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Locker Room, Coatroom, and Dressing Room Attendants

Occupation · SOC 39-3093.00

Provide personal items to patrons or customers in locker rooms, dressing rooms, or coatrooms.

Also called: Athletic Equipment Manager · Ladies Locker Room Attendant · Locker Room Attendant · Spa Attendant · Coat Check Attendant · Coat Checker · Coat Room Attendant · Fitting Room Attendant · Area Attendant · Attendant · Bath Attendant · Bath Steward

Job family: Personal Care and Service Occupations

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Download .md

A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch /roles/role-39-3093-00/context.md directly.

AI work map

A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.

Often handed to AI

Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.

  • Answer customer inquiries or explain cost, availability, policies, and procedures of facilities. · 0.4%
See how AI is used here →

Keep a human in the loop

Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.

  • Answer customer inquiries or explain cost, availability, policies, and procedures of facilities. · 97.4% need a human
See the boundary tasks →

35th-percentile task overlap — yet about 4,200 openings a year (+6.4% projected, BLS) . What exposure means →

AI & job outlook

What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.

Exposure to current AI

Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.

Measure Rank vs all occupations Percentile Score
Overall AI exposure (Felten et al.) Moderate 39th -0.3
LLM task exposure, γ (OpenAI / Eloundou) Low 9th 0.1
AI assistant applicability (Microsoft) Moderate 63rd 0.2

OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.1), and including AI-powered software (γ 0.1). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.

This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.

Historical automation estimate (2013)

A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.

Frey–Osborne probability 0.4 · 45th percentile among occupations · Moderate

How AI is actually used in this job

Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.

Answer customer inquiries or explain cost, availability, policies, and procedures of facilities. 1.6%

Job outlook

Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.

Outlook About average · +6.4% by 2034
Projected annual openings 4,200
Employment 2024 → 2034 15,600 → 16,600

“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.

Where this work sits on the global GenAI gradient

The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.

29% mean task exposure (2025)
55th percentile of 427 placed occupations
−9 pts shift 2023 → 2025
International occupation (ISCO-08) Task exposure (2025) Most tasks fall in
Elementary Workers Not Elsewhere Classified · 9629 29% Minimal

Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.

Working with AI in this job

How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.

Most common way people use AI here Directive · AI does it; you give the instruction
Typical AI autonomy 3.0 / 5 · higher = AI acts more independently

What people delegate to AI

The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.

Task How Usage
Answer customer inquiries or explain cost, availability, policies, and procedures of facilities. Directive 0.4%

Where a human is still needed

Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.

Answer customer inquiries or explain cost, availability, policies, and procedures of facilities. 97.4%

What people most often hand AI here

Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.

  • Help me answer customer inquiries or explain cost, availability, policies, and procedures of facilities.

    From: Answer customer inquiries or explain cost, availability, policies, and procedures of facilities. · 0.4% of measured AI use · directive

Tasks

All 23 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.

Emerging tasks

Newer responsibilities O*NET has flagged as growing for this occupation.

  • Maintain or repair athletic equipment.

Work activities

Knowledge, skills & abilities

O*NET importance rating, from 1 (not important) to 5 (extremely important).

Knowledge

Customer and Personal Service 3.7
English Language 3.4
Public Safety and Security 2.3
Education and Training 2.2
Sales and Marketing 2.1
Administration and Management 2.1
Mathematics 2.1
Communications and Media 2.1
Telecommunications 2.1

Essential skills

Speaking 3.6
Active Listening 3.5
Monitoring 2.8
Reading Comprehension 2.6
Critical Thinking 2.5
Writing 2.3
Active Learning 2.1

Transferable skills

Service Orientation 3.5
Social Perceptiveness 3.1
Coordination 2.8
Negotiation 2.5
Time Management 2.4
Persuasion 2.3
Judgment and Decision Making 2.3

Abilities

Speech Recognition 3.5
Oral Comprehension 3.4
Speech Clarity 3.4
Oral Expression 3.3
Problem Sensitivity 2.9
Trunk Strength 2.9
Near Vision 2.9
Written Comprehension 2.8
Written Expression 2.8
Deductive Reasoning 2.8
Information Ordering 2.8
Inductive Reasoning 2.6
Selective Attention 2.3
Extent Flexibility 2.3
Arm-Hand Steadiness 2.1
Stamina 2.1
Far Vision 2.1

Skills in demand

Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.

Tools & technology

Example Category
Facebook Web page creation and editing software Hot technology
Microsoft Excel Spreadsheet software Hot technology
Microsoft Office software Office suite software Hot technology
Microsoft Word Word processing software Hot technology
IBM Lotus 1-2-3 Spreadsheet software
IntelliTrack DMS Check In-Out Inventory management software
Inventory tracking software Inventory management software
SportSoft Equipment Manager Inventory management software
Web browser software Internet browser software

Work context

How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.

Contact With Others 4.7
Indoors, Environmentally Controlled 4.7
Face-to-Face Discussions with Individuals and Within Teams 4.4
Deal With External Customers or the Public in General 4.4
Spend Time Standing 4.2
Spend Time Using Your Hands to Handle, Control, or Feel Objects, Tools, or Controls 4.0
Work With or Contribute to a Work Group or Team 4.0
Freedom to Make Decisions 3.9
Determine Tasks, Priorities and Goals 3.6
Physical Proximity 3.6
Telephone Conversations 3.5
Dealing With Unpleasant, Angry, or Discourteous People 3.5
Spend Time Making Repetitive Motions 3.4
Health and Safety of Other Workers 3.3
Frequency of Decision Making 3.3
Spend Time Walking or Running 3.3
Time Pressure 3.2
Importance of Being Exact or Accurate 3.2
Coordinate or Lead Others in Accomplishing Work Activities 3.1
Work Outcomes and Results of Other Workers 3.0
Impact of Decisions on Co-workers or Company Results 2.9
Exposed to Contaminants 2.8
Importance of Repeating Same Tasks 2.8
Conflict Situations 2.8
Spend Time Bending or Twisting Your Body 2.6
Written Letters and Memos 2.1
Exposed to Sounds, Noise Levels that are Distracting or Uncomfortable 2.0
Spend Time Sitting 2.0
Spend Time Kneeling, Crouching, Stooping, or Crawling 1.9
Wear Common Protective or Safety Equipment such as Safety Shoes, Glasses, Gloves, Hearing Protection, Hard Hats, or Life Jackets 1.8
Exposed to Very Hot or Cold Temperatures 1.7
Consequence of Error 1.7
Outdoors, Exposed to All Weather Conditions 1.6
Degree of Automation 1.6
Exposed to Minor Burns, Cuts, Bites, or Stings 1.6
Level of Competition 1.6
Spend Time Keeping or Regaining Balance 1.5
Exposed to Disease or Infections 1.5
Indoors, Not Environmentally Controlled 1.5
Dealing with Violent or Physically Aggressive People 1.4

How to get in

Job zone
Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
Education
Usually requires a high school diploma or GED, though some occupations may not.
Typical entry-level education
High school diploma or equivalent · BLS, the typical path — not a requirement
Related experience
Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
Preparation level
SVP (Below 6.0) — total schooling plus on-the-job experience.

Education of current workers

Share of people in this occupation at each level of education.

High School Diploma 64.8%
Less than a High School Diploma 22.0%
Some College Courses 7.7%
Post-Secondary Certificate 5.5%

Interests & work styles

The interests and personal qualities O*NET associates with people who do this work.

Career interests (Holland / RIASEC)

Conventional 5.6
Realistic 4.2
Social 4.1
Enterprising 2.5
Artistic 1.6

Interest areas

Personal Service 4.4
Physical/Manual Labor 2.8
Protective Service 1.7
Athletics 1.7
Office Work 1.6
Social Service 1.6
Human Resources 1.5
Management/Administration 1.4

Work styles

Dependability 2.0
Cooperation 1.7
Integrity 1.5

Wages & employment

U.S. · annual wages (BLS OEWS)

$24k10th$29k25th$35kMedian$42k75th$51k90th
Annual wages by percentile — U.S. (BLS OEWS). The light band spans the 10th–90th percentile; the darker band is the middle half (25th–75th); the line is the median.
16k202417k2034 (proj.)+6.4% · About average
Projected U.S. employment, 2024–2034 (BLS Employment Projections). A labor-market forecast for the occupation, not an AI-impact forecast.
10th percentile $23,530
25th percentile $29,120
Median (50th) $34,800
75th percentile $41,530
90th percentile $50,790
People employed 14,960

Industries that employ this occupation

Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.

Industry Workers National median pay
Arts, Entertainment, and Recreation · Sector 5,870 $34,940
Other Services (except Public Administration) · Sector 3,490 $29,040
Accommodation and Food Services · Sector 3,390 $37,470
Educational Services · Sector 1,390 $44,270
Fitness and Recreational Sports Centers · National industry 1,220 $36,160
Casino Hotels · National industry 520 $33,030
Administrative and Support and Waste Management and Remediation Services · Sector 240 $33,610
Temporary Help Services · National industry 200 $33,000
Real Estate and Rental and Leasing · Sector 150 $35,710
Retail Trade · Sector 70 $26,860
Health Care and Social Assistance · Sector 70 $33,920

Where this work is most concentrated

Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).

Industry Concentration Workers
Arts, Entertainment, and Recreation · Sector 22.9× 5,870
Fitness and Recreational Sports Centers · National industry 19.95× 1,220
Casino Hotels · National industry 15.9× 520
Other Services (except Public Administration) · Sector 8.13× 3,490
Accommodation and Food Services · Sector 2.45× 3,390
Educational Services · Sector 1.05× 1,390
Temporary Help Services · National industry 0.78× 200
Real Estate and Rental and Leasing · Sector 0.65× 150

Part of the Arts, Entertainment, & Design career cluster.

Exposure quadrant: AI task-overlap percentile vs Median pay Locker Room, Coatroom, and Dressing Room Attendants sits at the 35th percentile of AI task-overlap and the 4th percentile of median pay, placed here against 12 adjacent occupations on the same two axes. Lower overlap · higher pay Higher overlap · higher pay Higher overlap · lower pay Lower overlap · lower pay Locker Room, Coatroom, and Dressing Room Attendants Maids and Housekeeping Cleaners Passenger Attendants Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop AI task-overlap percentile → ↑ Median pay
AI task-overlap percentile (horizontal) vs. median-pay percentile (vertical), across all scored occupations. This occupation is highlighted; related occupations are plotted alongside it. Overlap measures shared tasks with AI, not automation.

Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.

What you can do with this

Options the data surfaces for Locker Room, Coatroom, and Dressing Room Attendants — not advice or a forecast. Each is a real cross-link you can follow into the evidence.

Skills that travel

Capabilities this work builds that are used across many other occupations.

Adjacent roles

Occupations O*NET rates as related — the nearby moves on the map.

Paths in

How people typically prepare for this work.

Zoom out

On the global GenAI exposure gradient this work sits around the 55th percentile of 427 international occupations.

Write a report on thisheadline · factoids · citation

Locker Room, Coatroom, and Dressing Room Attendants show 35th-percentile AI task overlap — and about 4,200 annual U.S. openings

  • Locker Room, Coatroom, and Dressing Room Attendants rank in the 35th percentile (Moderate band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
  • The occupation is projected to see about 4,200 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
  • BLS projects employment to be about average (+6.4%) from 2024 to 2034.BLS Employment Projections 2024–34
  • Median annual pay is $34,800, across about 14,960 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Locker Room, Coatroom, and Dressing Room Attendants show 35th-percentile AI task overlap — and about 4,200 annual U.S. openings

• Locker Room, Coatroom, and Dressing Room Attendants rank in the 35th percentile (Moderate band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE)
• The occupation is projected to see about 4,200 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34)
• BLS projects employment to be about average (+6.4%) from 2024 to 2034. (BLS Employment Projections 2024–34)
• Median annual pay is $34,800, across about 14,960 U.S. workers. (BLS OEWS (May 2024))

Source: Singulariki — "Locker Room, Coatroom, and Dressing Room Attendants". https://singulariki.com/roles/role-39-3093-00
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.

AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom

Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

Data compiled June 2, 2026. Figures are estimates, not advice.

Cite this page
Plain

Singulariki. "Locker Room, Coatroom, and Dressing Room Attendants." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-39-3093-00

APA

Singulariki. (2026). Locker Room, Coatroom, and Dressing Room Attendants. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-39-3093-00

BibTeX 
@misc{singulariki-role-39-3093-00,
  title  = {Locker Room, Coatroom, and Dressing Room Attendants},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
  url    = {https://singulariki.com/roles/role-39-3093-00}
}

Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.

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