US dataswitch to UK
Locker Room, Coatroom, and Dressing Room Attendants
cleaning and polishing footwear, using brushes, sponges, cleaning fluid, polishes, waxes, cleaning facilities, floors or locker rooms and collecting soiled linen or clothing for laundering. If that's your week, this page is about your job.
The honest answer
AI changes the edges of this job, not the middle: monitoring patrons' facility use to ensure that rules and regulations is work software can't reach.
What shifts is reporting and documenting safety hazards: the overhead at the edges, not the middle you trained for.
Your week, as this page understands it
Provide personal items to patrons or customers in locker rooms, dressing rooms, or coatrooms. The job title says “locker room”, “coatroom” or “dressing room attendants”: officially one job, several names. The real job is the part underneath: monitoring patrons' facility use to ensure that rules and regulations. That is the thing someone has to be right about.
The exposed part of this job is specific, and we won’t pretend it is coming back. But locker room, coatroom, and dressing room attendants is not one task. It is 23 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is monitoring patrons' facility use to ensure that rules and regulations, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 0%
- changing shape
- 4%
- staying human
- 96%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 13 out of 100 (10–19 allowing for uncertainty): minimal exposure, across 23 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.
How we know this
What is measured: Every published task statement for locker room, coatroom, and dressing room attendants is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.
How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.
Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.
Your job, task by task
These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.
- 2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
0 tasksTasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.
Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.
Changing shape
1 taskTasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.
Reporting and documenting safety hazards
The software now makes the first pass at safety hazards, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · SupplementalSource: “Report and document safety hazards, potentially hazardous conditions, and unsafe practices and procedures.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing up hazards and unsafe practices is document work software helps with, though someone must see them first.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Staying human
22 tasksTasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.
Referring guest problems or complaints to supervisors
This work happens in the physical world: guest problems, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Refer guest problems or complaints to supervisors.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Passing a complaint up the chain is simple message handling, but it usually happens as a spoken word to the supervisor on shift.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Monitoring patrons' facility use to ensure that rules and regulations
This work happens in the physical world: patrons' facility, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Monitor patrons' facility use to ensure that rules and regulations are followed, and safety and order are maintained.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Keeping order among people using the changing rooms means being in the room with them.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Answering customer inquiries or explaining cost
This work happens in the physical world: customer inquiries, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Answer customer inquiries or explain cost, availability, policies, and procedures of facilities.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Prices, opening hours and rules are simple information a screen or chatbot can give, though patrons usually ask an attendant face to face.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Cleaning facilities, floors or locker rooms
This work happens in the physical world: facilities, floors or locker rooms, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Clean facilities such as floors or locker rooms.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Mopping floors and cleaning locker rooms is hands-on work in the room itself.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Assigning dressing room facilities, locker space or clothing containers to patrons of athletic or bathing establishments
This work happens in the physical world: room facilities, locker space or clothing containers, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Assign dressing room facilities, locker space, or clothing containers to patrons of athletic or bathing establishments.” (O*NET task statement)
How this row was scored
Exposure score: 16 out of 100 (9–23 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Working out who gets which locker is easy for software, but the attendant still hands over keys and space in person.
The five ratings: output a model can produce 3/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Checking supplies to ensure adequate availability
This work happens in the physical world: supplies, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Check supplies to ensure adequate availability, and order new supplies when necessary.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Half of this is walking the store cupboard; the reordering half is exactly the kind of routine paperwork software handles well.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Providing towels and sheets to clients in public baths
This work happens in the physical world: towels, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Provide towels and sheets to clients in public baths, steam rooms, and restrooms.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Handing towels and sheets to people in a bath house is done with hands, in person, so software cannot take it over.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Providing assistance to patrons by performing duties
This work happens in the physical world: assistance, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Provide assistance to patrons by performing duties such as opening doors or carrying bags.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Opening doors and carrying bags is help given with your body, in the moment.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 1/4.
Maintaining a lost-and-found collection
This work happens in the physical world: a lost-and-found collection, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Maintain a lost-and-found collection.” (O*NET task statement)
How this row was scored
Exposure score: 16 out of 100 (9–23 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: The logbook side is easy to automate, but someone still has to physically hold, sort and hand back the lost items.
The five ratings: output a model can produce 3/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Show the other 13 tasks
Maintaining inventories of clothing or uniforms
staying humanThis work happens in the physical world: inventories, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Maintain inventories of clothing or uniforms, accessories, equipment, or linens.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Counting stock is physical, but the record-keeping and reorder points behind it are straightforward for software.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Providing or arranging
staying humanThis work happens in the physical world: this work, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Provide or arrange for services such as clothes pressing, cleaning, or repair.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Arranging pressing or repairs is ordinary booking work, but collecting and returning the garments still needs a person.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Operating controls that regulate temperatures or room environments
staying humanThis work happens in the physical world: controls, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Operate controls that regulate temperatures or room environments.” (O*NET task statement)
How this row was scored
Exposure score: 19 out of 100 (12–26 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Deciding a comfortable temperature is simple, but the controls are on the wall and someone is expected to be there to work them.
The five ratings: output a model can produce 3/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Cleaning and polishing footwear, using brushes, sponges, cleaning fluid, polishes, waxes, liquid or sole dressing and daubers
staying humanThis work happens in the physical world: footwear, in a real place. Software cannot follow it there.
importance 5 · SupplementalSource: “Clean and polish footwear, using brushes, sponges, cleaning fluid, polishes, waxes, liquid or sole dressing, and daubers.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Polishing shoes with brushes and cleaning fluid is hand work on a physical object.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Operating washing machines and dryers to clean soiled apparel and towels
staying humanThis work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Operate washing machines and dryers to clean soiled apparel and towels.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Loading and running washers and dryers means being at the machines.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Procuring beverages, food and other items
staying humanThis work happens in the physical world: beverages, food and other items, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Procure beverages, food, and other items as requested.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Fetching drinks and food for patrons means walking and carrying things.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Activating emergency action plans and administering first aid
staying humanThis work happens in the physical world: emergency action plans, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Activate emergency action plans and administer first aid, as necessary.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: First aid and emergency response mean physically being with the person who needs help.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 1/4.
Storing personal possessions
staying humanThis work happens in the physical world: personal possessions, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Store personal possessions for patrons, issue claim checks for articles stored, and return articles on receipt of checks.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Taking, storing and handing back people's belongings is physical handling at a counter.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Collecting soiled linen or clothing for laundering
staying humanThis work happens in the physical world: soiled linen, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Collect soiled linen or clothing for laundering.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Gathering up used towels and clothing is hands-on collection work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Attending to needs of athletic teams in clubhouses
staying humanThis work happens in the physical world: needs of athletic teams, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Attend to needs of athletic teams in clubhouses.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Looking after teams in the clubhouse means being there while they are there.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 1/4.
Stenciling identifying information on equipment
staying humanThis work happens in the physical world: information, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Stencil identifying information on equipment.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Stencilling markings onto equipment is done by hand on the item itself.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Issuing gym clothes, uniforms, towels, athletic equipment and special athletic apparel
staying humanThis work happens in the physical world: gym clothes, uniforms, towels, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Issue gym clothes, uniforms, towels, athletic equipment, and special athletic apparel.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Issuing towels, kit and uniforms across a counter is a hands-on exchange.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Setting up various apparatus or athletic equipment
staying humanThis work happens in the physical world: various apparatus, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Set up various apparatus or athletic equipment.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Setting up apparatus and sports equipment means lifting and placing it by hand.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
What this job pays, and how many people do it
- Median pay
- $36,300a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
- People doing this job
- 15,560in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)
What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.
Why this is shifting
The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: safety hazards in, a record out. The rows above are exactly that shape: reporting and documenting safety hazards. What it cannot do is be there in the room, and that is still where patrons' facility gets done. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
Start with what does not change: monitoring patrons' facility use to ensure that rules and regulations is the middle of this job, and the evidence on this page says it stays with a person.
So, given all that: 0% of this job's task weight sits in rows the software is already learning, 4% in rows that change shape rather than disappear, and 96% in rows it is nowhere near. That is the position, measured across 23 scored tasks. It is not a forecast about you.
So the thing worth your attention is not the job going away. It is the layer around it. Reporting and documenting safety hazards is the part turning into software, and being the person who understands that layer is worth money.
This week: one thing
Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to safety hazards, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.
- What you end up holding
- a straight answer about what is actually being rolled out, and when
- How long it takes
- ten minutes
If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether patrons' facility is in scope or not. Nothing to log into, no license needed.
Over the next 90 days
Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near reporting and documenting safety hazards. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.
Over the next 12 months
On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.
The roads out of here, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to locker room, coatroom, and dressing room attendants (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was ushers, lobby attendants, and ticket takers: only about 7% of its durable work is work you already do. And on the numbers you do not need one. This job scores 13/100 here, with only 0% of the task list in the top band, and “refer guest problems or complaints to supervisors” is not work that hands over cleanly. None of them beats deepening what you already have.
How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.
3 moves I checked and rejected
These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.
Ushers, Lobby Attendants, and Ticket Takers
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “clean facilities such as floors or locker rooms”. Across the whole of both lists that adds up to about 7% of the work in that job the software is not taking.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step.
Recreation Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already monitor patrons' facility use to ensure that rules and regulations are followed, and…, and their equivalent is to enforce rules and regulations of recreational facilities to maintain discipline and ensure safety. Across both published task lists that is about 7% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step.
Amusement and Recreation Attendants
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already monitor patrons' facility use to ensure that rules and regulations are followed, and…, and their equivalent is to monitor activities to ensure adherence to rules and safety procedures, or arrange for…. Across both published task lists that is about 5% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 5% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $32,150 against your $36,300, 11.4% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
What I’d stop worrying about
A friend tells you what not to spend fear on. This is that list.
The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 0% of its task weight, across 23 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like monitoring patrons' facility use to ensure that rules and regulations arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Leisure and theme park attendants is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
The other groups this work is counted across:
In UK official statistics this job is counted as Leisure and theme park attendants and Parking and civil enforcement occupations. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
Why there is no community here
Collab365, who build this site, run paid Spaces for a small number of subjects, and none of them is built for this job. We are not going to point you at the nearest one and call it a fit.
So the free services listed on this page are the whole answer, and it is the same answer we would give a friend.
Noted, and thank you. We’ll email you if a Space for locker room / coatroom / dressing room attendants launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
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No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.
Questions people ask about this job
- Will AI replace Locker Room, Coatroom, and Dressing Room Attendants?
- Not as a job, but it is already doing parts of the work. Across the 23 official task statements scored for Locker Room, Coatroom, and Dressing Room Attendants (United States, SOC 39-3093), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 13 out of 100 (range 10–19, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
- Which tasks in “Locker Room, Coatroom, and Dressing Room Attendants” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Report and document safety hazards, potentially hazardous conditions, and unsafe practices and procedures” (56/100, partial); “Check supplies to ensure adequate availability, and order new supplies when necessary” (38/100, low); “Maintain inventories of clothing or uniforms, accessories, equipment, or linens” (38/100, low). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Locker Room, Coatroom, and Dressing Room Attendants” stay human?
- About 96% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Clean facilities such as floors or locker rooms” (0/100, minimal); “Set up various apparatus or athletic equipment” (0/100, minimal); “Issue gym clothes, uniforms, towels, athletic equipment, and special athletic apparel” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
- What should someone working in “Locker Room, Coatroom, and Dressing Room Attendants” do about AI?
- Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 96% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
- How is the AI exposure score for Locker Room, Coatroom, and Dressing Room Attendants calculated?
- Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 23 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.
Where these numbers come from
Worth knowing about these figures
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 2 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
- Task statements
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
- Pay and employment
- bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))
Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.
The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.
The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.
Using these figures?
Cite this
Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.
Plain text
Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).
BibTeX
@misc{collab365futureproof2026q41,
title = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
author = {{Collab365}},
year = {2026},
url = {https://futureproof.collab365.com/data/2026-q4.1},
note = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}Data as of release 2026-q4.1, published . Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.