Metal Production Process Controllers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Metal Production Process Controllers (ISCO-08 3135) score an average of 0.31 on a 0–1 exposure scale — more exposed than about 58% 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 7 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Maintaining shift log of production and other data and preparing production and other reports.”

Scores 0.56 on the 2025 scale. The task of maintaining shift logs of production and preparing production reports aligns with tasks that involve structured documentation and data entry, such as "Maintaining production process documentation" with an adjusted score of 0.455, and "Maintaining technological process documentation" with an adjusted score of 0.445. Generative AI can significantly assist in automating the documentation process by generating reports, organizing data, and ensuring consistency, which are integral parts of maintaining shift logs. However, human oversight is crucial for ensuring accuracy, handling exceptions, and applying context-specific insights that AI cannot fully comprehend. Given the context of a high-income country like Poland, where technology infrastructure supports the integration of AI, the potential for partial automation is relatively high. Thus, a score of 0.465 reflects the balance between AI's capabilities in automating routine documentation tasks and the necessity for human expertise in nuanced interpretation and verification.

Moving fastest, 2023 → 2025

“Operating multi-function central process control machinery to grind, separate, filter, melt, roast, treat, refine or otherwise process metals;”

Model capability on this task changed by +0.01 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 3135, 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.

• Metal-Refining Furnace Operators and Tenders

In context

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

Metal Production Process Controllers sit at the 58th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Metal Production Process Controllers rank in the 58th 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 0.10 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Maintaining shift log of production and other data and preparing production and other reports.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Metal Production Process Controllers". https://singulariki.com/gradient/3135-metal-production-process-controllers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.