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Elementary occupations · code 9112

Cleaners and helpers in offices, hotels and other establishments

AI can plan the cleaning rota. Someone still has to change the beds.

AI barely touches any of the four tasks: making beds and cleaning bathrooms, emptying bins, cleaning kitchens, and vacuuming and polishing.

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

Cleaners and helpers in offices, hotels and other establishments perform various cleaning tasks in order to keep clean and tidy the interiors and fixtures of hotels, offices and other establishments, as well as of aircraft, trains, buses and similar vehicles. Tasks include - (a) sweeping or vacuum cleaning, washing and polishing floors, furniture and other fixtures in buildings, coaches, buses, trams, trains and aircraft; (b) making beds, cleaning bathrooms, supplying towels, soap and related items; (c) cleaning kitchens and generally helping with kitchen work, including dishwashing; (d) picking up rubbish, emptying garbage containers and taking contents to waste areas to removal. Examples of the occupations classified here: - Aircraft cleaner - Hotel cleaner - Lavatory attendant - Office cleaner Some related occupations classified elsewhere: - Domestic housekeeper - 5152 - Building caretaker - 5153 - Domestic cleaner - 9111 - Dishwasher - 9412 - Kitchen helper - 9412 - Street sweeper - 9613 Note Workers who perform cleaning and helping tasks only in kitchens and other food preparation areas are classified in Unit Group 9412: Kitchen Helpers.

  • aircraft groomer
  • room attendant
  • building cleaner
  • furniture cleaner
  • toilet attendant
  • train cleaner

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

88 / 100

Strongly holds up against AI

Most of this job still needs an actual human. AI would mostly be making the tea.

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: Not Exposed

30 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 4 tasks for how much of it AI could do. They average 11.9 out of 100, so it holds up at 100 − 11.9 = 88.1.

That's the whole sum. No black box, no secret sauce: you can check it against the ILO's study. ILO group “Not Exposed”: None of this job's tasks scored high enough to count.

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

Pinch of salt: this number is probably too rosy

It only asks what AI software could do, and this job is 54 out of 100 hands-on. A chatbot can't do that part. A robot might, one day, and nobody measures how close that is.

Man v Bot · compare

Who holds up better?

Cleaners and helpers in offices, hotels and other establishments 88

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.

  • Rotas, room lists and task tracking.
  • Stock ordering.
  • Reporting faults and damage from a phone.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Cleaning bathrooms, kitchens and rooms to a standard.
  • Working around guests and staff.
  • Noticing and reporting what's wrong.

Both

What's arriving

New work that only exists because the AI does.

  • Supervising floor-cleaning robots.
  • Tighter hygiene standards.
  • Task apps that organise the day.

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

19 could do without you

It could take over very little of the work on its own. It could help with 44 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Some

54 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 fine handwork and fewer cramped spaces than a typical hands-on job. That clears one hurdle for machines. It doesn't mean machines are doing this work yet.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 33 out of 100. The higher it is, the longer the robot waits.

54 How
hands-on
33 How fiddly
and cramped

Kind of hands-on work Hands-on, less fiddly work

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 3 US jobs .

finger skill 35.1 · hand skill 35.7 · cramped spaces 28.3 · averaged · rule: hands-on 50+ · fiddly under 39.4

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 14, against an average of 12. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 14

    Making beds, cleaning bathrooms, supplying towels, soap and related items

    Very Low
  2. 14

    Picking up rubbish, emptying garbage containers and taking contents to waste areas to removal.

    Very Low
  3. 12

    Cleaning kitchens and generally helping with kitchen work, including dishwashing

    Very Low
  4. 9

    Sweeping or vacuum cleaning, washing and polishing floors, furniture and other fixtures in buildings, coaches, buses, trams, trains and aircraft

    Very Low

4 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
44
AI does it for you
19

Sidekick wins, by 25 points.

How good a match is this?

3 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, 8112, 8114

So, should cleaners be worried?

Short answer: not really. Put the kettle on.

No. Every task rates Very Low, from making beds to cleaning kitchens and floors. Cleaning robots handle some big open floors, but hotel rooms, bathrooms and offices full of furniture still need people.

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 above the family average.

Pit this against any other job →