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AI Automation Index
Will AI Replace Automotive Engineering Technicians?
Task-level automation exposure for Automotive Engineering Technicians: 18 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
46%
weighted task index
18 of 18 tasks scored
Median Salary
$74,510 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Medium
36,190 people hold this job — size, not growth
Projected 2025–35
+1.3%
employment change
3,100 openings a year · BLS line item
Pay by state
BLS OEWS publishes 45 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$47,660 – $102,800
Highest medians
Louisiana $102,800
New Mexico $97,560
Missouri $92,150
California $87,390
Connecticut $82,660
Lowest medians
West Virginia $47,660
Puerto Rico $51,860
South Dakota $53,670
Kansas $55,390
Florida $59,720
State figures come from the 17-3027 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 46%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 18%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 21%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 7%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 26/100
Moderate
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
18 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 46%
8 tasks
Medium exposure (≥ 35%) 9%
2 tasks
Low exposure (below 35%) 45%
8 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Constituent Tasks
#16606 · Core · 82% automatable
Document test results, using cameras, spreadsheets, documents, or other tools.
text generation — Document test results from provided data
Document design or operational test results.
#16599 · Core · 15% automatable
Set up mechanical, hydraulic, or electric test equipment in accordance with engineering specifications, standards, or test procedures.
physical manipulation — Hands-on control of industrial machinery in unstructured settings
Operate industrial equipment.
#16602 · Core · 80% automatable
Read and interpret blueprints, schematics, work specifications, drawings, or charts.
text generation — Review technical docs to plan work
Review technical documents to plan work.
#16605 · Core · 28% automatable
Inspect or test parts to determine nature or cause of defects or malfunctions.
data analysis — AI interprets results; human runs physical tests
Inspect finished products to locate flaws. Test performance of electrical, electronic, mechanical, or integrated systems or equipment.
#16601 · Core · 60% automatable
Monitor computer-controlled test equipment, according to written or verbal instructions.
data analysis — Efficiency metrics are digital and analyzable by AI
Monitor the productivity or efficiency of industrial operations.
#16608 · Core · 72% automatable
Analyze test data for automotive systems, subsystems, or component parts.
data analysis — Analyzes validation data; human confirms interpretation
Analyze test or validation data.
#16604 · Core · 8% automatable
Install equipment, such as instrumentation, test equipment, engines, or aftermarket products, to ensure proper interfaces.
physical manipulation — Instrumentation installation requires hands-on wiring
Install instrumentation or electronic equipment or systems.
#16611 · Core · 30% automatable
Perform or execute manual or automated tests of automotive system or component performance, efficiency, or durability.
data analysis — AI interprets results; human runs physical tests
Test performance of electrical, electronic, mechanical, or integrated systems or equipment.
#16603 · Core · 18% automatable
Maintain test equipment in operational condition by performing routine maintenance or making minor repairs or adjustments as needed.
physical manipulation — Physical calibration and upkeep of test equipment
Maintain test equipment.
#19696 · Core · 72% automatable
Analyze performance of vehicles or components that have been redesigned to increase fuel efficiency, such as camless or dual-clutch engines or alternative types of air-conditioning systems.
data analysis — Evaluates operations from data; human owns conclusions
Analyze operational data to evaluate operations, processes or products.
#19697 · Core · 30% automatable
Improve fuel efficiency by testing vehicles or components that use lighter materials, such as aluminum, magnesium alloy, or plastic.
physical manipulation — Functional product testing is hands-on
Test products for functionality or quality.
#16607 · Core · 15% automatable
Fabricate new or modify existing prototype components or fixtures.
physical manipulation — Physical prototype fabrication is hands-on work
Create physical models or prototypes.
#16598 · Core · 65% automatable
Order new test equipment, supplies, or replacement parts.
decision support — Procurement drafting and comparison; human commits funds
Purchase materials, equipment, or other resources.
#16610 · Core · 68% automatable
Recommend product or component design improvements, based on test data or observations.
decision support — Proposes design changes; human validates feasibility
Recommend technical design or process changes to improve efficiency, quality, or performance.
#16600 · Core · 68% automatable
Recommend tests or testing conditions in accordance with designs, customer requirements, or industry standards to ensure test validity.
decision support — Proposes design changes; human validates feasibility
Recommend technical design or process changes to improve efficiency, quality, or performance.
#19699 · Core · 30% automatable
Test performance of vehicles that use alternative fuels, such as alcohol blends, natural gas, liquefied petroleum gas, biodiesel, nano diesel, or alternative power methods, such as solar energy or hydrogen fuel cells.
physical manipulation — Testing green tech installations requires physical setup
Test green technologies or processes.
#19698 · Core · 35% automatable
Participate in research or testing of computerized automotive applications, such as telemetrics, intelligent transportation systems, artificial intelligence, or automatic control.
data analysis — Design analysis aided; simulation and build human
Research advanced engineering designs or applications. Test performance of electrical, electronic, mechanical, or integrated systems or equipment.
#16597 · Core · 50% automatable
Build instrumentation or laboratory test equipment for special purposes.
decision support — AI drafts electronics; engineer validates and tests
Design electronic or computer equipment or instrumentation.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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