Share and intensity of work current AI systems can materially affect.
SOC 49-9071
Maintenance and Repair Workers, General AI displacement risk
Every building needs someone who can diagnose a noise, repair a pump, patch drywall, and adjust a boiler in the same week. Smart-building sensors help prioritize work orders; the varied physical repair work itself resists automation almost entirely.
Likely potential for exposed tasks to move to software after workflow integration.
Generalist repair work is the opposite of the standardized tasks machines absorb. Building systems monitoring changes what gets fixed first, not who fixes it.
Distribution
Where Maintenance and Repair Workers, General sits across 620 tracked roles
Displacement pressure 22 — higher than 29% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
27 O*NET task statements matched to SOC 49-9071. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $49,590 (May 2025, US national). The latest BLS row matched SOC 49-9071.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Maintenance and Repair Workers, General
SOC 49-9071 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 22/100 role score and are not an occupation forecast.
+1.1% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +1.6% GDP and 3.9% unemployment.
+5.9% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +8.3% GDP and 4.6% unemployment.
+33.6% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Maintenance and Repair Workers, General
The current evidence import matched 27 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks27
SOC49-9071
- Core task / ID 20671
Perform routine maintenance, such as inspecting drives, motors, or belts, checking fluid levels, replacing filters, or doing other preventive maintenance actions.
- Core task / ID 3024
Inspect, operate, or test machinery or equipment to diagnose machine malfunctions.
- Core task / ID 3030
Adjust functional parts of devices or control instruments, using hand tools, levels, plumb bobs, or straightedges.
- Core task / ID 20672
Repair machines, equipment, or structures, using tools such as hammers, hoists, saws, drills, wrenches, or equipment such as precision measuring instruments or electrical or electronic testing devices.
- Core task / ID 20674
Order parts, supplies, or equipment from catalogs or suppliers.
- Core task / ID 3023
Diagnose mechanical problems and determine how to correct them, checking blueprints, repair manuals, or parts catalogs, as necessary.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Diagnose and repair equipment problems
Exposure 22, automation 8%, augmentation 36%.
O*NET evidence: Diagnose mechanical problems and determine how to correct them, checking blueprints, re... (ID 3023)
Perform preventive maintenance
Exposure 24, automation 12%, augmentation 30%.
O*NET evidence: Perform routine maintenance, such as inspecting drives, motors, or belts, checking flui... (ID 20671)
Repair building structures and fixtures
Exposure 14, automation 4%, augmentation 18%.
O*NET evidence: Design new equipment to aid in the repair or maintenance of machines, mechanical equipm... (ID 20679)
Record work and order parts
Exposure 50, automation 26%, augmentation 52%.
O*NET evidence: Order parts, supplies, or equipment from catalogs or suppliers. (ID 20674)
TaskExposureAutomationAugmentation
Diagnose and repair equipment problems228%36%
Perform preventive maintenance2412%30%
Repair building structures and fixtures144%18%
Record work and order parts5026%52%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
LowFacilities Maintenance Supervisor
Training horizon: 2-5 months. Skill overlap 78. Wage preservation signal 120.
- Own preventive maintenance programs
- Manage work order backlogs
- Supervise contractors
credentialed transition
LowBuilding Engineer
Training horizon: 6-18 months. Skill overlap 64. Wage preservation signal 130.
- Learn building systems operations
- Study boiler and HVAC licenses
- Practice energy management
Comparison guides
Compare the next move before you commit
2-5 months
Maintenance and Repair Workers, General to Facilities Maintenance Supervisor
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Maintenance and Repair Workers, General into Facilities Maintenance Supervisor.
6-18 months
Maintenance and Repair Workers, General to Building Engineer
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Maintenance and Repair Workers, General into Building Engineer.
What the AI risk score means for Maintenance and Repair Workers, General
The displacement pressure score for Maintenance and Repair Workers, General is 22. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Record work and order parts carries 26% automation pressure, while Record work and order parts carries 52% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $49,590 (May 2025, US national). Employment context: One of the largest fix-it occupations across all buildings. Typical education: High school diploma plus on-the-job training.
Wage vulnerability is 54, while transition feasibility is 66. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Low displacement pressure
- Smart buildings augment prioritization
- Generalist skills stay scarce
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Maintenance and Repair Workers, General, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Multi-trade repair
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Troubleshooting
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Work order systems
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Vendor coordination
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Facilities Maintenance Supervisor, such as own preventive maintenance programs.
- By 90 days, compare internal openings and external postings for Facilities Maintenance Supervisor or Building Engineer and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Maintenance and Repair Workers, General
Will AI replace Maintenance and Repair Workers, General?
Every building needs someone who can diagnose a noise, repair a pump, patch drywall, and adjust a boiler in the same week. Smart-building sensors help prioritize work orders; the varied physical repair work itself resists automation almost entirely. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Maintenance and Repair Workers, General work are most exposed to AI?
Record work and order parts and Perform preventive maintenance show the strongest automation pressure in this model. Record work and order parts and Diagnose and repair equipment problems are better treated as AI-augmented work.
What should Maintenance and Repair Workers, General learn next?
Start with Multi-trade repair, Troubleshooting, Work order systems. The most practical adjacent paths in this model are Facilities Maintenance Supervisor and Building Engineer.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
OECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics