Skip to content
man bot
Compare

Elementary occupations · code 9629

Elementary workers not elsewhere classified

The gate scans your ticket. The usher still gets you out in an emergency.

Selling and checking tickets are mostly out of AI's reach. Assigning lockers and guiding people out in an emergency it barely touches.

What they actually do (and what else they get called)

This unit group covers elementary workers not classified elsewhere in Major Group 9: Elementary Workers. For instance, the group includes those who issue and collect parking or admission tickets, provide personal items to patrons or customers in cloakrooms, and assist patrons at entertainment events. In such cases tasks would include - (a) selling admission tickets and passes to patrons at entertainment events or collecting tickets, passes and tags from patrons; (b) examining tickets or passes to verify authenticity, using criteria such as colour and date issued; (c) guiding patrons to exits or providing other instructions or assistance in case of emergency; (d) directing patrons to restrooms, concession stands and telephones; (e) directing vehicle drivers to parking spaces; (f) patrolling parking areas in order to prevent vehicle damage and vehicle property theft; (g) calculating parking charges and collecting fees from customers; (h) assigning dressing room facilities, locker space, or clothing containers to patrons of athletic or bathing establishments. Examples of the occupations classified here: - Cloakroom attendant - Fairground attendant - Parking attendant - Ticket collector - Usher Some related occupations classified elsewhere: - Parking valet - 8322

  • advertising installer
  • attraction operator
  • locker room attendant
  • laundromat attendant
  • usher
  • amusement and recreation attendant
  • cloak room attendant

Job titles from the EU's ESCO list. Search for any of them on the jobs page and you'll end up here.

71 / 100

Mostly holds up against AI

AI can take a few chores off your plate, but most of the day is still yours.

Not the odds you keep your job. It's how much of the everyday work still needs a person, out of 100.

← Robot's turf Human's turf →

The ILO's official verdict: Minimal Exposure

235 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 8 tasks for how much of it AI could do. They average 29.2 out of 100, so it holds up at 100 − 29.2 = 70.8.

That's the whole sum. No black box, no secret sauce: you can check it against the ILO's study. ILO group “Minimal Exposure”: A few tasks are affected, but the heart of the job isn't.

This job rates 41.8 out of 100 for how hands-on it is. The score above only looks at AI tools, not robots, but for work this far from the physical that matters less.

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Man v Bot · compare

Who holds up better?

Elementary workers not elsewhere classified 71

v

Go on, try your partner's job. Or your sibling's. We won't tell.

Man v Bot

Where the bot helps, and where the human wins

Bot

What AI takes

The routine stuff. You may not miss it.

  • Digital tickets and scanners.
  • Automatic parking payment.
  • Venue information by app.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Helping people find their way.
  • Emergencies and evacuations.
  • Keeping order in crowds.

Both

What's arriving

New work that only exists because the AI does.

  • Crowd safety roles at large events.
  • Accessibility support.
  • Cashless venues.

Written by a human, from the ILO's task ratings below. Yes, we see the irony. Read it as one person's take.

Meet your two rivals

They're not the same machine. AI software can only reach the parts of a job done on a screen or in words. A chatbot can't climb a ladder. A robot might, one day, if it doesn't fall off.

The AI

Lives in a screen. Never had to fix a boiler.

Threat level: Low

25 could do without you

It could take over very little of the work on its own. It could help with 42 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Some

42 how hands-on

A fair part of this job is hands-on, so robots would be needed for that part, not just software.

So could a robot do the physical part?

Less than half of this job is hands-on. Robots still matter, but for a smaller part of the work, so we don't sort it any further.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 26 out of 100. The higher it is, the longer the robot waits.

42 How
hands-on
26 How fiddly
and cramped

Kind of hands-on work Less than half hands-on

The small print

They aren't a robot score, and nothing on this page is. Nobody measures how far robots have got well enough to put a number on it. These are the closest thing we have: how hands-on the job is, and how much fiddly, cramped work it takes, which is what Frey and Osborne found hardest for machines.

From O*NET, averaged across 5 US jobs .

finger skill 30.7 · hand skill 31.5 · cramped spaces 15 · averaged · rule: hands-on under 50

estimated O*NET 31.0 BLS occupational crosswalk chain ISCO-08 to 2010 SOC (Aug 2012, rev. Jun 2015); 2010 to 2018 SOC (Nov 2017); O*NET-SOC 2019 taxonomy data 0.8-observed-use measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

The job, task by task

Every job is really a bundle of tasks. Here's this one unpacked, with how much of each AI could do, out of 100. The score is the average, and averages can hide a lot.

AI's favourite task here scores 49, against an average of 29. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 49

    Selling admission tickets and passes to patrons at entertainment events or collecting tickets, passes and tags from patrons

    Low
  2. 45

    Examining tickets or passes to verify authenticity, using criteria such as colour and date issued

    Low
  3. 38

    Calculating parking charges and collecting fees from customers

    Low
  4. 33

    Directing patrons to restrooms, concession stands and telephones

    Low
  5. 21

    Directing vehicle drivers to parking spaces

    Very Low
  6. 19

    Patrolling parking areas in order to prevent vehicle damage and vehicle property thefts

    Very Low
  7. 17

    Assigning dressing room facilities, locker space or clothing containers to patrons of athletic or bathing establishments.

    Very Low
  8. 14

    Guiding patrons to exits or providing other instructions or assistance in case of emergency

    Very Low

8 tasks · the High to Very Low labels are the ILO's

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Sidekick or replacement?

A tool that makes you faster is a very different thing from one that does the task while you watch. Jobs and Skills Australia measured the two separately, which is handy.

AI helps you
42
AI does it for you
25

Sidekick wins, by 18 points.

How good a match is this?

2 Australian jobs match this one, and we've simply averaged them. Treat the numbers as a rough guide.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.8-observed-use matched to Australian jobs 4319, 8999

Meanwhile, in real life…

Everything else here is about what AI could do. This is what people actually did. When they asked Claude for help with this kind of work, did they hand the whole task over, or roll their sleeves up and work through it together?

Tiny sample alert This job makes up under 0.01% of all the conversations: a tiny slice. People using AI for a job isn't the same as AI being able to do it.

Out of every 100 conversations

29
71

worked through it together handed it over

The small print

Anthropic publishes this about its own product, and it doesn't feed into any score here. These are people who use Claude, not workers in general, so little use here doesn't mean AI can't reach the job.

The data is sorted by US job, and this one matches 2 of them. We've combined them, counting each by how often it comes up in the conversations, so the busiest one counts most.

These two add up to 100. The bars elsewhere on the page don't, because each is scored on its own, so we don't put them side by side.

observed use Anthropic Economic Index release 2026-06-26, period beginning 2026-05-01 data 0.8-observed-use 2026-05-01 · Anthropic, about its own product

So, should ushers, attendants and ticket staff be worried?

Short answer: not much. Some of the chores will change, and good riddance.

Somewhat. Selling and checking tickets and collecting parking fees are the most exposed tasks, all Low; guiding people, patrolling and emergencies rate Very Low. Digital tickets and automatic barriers take on the routine checks; helping people stays human.

Worth remembering: we score tasks, not jobs. When AI takes on a task, the job usually reshuffles around it rather than vanishing.

Where this job could lead

None of the new jobs we've written up start from here yet. Could be you're the first. See the jobs AI is creating →

How are the neighbours doing?

There are 33 jobs in the “Elementary occupations” family, averaging 85. This one is below the family average.

Pit this against any other job →