Headline risk
7%
Low RiskLocker room, coatroom, and dressing room attendants
United States AI Work Index tracks this occupation on the shared structural baseline and then layers on local demand resilience, wages, and confidence.
Why This Score
Share of job tasks that overlap with current AI capabilities
Median annual wage
Projected employment change over 10 years
Typical preparation needed for this occupation
Occupation profile
Provide personal items to patrons or customers in locker rooms, dressing rooms, or coatrooms.
Task evidence
100% weighted task match · 0% effective coverage
Scores combine AI task overlap, human advantages, and local demand. How it works
United States Now
Median Wage
USD 34,800
Employment 2024
15.6K
Projected Change (2024–34)
6.4%
Openings (2024–34)
4.2K
Wage distribution
Demand outlook
Projections published, but no prose outlook available.
Role Profile
Tasks
- 1. Refer guest problems or complaints to supervisors. AI use: 0%
- 2. Monitor patrons' facility use to ensure that rules and regulations are followed, and safety and order are maintained. AI use: 0%
- 3. Answer customer inquiries or explain cost, availability, policies, and procedures of facilities. AI use: 0%
- 4. Clean facilities such as floors or locker rooms. AI use: 0%
- 5. Assign dressing room facilities, locker space, or clothing containers to patrons of athletic or bathing establishments. AI use: 0%
- 6. Check supplies to ensure adequate availability, and order new supplies when necessary. AI use: 0%
Technologies
Requirements
Work context
Worker profile
Median age 27.8 · 256K employed
Under 25: 47% · 25–54: 33% · 55+: 20%
Related
No direct US role match is available yet for this occupation.
Source coverage
11/11 source families · O*NET 30.2 / OEWS 2024 / ORS 2025 / OOH 2025-08-28 / Projections 2024-34 / CPS 2025 / Anthropic task penetration
Mapping quality
major_group_fallback · employment series present
Narrative & sources
Published limitations
This page shows the local country layer, not realised individual job outcomes. The global structural baseline is shared across countries; only the local demand and wage layer changes here.
Built from O*NET occupation descriptions, task statements, technology skills, work context, Job Zones, Anthropic task penetration, BLS OEWS wages, BLS projection tables, BLS ORS requirements, BLS OOH narrative content, BLS skills data, and BLS CPS occupation age tables.