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Street cleaners

removing litter from public areas, eradicating graffiti from public and private surfaces using appropriate removal techniques and driving heavy machinery with attachments to maintain paved surfaces and remove snow or ice. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: removing litter from public areas is work software can't reach.

Your move: what you can actually do about this ↓

What shifts is the routine end of the work. This page scores what today's tools actually do, not headlines.

Your week, as this page understands it

Street cleaners clean, sweep and remove refuse from public thoroughfares. The job title says “street cleaners”. The real job is the part underneath: removing litter from public areas. That is the thing someone has to be right about.

The exposed part of this job is specific, and we won’t pretend it is coming back. But street cleaners is not one task. It is 6 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is removing litter from public areas, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI
0%
changing shape
0%
staying human
100%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, across 6 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.

How we know this

What is measured: Every published task statement for street cleaners is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

Shifting to AI

0 tasks

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.

Changing shape

0 tasks

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.

Staying human

6 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

  • Removing litter from public areas

    This work happens in the physical world: litter, in a real place. Software cannot follow it there.

    importance 90 · 9222/00
    Source: “Remove litter from public areas.” (UK task statement)
    How this row was scored

    Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.

    Why it sits in this group: work that happens in the physical world.

    The rating behind it: Picking up litter is physical work in the place where the litter is.

    The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.

  • Clearing drains and ditches

    This work happens in the physical world: drains, in a real place. Software cannot follow it there.

    importance 80 · 9222/00
    Source: “Clear drains and ditches.” (UK task statement)
    How this row was scored

    Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.

    Why it sits in this group: work that happens in the physical world.

    The rating behind it: Clearing drains and ditches is physical outdoor work.

    The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.

  • Eradicating graffiti from public and private surfaces using appropriate removal techniques

    This work happens in the physical world: graffiti, in a real place. Software cannot follow it there.

    importance 75 · 9222/00
    Source: “Eradicate graffiti from public and private surfaces using appropriate removal techniques.” (UK task statement)
    How this row was scored

    Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.

    Why it sits in this group: work that happens in the physical world.

    The rating behind it: Removing graffiti means being at the wall with the right equipment.

    The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.

  • Driving heavy machinery with attachments to maintain paved surfaces and remove snow or ice

    This work happens in the physical world: heavy machinery, in a real place. Software cannot follow it there.

    importance 70 · 9222/00
    Source: “Drive heavy machinery with attachments to maintain paved surfaces and remove snow or ice.” (UK task statement)
    How this row was scored

    Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.

    Why it sits in this group: work that happens in the physical world.

    The rating behind it: Driving heavy machinery means sitting in the cab.

    The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.

  • Using a pressure washer to clean paved areas

    This work happens in the physical world: a pressure washer, in a real place. Software cannot follow it there.

    importance 70 · 9222/00
    Source: “Use a pressure washer to clean paved areas.” (UK task statement)
    How this row was scored

    Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.

    Why it sits in this group: work that happens in the physical world.

    The rating behind it: Pressure washing a pavement means someone holding the lance and walking the area.

    The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.

  • Using specialist machines to remove chewing gum from pavements

    This work happens in the physical world: specialist machines, in a real place. Software cannot follow it there.

    importance 60 · 9222/00
    Source: “Use specialist machines to remove chewing gum from pavements.” (UK task statement)
    How this row was scored

    Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.

    Why it sits in this group: work that happens in the physical world.

    The rating behind it: Gum removal machines have to be pushed and operated on the street by a person.

    The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.

What this job pays, and how many people do it

Median pay
£27,600a year, before tax, the middle of the range, so half earn more and half earn less.ashe-t14, 2025 · ASHE 2025 provisional (reference April 2025)Provisional, because the ONS revises this figure in the autumn.
How we know this

Source: ashe-t14

Reference period: ASHE 2025 provisional (reference April 2025)

Rounding: Shown to the nearest £100. The exact published figure is in the downloadable dataset. We do not render pounds the survey cannot support.

People doing this job
14,000in the UK, 2026.nomis-aps · Apr 2025-Mar 2026 (latest APS 12-month period)This headcount comes from a survey, not a census, so treat it as a good estimate rather than an exact count.

What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.

Why this is shifting

The reason is boringly specific. Almost none of this job is reading one thing and writing another (the shape today's tools are built for), because the work turns on litter, which happens with people and things rather than on a screen. The rows above are the evidence rather than the reassurance: removing litter from public areas and clearing drains and ditches. The parts that are changing are the paperwork and the tools around the job, not the middle of it, which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

Start with what does not change: removing litter from public areas is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 0% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 100% in rows it is nowhere near. That is the position, measured across 6 scored tasks. It is not a forecast about you.

So the thing worth your attention is not the job going away. It is the layer around it. The routine end of the work is the part turning into software, and being the person who understands that layer is worth money.

This week: one thing

Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to the routine work, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.

What you end up holding
a straight answer about what is actually being rolled out, and when
How long it takes
ten minutes

If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether litter is in scope or not. Nothing to log into, no licence needed.

Over the next 90 days

Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near removing litter from public areas. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.

Over the next 12 months

On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. Before you pay for anything, spend an hour with National Careers Service. It is free and government-funded, and it is listed below with the rest of the free routes.

The roads out of here, and why I am not sending you down them

I looked at the obvious moves out of this job, and here is what I found.

I checked the 12 nearest UK occupations to street cleaners (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was elementary sales occupations n.e.c.: only about 4% of its durable work is work you already do. And on the numbers you do not need one. This job scores 0/100 here, with only 0% of the task list in the top band, and “remove litter from public areas” is not work that hands over cleanly. None of them beats deepening what you already have.

How that was checked: this job was compared against all 412 UK occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.

3 moves I checked and rejected

These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.

  • Elementary sales occupations n.e.c.

    Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “remove litter from public areas”. Across the whole of both lists that adds up to about 4% of the work in that job the software is not taking.

    Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step. This release publishes no median pay for that job, so I cannot show you what the move costs or pays. I do not recommend a move I cannot price.

    Look at that job’s page anyway →

  • Industrial cleaning process occupations

    Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “eradicate graffiti from public and private surfaces using appropriate removal techniques”. Across the whole of both lists that adds up to about 3% of the work in that job the software is not taking.

    Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step.

    Look at that job’s page anyway →

  • Road construction operatives

    Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “drive heavy machinery with attachments to maintain paved surfaces and remove snow or ice”. Across the whole of both lists that adds up to about 3% of the work in that job the software is not taking.

    Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step.

    Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

  • The headline number you read somewhere

    The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 0% of its task weight, across 6 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.

  • The headlines about your trade disappearing

    They are usually about the technology, not the timetable. Changes to work like removing litter from public areas arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.

  • Retraining out of a job that is holding up

    On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.

  • The “obvious” next job everyone suggests

    I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.

You are reading the United Kingdom figures

The United States splits this work across more than one official group, of which Grounds Maintenance Workers, All Other is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United States page →partial match

In US official statistics this job is counted as Grounds Maintenance Workers, All Other. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

Your route through this

Where to go next, and what it costs

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

Why there is no community here

Collab365, who build this site, run paid Spaces for a small number of subjects, and none of them is built for this job. We are not going to point you at the nearest one and call it a fit.

So the free services listed on this page are the whole answer, and it is the same answer we would give a friend.

Noted, and thank you. We’ll email you if a Space for street cleaners launches. Nothing else.

That did not look like an email address, so nothing was saved. Have another go below.

We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.

No Space for street cleaners yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for street cleaners existed, with researched problems, courses and people in the same boat, would you want in?

We use your email address for one thing: to tell you if a Space for street cleaners launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.

Questions people ask about this job

Will AI replace Street cleaners?
Not as a job, but it is already doing parts of the work. Across the 6 official task statements scored for Street cleaners (United Kingdom, SOC 9222), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 0 out of 100 (range 0–4, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
Which tasks in “Street cleaners” can AI already do?
The highest-scoring tasks in release 2026-q4.1 are: “Remove litter from public areas” (0/100, minimal); “Clear drains and ditches” (0/100, minimal); “Eradicate graffiti from public and private surfaces using appropriate removal techniques” (0/100, minimal). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Street cleaners” stay human?
About 100% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Use specialist machines to remove chewing gum from pavements” (0/100, minimal); “Use a pressure washer to clean paved areas” (0/100, minimal); “Drive heavy machinery with attachments to maintain paved surfaces and remove snow or ice” (0/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
What should someone working in “Street cleaners” do about AI?
Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 100% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
How is the AI exposure score for Street cleaners calculated?
Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 6 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

About the data on this page

  • Provisional, because the ONS revises this figure in the autumn.
  • This headcount comes from a survey, not a census, so treat it as a good estimate rather than an exact count.
  • The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
  • 4 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
  • Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Task statements
gaisi-indexProcessing: catalogue-bridge → ssc-relatedness-weighting → task-scoring → score-aggregation
Task weights
gaisi-index (relatedness)
Scores
Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
Pay and employment
ashe-t14 (ASHE 2025 provisional (reference April 2025))nomis-aps (Apr 2025-Mar 2026 (latest APS 12-month period))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

How we score a jobDownload this releaseLook up another job

Using these figures?

Cite this

Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.

Plain text

Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).

BibTeX

@misc{collab365futureproof2026q41,
  title        = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
  author       = {{Collab365}},
  year         = {2026},
  url          = {https://futureproof.collab365.com/data/2026-q4.1},
  note         = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published . Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.