Pelt Dressers, Tanners and Fellmongers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 13 task statements that define Pelt Dressers, Tanners and Fellmongers (ISCO-08 7535) score an average of 0.11 on a 0–1 exposure scale — more exposed than about 4% 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 13 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Sorting and grading pelts, hides and skins according to colour, shading, size and density;”

Scores 0.15 on the 2025 scale. The task of sorting and grading pelts, hides, and skins according to color, shading, size, and density is primarily a manual task involving visual inspection, tactile assessment, and nuanced human judgment. This mirrors tasks such as evaluating materials and semi-finished products or grading items for quality, which scored low in automation potential due to reliance on human expertise. Generative AI can assist with data recording, suggesting categorizations based on input standards, or optimizing processes through digital tools, but cannot replace the physical and perceptual components required for this task. The similar tasks like "Classifying waste produced during leather cutting" (0.15) and "Collecting fabric samples" (0.25) had limited potential for automation, mostly due to the hands-on and sensory requirements inherent in these tasks. Given these considerations and the high reliance on human skills in sorting and grading, the automation potential for this task is low, justifying a score of 0.12. This reflects the need for human expertise in tactile and visual evaluation, which generative AI cannot replicate, even in a high-income country like Poland where technology is accessible.

Moving fastest, 2023 → 2025

“Treating hides and skins in tanning solution to convert them into leather;”

Model capability on this task changed by +0.06 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 7535, 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.

• Graders and Sorters, Agricultural Products
• Textile, Apparel, and Furnishings Workers, All Other

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.

Pelt Dressers, Tanners and Fellmongers sit at the 4th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Pelt Dressers, Tanners and Fellmongers rank in the 4th 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 0.00 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Sorting and grading pelts, hides and skins according to colour, shading, size and density;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Pelt Dressers, Tanners and Fellmongers". https://singulariki.com/gradient/7535-pelt-dressers-tanners-and-fellmongers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.