Materials engineers: AI exposure and career outlook
Materials engineers (SOC 17-2131) sit at the 38th percentile for measured AI exposure among the 342 occupations tracked here, measured from a composite of Microsoft Research and Anthropic Economic Index telemetry. An estimated 19% of tasks are already automated and 42% are being reshaped rather than replaced — both modelled figures, not direct measurements. Of the Claude usage observed in this occupation, 37% follows an automation pattern and 63% an augmentation pattern, which places it in the Orchestrator opportunity group: AI reaches deep into this work and amplifies it. The leverage goes to whoever directs it, and that can be you.
Figures last updated 2026-06. Every number on this page is labelled measured or modelled; where a source has no coverage for this occupation, it says so rather than showing a zero.
Key facts
- SOC code
- 17-2131
- Canada NOC 2021
- 21322
- TEER level
- 1 — University Degree
- COPS outlook
- Not assessed
What the measurements actually say
Three independent studies measure AI exposure for this occupation directly. The two percentages below them are ours, modelled from those measurements.
| Metric | Value | Provenance |
|---|---|---|
| AI applicability | 18% | Measured — Microsoft Research, from 200,000 Copilot conversations classified against O*NET work activities. |
| Observed AI usage | 0% | Measured — Anthropic Economic Index, share of tasks observed being performed with Claude (CC-BY 4.0). |
| Academic AI exposure | 76th percentile | Measured — Felten-Raj-Seamans AIOE index across 774 occupations; the index underlying Statistics Canada's Canadian estimates. |
| Automation-pattern usage | 37% | Measured — the share of observed Claude usage in this occupation where the task is handed over rather than iterated on (Anthropic Economic Index). |
| Augmentation-pattern usage | 63% | Measured — the complement of the row above; the two sum to 100% by construction. This is the pattern where the person stays in the loop. |
| Estimated task automation (modelled) | 19% | Modelled from this occupation's measured telemetry composite, mapped through anchors calibrated to Anthropic's published aggregate findings. Our estimate, not per-occupation telemetry. |
| Estimated task reshaping (modelled) | 42% | Share of tasks where AI acts as co-pilot rather than replacement. Modelled from the measured telemetry composite, calibrated to BCG's published aggregate reshaping rates. Our estimate. |
AI is transforming how this work is done. Professionals who adapt their workflows will thrive; those who don't face increasing competition.
In Canada: NOC 21322
Materials engineers map to NOC 21322 — Metallurgical and materials engineers at TEER 1 (University Degree). ESDC's COPS 2024–2033 projection for this unit group is not assessed.
Mapped via Statistics Canada's official SOC 2018 → NOC 2016 → NOC 2021 correspondence tables. TEER is the second digit of the official NOC code, so it cannot disagree with it.
What this job actually involves
These are the O*NET task statements for Materials engineers— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 12 of 21, core tasks first.
The exposure figures above are for the occupation as a whole. We do not have a per-task measurement: no published dataset tells us which of these specific tasks AI is doing. Read this as what the job is made of, not as a ranking of what is at risk.
2 of these 12statements do carry one measured signal: Anthropic publishes, per O*NET task, how much of the observed Claude usage on it looked like automation rather than iteration. Those are marked below. It still says how people use AI on that task, not how much of it AI can do — and the unmarked statements carry nothing rather than inheriting the occupation's figure.
Microsoft's applicability score was itself produced by classifying Copilot conversations against the O*NET work-activity catalogue — the same catalogue these groups come from. The published file gives one number per occupation, not one per activity, so we can tell you that 18% of this occupation's work activities are covered by observed AI usage, but not which ones.
Guiding, Directing, and Motivating Subordinates · 4 tasks
Guide technical staff in developing materials for specific uses in projected products or devices.
Core task · Importance 3.8/5 (O*NET)
Design and direct the testing or control of processing procedures.
Core task · Importance 3.8/5 (O*NET)
Supervise the work of technologists, technicians, and other engineers and scientists.
Core task · Importance 3.7/5 (O*NET)
Supervise production and testing processes in industrial settings, such as metal refining facilities, smelting or foundry operations, or nonmetallic materials production operations.
Core task · Importance 3.6/5 (O*NET)
Evaluating Information to Determine Compliance with Standards · 2 tasks
Review new product plans, and make recommendations for material selection, based on design objectives such as strength, weight, heat resistance, electrical conductivity, and cost.
Core task · Importance 4.3/5 (O*NET)
Plan and evaluate new projects, consulting with other engineers and corporate executives, as necessary.
Core task · Importance 3.8/5 (O*NET)
Analyzing Data or Information · 2 tasks
Evaluate technical specifications and economic factors relating to process or product design objectives.
Core task · Importance 4.1/5 (O*NET) · 35% of observed AI use on this task is automation-pattern
Analyze product failure data and laboratory test results to determine causes of problems and develop solutions.
Core task · Importance 3.9/5 (O*NET)
Inspecting Equipment, Structures, or Materials · 1 task
Conduct or supervise tests on raw materials or finished products to ensure their quality.
Core task · Importance 4.3/5 (O*NET)
Making Decisions and Solving Problems · 1 task
Determine appropriate methods for fabricating and joining materials.
Core task · Importance 3.9/5 (O*NET) · 69% of observed AI use on this task is automation-pattern
Organizing, Planning, and Prioritizing Work · 1 task
Plan and implement laboratory operations to develop material and fabrication procedures that meet cost, product specification, and performance standards.
Core task · Importance 4.0/5 (O*NET)
Resolving Conflicts and Negotiating with Others · 1 task
Solve problems in a number of engineering fields, such as mechanical, chemical, electrical, civil, nuclear, and aerospace.
Core task · Importance 4.0/5 (O*NET)
Task statements and work activities from the O*NET Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under CC BY 4.0. O*NET® is a trademark of USDOL/ETA. USDOL/ETA has not reviewed or approved this material.
That's the average. Is this you?
Everything above describes Materials engineers as a whole. Three questions, and nothing is stored or sent.
Where are you in your career?
What does your week mostly look like?
Is your employer deploying AI?
Where the leverage is
Upskill in AI-driven automation (e.g., Make, Zapier) and generative tools in your domain. The opportunity is to pivot from execution to directing AI-augmented workflows.
AI is transforming tasks rather than replacing entire jobs. Routine scheduling and communication automate, but human decision-making remains critical.
Upskill in AI prompt engineering, model validation, and governance. Focus on transition playbooks to pivot toward high-value advisory services.
This guidance is written per exposure band — three texts across all 342 occupations, framed on the ILO's transformation-versus-displacement distinction. It is editorial guidance for the reshaping band, not an occupation-specific research finding about materials engineers.
Sources for this occupation
Only the sources that hold data for materials engineers are listed. A study that does not cover this occupation is not cited here.
- Microsoft Research — Working with AI (Tomlinson et al., arXiv:2507.07935): Measured AI applicability, from 200,000 anonymised Copilot conversations classified against O*NET work activities.
- Anthropic Economic Index: Measured observed AI usage per occupation, from Anthropic's open per-SOC dataset (CC-BY 4.0).
- Felten, Raj & Seamans — AI Occupational Exposure index: Measured academic exposure percentile; the index underlying Statistics Canada's Canadian AI-exposure estimates (Mehdi & Morissette, 2024).
- O*NET Database 31.0 (USDOL/ETA), CC BY 4.0: The occupation's task statements and work activities, verbatim. Joined on SOC 2018 with no crosswalk — O*NET-SOC is built on SOC — and carrying no claim about which tasks AI touches.
- Anthropic Economic Index — collaboration split (release 2026-06-26, CC-BY 4.0): Measured share of observed Claude usage following an automation rather than an augmentation pattern. This is the second axis of the grouping above.
- Statistics Canada NOC 2021 concordance: Official SOC 2018 → NOC 2016 V1.3 → NOC 2021 V1.0 correspondence tables. TEER is the second digit of the resolved code, so we report it only where the concordance gave a single candidate.
- BCG — AI Will Reshape More Jobs Than It Replaces (April 3, 2026): Published aggregate reshaping rates, used to calibrate our modelled percentages. The per-occupation figures here are our estimates, not BCG's data.
Related occupations
Other roles in Technology.
This is the picture for the role. What about your skills?
These numbers describe an occupation, not a person. Map your own skills against them, see which pathways stay open, and plan the transition you actually want.