Will AI replace Locker Room, Coatroom, and Dressing Room Attendants?
AI reaches little of this job’s work.
13/100
Low exposure
More exposed to AI than 23% of US workers.
- AI already used in
- 0% of the work
- As AI tools mature: up to
- 21% of the work
What the job involves
Provide personal items to patrons or customers in locker rooms, dressing rooms, or coatrooms.
Where AI reaches the work
The job’s main tasks, heaviest first, by how far AI reaches them.
Within reach(5)
- Provide towels and sheets to clients in public baths, steam rooms, and restrooms.
- Report and document safety hazards, potentially hazardous conditions, and unsafe practices and procedures.
- Answer customer inquiries or explain cost, availability, policies, and procedures of facilities.
- Attend to needs of athletic teams in clubhouses.
- Stencil identifying information on equipment.
Out of reach(18)
- Clean facilities such as floors or locker rooms.
- Collect soiled linen or clothing for laundering.
- Check supplies to ensure adequate availability, and order new supplies when necessary.
- Refer guest problems or complaints to supervisors.
- Monitor patrons' facility use to ensure that rules and regulations are followed, and safety and order are maintained.
and 13 more
Also called
Coat Checker · Locker Attendant · Spa Attendant · Checkroom Attendant · Jockey Valet · Rest Room Attendant · Attendant · Ladies Locker Room Attendant
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