Mining Managers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 10 task statements that define Mining Managers (ISCO-08 1322) score an average of 0.35 on a 0–1 exposure scale — more exposed than about 63% 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 Minimal 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 10 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Controlling the preparation of production records and reports;”

Scores 0.55 on the 2025 scale. The task of controlling the preparation of production records and reports aligns with tasks involving documentation and data management, such as "Maintaining production process documentation" (0.455) and "Maintaining current documentation of foundry equipment operation" (0.45). These involve structured data handling, organization, and report generation, areas where Generative AI can assist by standardizing records, automating data entry, and generating report templates, enhancing productivity in repetitive processes. However, the need for human oversight to ensure data accuracy, context-specific adjustments, and compliance remains crucial, as seen in tasks like "Maintaining required records of process progress" (0.45) and "Preparing inventory plans before yearly inventories" (0.575). Given these considerations and the potential capabilities of AI to streamline yet not fully automate the task, an adjusted score of 0.495 reflects the moderate potential for automation while ensuring the necessary human oversight in a high-income, tech-equipped setting like Poland. This score considers the balance between AI's capability to process and organize structured data and the need for human judgment and decision-making in context-specific or non-routine scenarios.

Moving fastest, 2023 → 2025

“Evaluating efficiency of production sites to determine adequacy of personnel, equipment and technologies used, and make changes to work schedule or equipment when necessary;”

Model capability on this task changed by +0.14 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 1322, 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.

• Managers, All Other
• Regulatory Affairs Managers
• Compliance Managers
• Loss Prevention Managers
• Wind Energy Operations Managers
• Wind Energy Development Managers
• Brownfield Redevelopment Specialists and Site Managers

In context

Part of the 1 - Managers major group. Return to the full gradient to see how the whole group sits.

Mining Managers sit at the 63rd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Mining Managers rank in the 63rd 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: "Controlling the preparation of production records and reports;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Mining Managers". https://singulariki.com/gradient/1322-mining-managers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.