Automotive Service Technicians and Mechanics

Construction, Maintenance & Repair

AI exposure

  • Data source: BLSPublished: '26.08

    Moderate· relative

    LowFour relative bandsVery high

    Group-level value

    Scale, basis and source

    Four relative bands (Low / Moderate / High / Very high)

    831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: '26.03

    0.000

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: '25

    0.18

    0.09Range of values carried here0.70

    Group-level value

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    ISCO-08 unit group — every occupation sharing the code gets this value

    Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 90% score at or above this value.

    Source dataset

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Exposed tasks only

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

TaskβE1 + 0.5 × E2
Plan work procedures, using charts, technical manuals, and experience.

O*NET Task ID 23535

1.0
Inspect vehicles for damage and record findings so that necessary repairs can be made.

O*NET Task ID 23523

0.5
Estimate costs of vehicle repair.

O*NET Task ID 23526

0.5
Conduct visual inspections of compressed natural gas fuel systems to identify cracks, gouges, abrasions, discoloration, broken fibers, loose brackets, damaged gaskets, or other problems.

O*NET Task ID 23548

0.5
Test drive vehicles and test components and systems, using equipment such as infrared engine analyzers, compression gauges, and computerized diagnostic devices.

O*NET Task ID 23522

0.0
Test and adjust repaired systems to meet manufacturers' performance specifications.

O*NET Task ID 23524

0.0
Repair, reline, replace, and adjust brakes.

O*NET Task ID 23525

0.0
Review work orders and discuss work with supervisors.

O*NET Task ID 23527

0.0
Troubleshoot fuel, ignition, and emissions control systems, using electronic testing equipment.

O*NET Task ID 23528

0.0
Confer with customers to obtain descriptions of vehicle problems and to discuss work to be performed and future repair requirements.

O*NET Task ID 23529

0.0
Align vehicles' front ends.

O*NET Task ID 23530

0.0
Test electronic computer components in automobiles to ensure proper operation.

O*NET Task ID 23531

0.0
Tear down, repair, and rebuild faulty assemblies, such as power systems, steering systems, and linkages.

O*NET Task ID 23532

0.0
Perform routine and scheduled maintenance services, such as oil changes, lubrications, and tune-ups.

O*NET Task ID 23533

0.0
Follow checklists to ensure all important parts are examined, including belts, hoses, steering systems, spark plugs, brake and fuel systems, wheel bearings, and other potentially troublesome areas.

O*NET Task ID 23534

0.0
Maintain cleanliness of work area.

O*NET Task ID 23536

0.0
Align wheels, axles, frames, torsion bars, and steering mechanisms of automobiles, using special alignment equipment and wheel-balancing machines.

O*NET Task ID 23537

0.0
Tune automobile engines to ensure proper and efficient functioning.

O*NET Task ID 23538

0.0
Repair, replace, or adjust defective fuel injectors, carburetor parts, and gasoline filters.

O*NET Task ID 23539

0.0
Repair and service air conditioning, heating, engine cooling, and electrical systems.

O*NET Task ID 23540

0.0
Disassemble units and inspect parts for wear, using micrometers, calipers, and gauges.

O*NET Task ID 23541

0.0
Change spark plugs, fuel filters, air filters, and batteries in hybrid electric vehicles.

O*NET Task ID 23542

0.0
Overhaul or replace carburetors, blowers, generators, distributors, starters, and pumps.

O*NET Task ID 23543

0.0
Repair or replace parts such as pistons, rods, gears, valves, and bearings.

O*NET Task ID 23544

0.0
Rewire ignition systems, lights, and instrument panels.

O*NET Task ID 23545

0.0
Install, adjust, or repair hydraulic or electromagnetic automatic lift mechanisms used to raise and lower automobile windows, seats, and tops.

O*NET Task ID 23546

0.0
Rebuild parts, such as crankshafts and cylinder blocks.

O*NET Task ID 23547

0.0
Diagnose and replace or repair engine management systems or related sensors for flexible fuel vehicles (FFVs) with ignition timing, fuel rate, alcohol concentration, or air-to-fuel ratio malfunctions.

O*NET Task ID 23549

0.0
Repair or rebuild transmissions.

O*NET Task ID 23550

0.0
Retrofit vehicle fuel systems with aftermarket products, such as vapor transfer devices, evaporation control devices, swirlers, lean burn devices, and friction reduction devices, to enhance combustion and fuel efficiency.

O*NET Task ID 23551

0.0

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information

Recent Changes Affecting This Occupation

Mar 2026: New evergreen blog post published analyzing AI impact on auto mechanics. Automation risk 12%, BLS projects +4% growth through 2034.

[Source: AI Changing Work Blog]