Will AI replace Cleaners of Vehicles and Equipment?

Not as a job, but it is already doing parts of the work. Across the 20 official task statements scored for Cleaners of Vehicles and Equipment (United States, SOC 53-7061), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 4 out of 100 (range 2–8, 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 “Cleaners of Vehicles and Equipment” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Maintain inventories of supplies” (38/100, low); “Inspect parts, equipment, or vehicles for cleanliness, damage, and compliance with standards or regulations” (14/100, minimal); “Monitor operation of cleaning machines and stop machines or notify supervisors when malfunctions occur” (14/100, 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 “Cleaners of Vehicles and Equipment” 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: “Fit boot spoilers, side skirts, or mud flaps to cars” (0/100, minimal); “Clean the plastic work inside cars, using paintbrushes” (0/100, minimal); “Transport materials, equipment, or supplies to or from work areas, using carts or hoists” (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 “Cleaners of Vehicles and Equipment” 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 Cleaners of Vehicles and Equipment 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 20 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.

US dataswitch to UK

Cleaners of Vehicles and Equipment

rinsing objects and placing them on drying racks, inspecting parts and mixing cleaning solutions, abrasive compositions or other compounds. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: cleaning and polishing vehicle windows is work software can't reach.

Your move: what you can actually do about this ↓

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 4 out of 100 (2–8 allowing for uncertainty): minimal exposure, across 20 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 cleaners of vehicles and equipment 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.

What shifts is the routine end of the work: the paper around the work, not the work.

Your week, as this page understands it

Wash or otherwise clean vehicles, machinery, and other equipment. Use such materials as water, cleaning agents, brushes, cloths, and hoses. The job title says “cleaners of vehicles” or “equipment”: officially one job, two names. The real job is the part underneath: cleaning and polishing vehicle windows. 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 cleaners of vehicles and equipment is not one task. It is 20 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is cleaning and polishing vehicle windows, 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.

How we know this

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.

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.

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

20 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.

Inspecting parts, equipment or vehicles for cleanliness, damage and compliance with standards or regulations
This work happens in the physical world: parts, equipment or vehicles, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Inspect parts, equipment, or vehicles for cleanliness, damage, and compliance with standards or regulations.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (7–21 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Photographs can help flag damage, but judging whether a vehicle is properly clean means looking closely in person.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Cleaning and polishing vehicle windows
This work happens in the physical world: vehicle windows, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Clean and polish vehicle windows.” (O*NET 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: Cleaning and polishing glass is done by hand on the vehicle.
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 2/4.
Scrubbing scrape or spraying machine parts, equipment or vehicles, using scrapers, brushes, clothes, cleaners, disinfectants, insecticides, acid, abrasives, vacuums or hoses
This work happens in the physical world: scrape, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Scrub, scrape, or spray machine parts, equipment, or vehicles, using scrapers, brushes, clothes, cleaners, disinfectants, insecticides, acid, abrasives, vacuums, or hoses.” (O*NET 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: Scrubbing and spraying parts and vehicles is manual cleaning 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 2/4.
Sweeping shovel or vacuum loose debris or salvageable scrap into containers and removing containers from work areas
This work happens in the physical world: shovel, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Sweep, shovel, or vacuum loose debris or salvageable scrap into containers and remove containers from work areas.” (O*NET 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: Sweeping, shovelling and shifting waste containers is physical 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 2/4.
Rinsing objects and placing them on drying racks
This work happens in the physical world: objects, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Rinse objects and place them on drying racks or use cloth, squeegees, or air compressors to dry surfaces.” (O*NET 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: Rinsing and drying items is done by hand at the wash bay.
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 2/4.
Driving vehicles to or from workshops or customers' workplaces or homes
This work happens in the physical world: vehicles, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Drive vehicles to or from workshops or customers' workplaces or homes.” (O*NET 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: Moving vehicles between customers and the workshop means driving them.
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 2/4.
Mixing cleaning solutions, abrasive compositions or other compounds, according to formulas
This work happens in the physical world: solutions, abrasive compositions or other compounds, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Mix cleaning solutions, abrasive compositions, or other compounds, according to formulas.” (O*NET 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: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Mixing cleaning compounds to a formula is done by hand at the bench.
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 3/4.
Maintaining inventories of supplies
This work happens in the physical world: inventories of supplies, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Maintain inventories of supplies.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Supply records are simple to keep in a system, though checking what is actually on the shelf means being there.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Presoaking or rinsing machine parts
This work happens in the physical world: machine parts, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Pre-soak or rinse machine parts, equipment, or vehicles by immersing objects in cleaning solutions or water, manually or using hoists.” (O*NET 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: Soaking and rinsing parts involves lifting and immersing them.
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 2/4.
Turning valves or disconnect hoses to eliminate water
This work happens in the physical world: valves, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Turn valves or disconnect hoses to eliminate water, cleaning solutions, or vapors from machinery or tanks.” (O*NET 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: Turning valves and disconnecting hoses is hands-on work at the 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 2/4.

Show the other 10 tasks

Monitoring operation of cleaning machines and stop machines or notifying supervisors when malfunctions occur
staying human
This work happens in the physical world: operation of cleaning machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Monitor operation of cleaning machines and stop machines or notify supervisors when malfunctions occur.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (7–21 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Fault alerts can be flagged automatically, but watching a wash line and stopping it means being on the floor.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Pressing buttons to activate cleaning equipment or machines
staying human
This work happens in the physical world: buttons, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Press buttons to activate cleaning equipment or machines.” (O*NET 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: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Starting the machines means pressing the buttons on them.
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 3/4.
Turning valves or handles on equipment to regulate pressure or flow of water
staying human
This work happens in the physical world: valves, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Turn valves or handles on equipment to regulate pressure or flow of water, air, steam, or abrasives from sprayer nozzles.” (O*NET 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: Adjusting pressure and flow controls means operating the equipment directly.
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 2/4.
Lubricating machinery, vehicles or equipment or performing minor repairs or adjustments, using hand tools
staying human
This work happens in the physical world: machinery, vehicles or equipment or performing minor repairs, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Lubricate machinery, vehicles, or equipment or perform minor repairs or adjustments, using hand tools.” (O*NET 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: Greasing machinery and making small repairs is hands-on maintenance.
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 2/4.
Disassembling and reassembling machines or equipment or removing and reattach vehicle parts or trim, using hand tools
staying human
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Disassemble and reassemble machines or equipment or remove and reattach vehicle parts or trim, using hand tools.” (O*NET 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: Taking machines and vehicle parts apart and refitting them needs tools and hands.
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 2/4.
Connecting hoses or lines to pumps or other equipment
staying human
This work happens in the physical world: hoses, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Connect hoses or lines to pumps or other equipment.” (O*NET 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: Connecting hoses and lines to pumps is done by hand.
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 2/4.
Applying paints, dyes, polishes, reconditioners, waxes or masking materials to vehicles to preserve, protect or restoring color or condition
staying human
This work happens in the physical world: paints, dyes, polishes, reconditioners, waxes or masking materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Apply paints, dyes, polishes, reconditioners, waxes, or masking materials to vehicles to preserve, protect, or restore color or condition.” (O*NET 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: Applying polish, wax or paint to a vehicle is physical work on the bodywork.
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 2/4.
Transporting materials, equipment or supplies to or from work areas, using carts or hoists
staying human
This work happens in the physical world: materials, equipment or supplies, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Transport materials, equipment, or supplies to or from work areas, using carts or hoists.” (O*NET 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: Moving supplies and equipment around the site means pushing carts and lifting.
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 2/4.
Cleaning the plastic work inside cars
staying human
This work happens in the physical world: the plastic work inside cars, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Clean the plastic work inside cars, using paintbrushes.” (O*NET 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: Brushing out the plastic trim inside a car is close manual 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 2/4.
Fitting boot spoilers, side skirts or mud flaps to cars
staying human
This work happens in the physical world: boot spoilers, side skirts or mud flaps, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Fit boot spoilers, side skirts, or mud flaps to cars.” (O*NET 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: Fitting spoilers, skirts and mud flaps is bolting parts onto a car.
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 2/4.

What this job pays, and how many people do it

Median pay: $35,830a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
People doing this job: 380,430in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)

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 vehicle windows, which happens with people and things rather than on a screen. The rows above are the evidence rather than the reassurance: inspecting parts, equipment or vehicles for cleanliness, damage and compliance with standards or regulations and cleaning and polishing vehicle windows. 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: cleaning and polishing vehicle windows 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 20 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 vehicle windows are in scope or not. Nothing to log into, no license 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 inspecting parts, equipment or vehicles for cleanliness, damage and compliance with standards or regulations. 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, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, 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 US occupations to cleaners of vehicles and equipment (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was furnace, kiln, oven, drier, and kettle operators and tenders: only about 7% of its durable work is work you already do and there are far fewer of those jobs than of yours. And on the numbers you do not need one. This job scores 4/100 here, with only 0% of the task list in the top band, and “inspect parts, equipment, or vehicles for cleanliness, damage, and compliance with standards…” 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 830 US 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.

Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already transport materials, equipment, or supplies to or from work areas, using carts or…, and their equivalent is to transport materials and products to and from work areas, manually or using carts…. Across both published task lists that is about 7% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 14,280 of those jobs against 380,430 of yours (OEWS May 2025), 4% as many seats.
Look at that job’s page anyway →
Helpers--Installation, Maintenance, and Repair Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already lubricate machinery, vehicles, or equipment or perform minor repairs or adjustments, using hand…, and their equivalent is to clean or lubricate vehicles, machinery, equipment, instruments, tools, work areas, and other objects…. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already lubricate machinery, vehicles, or equipment or perform minor repairs or adjustments, using hand…, and their equivalent is to clean, adjust, and maintain equipment, using hand tools. Across both published task lists that is about 5% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 5% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 26,000 of those jobs against 380,430 of yours (OEWS May 2025), 7% as many seats.
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 20 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 cleaning and polishing vehicle windows 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 States figures

The United Kingdom splits this work across more than one official group, of which Hospital porters is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

The other groups this work is counted across:

In UK official statistics this job is counted as Hospital porters, Vehicle valeters and cleaners, Elementary cleaning occupations n.e.c. and Cleaners and domestics. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

Your route through this

Where to go next, and what it costs

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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 cleaners of vehicles / equipment launches. Nothing else.

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No Space for cleaners of vehicles / equipment 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 cleaners of vehicles / equipment existed, with researched problems, courses and people in the same boat, would you want in?

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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 Cleaners of Vehicles and Equipment?: Not as a job, but it is already doing parts of the work. Across the 20 official task statements scored for Cleaners of Vehicles and Equipment (United States, SOC 53-7061), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 4 out of 100 (range 2–8, 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 “Cleaners of Vehicles and Equipment” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Maintain inventories of supplies” (38/100, low); “Inspect parts, equipment, or vehicles for cleanliness, damage, and compliance with standards or regulations” (14/100, minimal); “Monitor operation of cleaning machines and stop machines or notify supervisors when malfunctions occur” (14/100, 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 “Cleaners of Vehicles and Equipment” 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: “Fit boot spoilers, side skirts, or mud flaps to cars” (0/100, minimal); “Clean the plastic work inside cars, using paintbrushes” (0/100, minimal); “Transport materials, equipment, or supplies to or from work areas, using carts or hoists” (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 “Cleaners of Vehicles and Equipment” 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 Cleaners of Vehicles and Equipment 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 20 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

Worth knowing about these figures

The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
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: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

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.

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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 2026-08-05. 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.