Helpers--Installation, Maintenance, and Repair Workers
AI is not doing this work today.
This occupation's activities barely register in measured AI usage. Generative AI is not doing this work today. The pressures that matter here are more likely economic or robotic than linguistic.
Where this occupation sits among all 785 measured. Bands are quintiles of the real distribution. Applicability measures task overlap with observed AI usage. It is not a probability of job loss.
What this job involves
Help installation, maintenance, and repair workers in maintenance, parts replacement, and repair of vehicles, industrial machinery, and electrical and electronic equipment. Perform duties such as furnishing tools, materials, and supplies to other workers; cleaning work area, machines, and tools; and holding materials or tools for other workers.
Where AI overlaps this work, activity by activity
The occupation's most important work activities (O*NET weights, normalized to this set), each with its measured AI performance across 200,000 real conversations: how often AI is used for it, how well it completes it, and how much of the activity it covers.
| Work activity | Share of role | AI performance |
|---|---|---|
| Assemble equipment or components. | 14.1% | Not observed 0.00 |
| Inspect commercial, industrial, or production systems or equipment. | 13.6% | Not observed 0.00 |
| Maintain tools or equipment. | 12.0% | Not observed 0.00 |
| Clean tools, equipment, facilities, or work areas. | 8.9% | Not observed 0.00 |
| Test performance of equipment or systems. | 8.0% | Moderate 0.55 |
| Monitor equipment operation. | 7.5% | Not observed 0.00 |
| Connect components or supply lines to equipment or tools. | 7.1% | High overlap 0.66 |
| Disassemble equipment. | 6.2% | Not observed 0.00 |
| Move materials, equipment, or supplies. | 6.0% | Not observed 0.00 |
| Position tools or equipment. | 5.7% | Not observed 0.00 |
| Repair electrical or electronic equipment. | 5.6% | Not observed 0.00 |
| Adjust equipment to ensure adequate performance. | 5.4% | High overlap 0.67 |
"Not observed" is a measurement, not missing data: that activity does not meaningfully appear in the usage sample. AI performance = completion × scope × coverage (user-goal view), 0–1.
What drives this score: the biggest AI-overlapped activity in this role is test performance of equipment or systems; the most important activity AI is not observed doing is assemble equipment or components. The plan below is built around exactly that split.
Low exposure is an asset. Spend it deliberately.
Next 30 days
- Don't buy the panic. This occupation's activities barely appear in AI usage data. Your near-term exposure is other people's headlines, not your tasks.
- Skim the gains anyway. Even here, an assistant helps with the thin admin layer: Test performance of equipment or systems is the one place the data shows any overlap.
Next 90 days
- Deepen the human core. Activities like Assemble equipment or components are measured as fully outside current AI. Mastery there is compounding, defensible value.
- Mentor the exposed. Colleagues in adjacent office-heavy roles are the ones facing redesign; understanding their tools makes you a better coordinator and leader.
The longer game
- Watch the actual frontier for this work: usually physical automation, demographics or regulation rather than language models. This dataset intentionally measures only generative AI.
- Revisit after major data updates. If this page's vintage changes and your band moves, the plan moves with it.
Where to put your learning time
Your moat here is the activity assemble equipment or components, which the data shows AI is not doing at all. Deepening that is worth more than chasing AI skills you do not need. The one useful exception is the paperwork fringe of the job: enough tool fluency to clear admin quickly and hand the time back to the core work.
- Full task, skill and pay profile for this occupation on O*NET OnLine Free
- Browse role-relevant courses on Coursera Partner link
Most Coursera courses can be audited free; payment covers graded work and certificates. The partner link earns this site a commission if you subscribe, at no extra cost to you, and it never changes a score, a ranking or a plan here.
- AI applicability score and per-activity metrics: Tomlinson, Jaffe, Wang, Counts & Suri, Working with AI: Measuring the Applicability of Generative AI to Occupations, Microsoft Research 2025 (arXiv:2507.07935), data CC BY 4.0.
- Occupation description and activity structure: O*NET 30.0 Database, USDOL/ETA, CC BY 4.0.
- Bands, personalization weights and action plans are JobRiskAI editorial layers; method and limitations on the methodology page.