Mining and Geological Engineers, Including Mining Safety Engineers
The routine layer of this job is compressing.
A substantial share of this occupation's activities overlaps with what AI already does well. Expect the routine layer to compress first, and the human-anchored layer to gain weight and value.
Where this occupation sits among all 785 measured. Bands are quintiles of the real distribution. Applicability measures task overlap with observed AI usage. It is not a probability of job loss.
What this job involves
Conduct subsurface surveys to identify the characteristics of potential land or mining development sites. May specify the ground support systems, processes, and equipment for safe, economical, and environmentally sound extraction or underground construction activities. May inspect areas for unsafe geological conditions, equipment, and working conditions. May design, implement, and coordinate mine safety programs.
Where AI overlaps this work, activity by activity
The occupation's most important work activities (O*NET weights, normalized to this set), each with its measured AI performance across 200,000 real conversations: how often AI is used for it, how well it completes it, and how much of the activity it covers.
| Work activity | Share of role | AI performance |
|---|---|---|
| Determine operational methods or procedures. | 16.7% | High overlap 0.73 |
| Monitor operations to ensure adequate performance. | 11.4% | Not observed 0.00 |
| Present research or technical information. | 10.5% | High overlap 0.75 |
| Select materials or equipment for operations or projects. | 10.2% | High overlap 0.63 |
| Inspect facilities or equipment. | 7.2% | Not observed 0.00 |
| Design industrial systems or equipment. | 7.1% | Not observed 0.00 |
| Plan work activities. | 6.7% | Moderate 0.60 |
| Schedule operational activities. | 6.1% | Not observed 0.00 |
| Prepare reports of operational or procedural activities. | 6.1% | Not observed 0.00 |
| Estimate project development or operational costs. | 6.1% | Not observed 0.00 |
| Read documents or materials to inform work processes. | 6.0% | Moderate 0.55 |
| Design structures or facilities. | 5.8% | Not observed 0.00 |
"Not observed" is a measurement, not missing data: that activity does not meaningfully appear in the usage sample. AI performance = completion × scope × coverage (user-goal view), 0–1.
What drives this score: the biggest AI-overlapped activity in this role is determine operational methods or procedures; the most important activity AI is not observed doing is monitor operations to ensure adequate performance. The plan below is built around exactly that split.
Move first: compress the routine before it compresses you.
Next 30 days
- Take the exposed layer yourself. Your most AI-overlapped activity, Determine operational methods or procedures, is exactly what a well-tooled colleague will accelerate. Be that colleague first.
- Time-box the routine. Measure how long your repeatable tasks actually take; that's the budget AI adoption will compete against, and the time you will reinvest.
Next 90 days
- Double down where humans hold. Grow the share of your week spent on work like Monitor operations to ensure adequate performance, measured as outside AI's current reach, and take on more of it explicitly.
- Build one AI-assisted workflow end-to-end for your team and own its quality. Being the local standard-setter compounds.
The longer game
- Position at the boundary. The valuable seat in partially-exposed occupations is the translator: someone fluent in both the domain and the tools. Certifications matter less than a demonstrated workflow portfolio.
- Recheck yearly. Elevated-band occupations move: new tools shift specific activities fast. The data vintage is printed at the top of this page.
- AI applicability score and per-activity metrics: Tomlinson, Jaffe, Wang, Counts & Suri, Working with AI: Measuring the Applicability of Generative AI to Occupations, Microsoft Research 2025 (arXiv:2507.07935), data CC BY 4.0.
- Occupation description and activity structure: O*NET 30.0 Database, USDOL/ETA, CC BY 4.0.
- Bands, personalization weights and action plans are JobRiskAI editorial layers; method and limitations on the methodology page.