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Elementary occupations · code 9129

Other cleaning workers

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

None of this job's tasks scored high enough to count. It ranks 14 of 427 for holding up against AI.

Also known as

  • 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.

89.8
Holds up
against AI Not Exposed

AI could take on 10.2 of 100, on average
so it holds up at 100 − 10.2 = 89.8

That's the whole sum, and you can check it against the ILO's study.

71.8
How hands-on
out of 100

From O*NET, matched to one US job

45.9
How fiddly
and cramped

finger skill 42.9 · hand skill 44.6 · cramped spaces 50.2 · averaged

Kind of hands-on work · hands-on 50+ · fiddly 39.4+ Hands-on, fiddly work in tight spaces

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.

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 0.7-work-setting measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

This number is probably too high

This score only looks at AI tools like chatbots. It doesn't look at robots at all — and this job rates 71.8 out of 100 for how hands-on it is, which is exactly where robots come in.

Have a look at the tasks below and judge for yourself. The ILO scored them low because a chatbot can't do them. Whether a machine with hands could is a different question, and we can't answer it yet.

The tasks behind the score

The score is the average of these. A job is really a bundle of tasks, and an average can hide how uneven that bundle is.

The task AI could do most of scores 12, while the average is 10.2. A big gap means some of this job is wide open to AI and some of it barely at all. That's quite different from a job where everything sits somewhere in the middle, even when the averages match.

  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. 9.3

    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 0.7-work-setting

Helped, or replaced?

The score above can't tell a tool that makes you faster from one that does the task instead of you. Jobs and Skills Australia looked at those two things separately, and this is their result, matched to this job.

One Australian job matches, but only partly: not everyone in it does this job. Treat the numbers as a rough guide.

AI helps you
44
AI does it for you
15

Helping wins by 29 points.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.7-work-setting matched to Australian jobs 8116

Compared with similar jobs

There are 33 jobs in the “Elementary occupations” group, averaging 84.8. This one is above that.