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AI Automation Index
Will AI Replace Non-Destructive Testing Specialists?
Task-level automation exposure for Non-Destructive Testing Specialists: 16 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
42%
weighted task index
16 of 16 tasks scored
Median Salary
$78,350 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Medium
59,930 people hold this job — size, not growth
Projected 2025–35
+2.8%
employment change
5,400 openings a year · BLS line item
Pay by state
BLS OEWS publishes 51 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$43,640 – $107,060
Highest medians
Maine $107,060
New Mexico $101,750
Maryland $99,970
Washington $99,720
Virginia $98,410
Lowest medians
Puerto Rico $43,640
Vermont $52,770
Tennessee $58,700
Louisiana $58,960
Arizona $59,580
State figures come from the 17-3029 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 42%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 6%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 32%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 13%
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 38/100
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
16 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) 32%
5 tasks
Medium exposure (≥ 35%) 0%
0 tasks
Low exposure (below 35%) 68%
11 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 drones for remote inspection of large or hard-to-reach structures, such as wind turbines, bridges, or tall buildings.
Constituent Tasks
#16626 · Core · 66% automatable
Interpret the results of all methods of non-destructive testing (NDT), such as acoustic emission, electromagnetic, leak, liquid penetrant, magnetic particle, neutron radiographic, radiographic, thermal or infrared, ultrasonic, vibration analysis, and visual testing.
data analysis — Interprets test results; context and judgment needed
Interpret design or operational test results.
#16620 · Core · 66% automatable
Interpret or evaluate test results in accordance with applicable codes, standards, specifications, or procedures.
data analysis — Interprets test results; context and judgment needed
Interpret design or operational test results.
#16622 · Core · 25% automatable
Identify defects in solid materials, using ultrasonic testing techniques.
physical manipulation — Material characteristic testing needs physical instruments
Test characteristics of materials or structures.
#16618 · Core · 30% automatable
Make radiographic images to detect flaws in objects while leaving objects intact.
physical manipulation — Physical/chemical property testing by hand
Measure physical or chemical properties of materials or objects.
#16625 · Core · 82% automatable
Prepare reports on non-destructive testing results.
text generation — Document test results from provided data
Document design or operational test results.
#16623 · Core · 29% automatable
Select, calibrate, or operate equipment used in the non-destructive testing of products or materials.
decision support — Tool selection needs context; human decides and procures
Select tools, equipment, or technologies for use in operations or projects. Calibrate scientific or technical equipment. Operate industrial equipment.
#16619 · Core · 25% automatable
Visually examine materials, structures, or components for signs of corrosion, metal fatigue, cracks, or other flaws, using tools and equipment such as endoscopes, closed-circuit television systems, and fiber optics.
visual inspection — Flaw location on physical products needs presence
Inspect finished products to locate flaws.
#16627 · Core · 20% automatable
Examine structures or vehicles such as aircraft, trains, nuclear reactors, bridges, dams, and pipelines, using non-destructive testing techniques.
visual inspection — Equipment/system inspection requires physical presence
Inspect equipment or systems.
#16616 · Core · 82% automatable
Document non-destructive testing methods, processes, or results.
text generation — Document test results from provided data
Document design or operational test results.
#16613 · Core · 30% automatable
Produce images of objects on film, using radiographic techniques.
physical manipulation — Physical/chemical property testing by hand
Measure physical or chemical properties of materials or objects.
#16612 · Core · 30% automatable
Supervise or direct the work of non-destructive testing trainees or staff.
interpersonal — Supervising engineers needs accountability and motivation
Supervise engineering or other technical personnel.
#16624 · Core · 30% automatable
Conduct liquid penetrant tests to locate surface cracks by coating objects with fluorescent dyes, cleaning excess penetrant, and applying developer.
data analysis — AI interprets results; human runs physical tests
Test performance of electrical, electronic, mechanical, or integrated systems or equipment.
#16617 · Core · 30% automatable
Map the presence of imperfections within objects, using sonic measurements.
physical manipulation — Physical/chemical property testing by hand
Measure physical or chemical properties of materials or objects.
#16615 · Core · 60% automatable
Develop or use new non-destructive testing methods, such as acoustic emission testing, leak testing, and thermal or infrared testing.
decision support — Protocols drafted; scientific validity human-owned
Devise research or testing protocols.
#16621 · Core · 25% automatable
Identify defects in concrete or other building materials, using thermal or infrared testing.
physical manipulation — Material characteristic testing needs physical instruments
Test characteristics of materials or structures.
#16614 · Supplemental · 30% automatable
Evaluate material properties, using radio astronomy, voltage and amperage measurement, or rheometric flow measurement.
physical manipulation — Physical/chemical property testing by hand
Measure physical or chemical properties of materials or objects.
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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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