrical and Electronic Equipment Assemblers
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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.
