Mining and geological engineers: AI exposure and career outlook
Mining and geological engineers (SOC 17-2151) sit at the 48th 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 24% of tasks are already automated and 50% are being reshaped rather than replaced — both modelled figures, not direct measurements. Of the Claude usage observed in this occupation, 50% follows an automation pattern and 50% an augmentation pattern — close enough to even that the grouping below should be read loosely, 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. In Canada the role maps to NOC 21330 (Mining engineers), and ESDC's COPS 2024–2033 outlook for that unit group is balance.
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
- 17-2151
- Canada NOC 2021
- 21330
- TEER level
- Not available
- COPS outlook
- Balance
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 | 23% | 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 | 92nd percentile | Measured — Felten-Raj-Seamans AIOE index across 774 occupations; the index underlying Statistics Canada's Canadian estimates. |
| Automation-pattern usage | 50% | 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 | 50% | 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) | 24% | 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) | 50% | 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. |
Microsoft and Anthropic place it 51 percentile points apart. The modelled figures above average the two, so for this occupation that average sits between two readings that do not agree rather than summarising a settled one. Read the two measured rows above in preference to the modelled percentages, and treat the band as provisional.
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 21330
Mining and geological engineers map to NOC 21330 — Mining engineers. ESDC's COPS 2024–2033 projection for this unit group is Balance.
Mapped via Statistics Canada's official SOC 2018 → NOC 2016 → NOC 2021 correspondence tables (closest match of several correspondence candidates). TEER is the second digit of the NOC code, so it is only as certain as the code is. Several correspondence candidates were available here and we took the closest by title, so we do not report a TEER level for it.
What this job actually involves
These are the O*NET task statements for Mining and geological engineers— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 12 of 18, 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.
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 23% of this occupation's work activities are covered by observed AI usage, but not which ones.
Getting Information · 2 tasks
Examine maps, deposits, drilling locations, or mines to determine the location, size, accessibility, contents, value, and potential profitability of mineral, oil, and gas deposits.
Core task · Importance 3.7/5 (O*NET)
Test air to detect toxic gases and recommend measures to remove them, such as installation of ventilation shafts.
Supplemental task · Importance 4.1/5 (O*NET)
Monitoring Processes, Materials, or Surroundings · 2 tasks
Monitor mine production rates to assess operational effectiveness.
Core task · Importance 3.8/5 (O*NET)
Design, implement, and monitor the development of mines, facilities, systems, or equipment.
Core task · Importance 3.7/5 (O*NET)
Making Decisions and Solving Problems · 2 tasks
Select or develop mineral location, extraction, and production methods, based on factors such as safety, cost, and deposit characteristics.
Core task · Importance 4.0/5 (O*NET)
Select locations and plan underground or surface mining operations, specifying processes, labor usage, and equipment that will result in safe, economical, and environmentally sound extraction of minerals and ores.
Core task · Importance 4.0/5 (O*NET)
Inspecting Equipment, Structures, or Materials · 1 task
Inspect mining areas for unsafe structures, equipment, and working conditions.
Core task · Importance 4.1/5 (O*NET)
Estimating the Quantifiable Characteristics of Products, Events, or Information · 1 task
Prepare schedules, reports, and estimates of the costs involved in developing and operating mines.
Core task · Importance 3.8/5 (O*NET)
Documenting/Recording Information · 1 task
Prepare technical reports for use by mining, engineering, and management personnel.
Core task · Importance 4.4/5 (O*NET)
Resolving Conflicts and Negotiating with Others · 1 task
Devise solutions to problems of land reclamation and water and air pollution, such as methods of storing excavated soil and returning exhausted mine sites to natural states.
Supplemental task · Importance 3.8/5 (O*NET)
Coordinating the Work and Activities of Others · 1 task
Implement and coordinate mine safety programs, including the design and maintenance of protective and rescue equipment and safety devices.
Supplemental task · Importance 3.9/5 (O*NET)
Training and Teaching Others · 1 task
Supervise, train, and evaluate technicians, technologists, survey personnel, engineers, scientists or other mine personnel.
Core task · Importance 3.8/5 (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 Mining and geological 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.
Master workflow automation platforms (e.g., Make, Zapier, CRM integrations) to double your output and position for fractional leadership roles.
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 mining and geological engineers.
Sources for this occupation
Only the sources that hold data for mining and geological 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.
- ESDC COPS 2024–2033: Projected labour-market assessment per NOC 2021 unit group, from the Canadian Occupational Projection System open dataset.
- 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.
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