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.
- Connect cables, tubes, and wiring, according to specifications.81
- Attach name plates and mark identifying information on parts.81
- Position, align, and adjust parts for proper fit and assembly.80
- Read blueprints and specifications to determine component parts and assembly sequences of electromechanical units.80
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.
- Assemble parts or units, and position, align, and fasten units to assemblies, subassemblies, or frames, using hand tools and power tools.01
- Clean and lubricate parts and subassemblies, using grease paddles or oilcans.02
- Inspect, test, and adjust completed units to ensure that units meet specifications, tolerances, and customer order requirements.03
- Operate or tend automated assembling equipment, such as robotics and fixed automation equipment.04
- File, lap, and buff parts to fit, using hand and power tools.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.
