Skills it runs on
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Ushers, Lobby Attendants, and Ticket Takers
Occupation · SOC 39-3031.00
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.
Also called: Lobby Attendant · Ticket Taker · Usher · Visitor Services Representative · Docent · Ticket Attendant · Visitor Services Assistant · Visitor Services Associate · Visitor Services Specialist · Admittance Attendant · Attractions Associate · Concessionist
Job family: Personal Care and Service Occupations
Take this to your AI
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-39-3031-00/context.md directly.
AI work map
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Market signal
Independent published positions, read together — not a forecast.
- About average employment outlook (+1.2% by 2034)
- 30,800 openings/yr
- High AI exposure
- Median pay $31,150/yr
- Can largely be done remotely (teleworkable)
- 2013 flagged this for computerization (91st); today's AI task-overlap is comparatively low (45th)
45th-percentile task overlap — yet about 30,800 openings a year (+1.2% projected, BLS) . What exposure means →
AI & job outlook
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Exposure to current AI
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) Moderate | 42nd | -0.2 | |
| LLM task exposure, γ (OpenAI / Eloundou) Low | 22nd | 0.2 | |
| AI assistant applicability (Microsoft) High | 74th | 0.2 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.1), with simple added tooling (β 0.1), and including AI-powered software (γ 0.2). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
Most of this job's tasks can be done remotely (Dingel–Neiman), which tends to track with higher digital and AI exposure.
Historical automation estimate (2013)
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 1.0 · 91st percentile among occupations · High
How AI is actually used in this job
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Lead tours and answer visitors' questions about the exhibits. | 1.3% | |
| Count and record number of tickets collected. | 0.2% |
Job outlook
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · +1.2% by 2034 |
| Projected annual openings | 30,800 |
| Employment 2024 → 2034 | 121,700 → 123,100 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
Where this work sits on the global GenAI gradient
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
Ushers, Lobby Attendants, and Ticket Takers sits at the 55th percentile of 427
occupations on the global GenAI task-exposure gradient
— exposure eased from 2023 to 2025. Each dot is one occupation; the
ringed one is this work. Exposure is task overlap, not automation or jobs lost.
29% mean task exposure (2025)
55th percentile of 427 placed occupations
−9 pts shift 2023 → 2025
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Elementary Workers Not Elsewhere Classified · 9629 | 29% | Minimal |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
Tasks
All 23 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
- Greet patrons attending entertainment events. Core · importance 4.3
- Operate refreshment stands during intermission or obtain refreshments for press box patrons during performances. Supplemental · importance 4.3
- Count and record number of tickets collected. Supplemental · importance 4.3
- Lead tours and answer visitors' questions about the exhibits. Supplemental · importance 4.3
- Sell or collect admission tickets, passes, or facility memberships from patrons at entertainment events. Core · importance 4.2
- Clean facilities. Core · importance 4.1
- Settle seating disputes or help solve other customer concerns. Core · importance 4.0
- Manage inventory or sale of artist merchandise. Supplemental · importance 4.0
- Examine tickets or passes to verify authenticity, using criteria such as color or date issued. Core · importance 3.9
- Provide assistance with patrons' special needs, such as helping those with wheelchairs. Core · importance 3.9
- Guide patrons to exits or provide other instructions or assistance in case of emergency. Core · importance 3.8
- Verify credentials of patrons desiring entrance into press box and permit only authorized persons to enter. Supplemental · importance 3.7
- Refuse admittance to undesirable persons or persons without tickets or passes. Core · importance 3.7
- Assist patrons by giving directions to points in or outside of the facility or providing information about local attractions. Core · importance 3.7
- Distribute programs to patrons. Supplemental · importance 3.7
- Assist patrons in finding seats, lighting the way with flashlights, if necessary. Core · importance 3.7
- Give door checks to patrons who are temporarily leaving establishments. Supplemental · importance 3.7
- Maintain order and ensure adherence to safety rules. Core · importance 3.7
- Search for lost articles or for parents of lost children. Core · importance 3.5
- Manage informational kiosks or displays of event signs or posters. Supplemental · importance 3.4
- Work with others to change advertising displays. Supplemental · importance 3.4
- Page individuals wanted at the box office. Supplemental · importance 3.4
- Schedule or manage staff, such as volunteer usher corps. Supplemental · importance 3.3
Work activities
- Sell products or services.
- Clean facilities or work areas.
- Provide patrons with directions to locales or attractions.
- Provide attraction or event information to patrons.
- Assign duties or work schedules to employees.
- Supervise service workers.
- Greet customers, patrons, or visitors.
- Verify patron or staff credentials.
- Usher patrons to seats or exits.
- Monitor environment to ensure safety.
- Assist individuals with special needs.
- Mediate disputes.
- Resolve customer complaints or problems.
- Respond to customer problems or complaints.
- Prepare operational reports or records.
- Collaborate with others to develop or refine designs.
- Maintain supply or equipment inventories.
- Respond to customer inquiries.
- Arrange artwork, products, or props.
- Provide notifications to customers or patrons.
Knowledge, skills & abilities
O*NET importance rating, from 1 (not important) to 5 (extremely important).
Knowledge
Transferable skills
| Social Perceptiveness | 3.4 | |
| Service Orientation | 3.3 | |
| Coordination | 3.0 | |
| Persuasion | 2.9 | |
| Negotiation | 2.9 | |
| Complex Problem Solving | 2.5 | |
| Time Management | 2.5 |
Abilities
| Speech Clarity | 3.4 | |
| Oral Comprehension | 3.3 | |
| Oral Expression | 3.3 | |
| Near Vision | 3.1 | |
| Problem Sensitivity | 3.0 | |
| Speech Recognition | 3.0 | |
| Written Comprehension | 2.9 | |
| Deductive Reasoning | 2.9 | |
| Selective Attention | 2.9 | |
| Inductive Reasoning | 2.8 | |
| Information Ordering | 2.8 | |
| Far Vision | 2.8 | |
| Perceptual Speed | 2.6 | |
| Trunk Strength | 2.6 | |
| Category Flexibility | 2.5 | |
| Flexibility of Closure | 2.5 | |
| Time Sharing | 2.5 | |
| Manual Dexterity | 2.5 | |
| Finger Dexterity | 2.5 | |
| Visual Color Discrimination | 2.5 |
Essential skills
| Speaking | 3.3 | |
| Active Listening | 3.1 | |
| Reading Comprehension | 2.8 | |
| Critical Thinking | 2.8 | |
| Monitoring | 2.8 | |
| Active Learning | 2.6 |
Skills in demand
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Tools & technology
| Example | Category | |
|---|---|---|
| Microsoft Excel | Spreadsheet software | Hot technology |
| Microsoft Office software | Office suite software | Hot technology |
| Microsoft Outlook | Electronic mail software | Hot technology |
| Microsoft PowerPoint | Presentation software | Hot technology |
| Microsoft Word | Word processing software | Hot technology |
| Microsoft Windows Mobile | Operating system software | |
| Ticket Alternative Express Entry | Optical character reader OCR or scanning software | |
| Ticket scanning software | Optical character reader OCR or scanning software |
Work context
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
How to get in
- Job zone
- Zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed
- Education
- Usually requires a high school diploma or GED, though some occupations may not.
- Typical entry-level education
- No formal educational credential · BLS, the typical path — not a requirement
- Related experience
- Some occupations may need little or no previous experience; others require several months to a year of experience. For example, landscaping and groundskeeping workers might require very little training or previous experience, while agricultural equipment operators can benefit from on-the job training.
- Preparation level
- SVP (Below 6.0) — total schooling plus on-the-job experience.
Education of current workers
Share of people in this occupation at each level of education.
| High School Diploma | 61.4% | |
| Less than a High School Diploma | 33.0% | |
| Associate's Degree (or other 2-year degree) | 2.7% | |
| Bachelor's Degree | 1.6% | |
| Some College Courses | 1.2% |
Interests & work styles
The interests and personal qualities O*NET associates with people who do this work.
Interest areas
| Personal Service | 5.2 | |
| Sales | 2.2 | |
| Protective Service | 2.2 | |
| Social Service | 2.1 | |
| Performing Arts | 1.9 | |
| Physical/Manual Labor | 1.8 | |
| Athletics | 1.8 |
Career interests (Holland / RIASEC)
| Conventional | 4.8 | |
| Social | 4.7 | |
| Enterprising | 4.2 | |
| Realistic | 3.2 |
Work styles
| Dependability | 3.0 | |
| Cooperation | 2.4 | |
| Social Orientation | 2.1 | |
| Optimism | 1.9 | |
| Empathy | 1.7 |
Wages & employment
U.S. · annual wages (BLS OEWS)
| 10th percentile | $22,880 |
| 25th percentile | $27,140 |
| Median (50th) | $31,150 |
| 75th percentile | $35,650 |
| 90th percentile | $40,210 |
| People employed | 119,210 |
Industries that employ this occupation
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Information · Sector | 52,070 | $29,490 |
| Arts, Entertainment, and Recreation · Sector | 40,430 | $32,410 |
| Accommodation and Food Services · Sector | 4,980 | $34,680 |
| Temporary Help Services · National industry | 4,920 | $29,410 |
| Theater Companies and Dinner Theaters · National industry | 4,480 | $35,610 |
| Educational Services · Sector | 3,730 | $28,530 |
| Casino Hotels · National industry | 1,220 | $35,840 |
| Other Services (except Public Administration) · Sector | 1,150 | $35,480 |
| Fitness and Recreational Sports Centers · National industry | 260 | $29,380 |
| Retail Trade · Sector | 50 | $32,640 |
| Health Care and Social Assistance · Sector | 40 | $34,000 |
| Professional, Scientific, and Technical Services · Sector | — | $36,170 |
Where this work is most concentrated
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Theater Companies and Dinner Theaters · National industry | 80.04× | 4,480 |
| Information · Sector | 23.16× | 52,070 |
| Arts, Entertainment, and Recreation · Sector | 19.79× | 40,430 |
| Casino Hotels · National industry | 4.68× | 1,220 |
| Temporary Help Services · National industry | 2.4× | 4,920 |
| Fitness and Recreational Sports Centers · National industry | 0.53× | 260 |
| Accommodation and Food Services · Sector | 0.45× | 4,980 |
| Educational Services · Sector | 0.35× | 3,730 |
Related occupations
Part of the Hospitality, Events, & Tourism career cluster.
- Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop · 35-9031.00 Compare →
- Baggage Porters and Bellhops · 39-6011.00 Compare →
- Hotel, Motel, and Resort Desk Clerks · 43-4081.00 Compare →
- Passenger Attendants · 53-6061.00 Compare →
- Reservation and Transportation Ticket Agents and Travel Clerks · 43-4181.00 Compare →
- Locker Room, Coatroom, and Dressing Room Attendants · 39-3093.00 Compare →
- Cashiers · 41-2011.00 Compare →
- Receptionists and Information Clerks · 43-4171.00 Compare →
- Counter and Rental Clerks · 41-2021.00 Compare →
- Flight Attendants · 53-2031.00 Compare →
- Concierges · 39-6012.00 Compare →
- Tour Guides and Escorts · 39-7011.00 Compare →
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
What you can do with this
Options the data surfaces for Ushers, Lobby Attendants, and Ticket Takers — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Skills that travel
Capabilities this work builds that are used across many other occupations.
Adjacent roles
Occupations O*NET rates as related — the nearby moves on the map.
Paths in
How people typically prepare for this work.
- No formal educational credential · typical entry level
- Job zone 2 — Job Zone 1-2: Very Little to Some Preparation Needed · preparation tier
Zoom out
On the global GenAI exposure gradient this work sits around the 55th percentile of 427 international occupations.
Write a report on thisheadline · factoids · citation
Ushers, Lobby Attendants, and Ticket Takers show 45th-percentile AI task overlap — and about 30,800 annual U.S. openings
- Ushers, Lobby Attendants, and Ticket Takers rank in the 45th percentile (Moderate band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated.Eloundou et al. (GPTs are GPTs) + Felten AIOE
- The occupation is projected to see about 30,800 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI.BLS Employment Projections 2024–34
- BLS projects employment to be about average (+1.2%) from 2024 to 2034.BLS Employment Projections 2024–34
- Median annual pay is $31,150, across about 119,210 U.S. workers.BLS OEWS (May 2024)
Copy the whole kit
Ushers, Lobby Attendants, and Ticket Takers show 45th-percentile AI task overlap — and about 30,800 annual U.S. openings • Ushers, Lobby Attendants, and Ticket Takers rank in the 45th percentile (Moderate band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 30,800 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be about average (+1.2%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $31,150, across about 119,210 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Ushers, Lobby Attendants, and Ticket Takers". https://singulariki.com/roles/role-39-3031-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Sources for this page
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
- O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
- BLS Occupational Employment and Wage Statistics (OEWS) May 2024 U.S. Bureau of Labor Statistics
- BLS Employment Projections 2024–2034 U.S. Bureau of Labor Statistics
- Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
- Microsoft “Working with AI” working-with-ai Microsoft Research
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
- AI Occupational Exposure (AIOE) Felten, Raj & Seamans academic
- ILO / Gmyrek et al. GenAI exposure gradient 2025 International Labour Organization
- IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022 Institute for Structural Research (IBS)
- Frey & Osborne (2013) frey-osborne-automation academic
- Dingel & Neiman (2020) dingel-neiman-workathome academic
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Plain
Singulariki. "Ushers, Lobby Attendants, and Ticket Takers." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-39-3031-00
APA
Singulariki. (2026). Ushers, Lobby Attendants, and Ticket Takers. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-39-3031-00
BibTeX
@misc{singulariki-role-39-3031-00,
title = {Ushers, Lobby Attendants, and Ticket Takers},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-39-3031-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.