Painters and Related Workers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Painters and Related Workers (ISCO-08 7131) score an average of 0.13 on a 0–1 exposure scale — more exposed than about 9% 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 5 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 preparing paints to required colours by mixing pigments and additives;”

Scores 0.19 on the 2025 scale. The task of selecting and preparing paints by mixing pigments and additives involves both cognitive and manual skills similar to tasks like preparing color extracts and handling materials for coatings. Generative AI can support by recommending color formulas and mixing techniques based on input data, yet the tactile aspect of mixing and manually ensuring color consistency is not within AI's current capabilities. Tasks with significant manual components, like painting roof coverings or preparing boards for painting, and preparing color extracts scored between 0.15 and 0.275, reflecting low automation potential. Furthermore, while AI can aid in advisory roles, the physical nature of the task limits the scope for full automation, as seen in other verified scores. Therefore, considering Poland's technological infrastructure that facilitates AI's role in data analysis but not in physical tasks, the adjusted score for this specific task reflects the moderate ability of AI to assist, but not fully automate, with a score of 0.23.

Moving fastest, 2023 → 2025

“Selecting and preparing paints to required colours by mixing pigments and additives;”

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 7131, 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.

• Painters, Construction and Maintenance
• Paperhangers

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.

Painters and Related Workers sit at the 9th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Painters and Related Workers rank in the 9th 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 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Selecting and preparing paints to required colours by mixing pigments and additives;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Painters and Related Workers". https://singulariki.com/gradient/7131-painters-and-related-workers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.