Will AI replace Amusement and Recreation Attendants?

Not as a job, but it is already doing parts of the work. Across the 17 official task statements scored for Amusement and Recreation Attendants (United States, SOC 39-3091), 3% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 22 out of 100 (range 19–26, band: low). 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 “Amusement and Recreation Attendants” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Schedule the use of recreation facilities, such as golf courses, tennis courts, bowling alleys, or softball diamonds” (79/100, high); “Provide information about facilities, entertainment options, and rules and regulations” (59/100, partial); “Record details of attendance, sales, receipts, reservations, or repair activities” (56/100, partial). 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 “Amusement and Recreation Attendants” stay human?

About 77% 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: “Sell and serve refreshments to customers” (0/100, minimal); “Provide assistance to patrons entering or exiting amusement rides, boats, or ski lifts, or mounting or dismounting animals” (0/100, minimal); “Verify, collect, or punch tickets before admitting patrons to venues, such as amusement parks and rides” (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 “Amusement and Recreation Attendants” do about AI?

Start from the ledger rather than the headline: 3% of this job's weighted core work is exposed, and roughly 77% 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 Amusement and Recreation 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 17 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.

US dataswitch to UK

Amusement and Recreation Attendants

selling tickets and collecting fees from customers, recording details of attendance, sales, receipts, reservations or repairing activities and providing assistance to patrons entering or exiting amusement rides. 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 activities to ensure adherence to rules and safety procedures is work software can't reach.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 3%
changing shape: 20%
staying human: 77%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 22 out of 100 (19–26 allowing for uncertainty): low exposure, across 17 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 amusement and recreation 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 2026-08-04. Read the full method.

What shifts is scheduling the use of recreation facilities: the paper around the work, not the work.

Your week, as this page understands it

Perform a variety of attending duties at amusement or recreation facility. May schedule use of recreation facilities, maintain and provide equipment to participants of sporting events or recreational pursuits, or operate amusement concessions and rides. The job title says “amusement” or “recreation attendants”: officially one job, two names. The real job is the part underneath: monitoring activities to ensure adherence to rules and safety procedures. 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 amusement and recreation attendants is not one task. It is 17 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is monitoring activities to ensure adherence to rules and safety procedures, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 3%
changing shape: 20%
staying human: 77%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

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 2026-08-04. 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.

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

1 task

Tasks 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.

Scheduling the use of recreation facilities
This is reading one thing and writing another: the use of recreation facilities in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Schedule the use of recreation facilities, such as golf courses, tennis courts, bowling alleys, or softball diamonds.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (75–83 allowing for uncertainty): high exposure, high 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: Booking courts, courses and lanes is exactly what online scheduling systems already do.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

Changing shape

2 tasks

Tasks 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.

Providing information about facilities, entertainment options and rules and regulations
The software now makes the first pass at information, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Provide information about facilities, entertainment options, and rules and regulations.” (O*NET task statement)
How this row was scored
Exposure score: 59 out of 100 (55–63 allowing for uncertainty): partial exposure, high 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: Facility details, opening times and rules are written information that apps and signs already give visitors.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Recording details of attendance, sales, receipts, reservations or repairing activities
The software now makes the first pass at details of attendance, sales, receipts, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Record details of attendance, sales, receipts, reservations, or repair activities.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (52–60 allowing for uncertainty): partial exposure, high 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: Attendance, takings and booking figures are recorded in systems that capture most of the detail automatically.
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

14 tasks

Tasks 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.

Monitoring activities to ensure adherence to rules and safety procedures
This work happens in the physical world: activities, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Monitor activities to ensure adherence to rules and safety procedures, or arrange for the removal of unruly patrons.” (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: Watching for rule-breaking and removing difficult visitors needs staff physically present.
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.
Selling tickets and collecting fees from customers
This work happens in the physical world: tickets, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Sell tickets and collect fees from customers.” (O*NET task statement)
How this row was scored
Exposure score: 39 out of 100 (35–43 allowing for uncertainty): low exposure, high 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: Ticket sales already run through online and self-service systems, but a booth also means taking money from people present.
The five ratings: output a model can produce 4/4 · 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.
Directing patrons to rides, seats or attractions
This work happens in the physical world: patrons, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Direct patrons to rides, seats, or attractions.” (O*NET task statement)
How this row was scored
Exposure score: 16 out of 100 (12–20 allowing for uncertainty): minimal exposure, high 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: Signs and apps can point people to rides and seats, but directing a crowd happens on the spot.
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.
Maintaining inventories of equipment, storing and retrieving items and assembling and disassembling equipment
This work happens in the physical world: inventories of equipment, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Maintain inventories of equipment, storing and retrieving items and assembling and disassembling equipment as necessary.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (10–18 allowing for uncertainty): minimal exposure, high 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: Tracking what equipment you hold is simple, but storing, fetching and assembling it is physical work.
The five ratings: output a model can produce 2/4 · 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.
Keeping informed of shut-down and emergency evacuation procedures
This work happens in the physical world: informed of shut-down, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Keep informed of shut-down and emergency evacuation procedures.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: The procedures are written down, but the point is being on site and able to act in an emergency.
The five ratings: output a model can produce 1/4 · 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 sporting equipment, vehicles, rides, booths, facilities or grounds
This work happens in the physical world: equipment, vehicles, rides, booths, facilities or grounds, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Clean sporting equipment, vehicles, rides, booths, facilities, or grounds.” (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: Cleaning equipment, rides and grounds is physical work on site.
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.
Inspecting equipment to detect wear and damage and perform minor repairs
This work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Inspect equipment to detect wear and damage and perform minor repairs, adjustments, or maintenance tasks, such as oiling parts.” (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: Spotting worn parts and oiling or adjusting them means hands on the equipment.
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.

Show the other 7 tasks

Announcing or describing amusement park attractions to patrons to entice customers to games and other entertainment
staying human
This work happens in the physical world: amusement park attractions, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Announce or describe amusement park attractions to patrons to entice customers to games and other entertainment.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (19–33 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: The announcement script is easy to write, but drawing a passing crowd in works through a live voice at the stall.
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 2/4 · how much data exists 3/4.
Renting, selling or issuing sporting equipment and supplies, such as bowling shoes, golf balls, swimming suits or beach chairs
staying human
This work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Rent, sell, or issue sporting equipment and supplies, such as bowling shoes, golf balls, swimming suits, or beach chairs.” (O*NET task statement)
How this row was scored
Exposure score: 16 out of 100 (12–20 allowing for uncertainty): minimal exposure, high 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 hire record is simple, but handing over shoes, balls or chairs happens at the counter.
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.
Fastening safety devices
staying human
This work happens in the physical world: safety devices, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Fasten safety devices for patrons, or provide them with directions for fastening devices.” (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: Fastening a safety harness for a rider is done by hand at the ride.
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, driving
staying human
This work happens in the physical world: the use of mechanical riding devices, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Operate, drive, or explain the use of mechanical riding devices or other automatic equipment in amusement parks, carnivals, or recreation areas.” (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: Operating a ride or driving equipment means a person at the controls.
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.
Verifying, collecting or punch tickets before admitting patrons to venues, such as amusement parks and rides
staying human
This work happens in the physical world: punch tickets before admitting patrons, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Verify, collect, or punch tickets before admitting patrons to venues, such as amusement parks and rides.” (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: Checking and taking tickets at the gate means standing at the entrance.
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.
Providing assistance to patrons entering or exiting amusement rides
staying human
This work happens in the physical world: assistance, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Provide assistance to patrons entering or exiting amusement rides, boats, or ski lifts, or mounting or dismounting animals.” (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: Helping visitors on and off rides, boats or animals is hands-on assistance.
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.
Selling and serving refreshments to customers
staying human
This work happens in the physical world: refreshments, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Sell and serve refreshments to customers.” (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: Serving food and drinks to customers happens at the 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 0/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $32,150a 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: 397,830in 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: the use of recreation facilities in, a record out. The rows above are exactly that shape: scheduling the use of recreation facilities and providing information about facilities, entertainment options and rules and regulations. What it cannot do is be there in the room, and that is still where activities get 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 activities to ensure adherence to rules and safety procedures is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 3% of this job's task weight sits in rows the software is already learning, 20% in rows that change shape rather than disappear, and 77% in rows it is nowhere near. That is the position, measured across 17 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. Scheduling the use of recreation facilities 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 the use of recreation facilities, 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 activities are 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 providing information about facilities, entertainment options and rules and regulations. 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 amusement and recreation 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 12% of its durable work is work you already do. And on the numbers you do not need one. This job scores 22/100 here, with only 3% of the task list in the top band, and “provide information about facilities, entertainment options, and rules and regulations” 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 on the work AI is not taking: you already sell tickets and collect fees from customers, and their equivalent is to sell or collect admission tickets, passes, or facility memberships from patrons at entertainment…. Across both published task lists that is about 12% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 12% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Look at that job’s page anyway →
Entertainment and Recreation Managers, Except Gambling
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already provide information about facilities, entertainment options, and rules and regulations, and their equivalent is to explain rules and regulations of facilities and entertainment attractions to customers. Across both published task lists that is about 9% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 9% of the durable side of that job. That is a different job, not a next step. The pay gap is the market pricing a barrier: $79,520 against your $32,150 is 2.47× (OEWS May 2025 (both)), and you would be crossing it holding about 9% of their durable work. A gap that size with an overlap that small is a wish, not a route. And it is a narrow door: about 37,980 of those jobs against 397,830 of yours (OEWS May 2025), 10% as many seats.
Look at that job’s page anyway →
Parking Attendants
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already sell tickets and collect fees from customers, and their equivalent is to explain and calculate parking charges, collect fees from customers, and respond to customer…. 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.
Look at that job’s page anyway →

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: 3% of its task weight, across 17 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 activities to ensure adherence to rules and safety procedures 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, Crane drivers 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:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
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 amusement / recreation attendants launches. Nothing else.

That did not look like an email address, so nothing was saved. Have another go below.

We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.

No Space for amusement / recreation attendants yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for amusement / recreation attendants existed, with researched problems, courses and people in the same boat, would you want in?

Your email

We use your email address for one thing: to tell you if a Space for amusement / recreation attendants launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

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 Amusement and Recreation Attendants?: Not as a job, but it is already doing parts of the work. Across the 17 official task statements scored for Amusement and Recreation Attendants (United States, SOC 39-3091), 3% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 22 out of 100 (range 19–26, band: low). 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 “Amusement and Recreation Attendants” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Schedule the use of recreation facilities, such as golf courses, tennis courts, bowling alleys, or softball diamonds” (79/100, high); “Provide information about facilities, entertainment options, and rules and regulations” (59/100, partial); “Record details of attendance, sales, receipts, reservations, or repair activities” (56/100, partial). 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 “Amusement and Recreation Attendants” stay human?: About 77% 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: “Sell and serve refreshments to customers” (0/100, minimal); “Provide assistance to patrons entering or exiting amusement rides, boats, or ski lifts, or mounting or dismounting animals” (0/100, minimal); “Verify, collect, or punch tickets before admitting patrons to venues, such as amusement parks and rides” (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 “Amusement and Recreation Attendants” do about AI?: Start from the ledger rather than the headline: 3% of this job's weighted core work is exposed, and roughly 77% 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 Amusement and Recreation 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 17 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.
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 2026-08-04.
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.

How we score a job Download this release Look up another job

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}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. 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.