Will AI replace Ushers, Lobby Attendants, and Ticket Takers?

Not as a job, but it is already doing parts of the work. Across the 23 official task statements scored for Ushers, Lobby Attendants, and Ticket Takers (United States, SOC 39-3031), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 5 out of 100 (range 4–10, 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 “Ushers, Lobby Attendants, and Ticket Takers” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Schedule or manage staff, such as volunteer usher corps” (48/100, partial); “Manage informational kiosks or displays of event signs or posters” (38/100, low); “Manage inventory or sale of artist merchandise” (32/100, 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 “Ushers, Lobby Attendants, and Ticket Takers” stay human?

About 98% 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: “Lead tours and answer visitors' questions about the exhibits” (0/100, minimal); “Clean facilities” (0/100, minimal); “Sell or collect admission tickets, passes, or facility memberships from patrons at entertainment events” (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 “Ushers, Lobby Attendants, and Ticket Takers” do about AI?

Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 98% 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 Ushers, Lobby Attendants, and Ticket Takers 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.

US dataswitch to UK

Ushers, Lobby Attendants, and Ticket Takers

greeting patrons attending entertainment events, managing inventory or sale of artist merchandise and examining tickets or passes to verify authenticity. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: greeting patrons attending entertainment events 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: 0%
changing shape: 2%
staying human: 98%

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

Whole-job exposure score 5 out of 100 (4–10 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 ushers, lobby attendants, and ticket takers 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 or managing staff, such as volunteer usher corps: the paper around the work, not the work.

Your week, as this page understands it

Assist patrons at entertainment events by performing duties, such as collecting admission tickets and passes from patrons, assisting in finding seats, searching for lost articles, and helping patrons locate such facilities as restrooms and telephones. The job title says “ushers”, “lobby attendants” or “ticket takers”: officially one job, several names. The real job is the part underneath: greeting patrons attending entertainment events. 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 ushers, lobby attendants, and ticket takers 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 greeting patrons attending entertainment events, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 0%
changing shape: 2%
staying human: 98%

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.

One row is marked low confidence, so treat it as a ballpark rather than a fine measurement.
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 tasks

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.

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 task

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.

Scheduling or managing staff, such as volunteer usher corps
The software now makes the first pass at staff, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · Supplemental
Source: “Schedule or manage staff, such as volunteer usher corps.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (41–55 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: Building schedules and coordinating a volunteer team is scheduling and messaging work.
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 1/4 · how much data exists 3/4.

Staying human

22 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.

Greeting patrons attending entertainment events
This work happens in the physical world: patrons attending entertainment events, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Greet patrons attending entertainment events.” (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: Greeting people as they arrive is about a person being there in the doorway.
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 2/4.
Selling or collecting admission tickets
This work happens in the physical world: admission tickets, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Sell or collect admission tickets, passes, or facility memberships from patrons at entertainment events.” (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 rating behind it: Taking tickets and money from people at the door needs someone standing 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 1/4 · how much data exists 3/4.
Assisting patrons by giving directions to points in or outside of the facility or providing information about local attractions
This work happens in the physical world: patrons, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Assist patrons by giving directions to points in or outside of the facility or providing information about local attractions.” (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; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: The directions themselves are easy to generate, but the usher gives them face to face in the venue.
The five ratings: output a model can produce 4/4 · 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.
Providing assistance with patrons' special needs
This work happens in the physical world: assistance, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Provide assistance with patrons' special needs, such as helping those with wheelchairs.” (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: Helping someone with a wheelchair is direct physical 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 2/4 · how much data exists 2/4.
Examining tickets or passes to verify authenticity
This work happens in the physical world: tickets, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Examine tickets or passes to verify authenticity, using criteria such as color or date issued.” (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 rating behind it: Checking a ticket for date or color means holding it at the entrance.
The five ratings: output a model can produce 1/4 · 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 3/4.
Settling seating disputes or help solve other customer concerns
This work happens in the physical world: disputes, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Settle seating disputes or help solve other customer concerns.” (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: Sorting out a seating dispute means being there with the people involved.
The five ratings: output a model can produce 1/4 · 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 2/4.
Cleaning facilities
This work happens in the physical world: facilities, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Clean 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: Cleaning a venue is physical work with cloths, bins and brushes.
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.
Searching for lost articles or for parents of lost children
This work happens in the physical world: lost articles, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Search for lost articles or for parents of lost children.” (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: Searching a venue for a lost bag or a lost child means physically looking around it.
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.
Refusing admittance to undesirable persons or persons without tickets or passes
This work happens in the physical world: admittance, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Refuse admittance to undesirable persons or persons without tickets or passes.” (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: Turning someone away at the door requires a person standing at that door.
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.

Show the other 13 tasks

Managing informational kiosks or displays of event signs or posters
staying human
This work happens in the physical world: informational kiosks, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Manage informational kiosks or displays of event signs or posters.” (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: Designing signs and kiosk content is screen work, but putting displays up is not.
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.
Managing inventory or sale of artist merchandise
staying human
This work happens in the physical world: inventory, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Manage inventory or sale of artist merchandise.” (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: Stock records and reordering are screen work, though selling merchandise happens at a 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 1/4 · how much data exists 3/4.
Paging individuals wanted at the box office
staying human
This work happens in the physical world: individuals, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Page individuals wanted at the box office.” (O*NET task statement)
How this row was scored
Exposure score: 20 out of 100 (8–32 allowing for uncertainty): low exposure, low 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 announcement wording is trivial to produce, but someone is at the venue microphone.
The five ratings: output a model can produce 4/4 · 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.
Counting and recording number of tickets
staying human
This work happens in the physical world: number of tickets, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Count and record number of tickets collected.” (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: The tally is simple to record, but the ticket stubs have to be counted by hand.
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.
Guiding patrons to exits or providing other instructions or assistance in case of emergency
staying human
This work happens in the physical world: patrons, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Guide patrons to exits or provide other instructions or assistance in case of emergency.” (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: Guiding people to exits in an emergency means being in the building 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.
Maintaining order and ensuring adherence to safety rules
staying human
This work happens in the physical world: order, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Maintain order and ensure adherence to safety rules.” (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 in a crowd depends on a person being present among 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.
Assisting patrons in finding seats
staying human
This work happens in the physical world: patrons, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Assist patrons in finding seats, lighting the way with flashlights, if 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 rating behind it: Walking people to their seats with a flashlight is physical guidance.
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.
Operating refreshment stands during intermission or obtaining refreshments for press box patrons during performances
staying human
This work happens in the physical world: refreshment stands during intermission, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Operate refreshment stands during intermission or obtain refreshments for press box patrons during performances.” (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 refreshments is hands-on work behind a stand.
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.
Verify credentials of patrons desiring entrance into press box and permit only authorized persons to enter
staying human
This work happens in the physical world: credentials of patrons desiring entrance, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Verify credentials of patrons desiring entrance into press box and permit only authorized persons to enter.” (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: Checking press credentials at the press box door needs someone at that door.
The five ratings: output a model can produce 1/4 · 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.
Distributing programs to patrons
staying human
This work happens in the physical world: programs, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Distribute programs to 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: Handing programs to people arriving is a physical task.
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.
Working with others to change advertising displays
staying human
This work happens in the physical world: this work, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Work with others to change advertising displays.” (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: Changing advertising displays means physically taking them down and putting new ones up.
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.
Giving door checks to patrons who are temporarily leaving establishments
staying human
This work happens in the physical world: door checks, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Give door checks to patrons who are temporarily leaving establishments.” (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: Giving out door checks to people stepping outside happens at the door.
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.
Leading tours and answering visitors' questions about the exhibits
staying human
This work happens in the physical world: tours, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Lead tours and answer visitors' questions about the exhibits.” (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: Leading a tour means walking a group around the building in person.
The five ratings: output a model can produce 1/4 · 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 3/4.

What this job pays, and how many people do it

Median pay: $32,910a 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: 121,770in 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: staff in, a record out. The rows above are exactly that shape: scheduling or managing staff, such as volunteer usher corps. What it cannot do is be there in the room, and that is still where patrons attending entertainment events 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: greeting patrons attending entertainment events 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, 2% in rows that change shape rather than disappear, and 98% 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. Scheduling or managing staff, such as volunteer usher corps 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 staff, 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 attending entertainment events 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 scheduling or managing staff, such as volunteer usher corps. 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 ushers, lobby attendants, and ticket takers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was reservation and transportation ticket agents and travel clerks: only about 11% of its durable work is work you already do. And on the numbers you do not need one. This job scores 5/100 here, with only 0% of the task list in the top band, and “greet patrons attending entertainment events” 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.

Reservation and Transportation Ticket Agents and Travel Clerks
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already clean facilities, and their equivalent is to keep information facilities clean during operation. Across both published task lists that is about 11% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 11% 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 →
Amusement and Recreation Attendants
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already maintain order and ensure adherence to safety rules, 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 11% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 11% 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 →
Locker Room, Coatroom, and Dressing Room Attendants
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”. Across the whole of both lists that adds up to about 8% 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 8% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 15,560 of those jobs against 121,770 of yours (OEWS May 2025), 13% as many seats.
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: 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 greeting patrons attending entertainment events 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:

Parking and civil enforcement occupations

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:
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 ushers / lobby attendants / ticket takers 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 ushers / lobby attendants / ticket takers 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 ushers / lobby attendants / ticket takers 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 ushers / lobby attendants / ticket takers 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 Ushers, Lobby Attendants, and Ticket Takers?: Not as a job, but it is already doing parts of the work. Across the 23 official task statements scored for Ushers, Lobby Attendants, and Ticket Takers (United States, SOC 39-3031), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 5 out of 100 (range 4–10, 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 “Ushers, Lobby Attendants, and Ticket Takers” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Schedule or manage staff, such as volunteer usher corps” (48/100, partial); “Manage informational kiosks or displays of event signs or posters” (38/100, low); “Manage inventory or sale of artist merchandise” (32/100, 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 “Ushers, Lobby Attendants, and Ticket Takers” stay human?: About 98% 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: “Lead tours and answer visitors' questions about the exhibits” (0/100, minimal); “Clean facilities” (0/100, minimal); “Sell or collect admission tickets, passes, or facility memberships from patrons at entertainment events” (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 “Ushers, Lobby Attendants, and Ticket Takers” do about AI?: Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 98% 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 Ushers, Lobby Attendants, and Ticket Takers 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

About the data on this page

One row is marked low confidence, so treat it as a ballpark rather than a fine measurement.
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 2026-08-04.
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.

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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.