Share and intensity of work current AI systems can materially affect.
SOC 49-1011
First-Line Supervisors of Mechanics and Repairers AI displacement risk
Mechanic supervisors direct repair crews, inspect completed work, compute estimates, and manage shop inventories. Fleet-management software and predictive-maintenance alerts now schedule much of the work — supervisors interpret the alerts, verify the repairs, and answer for quality.
Likely potential for exposed tasks to move to software after workflow integration.
This is the advancement path as diagnostic software compresses troubleshooting time: the supervisor's job shifts toward workflow management and quality verification of repairs the software only suspects.
Distribution
Where First-Line Supervisors of Mechanics and Repairers sits across 620 tracked roles
Displacement pressure 20 — higher than 23% 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.
22 O*NET task statements matched to SOC 49-1011. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $79,860 (May 2025, US national). The latest BLS row matched SOC 49-1011.
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 First-Line Supervisors of Mechanics and Repairers
SOC 49-1011 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 20/100 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 First-Line Supervisors of Mechanics and Repairers
The current evidence import matched 22 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 tasks22
SOC49-1011
- Core task / ID 2929
Inspect, test, and measure completed work, using devices such as hand tools or gauges to verify conformance to standards or repair requirements.
- Core task / ID 15263
Inspect and monitor work areas, examine tools and equipment, and provide employee safety training to prevent, detect, and correct unsafe conditions or violations of procedures and safety rules.
- Core task / ID 2920
Interpret specifications, blueprints, or job orders to construct templates and lay out reference points for workers.
- Core task / ID 2914
Monitor employees' work levels and review work performance.
- Core task / ID 2925
Perform skilled repair or maintenance operations, using equipment such as hand or power tools, hydraulic presses or shears, or welding equipment.
- Core task / ID 2919
Compute estimates and actual costs of factors such as materials, labor, or outside contractors.
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
Inspect, test, and measure completed work for conformance
Exposure 30, automation 13%, augmentation 50%.
O*NET evidence: Inspect, test, and measure completed work, using devices such as hand tools or gauges t... (ID 2929)
Compute estimates and actual costs of repairs
Exposure 50, automation 27%, augmentation 56%.
O*NET evidence: Compute estimates and actual costs of factors such as materials, labor, or outside cont... (ID 2919)
Monitor work levels and review performance
Exposure 38, automation 18%, augmentation 50%.
O*NET evidence: Monitor employees' work levels and review work performance. (ID 2914)
Monitor tool and part inventories and shop condition
Exposure 40, automation 20%, augmentation 48%.
O*NET evidence: Monitor tool and part inventories and the condition and maintenance of shops to ensure ... (ID 15262)
TaskExposureAutomationAugmentation
Inspect, test, and measure completed work for conformance3013%50%
Compute estimates and actual costs of repairs5027%56%
Monitor work levels and review performance3818%50%
Monitor tool and part inventories and shop condition4020%48%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
LowService Manager
Training horizon: 6-12 months. Skill overlap 68. Wage preservation signal 122.
- Own shop P&L basics
- Lead technician teams
- Manage customer approvals
role redesign
LowFleet Maintenance Manager
Training horizon: 6-12 months. Skill overlap 64. Wage preservation signal 118.
- Run fleet PM programs
- Master telematics platforms
- Manage vendor repairs
Comparison guides
Compare the next move before you commit
6-12 months
First-Line Supervisors of Mechanics and Repairers to Service Manager
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from First-Line Supervisors of Mechanics and Repairers into Service Manager.
6-12 months
First-Line Supervisors of Mechanics and Repairers to Fleet Maintenance Manager
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from First-Line Supervisors of Mechanics and Repairers into Fleet Maintenance Manager.
What the AI risk score means for First-Line Supervisors of Mechanics and Repairers
The displacement pressure score for First-Line Supervisors of Mechanics and Repairers is 20. 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. Compute estimates and actual costs of repairs carries 27% automation pressure, while Compute estimates and actual costs of repairs carries 56% 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: $79,860 (May 2025, US national). Employment context: The near-move target for mechanics and installers. Typical education: Extensive mechanic or installer experience.
Wage vulnerability is 30, 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
- Predictive alerts schedule, supervisors verify
- Quality accountability stays human
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For First-Line Supervisors of Mechanics and Repairers, 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.
Repair quality verification
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.
Cost estimating
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.
Crew supervision
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.
Shop inventory management
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 Service Manager, such as own shop p&l basics.
- By 90 days, compare internal openings and external postings for Service Manager or Fleet Maintenance Manager and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and First-Line Supervisors of Mechanics and Repairers
Will AI replace First-Line Supervisors of Mechanics and Repairers?
Mechanic supervisors direct repair crews, inspect completed work, compute estimates, and manage shop inventories. Fleet-management software and predictive-maintenance alerts now schedule much of the work — supervisors interpret the alerts, verify the repairs, and answer for quality. 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 First-Line Supervisors of Mechanics and Repairers work are most exposed to AI?
Compute estimates and actual costs of repairs and Monitor tool and part inventories and shop condition show the strongest automation pressure in this model. Compute estimates and actual costs of repairs and Inspect, test, and measure completed work for conformance are better treated as AI-augmented work.
What should First-Line Supervisors of Mechanics and Repairers learn next?
Start with Repair quality verification, Cost estimating, Crew supervision. The most practical adjacent paths in this model are Service Manager and Fleet Maintenance 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