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Elementary occupations · code 9622

Odd job persons

Odd-job persons clean, paint and maintain buildings, grounds and facilities, and undertake simple repairs. Tasks include - (a) repairing broken windows, screens, doors, fences, barbecues, picnic tables, shelves, cupboards and other items; (b) replacing defective items such as light bulbs; (c) repairing and painting interior and exterior surfaces such as walls, ceilings and fences; (d) adjusting doors and windows; (e) replacing tap washers; (f) putting up handrails and grab rails; (g) unloading coal or wood and putting it into cellars of private households or establishments. Examples of the occupations classified here: - Handyperson - Hotel useful - Odd-job person

None of this job's tasks scored high enough to count. It ranks 19 of 427 for holding up against AI.

Also known as

  • handyman

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

89
Holds up
against AI Not Exposed

AI could take on 11 of 100, on average
so it holds up at 100 − 11 = 89

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

74.2
How hands-on
out of 100

From O*NET, averaged across 3 US jobs

46.8
How fiddly
and cramped

finger skill 45.8 · hand skill 45.8 · cramped spaces 48.7 · 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 74.2 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 15, while the average is 11. 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. 15

    Adjusting doors and windows

    Very Low
  2. 13.9

    Replacing defective items such as light bulbs

    Very Low
  3. 12.3

    Repairing and painting interior and exterior surfaces such as walls, ceilings and fences

    Very Low
  4. 10

    Repairing broken windows, screens, doors, fences, barbecues, picnic tables, shelves, cupboards and other items

    Very Low
  5. 10

    Unloading coal or wood and putting it into cellars of private households or establishments.

    Very Low
  6. 8

    Replacing tap washers

    Very Low
  7. 8

    Putting up handrails and grab rails

    Very Low

7 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 this one closely, so this is about as reliable as it gets here.

AI helps you
43
AI does it for you
10

Helping wins by 33 points.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.7-work-setting matched to Australian jobs 8993

Compared with similar jobs

There are 33 jobs in the “Elementary occupations” group, averaging 84.9. This one is above that.