Handicraft Workers in Textile, Leather and Related Materials

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 14 task statements that define Handicraft Workers in Textile, Leather and Related Materials (ISCO-08 7318) score an average of 0.13 on a 0–1 exposure scale — more exposed than about 12% 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 14 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Grading and classifying natural textile fibres;”

Scores 0.21 on the 2025 scale. The task of grading and classifying natural textile fibers involves both visual inspection to assess fiber quality and determining the classification based on established criteria. This task shares similarities with "Checking the structural parameters of knitted fabrics" (score 0.27) and "Identifying the material from which the carpet / rug is made" (score 0.35), both of which require human sensory perception and judgment that Generative AI cannot perform. However, AI could assist with comparing features against data and records once fibers are digitized, similar to the role it plays in data management tasks. Overall, the task's manual and sensory nature limit full automation potential, aligning it more closely with the lower scores for tasks like "Collecting fabric samples" (0.25), which involve significant human input, but acknowledging potential minor AI assistance in data handling aspects. Therefore, the score is adjusted to reflect the limited yet possible AI facilitation in some aspects of the task within a high-income, technology-equipped environment like Poland.

Moving fastest, 2023 → 2025

“Cleaning and fluffing textile fibres;”

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 7318, 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.

• Shoe and Leather Workers and Repairers

In context

Part of the 7 - Craft and related trades workers major group. Return to the full gradient to see how the whole group sits.

Handicraft Workers in Textile, Leather and Related Materials sit at the 12th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Handicraft Workers in Textile, Leather and Related Materials rank in the 12th 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: "Grading and classifying natural textile fibres;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Handicraft Workers in Textile, Leather and Related Materials". https://singulariki.com/gradient/7318-handicraft-workers-in-textile-leather-and-related-materials.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.