Vehicle Cleaners

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Vehicle Cleaners (ISCO-08 9122) score an average of 0.09 on a 0–1 exposure scale — more exposed than about 0% 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 6 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Applying cleaning agents to remove stains from vehicle exteriors and interiors;”

Scores 0.10 on the 2025 scale. The task of applying cleaning agents to remove stains from vehicle exteriors and interiors is fundamentally a manual and tactile task. Similar tasks within the context, such as cleaning surfaces, maintaining appearance, and washing or polishing fabric items, have received low adjusted scores, typically around 0.1 or lower, reflecting the limited potential for automation due to the physical and manual nature required. Generative AI, while capable of providing instructions or advice through language models, cannot physically execute or enhance such tasks due to the absence of physical embodiment or sensory capabilities. The task, set in a high-income country like Poland where technology access is prevalent, benefits slightly in terms of procedural optimization or instruction but remains principally reliant on human skill and physical execution. Therefore, an adjusted score of 0.1 reflects the predominant need for human involvement consistent with similar manual tasks rated within the context.

Moving fastest, 2023 → 2025

“Washing tyres and wheel arches and blackening tyres;”

Model capability on this task changed by +0.03 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 9122, 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.

• Cleaners of Vehicles and Equipment

In context

Part of the 9 - Elementary occupations major group. Return to the full gradient to see how the whole group sits.

Vehicle Cleaners sit at the 0th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Vehicle Cleaners rank in the 0th 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 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Applying cleaning agents to remove stains from vehicle exteriors and interiors;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Vehicle Cleaners". https://singulariki.com/gradient/9122-vehicle-cleaners.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.