Mining Engineers, Metallurgists and Related Professionals

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Mining Engineers, Metallurgists and Related Professionals (ISCO-08 2146) score an average of 0.29 on a 0–1 exposure scale — more exposed than about 55% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Not exposed band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 9 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Planning and directing storage, initial treatment and transportation of water, oil or gas;”

Scores 0.36 on the 2025 scale. The task of planning and directing storage, initial treatment, and transportation of water, oil, or gas involves a combination of logistical planning, regulatory compliance, and real-time decision-making, which can benefit significantly from AI support. Generative AI could potentially assist in optimizing storage layouts, scheduling, and route planning using predictive analytics and real-time data analysis, similar to tasks like "Planning of domestic and international transportation" and "Operating computerized passport record systems," both of which reflect moderate automation potential with scores close to 0.38-0.69. However, the practical execution, compliance with environmental regulations, and emergency response require human expertise, akin to tasks such as "Planning tasks for the fire department" or "Dewatering the excavation," which have lower automation scores closer to 0.2-0.31 due to the need for human oversight and physical presence. While the technological infrastructure in Poland may facilitate AI integration in planning and analysis, human intervention remains essential for strategic oversight and risk management. Therefore, the adjusted score reflects the substantial role AI can play in augmenting but not fully replacing human responsibilities in this complex task.

Moving fastest, 2023 → 2025

“Investigating properties of metals and alloys, developing new alloys and advising on and supervising technical aspects of metal and alloy manufacture and processing;”

Model capability on this task changed by +0.09 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 2146, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Materials Engineers
• Petroleum Engineers
• Materials Scientists
• Mining and Geological Engineers, Including Mining Safety Engineers

In context

Part of the 2 - Professionals major group. Return to the full gradient to see how the whole group sits.

Mining Engineers, Metallurgists and Related Professionals sit at the 55th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Mining Engineers, Metallurgists and Related Professionals rank in the 55th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure rose by 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Planning and directing storage, initial treatment and transportation of water, oil or gas;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Mining Engineers, Metallurgists and Related Professionals". https://singulariki.com/gradient/2146-mining-engineers-metallurgists-and-related-professionals.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.