US dataswitch to UK
Entertainment and Recreation Managers, Except Gambling
planning, organizing, interviewing and hiring associates to fill staff vacancies and talking to customers to convey information about events or activities. If that's your week, this page is about your job.
The honest answer
AI is already taking a real slice of the routine work here: calculating and recording department expenses and revenue. That is a slice of tasks, not of you.
That slice is not coming back; the core of the job, administering first aid in emergency situations, stays yours. New tools, same person answering for it.
Your week, as this page understands it
Plan, direct, or coordinate entertainment and recreational activities and operations of a recreational facility, including cruise ships and parks. The job title says “entertainment”, “recreation managers” or “except gambling”: officially one job, several names. The real job is the part underneath: administering first aid in emergency situations. 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 entertainment and recreation managers, except gambling 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 administering first aid in emergency situations, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 31%
- changing shape
- 0%
- staying human
- 69%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 34 out of 100 (30–40 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 entertainment and recreation managers, except gambling 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.
- 3 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
5 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.
Planning programs of events or schedules of activities
This is reading one thing and writing another: programs of events in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Plan programs of events or schedules of activities.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 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: Building an events program from venue, staffing and seasonal data is planning work software already drafts well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Calculating and recording department expenses and revenue
This is reading one thing and writing another: department expenses in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Calculate and record department expenses and revenue.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very 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: Adding up and logging departmental income and spending is routine arithmetic that accounting software already handles reliably.
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 0/4 · how much data exists 3/4.
Assigning tasks and work hours to staff
This is reading one thing and writing another: tasks in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Assign tasks and work hours to staff.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (60–68 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: Matching shifts and duties to staff availability is a scheduling problem software solves well.
The five ratings: output a model can produce 3/4 · 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.
Writing budgets to plan recreational activities or programs
This is reading one thing and writing another: budgets in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Write budgets to plan recreational activities or programs.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 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: A recreation budget is figures and assumptions on a spreadsheet, which software drafts quickly from past costs.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Writing and presenting strategies for recreational facility programming using customer or employee data
This is reading one thing and writing another: strategies in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Write and present strategies for recreational facility programming using customer or employee data.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 allowing for uncertainty): high 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: Turning customer and staff numbers into a written programming strategy is analysis and writing, both strong points for AI.
The five ratings: output a model can produce 3/4 · 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
0 tasksTasks 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.
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.
Staying human
12 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.
Talking to customers to convey information about events or activities
The ratings behind this row put customers well outside what today's tools can do on their own.
importance 4 · CoreSource: “Talk to customers to convey information about events or activities.” (O*NET task statement)
How this row was scored
Exposure score: 37 out of 100 (30–44 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.
The rating behind it: The information is easy to prepare, but the job here is the conversation itself with a customer.
The five ratings: output a model can produce 2/4 · 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.
Talking to coworkers using electronic devices
The value here is that a specific person handles coworkers and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Talk to coworkers using electronic devices, such as computers and radios.” (O*NET task statement)
How this row was scored
Exposure score: 17 out of 100 (10–24 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Live radio and message traffic with colleagues on site is coordination in the moment, not a document.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Explaining rules and regulations of facilities and entertainment attractions to customers
This work happens in the physical world: rules, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Explain rules and regulations of facilities and entertainment attractions to customers.” (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: Facility rules are written down and easy to explain, though most explaining still happens face to face on site.
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.
Planning, organizing or leading group activities for customers, such as exercise routines, athletic events or arts and crafts
This work happens in the physical world: group activities, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Plan, organize, or lead group activities for customers, such as exercise routines, athletic events, or arts and crafts.” (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 value is that a specific person does it.
The rating behind it: Designing the activity is easy for software, but someone has to be in the room leading the group.
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 2/4 · how much data exists 3/4.
Resolving customer complaints regarding worker performance or services
The value here is that a specific person handles customer complaints regarding worker performance and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Resolve customer complaints regarding worker performance or services rendered.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Complaint replies are easy to draft, but calming an unhappy customer usually takes a person.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Show the other 7 tasks
Training workers in company procedures or policy
staying humanThis work happens in the physical world: workers, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Train workers in company procedures or policy.” (O*NET task statement)
How this row was scored
Exposure score: 20 out of 100 (13–27 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: Training material writes itself easily, but delivering it to a room of new staff is done in person.
The five ratings: output a model can produce 2/4 · 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.
Interviewing and hiring associates to fill staff vacancies
staying humanThe value here is that a specific person handles associates and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Interview and hire associates to fill staff vacancies.” (O*NET task statement)
How this row was scored
Exposure score: 17 out of 100 (10–24 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Software can screen resumes and draft questions, but sizing up a candidate face to face stays human.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Administering first aid in emergency situations
staying humanThis work happens in the physical world: first aid, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Administer first aid in emergency situations.” (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: 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: First aid means physically treating someone in front of you, which no software can do.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Cleaning equipment and areas of amusement park
staying humanThis work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Clean equipment and areas of amusement park, cruise ship, or other recreational facility.” (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 and park areas is physical work that has to be done on the spot.
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 1/4.
Inspecting equipment, such as rides, games and vehicles, to detect wear and damage
staying humanThis work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Inspect equipment, such as rides, games, and vehicles, to detect wear and damage.” (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 wear on a ride or vehicle means being there with your eyes and hands on it.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Operating, driving or explaining the use of mechanical equipment in amusement parks, cruise ships or other recreational facilities
staying humanThis work happens in the physical world: the use of mechanical equipment, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Operate, drive, or explain the use of mechanical equipment in amusement parks, cruise ships, or other recreational facilities.” (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: Driving and operating park machinery is hands-on work 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 1/4 · how much data exists 2/4.
Storing and retrieving equipment, such as vehicles, radios and ride components
staying humanThis work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Store and retrieve equipment, such as vehicles, radios, and ride components.” (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: Moving vehicles, radios and ride parts in and out of storage needs hands and a body.
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
- $79,520a 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
- 37,980in 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: department expenses in, a record out. The rows above are exactly that shape: calculating and recording department expenses and revenue and planning programs of events or schedules of activities. What it cannot do is be there in the room, and that is still where first aid 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
Your week is splitting in two, and which half fills it is the whole question. Calculating and recording department expenses and revenue is going; administering first aid in emergency situations is not.
So, given all that: 31% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 69% in rows it is nowhere near. That is the position, measured across 17 scored tasks. It is not a forecast about you.
The occupation is an average and you are not, so the first job below is finding out where your own week actually sits.
This week: one thing
Print the task ledger above and put your own hours next to each row. Roughly, in pen, in one sitting. Then look at which group your hours actually pile up in. Twenty minutes, this week.
- What you end up holding
- your own week, on one page, sorted into what is shifting and what is not
- How long it takes
- about twenty minutes
If printing it isn’t practical: Read the rows off this page and write the same list on the back of an envelope. Same twenty minutes, same page, and it works just as well said out loud to someone who knows the job. The point is your hours next to the rows, not the paper it is on.
Over the next 90 days
Volunteer toward the durable end, visibly. Over the next ninety days put your hand up for the work in the bottom group (administering first aid in emergency situations) and let people see you doing it. Not a new project: the same job, with your mix deliberately tilted. The point is that when the rota or the reorganisation comes, the version of you people picture is the one doing the part that stays.
Over the next 12 months
Claim a specialism at the durable end and let the other end go. Over a year, deliberately become the person who handles administering first aid in emergency situations, and deliberately stop being the first choice for the rows in the top group. That trade costs something, because the exposed work is often the comfortable work. Decide it on purpose rather than by drift. 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 entertainment and recreation managers, except gambling (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was first-line supervisors of entertainment and recreation workers, except gambling services: only about 15% of its durable work is work you already do and it pays 38.9% less. And on the numbers you do not need one. This job scores 34/100 here, with only 31% of the task list in the top band, and “talk to customers to convey information about events or activities” 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.
First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “resolve customer complaints regarding worker performance or services rendered”. Across the whole of both lists that adds up to about 15% of the work in that job the software is not taking.
Why I am not recommending it: You would be starting most of it from nothing: about 15% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. It is a pay cut, in those words: $48,560 against your $79,520, 38.9% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
First-Line Supervisors of Personal Service Workers
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “resolve customer complaints regarding worker performance or services rendered”. Across the whole of both lists that adds up to about 14% of the work in that job the software is not taking.
Why I am not recommending it: You would be starting most of it from nothing: about 14% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. It is a pay cut, in those words: $48,590 against your $79,520, 38.9% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Gambling Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already train workers in company procedures or policy, and their equivalent is to train new workers or evaluate their performance. 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. And it is a narrow door: about 5,030 of those jobs against 37,980 of yours (OEWS May 2025), 13% as many seats.
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: 31% of its task weight, across 17 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The whole-job doom story
Nothing on this page says this job ends. It says the mix inside it moves. Half the rows above are unchanged or changing shape, and the plan is about which half your week sits in. That is a very different problem, and a solvable one.
Panic-buying a course
Do the twenty-minute sorting exercise first. Most people who buy a course before they have done it buy the wrong one, and the free services listed below will tell you the same thing without charging for it.
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.
If you run a team doing this job
If you run a team doing this job, the conversation you owe them is the one on this page, and sooner than feels comfortable. Show them the ledger rather than a reassurance: the rows moving toward the software are calculating and recording department expenses and revenue, and the rows that are not are where you want your people visible. Ask each of them to do the this-week move and bring the list to your next one-to-one. It turns a rumour into a piece of work, and it tells you which parts of your team's week are actually at stake. And say the thing out loud that a team lead usually leaves unsaid: a shrinking team is your exposure too, so do the move yourself as well.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Leisure and sports managers and proprietors 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:
- Hairdressing and beauty salon managers and proprietors
- Garage managers and proprietors
- Travel agency managers and proprietors
- Senior officers in fire, ambulance, prison and related services
- Hire services managers and proprietors
- Managers and proprietors in other services n.e.c.
- Charitable organisation managers and directors
In UK official statistics this job is counted as Leisure and sports managers and proprietors, Hairdressing and beauty salon managers and proprietors, Garage managers and proprietors, Travel agency managers and proprietors, Senior officers in fire, ambulance, prison and related services, Hire services managers and proprietors, Managers and proprietors in other services n.e.c. and Charitable organisation managers and directors. 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
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for entertainment / recreation managers / except gambling, and we are not going to point you at the nearest one and call it a fit.
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 31% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for entertainment / recreation managers / except gambling. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for entertainment / recreation managers / except gambling 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 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 Entertainment and Recreation Managers, Except Gambling?
- Not as a job, but it is already doing parts of the work. Across the 17 official task statements scored for Entertainment and Recreation Managers, Except Gambling (United States, SOC 11-9072), 31% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 34 out of 100 (range 30–40, 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 “Entertainment and Recreation Managers, Except Gambling” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Calculate and record department expenses and revenue” (93/100, very high); “Plan programs of events or schedules of activities” (75/100, high); “Write budgets to plan recreational activities or programs” (75/100, high). 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 “Entertainment and Recreation Managers, Except Gambling” stay human?
- About 69% 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: “Store and retrieve equipment, such as vehicles, radios, and ride components” (0/100, minimal); “Operate, drive, or explain the use of mechanical equipment in amusement parks, cruise ships, or other recreational facilities” (0/100, minimal); “Inspect equipment, such as rides, games, and vehicles, to detect wear and damage” (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 “Entertainment and Recreation Managers, Except Gambling” do about AI?
- Start from the ledger rather than the headline: 31% of this job's weighted core work is exposed, and roughly 69% 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 Entertainment and Recreation Managers, Except Gambling 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.
- 3 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.