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AI Automation Index
Will AI Replace Materials Engineers?
Task-level automation exposure for Materials 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
49%
weighted task index
21 of 21 tasks scored
Median Salary
$112,860 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
22,770 people hold this job — size, not growth
Projected 2025–35
+7.5%
employment change
1,300 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.
$64,270 – $165,600
Highest medians
New Mexico $165,600
Maryland $140,640
Washington $139,840
Delaware $131,320
Louisiana $128,670
Lowest medians
Iowa $64,270
Montana $76,030
Mississippi $78,700
Rhode Island $87,180
Utah $88,080
State figures come from the 17-2131 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 49%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 3%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 67%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 10%
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 16/100
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) 43%
8 tasks
Medium exposure (≥ 35%) 36%
9 tasks
Low exposure (below 35%) 21%
4 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
#9006 · Core · 25% automatable
Conduct or supervise tests on raw materials or finished products to ensure their quality.
physical manipulation — Material characteristic testing needs physical instruments
Test characteristics of materials or structures.
#9010 · Core · 63% automatable
Review new product plans, and make recommendations for material selection, based on design objectives such as strength, weight, heat resistance, electrical conductivity, and cost.
decision support — Proposes design changes; human validates feasibility
Evaluate plans or specifications to determine technological or environmental implications. Recommend technical design or process changes to improve efficiency, quality, or performance.
#9005 · Core · 62% automatable
Evaluate technical specifications and economic factors relating to process or product design objectives.
data analysis — Evaluates technical data impact; engineer owns design
Evaluate technical data to determine effect on designs or plans.
#9008 · Core · 45% automatable
Solve problems in a number of engineering fields, such as mechanical, chemical, electrical, civil, nuclear, and aerospace.
decision support — AI diagnoses; human executes operational fixes
Resolve operational performance problems.
#9014 · Core · 72% automatable
Plan and implement laboratory operations to develop material and fabrication procedures that meet cost, product specification, and performance standards.
text generation — Detailed work plans drafted from specifications
Prepare detailed work plans.
#9001 · Core · 68% automatable
Analyze product failure data and laboratory test results to determine causes of problems and develop solutions.
data analysis — Quantitative failure analysis; domain judgment needed
Conduct quantitative failure analyses of operational data.
#9015 · Core · 50% automatable
Determine appropriate methods for fabricating and joining materials.
decision support — Operational methods aided; human decides
Determine operational methods.
#9013 · Core · 35% automatable
Guide technical staff in developing materials for specific uses in projected products or devices.
interpersonal — Directing design work needs accountability and judgment
Direct design or development activities.
#9004 · Core · 35% automatable
Design and direct the testing or control of processing procedures.
interpersonal — Directing QC needs human oversight of physical processes
Direct quality control activities.
#9009 · Core · 62% automatable
Plan and evaluate new projects, consulting with other engineers and corporate executives, as necessary.
text generation — Detailed work plans drafted from specifications
Evaluate plans or specifications to determine technological or environmental implications. Prepare detailed work plans. Confer with technical personnel to prepare designs or operational plans.
#9003 · Core · 30% automatable
Supervise the work of technologists, technicians, and other engineers and scientists.
interpersonal — Supervising engineers needs accountability and motivation
Supervise engineering or other technical personnel.
#9017 · Core · 28% automatable
Supervise production and testing processes in industrial settings, such as metal refining facilities, smelting or foundry operations, or nonmetallic materials production operations.
interpersonal — Industrial production direction is human supervisory control
Direct industrial production activities.
#9007 · Core · 70% automatable
Perform managerial functions, such as preparing proposals and budgets, analyzing labor costs, and writing reports.
text generation — Generate operational reports from structured data
Prepare proposal documents. Prepare operational reports. Prepare project budgets.
#9002 · Core · 60% automatable
Monitor material performance, and evaluate its deterioration.
data analysis — Efficiency metrics are digital and analyzable by AI
Monitor the productivity or efficiency of industrial operations.
#9019 · Core · 60% automatable
Replicate the characteristics of materials and their components, using computers.
data analysis — AI builds computational models; engineer validates assumptions
Create models of engineering designs or methods.
#9016 · Core · 40% automatable
Conduct training sessions on new material products, applications, or manufacturing methods for customers and their employees.
interpersonal — Operational procedure training needs live demonstration
Train personnel on proper operational procedures.
#9012 · Supplemental · 10% automatable
Modify properties of metal alloys, using thermal and mechanical treatments.
physical manipulation — Physical preparation of materials by hand
Prepare materials for processing.
#9020 · Supplemental · 55% automatable
Teach in colleges and universities.
text generation — Social science lectures draftable; delivery and grading human
Teach classes in area of specialization. Teach social science courses at the college level.
#9011 · Supplemental · 45% automatable
Design processing plants and equipment.
decision support — AI models process systems; engineer validates feasibility
Design industrial processing systems.
#21167 · Supplemental · 55% automatable
Present technical information at conferences.
interpersonal — Presentation delivery and Q&A are human; AI drafts slides
Present research results to others.
#9018 · Supplemental · 55% automatable
Write for technical magazines, journals, and trade association publications.
text generation — AI drafts; original expertise and authorship are human
Write articles, books or other original materials in area of expertise.
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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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