Manufacturing Labourers Not Elsewhere Classified

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 4 task statements that define Manufacturing Labourers Not Elsewhere Classified (ISCO-08 9329) score an average of 0.12 on a 0–1 exposure scale — more exposed than about 7% 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 4 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Carrying out manual sorting of products or components.”

Scores 0.14 on the 2025 scale. The task of carrying out manual sorting of products or components involves significant physical manipulation and real-time decision-making, which current Generative AI cannot directly perform. Similar tasks, such as sorting secondary raw materials or fiber waste, have been scored low (0.1417 and 0.125, respectively) due to their manual nature requiring human intervention for tactile and visual assessment. Unlike tasks apt for Generative AI's strengths in data processing or decision-support, this task requires dexterity and perception, limiting AI's direct role. However, AI could potentially assist by providing guidelines, optimizing sorting processes, or integrating with machinery to help streamline the operation. Considering the physical constraints alongside potential for indirect AI assistance, and the broader context of performing this task in a well-equipped country like Poland, a score of 0.135 reflects the moderate potential for AI support while recognizing the predominantly manual nature of the task.

Moving fastest, 2023 → 2025

“Carrying out manual sorting of products or components.”

Model capability on this task changed by +0.04 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 9329, 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.

• Laborers and Freight, Stock, and Material Movers, Hand
• Recycling and Reclamation Workers
• Production Workers, All Other
• Helpers--Production Workers
• Machine Feeders and Offbearers

In context

Part of the 9 - Elementary occupations major group. Return to the full gradient to see how the whole group sits.

Manufacturing Labourers Not Elsewhere Classified sit at the 7th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Manufacturing Labourers Not Elsewhere Classified rank in the 7th 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 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Carrying out manual sorting of products or components.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Manufacturing Labourers Not Elsewhere Classified". https://singulariki.com/gradient/9329-manufacturing-labourers-not-elsewhere-classified.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.