Installation, Maintenance, and Repair · United States (national)
Maintenance Workers, Machinery
How exposed this work is to automation, shown as ranges with the reasoning and sources.
The short version: this role is moderately exposed to automation, and a near-term shift looks unlikely.
It is a sourced range, not a verdict, and the detail is below.
Built on O*NET tasks, BLS pay and projections, and peer-reviewed AI-exposure research. Sources shown.
Data snapshot SNAPSHOT-2026-11-06. How it works
AI / softwarerange 0.0 to 23.4
Physical / roboticsrange 52.2 to 76.2
When the change is likely
Largely unaffectedlocal demand growing
What to do about it
Use AI to outpace AI.
Strategies to protect your livelihood, most important first.
1
Stay sharp; urgency is low
This role looks durable. Keep your skills current and watch for shifts in your field.
2
Move up from the task to the system
Robotics targets the physical work. Shift toward supervising, maintaining, or programming the automation instead of competing with it.
3
Pivot toward Industrial Machinery Mechanics
It is skill-adjacent and less exposed (58.8 vs 64). A lateral move now beats a forced one later.
4
Build a financial buffer
Keep 6 to 12 months of expenses and diversify your income, so a disruption is a setback rather than a catastrophe.
5
Add a recognised credential
A certification or license raises the bar against automation and signals value in a tightening market.
Keep the monitor on
Free shows where you stand today. JobBreak Monitor writes your full AI-researched plan and keeps watching, so you know the moment your outlook shifts.
Free
$0
See exactly where you stand.
Full job-risk score & timeline
Task-by-task exposure breakdown
One starter strategy
AI-researched personal plan
Ongoing monitoring & alerts
Current plan
Keep ahead
JobBreak Monitor
$3.99/mo
$29/yr
or $29/yr save 39%
that's about $2.42/mo, billed yearly
Your full researched plan, plus a watch on your job.
Everything in Free
Full AI-researched, cited action plan
Resume-aware personalisation
Re-run your plan any time
Alerts when your outlook shifts
Start monitoring
Secure checkout by Stripe. Cancel anytime.
Which of my tasks are exposed
Dismantle machines and remove parts for repair, using hand tools, chain falls, jacks, cranes, or hoists.
64.2
Reassemble machines after the completion of repair or maintenance work.
64.2
Record production, repair, and machine maintenance information.
64.2
Lubricate or apply adhesives or other materials to machines, machine parts, or other equipment according to specified procedures.
64.2
Install, replace, or change machine parts and attachments, according to production specifications.
64.2
Set up and operate machines, and adjust controls to regulate operations.
100.0
Collaborate with other workers to repair or move machines, machine parts, or equipment.
64.2
Read work orders and specifications to determine machines and equipment requiring repair or maintenance.
64.2
Longer, orange bars are more automatable; short green bars are resistant.
What is driving my result
lowers risk
raises risk
Task: Set up and operate machines, and adjust controls to regulate operations.
Task: Dismantle machines and remove parts for repair, using hand tools, chain falls, jacks, cranes, or hoists.
Local demand is growing
Lower-risk adjacent roles
Industrial Machinery Mechanics
58.8
Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic
60.5
Electric Motor, Power Tool, and Related Repairers
63.2
A full, ranked mitigation plan is part of the paid tiers.
How this score is calculated
Map Maintenance Workers, Machinery to its standard O*NET task list (8 core tasks).
Score each task for AI/software exposure (Eloundou et al. 2023) and physical/robotics exposure (an O*NET work-context proxy), each 0 to 100.
The two axis scores are the task averages: AI 11.4, robotics 64.2.
The timeline comes from the higher axis (64.2), shifted for local demand (growing), and seniority. It is read against your remaining working years.
Ranges are about ±12 points. A backtest against real BLS employment change shows the risk ranking holds in aggregate, but individual outcomes are predicted weakly, so results are ranges, never a flat verdict.
Sources: O*NET, Eloundou et al. (2023), GPTs are GPTs, O*NET Work Context, BLS CPI-U, BLS OEWS.
Snapshot SNAPSHOT-2026-11-06. Full method and backtest on How it works.