Ushers, Lobby Attendants, and Ticket Takers
The routine layer of this job is compressing.
A substantial share of this occupation's activities overlaps with what AI already does well. Expect the routine layer to compress first, and the human-anchored layer to gain weight and value.
Where this occupation sits among all 785 measured. Bands are quintiles of the real distribution. Applicability measures task overlap with observed AI usage. It is not a probability of job loss.
What this job involves
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.
Where AI overlaps this work, activity by activity
The occupation's most important work activities (O*NET weights, normalized to this set), each with its measured AI performance across 200,000 real conversations: how often AI is used for it, how well it completes it, and how much of the activity it covers.
| Work activity | Share of role | AI performance |
|---|---|---|
| Provide information to guests, clients, or customers. | 13.9% | High overlap 0.81 |
| Sell products or services. | 13.5% | Not observed 0.00 |
| Verify personal information. | 13.2% | Moderate 0.41 |
| Respond to customer problems or inquiries. | 12.7% | High overlap 0.78 |
| Escort others. | 9.1% | Not observed 0.00 |
| Provide general assistance to others, such as customers, patrons, or motorists. | 8.5% | High overlap 0.65 |
| Assist individuals with special needs. | 5.9% | Not observed 0.00 |
| Mediate disputes. | 5.8% | Not observed 0.00 |
| Clean tools, equipment, facilities, or work areas. | 5.6% | Not observed 0.00 |
| Monitor safety or security of work areas, facilities, or properties. | 4.5% | Not observed 0.00 |
| Prepare reports of operational or procedural activities. | 4.4% | Not observed 0.00 |
| Replenish inventories of materials, equipment, or products. | 2.7% | Not observed 0.00 |
"Not observed" is a measurement, not missing data: that activity does not meaningfully appear in the usage sample. AI performance = completion × scope × coverage (user-goal view), 0–1.
What drives this score: the biggest AI-overlapped activity in this role is provide information to guests, clients, or customers; the most important activity AI is not observed doing is sell products or services. The plan below is built around exactly that split.
Move first: compress the routine before it compresses you.
Next 30 days
- Take the exposed layer yourself. Your most AI-overlapped activity, Provide information to guests, clients, or customers, is exactly what a well-tooled colleague will accelerate. Be that colleague first.
- Time-box the routine. Measure how long your repeatable tasks actually take; that's the budget AI adoption will compete against, and the time you will reinvest.
Next 90 days
- Double down where humans hold. Grow the share of your week spent on work like Sell products or services, measured as outside AI's current reach, and take on more of it explicitly.
- Build one AI-assisted workflow end-to-end for your team and own its quality. Being the local standard-setter compounds.
The longer game
- Position at the boundary. The valuable seat in partially-exposed occupations is the translator: someone fluent in both the domain and the tools. Certifications matter less than a demonstrated workflow portfolio.
- Recheck yearly. Elevated-band occupations move: new tools shift specific activities fast. The data vintage is printed at the top of this page.
- AI applicability score and per-activity metrics: Tomlinson, Jaffe, Wang, Counts & Suri, Working with AI: Measuring the Applicability of Generative AI to Occupations, Microsoft Research 2025 (arXiv:2507.07935), data CC BY 4.0.
- Occupation description and activity structure: O*NET 30.0 Database, USDOL/ETA, CC BY 4.0.
- Bands, personalization weights and action plans are JobRiskAI editorial layers; method and limitations on the methodology page.