Spray Painters and Varnishers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 3 task statements that define Spray Painters and Varnishers (ISCO-08 7132) score an average of 0.12 on a 0–1 exposure scale — more exposed than about 7% 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 3 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Painting cars, buses, trucks and other vehicles, and applying varnish and other protective coatings;”

Scores 0.12 on the 2025 scale. The task of painting vehicles and applying protective coatings requires physical dexterity and manual precision, similar to tasks like painting roof coverings or applying paint coatings manually, which were given low adjusted scores around 0.115 to 0.12. These tasks highlight the limitations of Generative AI in automating manual, tactile operations, where physical manipulation, real-time adjustments, and sensory feedback are crucial. While Generative AI can offer assistance in planning, optimizing workflow, or advising on techniques, the actual painting process for vehicles requires human expertise. The similarity with other low-scored tasks in manual painting and preparation suggests minimal potential for automation. Factoring in Poland's high technological context doesn't substantially increase automation capability for this predominantly physical task, leading to an adjusted score of 0.12, indicating limited potential for full AI automation.

Moving fastest, 2023 → 2025

“Painting cars, buses, trucks and other vehicles, and applying varnish and other protective coatings;”

Model capability on this task changed by +0.02 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 7132, 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.

No U.S. role resolves through the crosswalk for this occupation. Search the encyclopedia for the closest match →

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.

Spray Painters and Varnishers sit at the 7th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Spray Painters and Varnishers rank in the 7th 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 0.01 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Painting cars, buses, trucks and other vehicles, and applying varnish and other protective coatings;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Spray Painters and Varnishers". https://singulariki.com/gradient/7132-spray-painters-and-varnishers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.