Will AI replace Laundry and Dry-Cleaning Workers?

Not as a job, but it is already doing parts of the work. Across the 23 official task statements scored for Laundry and Dry-Cleaning Workers (United States, SOC 51-6011), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 1 out of 100 (range 1–6, 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 “Laundry and Dry-Cleaning Workers” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Determine spotting procedures and proper solvents, based on fabric and stain types” (25/100, low); “Inspect soiled articles to determine sources of stains, to locate color imperfections, and to identify items requiring special treatment” (8/100, minimal); “Match sample colors, applying knowledge of bleaching agent and dye properties, and types, construction, conditions, and colors of articles” (6/100, 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 “Laundry and Dry-Cleaning Workers” 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: “Immerse articles in bleaching baths to strip colors” (0/100, minimal); “Hang curtains, drapes, blankets, pants, and other garments on stretch frames to dry” (0/100, minimal); “Iron or press articles, fabrics, and furs, using hand irons or pressing machines” (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 “Laundry and Dry-Cleaning Workers” 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 Laundry and Dry-Cleaning Workers 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 23 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

Laundry and Dry-Cleaning Workers

loading articles into washers or dry-cleaning machines, examining and sorting into lots articles and determining spotting procedures and proper solvents. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: starting washers, dry cleaners 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 1 out of 100 (1–6 allowing for uncertainty): minimal exposure, across 23 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 laundry and dry-cleaning workers 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

Operate or tend washing or dry-cleaning machines to wash or dry-clean industrial or household articles, such as cloth garments, suede, leather, furs, blankets, draperies, linens, rugs, and carpets. Includes spotters and dyers of these articles. The job title says “laundry” or “dry-cleaning workers”: officially one job, two names. The real job is the part underneath: starting washers, dry cleaners, driers or extractors and turn valves or levers to regulate machine processes and the volume of soap, detergent, water, bleach, starch and other additives. 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 laundry and dry-cleaning workers is not one task. It is 23 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is starting washers, dry cleaners, driers or extractors and turn valves or levers to regulate machine processes and the volume of soap, detergent, water, bleach, starch and other additives, 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.

1 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.

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

23 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.

Starting washers, dry cleaners
This work happens in the physical world: washers, dry cleaners, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Start washers, dry cleaners, driers, or extractors, and turn valves or levers to regulate machine processes and the volume of soap, detergent, water, bleach, starch, and other additives.” (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: Starting machines and setting valves means hands on the controls.
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.
Removing items from washers or dry-cleaning machines
This work happens in the physical world: items, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Remove items from washers or dry-cleaning machines, or direct other workers to do so.” (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 items out of machines is physical handling.
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.
Sorting and counting articles removed from dryers
This work happens in the physical world: articles, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Sort and count articles removed from dryers, and fold, wrap, or hang them.” (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: Sorting, folding and hanging clean items 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.
Examining and sorting into lots articles
This work happens in the physical world: lots articles, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Examine and sort into lots articles to be cleaned, according to color, fabric, dirt content, and cleaning technique required.” (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: Examining and sorting dirty articles means handling every one of 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.
Loading articles into washers or dry-cleaning machines
This work happens in the physical world: articles, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Load articles into washers or dry-cleaning machines, or direct other workers to perform loading.” (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: Loading machines means physically lifting and placing articles.
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.
Operating extractors and driers or directing their operation
This work happens in the physical world: extractors, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Operate extractors and driers, or direct their operation.” (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: Running extractors and dryers is hands-on machine 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.
Cleaning machine filters and lubricate equipment
This work happens in the physical world: machine filters, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Clean machine filters, and lubricate 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: Cleaning filters and oiling machines 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.
Receiving and marking articles for laundry or dry cleaning with identifying code numbers or names
This work happens in the physical world: articles, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Receive and mark articles for laundry or dry cleaning with identifying code numbers or names, using hand or machine markers.” (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: Marking each article requires physically tagging the garment.
The five ratings: output a model can produce 1/4 · 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.
Inspecting soiled articles to determine sources of stains
This work happens in the physical world: soiled articles, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Inspect soiled articles to determine sources of stains, to locate color imperfections, and to identify items requiring special treatment.” (O*NET task statement)
How this row was scored
Exposure score: 8 out of 100 (1–15 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Written guides help explain stains, but finding them means inspecting the actual garment in your hands.
The five ratings: output a model can produce 1/4 · 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 2/4.
Sprinkling chemical solvents over stains
This work happens in the physical world: chemical solvents over stains, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Sprinkle chemical solvents over stains, and pat areas with brushes or sponges to remove stains.” (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: Sprinkling solvents and patting stains 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.

Show the other 13 tasks

Determining spotting procedures and proper solvents
staying human
This work happens in the physical world: procedures, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Determine spotting procedures and proper solvents, based on fabric and stain types.” (O*NET task statement)
How this row was scored
Exposure score: 25 out of 100 (18–32 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Choosing the right solvent for a fabric and stain follows documented guidance, though someone must handle the garment first to judge it.
The five ratings: output a model can produce 2/4 · 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 2/4.
Matching sample colors
staying human
This work happens in the physical world: sample colors, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Match sample colors, applying knowledge of bleaching agent and dye properties, and types, construction, conditions, and colors of articles.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (0–13 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Color matching leans on documented dye knowledge but still needs someone holding the article and the sample under the light.
The five ratings: output a model can produce 1/4 · 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 1/4.
Mixing and adding detergents, dyes, bleaches, starches and other solutions and chemicals to clean, color, dry or stiffen articles
staying human
This work happens in the physical world: detergents, dyes, bleaches, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Mix and add detergents, dyes, bleaches, starches, and other solutions and chemicals to clean, color, dry, or stiffen articles.” (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: Mixing and adding chemicals to machines 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.
Spraying steam, water or air over spots to flush out chemicals, dry material, raise naps or brighten colors
staying human
This work happens in the physical world: steam, water or air over spots, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Spray steam, water, or air over spots to flush out chemicals, dry material, raise naps, or brighten colors.” (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: Spraying and flushing spots is hands-on cleaning.
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.
Presoaking sterilize, scrub, spot-clean and dry contaminated or stained articles, using neutralizer solutions and portable machines
staying human
This work happens in the physical world: sterilize, scrub, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Pre-soak, sterilize, scrub, spot-clean, and dry contaminated or stained articles, using neutralizer solutions and portable 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: work that happens in the physical world.
The rating behind it: Soaking, scrubbing and drying stained items 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.
Operating machines that comb, dry and polish furs, clean
staying human
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Operate machines that comb, dry and polish furs, clean, sterilize and fluff feathers and blankets, or roll and package towels.” (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: Operating these specialist fur, feather and towel machines is hands-on 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.
Spreading soiled articles on work tables
staying human
This work happens in the physical world: soiled articles, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Spread soiled articles on work tables, and position stained portions over vacuum heads or on marble slabs.” (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: Spreading and positioning articles on work tables 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 1/4.
Applying bleaching powders to spots and spraying them with steam to remove stains from fabrics that do not respond to other cleaning solvents
staying human
This work happens in the physical world: powders, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Apply bleaching powders to spots and spray them with steam to remove stains from fabrics that do not respond to other cleaning solvents.” (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 bleach and steaming spots is hands-on treatment.
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 bleaching agents with hot water in vats
staying human
This work happens in the physical world: agents, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Mix bleaching agents with hot water in vats, and soak material until it is bleached.” (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: Mixing bleach in vats and soaking material 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.
Identifying articles' fabrics and original dyes by sight and touch
staying human
This work happens in the physical world: articles' fabrics, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Identify articles' fabrics and original dyes by sight and touch, or by testing samples with fire or chemical reagents.” (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: Judging fabric by touch, or burning a sample to test it, needs the article in 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 1/4.
Ironing or pressing articles, fabrics and furs, using hand irons or pressing machines
staying human
This work happens in the physical world: articles, fabrics and furs, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Iron or press articles, fabrics, and furs, using hand irons or pressing 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: work that happens in the physical world.
The rating behind it: Ironing and pressing is physical work at the machine.
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.
Hanging curtains, drapes, blankets, pants and other garments on stretch frames to dry
staying human
This work happens in the physical world: curtains, drapes, blankets, pants and other garments, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Hang curtains, drapes, blankets, pants, and other garments on stretch frames to dry.” (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: Hanging garments on stretch frames 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 1/4.
Immersing articles in bleaching baths to strip colors
staying human
This work happens in the physical world: articles, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Immerse articles in bleaching baths to strip colors.” (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: Immersing articles in bleaching baths 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.

What this job pays, and how many people do it

Median pay: $34,890a 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: 198,040in 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 washers, dry cleaners, which happens with people and things rather than on a screen. The rows above are the evidence rather than the reassurance: starting washers, dry cleaners and removing items from washers or dry-cleaning machines. 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: starting washers, dry cleaners 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 23 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 washers, dry cleaners 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 starting washers, dry cleaners. 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 laundry and dry-cleaning workers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was pressers, textile, garment, and related materials: only about 5% 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 1/100 here, with only 0% of the task list in the top band, and “start washers, dry cleaners, driers, or extractors, and turn valves or levers…” 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.

Pressers, Textile, Garment, and Related Materials
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already sort and count articles removed from dryers, and fold, wrap, or hang them, and their equivalent is to hang, fold, package, and tag finished articles for delivery to customers. 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 26,120 of those jobs against 198,040 of yours (OEWS May 2025), 13% as many seats.
Look at that job’s page anyway →
Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already clean machine filters, and lubricate equipment, and their equivalent is to clean and lubricate machines. Across both published task lists that is about 3% of the durable work in that job.
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 →
Maintenance Workers, Machinery
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already clean machine filters, and lubricate equipment, and their equivalent is to lubricate or apply adhesives or other materials to machines, machine parts, or other…. Across both published task lists that is about 3% of the durable work in that job.
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 23 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 starting washers, dry cleaners 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 Launderers, dry cleaners and pressers 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

In UK official statistics this job is counted as Launderers, dry cleaners and pressers. 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.

Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

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 laundry / dry-cleaning workers 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 laundry / dry-cleaning workers 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 laundry / dry-cleaning workers existed, with researched problems, courses and people in the same boat, would you want in?

Your email

We use your email address for one thing: to tell you if a Space for laundry / dry-cleaning workers 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 Laundry and Dry-Cleaning Workers?: Not as a job, but it is already doing parts of the work. Across the 23 official task statements scored for Laundry and Dry-Cleaning Workers (United States, SOC 51-6011), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 1 out of 100 (range 1–6, 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 “Laundry and Dry-Cleaning Workers” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Determine spotting procedures and proper solvents, based on fabric and stain types” (25/100, low); “Inspect soiled articles to determine sources of stains, to locate color imperfections, and to identify items requiring special treatment” (8/100, minimal); “Match sample colors, applying knowledge of bleaching agent and dye properties, and types, construction, conditions, and colors of articles” (6/100, 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 “Laundry and Dry-Cleaning Workers” 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: “Immerse articles in bleaching baths to strip colors” (0/100, minimal); “Hang curtains, drapes, blankets, pants, and other garments on stretch frames to dry” (0/100, minimal); “Iron or press articles, fabrics, and furs, using hand irons or pressing machines” (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 “Laundry and Dry-Cleaning Workers” 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 Laundry and Dry-Cleaning Workers 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 23 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.
1 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: 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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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 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.