Mining Supervisors

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Mining Supervisors (ISCO-08 3121) score an average of 0.29 on a 0–1 exposure scale — more exposed than about 54% 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 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Providing reports and other information to mining managers about all aspects of mining or quarrying operations;”

Scores 0.40 on the 2025 scale. The task of providing reports and information about mining or quarrying operations involves both structured data analysis and nuanced understanding of site-specific conditions, which are well within the capabilities of generative AI technologies. Generative AI can significantly aid in data processing, generating preliminary reports, and even suggesting insights based on patterns from large datasets. However, the interpretation of complex, site-specific operational details, and the necessity for human oversight and strategic decision-making limits full automation. Similar tasks such as "Developing safety instructions" and "Monitoring and evaluating the technical condition of machines and devices" received scores around the 0.42 to 0.46 range, indicating a moderate potential for AI assistance while acknowledging the need for human expertise. Given Poland's robust digital infrastructure, allowing for efficient AI implementation, the adjusted score of 0.45 reflects a balanced integration of AI capabilities with essential human intervention in mining reporting tasks.

Moving fastest, 2023 → 2025

“Providing reports and other information to mining managers about all aspects of mining or quarrying operations;”

Model capability on this task changed by +0.10 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 3121, 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.

• First-Line Supervisors of Construction Trades and Extraction Workers
• Solar Energy Installation Managers

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 Supervisors sit at the 54th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Mining Supervisors rank in the 54th 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: "Providing reports and other information to mining managers about all aspects of mining or quarrying operations;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Mining Supervisors". https://singulariki.com/gradient/3121-mining-supervisors.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.