Packing, Bottling and Labelling Machine Operators

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 3 task statements that define Packing, Bottling and Labelling Machine Operators (ISCO-08 8183) score an average of 0.22 on a 0–1 exposure scale — more exposed than about 40% 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 3 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Operating and monitoring machines that, by gluing or other methods, label products, packages and various containers.”

Scores 0.23 on the 2025 scale. The task of operating and monitoring machines that label products, packages, and various containers is similar to monitoring and operating machinery tasks in manufacturing, which often involve physical manipulation, real-time adjustments, and quality assurance checks. In the provided context, such as "Operating and supervising machines for filling and sealing packages" (weighing 0.1725) and "Operating basic machines for production" (scored at 0.3), the tasks require a significant degree of human oversight to ensure precision and handle real-time operational issues. Generative AI can assist with optimizing machine settings and providing data-driven insights, but the physical and manual components cannot be fully automated—important when considering dual capabilities in monitoring and labeling. Given the technological infrastructure in high-income countries like Poland, where generative AI can be efficiently used to support repetitive and data-driven aspects of this task, the adjusted score should reflect the balance between AI's role in enhancing efficiency and the indispensable human oversight for physical operations and intricate quality control. Hence, an adjusted score of 0.31 considers these factors, indicating potential AI assistance with current limitations.

Moving fastest, 2023 → 2025

“Operating and monitoring machines that weigh, wrap, seal and pack various products;”

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 8183, 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.

• Packaging and Filling Machine Operators and Tenders

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.

Packing, Bottling and Labelling Machine Operators sit at the 40th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Packing, Bottling and Labelling Machine Operators rank in the 40th 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 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Operating and monitoring machines that, by gluing or other methods, label products, packages and various containers.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Packing, Bottling and Labelling Machine Operators". https://singulariki.com/gradient/8183-packing-bottling-and-labelling-machine-operators.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.