How much of this occupation’s work can be materially affected by current AI systems.
How likely exposure is to translate into reduced human demand.
Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured
Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.
What is driving the score?
Occupation scores are built from the task mix—not a single prediction about a job title.
Where AI can do more
Routine, digitized, and highly repeatable tasks face the greatest pressure.
- Prepare technical reports for use by mining, engineering, and management personnel.73
- 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.73
- Select or develop mineral location, extraction, and production methods, based on factors such as safety, cost, and deposit characteristics.72
- Prepare schedules, reports, and estimates of the costs involved in developing and operating mines.72
Where people still matter
These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.
- Test air to detect toxic gases and recommend measures to remove them, such as installation of ventilation shafts.01
- Examine maps, deposits, drilling locations, or mines to determine the location, size, accessibility, contents, value, and potential profitability of mineral, oil, and gas deposits.02
- Monitor mine production rates to assess operational effectiveness.03
- Implement and coordinate mine safety programs, including the design and maintenance of protective and rescue equipment and safety devices.04
- Prepare technical reports for use by mining, engineering, and management personnel.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Petroleum Engineers
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Compare these careers →Adoption and labour-market outlook
Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.
O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.
