Share and intensity of work current AI systems can materially affect.
SOC 49-3042
Mobile Heavy Equipment Mechanics, Except Engines AI displacement risk
Repairing bulldozers, excavators, and cranes — often in the field — is heavy physical troubleshooting. Telematics and diagnostic software flag problems early; someone still crawls under the machine with tools.
Likely potential for exposed tasks to move to software after workflow integration.
Equipment telematics made this trade more efficient, not smaller: sensors predict failures, and mechanics perform the repairs at remote sites. Construction fleet growth and mechanic shortages keep wages rising.
Distribution
Where Mobile Heavy Equipment Mechanics, Except Engines sits across 620 tracked roles
Displacement pressure 18 — higher than 20% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
20 O*NET task statements matched to SOC 49-3042. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $65,510 (May 2025, US national). The latest BLS row matched SOC 49-3042.
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 Mobile Heavy Equipment Mechanics, Except Engines
SOC 49-3042 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 18/100 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 Mobile Heavy Equipment Mechanics, Except Engines
The current evidence import matched 20 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 tasks20
SOC49-3042
- Core task / ID 3001
Repair and replace damaged or worn parts.
- Core task / ID 3000
Test mechanical products and equipment after repair or assembly to ensure proper performance and compliance with manufacturers' specifications.
- Core task / ID 3002
Operate and inspect machines or heavy equipment to diagnose defects.
- Core task / ID 3008
Read and understand operating manuals, blueprints, and technical drawings.
- Core task / ID 3004
Dismantle and reassemble heavy equipment using hoists and hand tools.
- Core task / ID 3009
Overhaul and test machines or equipment to ensure operating efficiency.
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 faults with test equipment
Exposure 36, automation 15%, augmentation 58%.
O*NET evidence: Diagnose faults or malfunctions to determine required repairs, using engine diagnostic ... (ID 3003)
Dismantle and reassemble equipment
Exposure 16, automation 6%, augmentation 28%.
O*NET evidence: Dismantle and reassemble heavy equipment using hoists and hand tools. (ID 3004)
Repair hydraulic and mechanical systems
Exposure 18, automation 7%, augmentation 32%.
O*NET evidence: Fit bearings to adjust, repair, or overhaul mobile mechanical, hydraulic, and pneumatic... (ID 3011)
Schedule and document maintenance
Exposure 48, automation 26%, augmentation 58%.
O*NET evidence: Schedule maintenance for industrial machines and equipment, and keep equipment service ... (ID 3007)
TaskExposureAutomationAugmentation
Diagnose faults with test equipment3615%58%
Dismantle and reassemble equipment166%28%
Repair hydraulic and mechanical systems187%32%
Schedule and document maintenance4826%58%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
LowFleet Maintenance Supervisor
Training horizon: 2-5 months. Skill overlap 74. Wage preservation signal 122.
- Own preventive maintenance programs
- Analyze fleet health data
- Manage shop schedules
adjacent role
LowEquipment Service Manager
Training horizon: 3-8 months. Skill overlap 62. Wage preservation signal 130.
- Run service department P&L
- Manage dealer relationships
- Lead technician recruiting
Comparison guides
Compare the next move before you commit
2-5 months
Mobile Heavy Equipment Mechanics, Except Engines to Fleet Maintenance Supervisor
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Mobile Heavy Equipment Mechanics, Except Engines into Fleet Maintenance Supervisor.
3-8 months
Mobile Heavy Equipment Mechanics, Except Engines to Equipment Service Manager
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Mobile Heavy Equipment Mechanics, Except Engines into Equipment Service Manager.
What the AI risk score means for Mobile Heavy Equipment Mechanics, Except Engines
The displacement pressure score for Mobile Heavy Equipment Mechanics, Except Engines is 18. 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. Schedule and document maintenance carries 26% automation pressure, while Diagnose faults with test equipment carries 58% 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: $65,510 (May 2025, US national). Employment context: Construction equipment repair trade with strong demand. Typical education: Postsecondary certificate or on-the-job training.
Wage vulnerability is 48, 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
- Telematics flag, mechanics fix
- Mechanic shortage drives wages
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Mobile Heavy Equipment Mechanics, Except Engines, 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.
Hydraulic 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.
Diagnostics
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.
Field 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.
Welding
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 Fleet Maintenance Supervisor, such as own preventive maintenance programs.
- By 90 days, compare internal openings and external postings for Fleet Maintenance Supervisor or Equipment Service Manager and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Mobile Heavy Equipment Mechanics, Except Engines
Will AI replace Mobile Heavy Equipment Mechanics, Except Engines?
Repairing bulldozers, excavators, and cranes — often in the field — is heavy physical troubleshooting. Telematics and diagnostic software flag problems early; someone still crawls under the machine with tools. 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 Mobile Heavy Equipment Mechanics, Except Engines work are most exposed to AI?
Schedule and document maintenance and Diagnose faults with test equipment show the strongest automation pressure in this model. Diagnose faults with test equipment and Schedule and document maintenance are better treated as AI-augmented work.
What should Mobile Heavy Equipment Mechanics, Except Engines learn next?
Start with Hydraulic systems, Diagnostics, Field repair. The most practical adjacent paths in this model are Fleet Maintenance Supervisor and Equipment Service Manager.
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