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AI Automation Index
Will AI Replace Validation Engineers?
Task-level automation exposure for Validation Engineers: 21 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
52%
weighted task index
21 of 21 tasks scored
Median Salary
$102,440 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Very High
365,740 people hold this job — size, not growth
Projected 2025–35
+12.4%
employment change
23,100 openings a year · BLS line item
Pay by state
BLS OEWS publishes 52 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$81,820 – $156,510
Highest medians
Alaska $156,510
Washington $129,050
Oregon $128,290
New Mexico $128,090
California $124,600
Lowest medians
Puerto Rico $81,820
South Dakota $93,200
Mississippi $95,830
Kentucky $96,820
North Dakota $96,940
State figures come from the 17-2112 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 52%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 10%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 43%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 11%
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 25/100
Low
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
21 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%
9 tasks
Medium exposure (≥ 35%) 44%
10 tasks
Low exposure (below 35%) 10%
2 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
#21051 · Core · 80% automatable
Study product characteristics or customer requirements to determine validation objectives and standards.
text generation — Review technical docs to plan work
Review technical documents to plan work.
#18131 · Core · 72% automatable
Analyze validation test data to determine whether systems or processes have met validation criteria or to identify root causes of production problems.
data analysis — Analyzes validation data; human confirms interpretation
Analyze test or validation data.
#18129 · Core · 72% automatable
Develop validation master plans, process flow diagrams, test cases, or standard operating procedures.
text generation — Detailed work plans drafted from specifications
Prepare detailed work plans.
#18137 · Core · 82% automatable
Prepare detailed reports or design statements, based on results of validation and qualification tests or reviews of procedures and protocols.
text generation — Draft technical design docs from provided details
Document technical design details.
#18144 · Core · 18% automatable
Maintain validation test equipment.
physical manipulation — Physical calibration and upkeep of test equipment
Maintain test equipment.
#18127 · Core · 35% automatable
Conduct validation or qualification tests of new or existing processes, equipment, or software in accordance with internal protocols or external standards.
decision support — AI can plan/analyze tests; execution is physical
Conduct validation tests of equipment or processes.
#18142 · Core · 62% automatable
Communicate with regulatory agencies regarding compliance documentation or validation results.
text generation — Technical communication drafting well-supported
Communicate technical information to suppliers, contractors, or regulatory agencies.
#18146 · Core · 79% automatable
Prepare, maintain, or review validation and compliance documentation, such as engineering change notices, schematics, or protocols.
text generation — Draft technical design docs from provided details
Maintain operational records or records systems. Document technical design details.
#21052 · Core · 47% automatable
Recommend resolution of identified deviations from established product or process standards.
decision support — Proposes design changes; human validates feasibility
Inspect finished products to locate flaws. Recommend technical design or process changes to improve efficiency, quality, or performance.
#18128 · Core · 60% automatable
Design validation study features, such as sampling, testing, or analytical methodologies.
decision support — Protocols drafted; scientific validity human-owned
Devise research or testing protocols.
#18130 · Core · 60% automatable
Prepare validation or performance qualification protocols for new or modified manufacturing processes, systems, or equipment for production of pharmaceuticals, electronics, or other products.
decision support — Protocols drafted; scientific validity human-owned
Devise research or testing protocols.
#18133 · Core · 35% automatable
Create, populate, or maintain databases for tracking validation activities, test results, or validated systems.
physical manipulation — Operating systems requires hands-on interaction
Operate computer systems.
#18139 · Core · 45% automatable
Resolve testing problems by modifying testing methods or revising test objectives and standards.
decision support — AI diagnoses; human executes operational fixes
Resolve operational performance problems.
#18132 · Core · 42% automatable
Conduct audits of validation or performance qualification processes to ensure compliance with internal or regulatory requirements.
decision support — AI aids process inspection; physical verification is human
Inspect operational processes.
#21053 · Core · 8% automatable
Draw samples of raw materials, intermediate products, or finished products for validation testing.
physical manipulation — Hands-on material sampling
Collect samples of raw materials or finished products.
#18134 · Core · 35% automatable
Direct validation activities, such as protocol creation or testing.
interpersonal — Directing QC needs human oversight of physical processes
Direct quality control activities.
#18143 · Core · 35% automatable
Coordinate the implementation or scheduling of validation testing with affected departments and personnel.
interpersonal — Directing QC needs human oversight of physical processes
Direct quality control activities.
#18145 · Core · 80% automatable
Participate in internal or external training programs to maintain knowledge of validation principles, industry trends, or novel technologies.
information retrieval — AI tracks technical updates; human validates depth
Update technical knowledge.
#19647 · Core · 35% automatable
Validate or characterize sustainable or environmentally friendly products, using electronic testing platforms.
decision support — AI can plan/analyze tests; execution is physical
Conduct validation tests of equipment or processes.
#18141 · Core · 40% automatable
Assist in training equipment operators or other staff on validation protocols and standard operating procedures.
interpersonal — Operational procedure training needs live demonstration
Train personnel on proper operational procedures.
#21054 · Supplemental · 50% automatable
Devise automated lab validation test stations or other test fixtures or equipment.
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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