Cement, Stone and Other Mineral Products Machine Operators

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 10 task statements that define Cement, Stone and Other Mineral Products Machine Operators (ISCO-08 8114) score an average of 0.23 on a 0–1 exposure scale — more exposed than about 41% 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 10 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Checking and maintaining production records, including information about quantities, dimensions and types of materials and goods produced;”

Scores 0.50 on the 2025 scale. The task of checking and maintaining production records, including information about quantities, dimensions, and types of materials and goods produced, shares similarities with tasks such as "Maintaining technological process documentation" and "Maintaining production process documentation," which received scores of 0.445 and 0.455, respectively. These tasks involve structured data entry and management, areas where Generative AI can facilitate automation through organizing records, generating standard reports, and assisting with data consistency checks. However, this task also requires context-specific judgment and accuracy, such as understanding production metrics and potential discrepancies, which necessitate human oversight. The automation potential is further limited by the need for on-site verification and adjustments based on real-time production changes. Considering the context of high digital literacy and access to technology in Poland, the automation potential is moderate but significant, reflecting the abilities of AI in managing repetitive and structured data tasks, balanced against the need for human expertise to ensure compliance and accuracy in record-keeping.

Moving fastest, 2023 → 2025

“Collecting and examining samples of mixtures and finished products for conformity to specifications and adjusting machine settings accordingly;”

Model capability on this task changed by +0.18 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 8114, 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.

• Mixing and Blending Machine Setters, Operators, and Tenders
• Cutting and Slicing Machine Setters, Operators, and Tenders
• Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders
• Plant and System Operators, All Other
• Biofuels Processing Technicians

In context

Part of the 8 - Plant and machine operators, and assemblers major group. Return to the full gradient to see how the whole group sits.

Cement, Stone and Other Mineral Products Machine Operators sit at the 41st percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Cement, Stone and Other Mineral Products Machine Operators rank in the 41st 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: "Checking and maintaining production records, including information about quantities, dimensions and types of materials and goods produced;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Cement, Stone and Other Mineral Products Machine Operators". https://singulariki.com/gradient/8114-cement-stone-and-other-mineral-products-machine-operators.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.