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Elementary occupations · code 9129

Other cleaning workers

AI can schedule the deep clean. It won't climb into the cooling tower.

AI barely touches any of the six tasks, from cleaning carpets and upholstery to pressure-washing stonework and clearing deposits with suction kit.

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

This unit group includes cleaning workers not classified elsewhere. The group includes, for instance, those who clean surfaces, materials and objects such as carpets, walls, swimming pools and cooling towers, using specialized cleaning equipment and chemicals. In such instances tasks would include - (a) cleaning carpets and upholstered furniture using cleaning machines and their attachments; (b) selecting and applying cleaning agents to remove stains from carpets; (c) treating carpets with soil-repellent chemicals and deodorants, and treating for pests; (d) cleaning stone walls, metal surfaces and fascias using high-pressure water cleaners and solvents; (e) applying chemicals and high-pressure cleaning methods to remove micro-organisms from water and filtration systems; (f) using wet vacuums and other suction equipment to remove scale, accumulated dirt and other deposits from swimming pools, cooling tower components and drains. Examples of the occupations classified here: - Carpet cleaner - Cooling tower cleaner - Graffiti cleaner - Swimming pool cleaner - Water blaster

  • sewerage cleaner
  • drapery and carpet cleaner
  • swimming facility attendant

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

90 / 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

14 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 6 tasks for how much of it AI could do. They average 10.2 out of 100, so it holds up at 100 − 10.2 = 89.8.

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 72 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?

Other cleaning workers 90

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.

  • Bookings, quotes and job sheets.
  • Chemical safety data and records.
  • Identifying stains and materials from photos.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Choosing the right chemical and method for each surface.
  • Working safely in confined or hazardous spaces.
  • Restoring things customers care about.

Both

What's arriving

New work that only exists because the AI does.

  • Hygiene and infection control work to higher standards.
  • Supervising cleaning robots on large, simple floors.
  • Greener cleaning products and methods.

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

15 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: Higher

72 how hands-on

Most of this job needs hands, a body or turning up. That's robot territory, and nobody measures how far robots have got well enough to give you a number. We won't make one up.

So could a robot do the physical part?

More fine handwork, and more time in cramped or awkward spots, than a typical hands-on job. That is exactly what machines find hardest: they do best in tidy, predictable places.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 46 out of 100. The higher it is, the longer the robot waits.

72 How
hands-on
46 How fiddly
and cramped

Kind of hands-on work Hands-on, fiddly work in tight spaces

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, matched to one US job .

finger skill 42.9 · hand skill 44.6 · cramped spaces 50.2 · averaged · rule: hands-on 50+ · fiddly 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 12, against an average of 10. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 12

    Applying chemicals and high-pressure cleaning methods to remove micro-organisms from water and filtration systems

    Very Low
  2. 12

    Using wet vacuums and other suction equipment to remove scale, accumulated dirt and other deposits from swimming pools, cooling tower components and drains.

    Very Low
  3. 10

    Selecting and applying cleaning agents to remove stains from carpets

    Very Low
  4. 9

    Treating carpets with soil-repellent chemicals and deodorants, and treating for pests

    Very Low
  5. 9

    Cleaning carpets and upholstered furniture using cleaning machines and their attachments;.

    Very Low
  6. 9

    Cleaning stone walls, metal surfaces and fascias using high pressure water cleaners and solvents;.

    Very Low

6 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
15

Sidekick wins, by 29 points.

How good a match is this?

One Australian job matches, but only partly: not everyone in it does this job. 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 8116

So, should specialist cleaners be worried?

Short answer: not really. Put the kettle on.

No. Every task rates Very Low: cleaning filtration systems, pools, carpets and stone walls with specialist kit. AI helps with bookings, quotes and safety records. The skill is knowing the chemicals and methods, and using them safely.

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 →