Hand Launderers and Pressers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 4 task statements that define Hand Launderers and Pressers (ISCO-08 9121) score an average of 0.14 on a 0–1 exposure scale — more exposed than about 16% 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

“Placing articles on shelves and hanging articles for delivery and collection.”

Scores 0.17 on the 2025 scale. The task of placing articles on shelves and hanging articles for delivery and collection is primarily physical and manual, similar to tasks such as "Arranging goods on store shelves" and "Arranging small items according to the supervisor's instructions," both of which received low automation scores due to their physical nature and spatial reasoning requirements. Generative AI can potentially assist with planning or optimizing inventory layouts but cannot perform the physical act of placing and hanging articles, limiting the potential for automation. The survey contexts suggest very low to moderate potential for automation in tasks involving significant manual labor, supported by scores from 0.084 to 0.19 in semantically similar tasks. Given that the task is performed in a high-income country like Poland, where AI-supported tools for optimization might be available, a slight upward adjustment from the pure physical limit is justified, akin to the optimization observed in "Arranging goods at the station" and "Replenishing the assortment of goods." Therefore, an adjusted score of 0.17 appropriately reflects the limited role AI can play in assisting but not automating this task.

Moving fastest, 2023 → 2025

“Placing articles on shelves and hanging articles for delivery and collection.”

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 9121, 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.

• Pressers, Textile, Garment, and Related Materials

In context

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

Hand Launderers and Pressers sit at the 16th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Hand Launderers and Pressers rank in the 16th 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: "Placing articles on shelves and hanging articles for delivery and collection.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Hand Launderers and Pressers". https://singulariki.com/gradient/9121-hand-launderers-and-pressers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.