Personal Care and Service · United States (national)
Ushers, Lobby Attendants, and Ticket Takers
How exposed this work is to automation, shown as ranges with the reasoning and sources.
The short version: this role is less exposed to automation, and a near-term shift looks unlikely.
It is a sourced range, not a verdict, and the detail is below.
Built on O*NET tasks, BLS pay and projections, and peer-reviewed AI-exposure research. Sources shown.
Data snapshot SNAPSHOT-2026-11-06. How it works
AI / softwarerange 8.0 to 32.0
Physical / roboticsrange 19.6 to 43.6
When the change is likely
Largely unaffectedlocal demand growing
What to do about it
Use AI to outpace AI.
Strategies to protect your livelihood, most important first.
1
Stay sharp; urgency is low
This role looks durable. Keep your skills current and watch for shifts in your field.
2
Pivot toward Passenger Attendants
It is skill-adjacent and less exposed (25.2 vs 32). A lateral move now beats a forced one later.
3
Build a financial buffer
Keep 6 to 12 months of expenses and diversify your income, so a disruption is a setback rather than a catastrophe.
4
Add a recognised credential
A certification or license raises the bar against automation and signals value in a tightening market.
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Which of my tasks are exposed
Greet patrons attending entertainment events.
31.6
Sell or collect admission tickets, passes, or facility memberships from patrons at entertainment events.
31.6
Clean facilities.
31.6
Settle seating disputes or help solve other customer concerns.
100.0
Examine tickets or passes to verify authenticity, using criteria such as color or date issued.
31.6
Provide assistance with patrons' special needs, such as helping those with wheelchairs.
31.6
Guide patrons to exits or provide other instructions or assistance in case of emergency.
31.6
Refuse admittance to undesirable persons or persons without tickets or passes.
31.6
Longer, orange bars are more automatable; short green bars are resistant.
What is driving my result
lowers risk
raises risk
Task: Settle seating disputes or help solve other customer concerns.
Local demand is growing
Task: Greet patrons attending entertainment events.
Lower-risk adjacent roles
Passenger Attendants
25.2
A full, ranked mitigation plan is part of the paid tiers.
How this score is calculated
Map Ushers, Lobby Attendants, and Ticket Takers to its standard O*NET task list (8 core tasks).
Score each task for AI/software exposure (Eloundou et al. 2023) and physical/robotics exposure (an O*NET work-context proxy), each 0 to 100.
The two axis scores are the task averages: AI 20.0, robotics 31.6.
The timeline comes from the higher axis (31.6), shifted for local demand (growing), and seniority. It is read against your remaining working years.
Ranges are about ±12 points. A backtest against real BLS employment change shows the risk ranking holds in aggregate, but individual outcomes are predicted weakly, so results are ranges, never a flat verdict.
Sources: O*NET, Eloundou et al. (2023), GPTs are GPTs, O*NET Work Context, BLS CPI-U, BLS OEWS.
Snapshot SNAPSHOT-2026-11-06. Full method and backtest on How it works.