As of August 2026,
Electromechanical Equipment Assemblers has an AI-exposure score of 51/100
(Elevated exposure) on the AI-Safe Careers index. This is an estimate of task
exposure, not a prediction of job loss.
Score inputs for this role:
This role starts from O*NET 29.1 occupational descriptors and is empirically grounded by Penn/OpenAI GPTs are GPTs study, Felten, Raj and Seamans AIOE index. BLS labor-market figures are separate context, not score inputs.
Electromechanical Equipment Assemblers
More exposed than 34% of the roles we track.
Will AI replace Electromechanical Equipment Assemblers?
No exposure score can predict whether AI will replace this role. The 51/100 score means our current model estimates elevated task exposure from the sources listed on this page. It does not predict an employer decision, headcount, or an individual outcome. The task map below shows the work assessed and where human judgment remains important.
Early-career context (study ages 22-25)
This role's task mix is automation-heavy, but it sits in the Elevated exposure band rather than our highest-exposure bands. That is only a partial match to the study's cohort, so the headline figure is context rather than a direct forecast for this role.
The November 2025 revision reports a 16% relative employment decline for workers ages 22-25 in the most AI-exposed U.S. occupations, after firm-level controls, relative to workers in less-exposed fields and more experienced workers in the same occupations.
Use the task map to identify durable work and the skills worth building early.
Stanford Digital Economy Lab, Canaries in the Coal Mine - November 2025 revision
This is group-level U.S. payroll evidence, not a personal forecast. The authors do not claim that AI alone caused the change, and this context does not alter the exposure score.
How this role compares to similar Production roles
What this role usually involves
Assemble or modify electromechanical equipment or devices, such as servomechanisms, gyros, dynamometers, magnetic drums, tape drives, brakes, control linkage, actuators, and appliances.
Broad guidance for this preparation level; exact requirements vary by role and employer.
Source: O*NET 29.1 exact occupation - Electromechanical Equipment Assemblers, SOC 51-2023.00. Context describes the role; the AI-exposure score remains a separate task-exposure estimate.
Electromechanical Equipment Assemblers tasks, by AI exposure
- File, lap, and buff parts to fit, using hand and power tools.
- Attach name plates and mark identifying information on parts.
- Pack or fold insulation between panels.
- Drill, tap, ream, countersink, and spot-face bolt holes in parts, using drill presses and portable power drills.
- Disassemble units to replace parts or to crate them for shipping.
No durable tasks identified for this role - its individually-assessed tasks split 79% automatable / 21% augmentable.
We analyzed all 14 Electromechanical Equipment Assemblers tasks - 11 automatable and 3 augmentable. The full task map - every task with exactly what to do about each - is in your Career Report.
Your report unlocks three concrete artifacts
Every task scored with what to automate, augment, or protect.
Related roles with exposure deltas, salary, demand, and reachability. Lower-exposure options appear only when the data supports them.
A keepable roadmap plus resume and LinkedIn repositioning.
Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate - not generic advice.
Adjacent career paths
Relatedness shows how reachable a move may be. A path is labeled lower exposure only when its score is at least 6 points lower; every row shows the measured difference.
File, lap, and buff parts to fit, using hand and power tools.
Your AI-Safe Career Report
Every task scored with what to do about it; adjacent paths with honest exposure deltas, salary, demand, and reachability; a skill-gap map; a 30/60/90-day roadmap; Agent Reality Check; plus a résumé and LinkedIn rewrite and professional PDF.
Grounded in O*NET-linked or curated role data, with Penn, Anthropic Economic Index, and AIOE signals where matched. BLS labor-market context is separate.
Workers with AI skills earn a roughly 62% wage premium - adapting pays. - PwC Global AI Jobs Barometer, 2026
Generation starts after checkout; reports are typically ready within a few minutes.