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AI Automation Index
Will AI Replace Aerospace Engineering and Operations Technologists and Technicians?
Task-level automation exposure for Aerospace Engineering and Operations Technologists and Technicians: 11 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
38%
weighted task index
11 of 11 tasks scored
Median Salary
$82,890 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
11,280 people hold this job — size, not growth
Projected 2025–35
+11.9%
employment change
1,100 openings a year · BLS line item
Pay by state
BLS OEWS publishes 24 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$45,590 – $125,160
Highest medians
Washington $125,160
Alaska $105,780
Nevada $102,980
California $99,000
Colorado $98,990
Lowest medians
Oklahoma $45,590
Pennsylvania $60,770
Alabama $62,130
Indiana $62,520
New York $64,480
State figures come from the 17-3021 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 38%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 25%
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 16%
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 30/100
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
11 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) 27%
3 tasks
Medium exposure (≥ 35%) 21%
2 tasks
Low exposure (below 35%) 53%
6 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 →Emerging & rewritten tasks
Tasks O*NET added or rewrote for this occupation in its recent updates (05/2025 – 08/2026), with who proposed them. Additions are not in the scored task inventory — they are new work O*NET is still measuring, so they appear here and nowhere in the index. A Revision replaces a task that is scored, which is why only those move the "Rewritten by O*NET" share.
- New 05/2025 · AI/SME
Operate, test, and troubleshoot uncrewed aerial systems, commonly known as drones, to ensure optimal performance.
Constituent Tasks
#10888 · Core · 30% automatable
Test aircraft systems under simulated operational conditions, performing systems readiness tests and pre- and post-operational checkouts, to establish design or fabrication parameters.
data analysis — AI interprets results; human runs physical tests
Test performance of electrical, electronic, mechanical, or integrated systems or equipment.
#10885 · Core · 68% automatable
Identify required data, data acquisition plans, and test parameters, setting up equipment to conform to these specifications.
data analysis — Estimates from specs; human validates assumptions and risk
Estimate technical or resource requirements for development or production projects.
#10881 · Core · 20% automatable
Inspect, diagnose, maintain, and operate test setups and equipment to detect malfunctions.
visual inspection — Equipment/system inspection requires physical presence
Inspect equipment or systems.
#10883 · Core · 55% automatable
Confer with engineering personnel regarding details and implications of test procedures and results.
interpersonal — Conferring with technical staff on plans is relational work
Confer with technical personnel to prepare designs or operational plans.
#10887 · Core · 35% automatable
Operate and calibrate computer systems and devices to comply with test requirements and to perform data acquisition and analysis.
physical manipulation — Operating systems requires hands-on interaction
Operate computer systems.
#10882 · Core · 74% automatable
Record and interpret test data on parts, assemblies, and mechanisms.
text generation — Document test results from provided data
Interpret design or operational test results. Document design or operational test results.
#10884 · Core · 18% automatable
Adjust, repair, or replace faulty components of test setups and equipment.
physical manipulation — Physical calibration of instruments requires hands
Calibrate scientific or technical equipment. Maintain test equipment.
#10889 · Core · 9% automatable
Fabricate and install parts and systems to be tested in test equipment, using hand tools, power tools, and test instruments.
physical manipulation — Fabricating devices needs dexterous hands-on assembly
Install production equipment or systems. Fabricate devices or components.
#10890 · Supplemental · 28% automatable
Finish vehicle instrumentation and deinstrumentation.
visual inspection — Quality testing of materials is hands-on inspection
Test quality of materials or finished products.
#10886 · Supplemental · 12% automatable
Construct and maintain test facilities for aircraft parts and systems, according to specifications.
physical manipulation — Physical calibration and upkeep of test equipment
Assemble equipment or components. Maintain test equipment.
#21102 · Supplemental · 64% automatable
Design electrical and mechanical systems for avionic instrumentation applications.
text generation — Draft technical design docs from provided details
Design electrical equipment or systems. Document technical design details.
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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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