Laundry Machine Operators

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 9 task statements that define Laundry Machine Operators (ISCO-08 8157) score an average of 0.16 on a 0–1 exposure scale — more exposed than about 19% 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 9 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Packaging articles and preparing orders for despatch.”

Scores 0.23 on the 2025 scale. The task of packaging articles and preparing orders for dispatch involves physical handling and manual coordination, similar to tasks such as "Packing goods and preparing them for shipment" and "Organizing parcels taken out of boxes," which have scores of 0.1535714285714285 and 0.275, respectively. These tasks require human intervention for tasks like arranging, wrapping, and ensuring the security of packaged goods. Generative AI can assist in optimizing packaging arrangements or providing guidelines based on data input, yet it cannot replace the human effort needed for physical actions such as lifting, arranging, and securing packages. Considering the context of a high-income country like Poland, where AI can support procedural elements but not execute physical tasks, an adjusted score of 0.2 reflects the limited potential for automation, emphasizing the necessity of human roles in the physical execution involved in packaging and dispatching articles.

Moving fastest, 2023 → 2025

“Loading and unloading washing machines, driers and extractors;”

Model capability on this task changed by +0.09 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 8157, 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.

• Laundry and Dry-Cleaning Workers

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.

Laundry Machine Operators sit at the 19th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Laundry Machine Operators rank in the 19th 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 0.03 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Packaging articles and preparing orders for despatch.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Laundry Machine Operators". https://singulariki.com/gradient/8157-laundry-machine-operators.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.