Elementary occupations · code 9123
Window cleaners
AI can book your window clean. It can't abseil down the building.
AI barely touches any of the three tasks: picking the right tools, washing and polishing glass, and working from ladders, cradles and cherry pickers.
What they actually do (and what else they get called)
Window cleaners wash and polish windows and other glass fittings. Tasks include - (a) washing windows or other glass surfaces with water or various solutions, and drying and polishing them; (b) using ladders, swinging scaffolds, bosun’s chairs, hydraulic bucket trucks and other equipment to reach and clean windows in multistorey buildings; (c) selecting appropriate cleaning or polishing implements. Examples of the occupations classified here: - Window cleaner
- window cleaner
Job titles from the EU's ESCO list. Search for any of them on the jobs page and you'll end up here.
89 / 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.
The ILO's official verdict: Not Exposed
21 of 427 for holding up against AI
Not quite your week? Make it yours ↓
Why this score?
The ILO rated each of this job's 3 tasks for how much of it AI could do. They average 11.1 out of 100, so it holds up at 100 − 11.1 = 88.9.
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 53 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?
Window cleaners 89
vGo 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, routes and payments.
- Customer reminders.
- Quotes for commercial contracts.
Man
What people still do better
Judgement, trust and skilled hands. Hard to download.
- Working safely at height.
- Getting a streak-free finish.
- Choosing the right kit for each building.
Both
What's arriving
New work that only exists because the AI does.
- Water-fed poles replacing ladders.
- Working with facade robots and drones on large buildings.
- Cleaning solar panels.
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: Some
53 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 32 out of 100. The higher it is, the longer the robot waits.
hands-on
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, matched to one US job .
finger skill 35.7 · hand skill 34 · cramped spaces 25.5 · 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 15, against an average of 11. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.
- 15
Selecting appropriate cleaning or polishing implement.
Very Low - 10
Washing windows or other glass surfaces with water or various solutions, drying and polishing them
Very Low - 9
Using ladders, swinging scaffolds, bosun´s chairs, hydraulic bucket trucks and other equipment to reach and clean windows in multistorey buildings
Very Low
3 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
New · the weekly task audit
Your week, your score
The score above assumes window cleaners spend the same time on every task. You don't. Slide each task to how much of your week it takes, and we'll re-weight the same ILO ratings to fit your job.
- Some
- Some
- Some
Your score
89 / 100
Same as the job as a whole, so far.
Move the sliders to make it yours.
The job as a whole: 89 / 100
Link copied.
Nothing you enter leaves your browser. The link just carries your slider positions.
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 this one closely, so this is about as reliable as it gets here.
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 window cleaners be worried?
Short answer: not really. Put the kettle on.
No. Every task rates Very Low. Some big buildings use automated facade systems, and drones are being tried, but most windows are still cleaned by people with ladders, poles and ropes.
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.
- Vehicle cleaners 91
- Water and firewood collectors 91
- Crop farm labourers 91
- Garbage and recycling collectors 91
- Civil engineering labourers 91
- Forestry labourers 91
- Building construction labourers 91
- Sweepers and related labourers 91
- Other cleaning workers 90
- Mining and quarrying labourers 89
- Odd job persons 89
- Fishery and aquaculture labourers 89
- Mixed crop and livestock farm laboure… 89
- Garden and horticultural labourers 88
- Cleaners and helpers in offices, hote… 88
- Livestock farm labourers 88
- Manufacturing labourers not elsewhere… 88
- Kitchen helpers 87
- Drivers of animal-drawn vehicles and … 87
- Freight handlers 86
- Domestic cleaners and helpers 86
- Hand launderers and pressers 86
- Street and related service workers 82
- Refuse sorters 82
- Fast food preparers 82
- Hand packers 80
- Shelf fillers 80
- Street vendors (excluding food) 80