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.
- Note differences in wire and cable colors so that work can be performed correctly.69
- Test connections to ensure that power supplies are adequate and that communications links function.69
- Collaborate with other workers to locate and correct malfunctions.69
- Refer to manufacturers' manuals to obtain maintenance instructions pertaining to specific malfunctions.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.
- Test repaired, newly installed, or updated equipment to ensure that it functions properly and conforms to specifications, using test equipment and observation.01
- Repair or replace faulty equipment, such as defective and damaged telephones, wires, switching system components, and associated equipment.02
- Request support from technical service centers when on-site procedures fail to solve installation or maintenance problems.03
- Dig holes or trenches as necessary for equipment installation and access.04
- Clean and maintain tools, test equipment, and motor vehicles.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.
