Mining and metallurgical technicians

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Mining and metallurgical technicians (ISCO-08 3117) score an average of 0.28 on a 0–1 exposure scale — more exposed than about 53% 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 8 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Preparing detailed estimates of quantities and costs of materials and labour required for mineral, oil and natural gas exploration, extraction, processing and transport projects;”

Scores 0.40 on the 2025 scale. The task of preparing detailed estimates of quantities and costs for mineral, oil, and natural gas projects involves a mix of interpretative analysis, data processing, and planning—areas where Generative AI can notably contribute. Similar tasks like "Calculating material requirements based on project drawings" (adjusted score: 0.25), and "Determining material and time standards and price calculation" (adjusted score: 0.4775) indicate that while AI can automate data input and preliminary analysis, full automation is constrained by the need for human oversight in interpreting complex and varied project-specific documentation. Given the structured nature of estimating, which AI can aid through computational efficiency, yet accounting for the nuanced judgment still needed for precise assessments and anomaly detection, a moderate automation score of 0.45 is justified. This score reflects the potential of AI to perform significant supportive functions but acknowledges the continued necessity for human expertise in detailed estimation processes specific to this task.

Moving fastest, 2023 → 2025

“Assisting scientists in the use of electrical, sonic or nuclear measuring instruments in both laboratory and production activities to obtain data indicating potential sources of metallic ore, gas, or petroleum.”

Model capability on this task changed by +0.21 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 3117, 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.

• Engineering Technologists and Technicians, Except Drafters, All Other
• Non-Destructive Testing Specialists
• Photonics Technicians

In context

Part of the 3 - Technicians and associate professionals major group. Return to the full gradient to see how the whole group sits.

Mining and metallurgical technicians sit at the 53rd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Mining and metallurgical technicians rank in the 53rd 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 fell by 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Preparing detailed estimates of quantities and costs of materials and labour required for mineral, oil and natural gas exploration, extraction, processing and transport projects;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Mining and metallurgical technicians". https://singulariki.com/gradient/3117-mining-and-metallurgical-technicians.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.