Tailors, Dressmakers, Furriers and Hatters

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 11 task statements that define Tailors, Dressmakers, Furriers and Hatters (ISCO-08 7531) score an average of 0.15 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 11 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Selecting and modifying commercial patterns to customers' and clothing manufacturers' specifications and fit;”

Scores 0.30 on the 2025 scale. The task of selecting and modifying commercial patterns to customers' and clothing manufacturers' specifications and fit involves a combination of creative and technical skills that are not entirely automatable with current Generative AI capabilities. However, AI can assist in suggesting pattern modifications, simulating pattern layouts, and optimizing fit based on standard measurements. This aligns with the context of tasks that involve creative and manual inputs, such as "Designing wigs" (0.28) and "Conducting research, design, implementation and development work in the field of textile industry" (0.415). These tasks, like pattern modification, require significant human judgment and customization, limiting their full automation potential. Compared to tasks with higher physical and tactile demands, like "Operating sewing machines" (0.115), this task has a higher automation potential due to its cognitive elements. Nonetheless, the core creativity and dexterous adjustments needed in pattern selection and modification keep the automation potential moderate. Given the technological context of a high-income country such as Poland, where access to digital tools could aid the task's cognitive components, a score of 0.35 reflects a balanced potential for AI support in this creative, technical, and manual task.

Moving fastest, 2023 → 2025

“Folding, twisting and draping material, such as satin or silk, or sewing ribbon or cloth in the form of artificial flowers or bows around crown and brim to shape and decorate hats;”

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 7531, 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.

• Tailors, Dressmakers, and Custom Sewers

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.

Tailors, Dressmakers, Furriers and Hatters sit at the 16th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Tailors, Dressmakers, Furriers and Hatters 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 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Selecting and modifying commercial patterns to customers' and clothing manufacturers' specifications and fit;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Tailors, Dressmakers, Furriers and Hatters". https://singulariki.com/gradient/7531-tailors-dressmakers-furriers-and-hatters.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.