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Will AI replace Maintenance Workers, Machinery?

Lubricate machinery, change parts, or perform other routine machinery maintenance. The percentage shown is a task-exposure model, not a prediction of certain job loss.

O*NET 30.3 · 49-9043.00 · modeled exposure, not guaranteed job loss
LOW TASK EXPOSURE
26%
Estimated share of representative tasks exposed to current AI capabilities.
Task automation shareⓘ
26%Automatable
Automatable26%
Augmentable41%
Hard to automate33%
Based on analysis of representative responsibilities and current AI capability maturity.
Category#
Trades & Physical
Field & Physical
Modeled pay midpoint$
$94,100/yr
Directional estimate — verify locally
Time until impact⌁
6–10+ years
Earliest 2032 · widespread 2036+
Task-level replaceability ⓘ
Representative tasks ranked from highest to lowest AI exposure.
◇Dismantle machines and remove parts for repair, using hand tools, chain falls, jacks, cranes, or hoists.36%
✦Physical dexterity in unpredictable settings25%
⚙Reassemble machines after the completion of repair or maintenance work.23%
✦Safety responsibility and on-site judgment21%
▥Record production, repair, and machine maintenance information.12%
Impact timeline ⓘ
How AI assistance may move from early support to wider adoption.
2032Next2036+
Light supportAI provides limited help with dismantle machines and remove parts for repair, using hand tools, chain falls, jacks, cranes, or hoists..
AugmentationTools improve productivity without owning outcomes.
Human-ledCore work stays anchored in physical dexterity in unpredictable settings.
6–10+ yearsdirectional confidence window
HOW AI CHANGES THIS JOBtyping the logic in plain English
01>Start with the real work done by Maintenance Workers, Machinery.
02>AI can help with: Dismantle machines and remove parts for repair, using hand tools, chain falls, jacks, cranes, or hoists.
03>If a task is repetitive and easy to check, AI pressure goes up.
04>But a person is still needed for: Physical dexterity in unpredictable settings.
05>Likely result: AI mostly acts as a helper while the core job stays human-led.
06>Important: 26% is estimated task exposure—not a 26% chance of losing the job.
What AI can do
Dismantle machines and remove parts for repair, using hand tools, chain falls, jacks, cranes, or hoists.LOW MATCH
Reassemble machines after the completion of repair or maintenance work.LOW MATCH
Record production, repair, and machine maintenance information.LOW MATCH
How estimates work →
Your human moat
◆

Physical dexterity in unpredictable settings

HIGH MOAT

Combined with safety responsibility and on-site judgment, this keeps a human in the loop.

Strength8.9
Similar-role comparison
RoleRisk
Maintenance Workers, Machinery26%
Motorcycle Mechanics26%
Fishing and Hunting Workers26%
Automotive Glass Installers and Repairers26%
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Why this score?

✓The model evaluates 3 representative core tasks.
✓The core work depends on presence, dexterity, or accountability.
✓Occupation and task language comes from O*NET 30.3.
✓The strongest human advantage is physical dexterity in unpredictable settings.
Research basis

O*NET 30.3 occupation tasks plus the ILO 2025 exposure framework. Scores are directional estimates.

Read ILO research →
Data: O*NET 30.3 · Model framework: ILO 2025About O*NETShare report
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Motorcycle Mechanics · 26% exposureFishing and Hunting Workers · 26% exposureAutomotive Glass Installers and Repairers · 26% exposure