Shoemakers and Related Workers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 13 task statements that define Shoemakers and Related Workers (ISCO-08 7536) score an average of 0.17 on a 0–1 exposure scale — more exposed than about 22% 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

“Studying drawings and other specifications to make footwear according to customer‚Äôs needs;”

Scores 0.32 on the 2025 scale. The task of studying drawings and specifications to make footwear according to customer needs involves interpreting design sketches and tailoring footwear solutions, which combines creative visualization and technical interpretation. It is similar to semi-manual tasks like "Evaluating materials for orthopedic footwear" and "Preparing materials and developing footwear components," with adjusted scores of 0.325 and 0.35, respectively. These tasks involve design interpretation and material handling, paralleling the need for human expertise in evaluating and customizing footwear based on design specifications. Generative AI can support this task through design visualization and providing suggestions but lacks the nuanced understanding and manual skills required for personalizing footwear to customer preferences. In a high-income country like Poland, AI utilization could facilitate specific aspects of design analysis and generate options, but the core task requires significant human intervention for bespoke customization based on customer needs. Hence, the assigned score reflects a moderate level of potential AI support, acknowledging the substantial portion that remains dependent on human expertise in design and customization for footwear production.

Moving fastest, 2023 → 2025

“Checking the texture, colour and strength of leather to ensure that it is adequate for a particular purpose;”

Model capability on this task changed by +0.16 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 7536, 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.

Shoemakers and Related Workers sit at the 22nd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Shoemakers and Related Workers rank in the 22nd 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 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Studying drawings and other specifications to make footwear according to customer‚Äôs needs;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Shoemakers and Related Workers". https://singulariki.com/gradient/7536-shoemakers-and-related-workers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.