Will AI replace Cleaners of Vehicles and Equipment?
AI reaches little of this job’s work.
0/100
Low exposure
More exposed to AI than 2% of US workers.
- AI already used in
- 0% of the work
- As AI tools mature: up to
- 0% of the work
What the job involves
Wash or otherwise clean vehicles, machinery, and other equipment. Use such materials as water, cleaning agents, brushes, cloths, and hoses.
Where AI reaches the work
The job’s main tasks, heaviest first, by how far AI reaches them.
Out of reach(20)
- Inspect parts, equipment, or vehicles for cleanliness, damage, and compliance with standards or regulations.
- Clean and polish vehicle windows.
- Scrub, scrape, or spray machine parts, equipment, or vehicles, using scrapers, brushes, clothes, cleaners, disinfectants, insecticides, acid, abrasives, vacuums, or hoses.
- Sweep, shovel, or vacuum loose debris or salvageable scrap into containers and remove containers from work areas.
- Rinse objects and place them on drying racks or use cloth, squeegees, or air compressors to dry surfaces.
and 15 more
Also called
Automotive Detailer (Auto Detailer) · Auto Detailer · Aircraft Cleaner · Car Washer · Truck Washer · Bus Cleaner · Car Detailer · Cleaner
Related jobs
What these numbers mean
The score is the share of this job’s work, weighted by how important and frequent each task is, that AI could do in half the time: tasks an AI model could do alone count in full, tasks that need software built on AI count half.
“AI already used” is the share of the work in tasks where people are recorded using AI today. “As AI tools mature” counts every task AI could halve, with or without extra software.
The percentile compares the score with every job’s, weighted by how many people in the US do each one. Exposure means AI could change how the work is done, not that the job goes away. These are estimates for the typical job, not predictions.
Sources
- O*NET 31.0 Database, U.S. Department of Labor, Employment and Training Administration
- GPTs are GPTs: Labor market impact potential of LLMs (task labels), Eloundou, Manning, Mishkin & Rock (2023), OpenAI and University of Pennsylvania
- Anthropic Economic Index: labor market impacts (task penetration), Anthropic
- Occupational Employment and Wage Statistics, May 2025 (national and metropolitan areas), U.S. Bureau of Labor Statistics