Metal Working Machine Tool Setters and Operators

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Metal Working Machine Tool Setters and Operators (ISCO-08 7223) score an average of 0.18 on a 0–1 exposure scale — more exposed than about 28% 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 6 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Inspecting work pieces for defects, and measuring work pieces to determine accuracy of machine operation, using rules, templates or other measuring instruments;”

Scores 0.25 on the 2025 scale. The task of inspecting workpieces for defects and measuring them to determine the accuracy of machine operation involves both physical inspection and the precise use of measurement tools. Based on the provided context, the task's nature is similar to tasks involving physical inspection and measurement, such as "Detecting and removing defective work and leaks in precision devices and instruments" with a score of 0.285, and "Performing control measurements of hull structures" with a score of 0.25. These tasks require human sensory input and manual dexterity, which generative AI currently cannot replicate. While AI can assist with data analysis or offering suggestions based on measurement data, the core physical aspects remain human-dependent. Given that the task is conducted in a high-income country like Poland, where access to technology and digital tools is widespread, the potential for AI support is recognized but insufficient for full automation. Hence, the adjusted score reflects the limited role AI can play in supporting, but not replacing, the human effort required in this inspection task.

Moving fastest, 2023 → 2025

“Inspecting work pieces for defects, and measuring work pieces to determine accuracy of machine operation, using rules, templates or other measuring instruments;”

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 7223, 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.

• Machinists
• Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic
• Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic
• Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic
• Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic
• Rolling Machine Setters, Operators, and Tenders, Metal and Plastic
• Metal Workers and Plastic Workers, All Other
• Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic

In context

Part of the 7 - Craft and related trades workers major group. Return to the full gradient to see how the whole group sits.

Metal Working Machine Tool Setters and  Operators sit at the 28th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Metal Working Machine Tool Setters and  Operators rank in the 28th 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 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Inspecting work pieces for defects, and measuring work pieces to determine accuracy of machine operation, using rules, templates or other measuring instruments;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Metal Working Machine Tool Setters and  Operators". https://singulariki.com/gradient/7223-metal-working-machine-tool-setters-and-operators.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.