Plastic Products Machine Operators

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 7 task statements that define Plastic Products Machine Operators (ISCO-08 8142) score an average of 0.17 on a 0–1 exposure scale — more exposed than about 24% 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 7 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Examining outputs for defects and conformity to specifications;”

Scores 0.28 on the 2025 scale. The task of "examining outputs for defects and conformity to specifications" in the context of machine operations involves significant hands-on inspection and decision-making, which are not fully automatable by Generative AI. Comparable tasks—such as checking the quality of sewing completion (0.265), detecting and removing defects in precision devices (0.285), and controlling the quality of orthopedic products (0.2195)—all indicate limited automation potential due to the requirement for human sensory input and nuanced judgment. AI can assist in preliminary defect detection and data analysis, yet the core inspection remains human-dependent. Given that the work is conducted in a high-income country like Poland with access to advanced technology, there is room for AI augmentation. However, the reliance on sensory and evaluative skills justifies maintaining a predominantly manual role, aligning the automation potential closer to these similar tasks with some technological support. Therefore, an adjusted score of 0.27 accurately reflects the current capabilities of Generative AI in this domain.

Moving fastest, 2023 → 2025

“Operating and monitoring machines which laminate plastics and plastic-impregnated materials or produce fibreglass;”

Model capability on this task changed by +0.07 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 8142, 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.

• Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic
• Molding, Coremaking, and Casting Machine 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
• Plating 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 8 - Plant and machine operators, and assemblers major group. Return to the full gradient to see how the whole group sits.

Plastic Products Machine Operators sit at the 24th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Plastic Products Machine Operators rank in the 24th 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 0.03 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Examining outputs for defects and conformity to specifications;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Plastic Products Machine Operators". https://singulariki.com/gradient/8142-plastic-products-machine-operators.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.