Will AI replace Painters, Construction and Maintenance?

Not as a job, but it is already doing parts of the work. Across the 17 official task statements scored for Painters, Construction and Maintenance (United States, SOC 47-2141), 5% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 8 out of 100 (range 7–13, 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 “Painters, Construction and Maintenance” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Calculate amounts of required materials and estimate costs, based on surface measurements or work orders” (69/100, high); “Select and purchase tools or finishes for surfaces to be covered, considering durability, ease of handling, methods of application, and customers' wishes” (48/100, partial); “Read work orders or receive instructions from supervisors or homeowners to determine work requirements” (37/100, low). 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 “Painters, Construction and Maintenance” stay human?

About 91% 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: “Cut stencils and brush or spray lettering or decorations on surfaces” (0/100, minimal); “Waterproof buildings, using waterproofers or caulking” (0/100, minimal); “Use special finishing techniques such as sponging, ragging, layering, or faux finishing” (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 “Painters, Construction and Maintenance” do about AI?

Start from the ledger rather than the headline: 5% of this job's weighted core work is exposed, and roughly 91% 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 Painters, Construction and Maintenance 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 17 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

Painters, Construction and Maintenance

covering surfaces with dropcloths or masking tape and paper to protect surfaces during painting, erecting scaffolding or swing gates and washing and treating surfaces with oil. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: covering surfaces with dropcloths or masking tape and paper to protect surfaces during painting 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: 5%
changing shape: 4%
staying human: 91%

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

Whole-job exposure score 8 out of 100 (7–13 allowing for uncertainty): minimal exposure, across 17 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 painters, construction and maintenance 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 calculating amounts of required materials and estimating costs: the paper around the work, not the work.

Your week, as this page understands it

Paint walls, equipment, buildings, bridges, and other structural surfaces, using brushes, rollers, and spray guns. May remove old paint to prepare surface prior to painting. May mix colors or oils to obtain desired color or consistency. The job title says “painters”, “construction” or “maintenance”: officially one job, several names. The real job is the part underneath: covering surfaces with dropcloths or masking tape and paper to protect surfaces during painting. 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 painters, construction and maintenance is not one task. It is 17 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is covering surfaces with dropcloths or masking tape and paper to protect surfaces during painting, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 5%
changing shape: 4%
staying human: 91%

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

1 task

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.

Calculating amounts of required materials and estimating costs
This is reading one thing and writing another: amounts of required materials in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Calculate amounts of required materials and estimate costs, based on surface measurements or work orders.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Once measurements are taken, working out paint quantities and costs is straightforward arithmetic software does instantly.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

Changing shape

1 task

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.

Selecting and purchasing tools or finishes for surfaces
The software now makes the first pass at tools, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 3 · Core
Source: “Select and purchase tools or finishes for surfaces to be covered, considering durability, ease of handling, methods of application, and customers' wishes.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (41–55 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Comparing products on durability, coverage and price is documented research, even if the buying trip is physical.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

Staying human

15 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.

Covering surfaces with dropcloths or masking tape and paper to protect surfaces during painting
This work happens in the physical world: surfaces, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Cover surfaces with dropcloths or masking tape and paper to protect surfaces during painting.” (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: Laying dust sheets and masking up is physical preparation 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.
Filling cracks, holes or joints with caulk, putty, plaster or other fillers
This work happens in the physical world: cracks, holes or joints, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Fill cracks, holes, or joints with caulk, putty, plaster, or other fillers, using caulking guns or putty knives.” (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: Filling cracks and holes needs hands and tools on the surface.
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.
Reading work orders or receiving instructions from supervisors or homeowners to determine work requirements
The ratings behind this row put work orders well outside what today's tools can do on their own.
importance 5 · Core
Source: “Read work orders or receive instructions from supervisors or homeowners to determine work requirements.” (O*NET task statement)
How this row was scored
Exposure score: 37 out of 100 (30–44 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.
The rating behind it: Written work orders are easy to interpret, but understanding what a customer actually wants often happens on site.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Smoothing surfaces, using sandpaper, scrapers, brushes, steel wool or sanding machines
This work happens in the physical world: surfaces, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Smooth surfaces, using sandpaper, scrapers, brushes, steel wool, or sanding 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: Sanding and smoothing a surface 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.
Erecting scaffolding or swing gates
This work happens in the physical world: gates, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Erect scaffolding or swing gates, or set up ladders, to work above ground level.” (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: Putting up scaffolding and ladders is physical work on site.
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 paint, stain, varnish, enamel or other finishes to equipment, buildings, bridges or other structures, using brushes, spray guns or rollers
This work happens in the physical world: paint, stain, varnish, enamel or other finishes, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Apply paint, stain, varnish, enamel, or other finishes to equipment, buildings, bridges, or other structures, using brushes, spray guns, or rollers.” (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 paint with a brush, roller or spray gun is entirely hands-on.
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 primers or sealers to prepare new surfaces
This work happens in the physical world: primers, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Apply primers or sealers to prepare new surfaces, such as bare wood or metal, for finish coats.” (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: Priming and sealing surfaces is hands-on preparation.
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.
Washing and treating surfaces with oil
This work happens in the physical world: surfaces, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Wash and treat surfaces with oil, turpentine, mildew remover, or other preparations, and sand rough spots to ensure that finishes will adhere properly.” (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: Washing, treating and sanding surfaces is done by hand before painting starts.
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 7 tasks

Removing fixtures, pictures, door knobs, lamps or electric switch covers prior to painting
staying human
This work happens in the physical world: fixtures, pictures, door knobs, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Remove fixtures such as pictures, door knobs, lamps, or electric switch covers prior to painting.” (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 down fittings before painting needs hands on site.
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 and matching colors of paint
staying human
This work happens in the physical world: colors of paint, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Mix and match colors of paint, stain, or varnish with oil or thinning and drying additives to obtain desired colors and consistencies.” (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 matching colours by eye in a real bucket of paint is hands-on craft.
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 old finishes
staying human
This work happens in the physical world: old finishes, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Remove old finishes by stripping, sanding, wire brushing, burning, or using water or abrasive blasting.” (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: Stripping and blasting old finishes is physical work with tools.
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.
Polishing final coats to specified finishes
staying human
This work happens in the physical world: final coats, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Polish final coats to specified finishes.” (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: Polishing a finish to specification 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.
Using special finishing techniques, sponging, ragging, layering or faux finishing
staying human
This work happens in the physical world: special finishing techniques, sponging, ragging, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Use special finishing techniques such as sponging, ragging, layering, or faux finishing.” (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: Sponging, ragging and faux finishes are skilled handwork on the wall.
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.
Waterproofing buildings, using waterproofers or caulking
staying human
This work happens in the physical world: buildings, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Waterproof buildings, using waterproofers or caulking.” (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: Waterproofing a building means applying materials by hand on site.
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.
Cutting stencils and brush or spraying lettering or decorations on surfaces
staying human
This work happens in the physical world: stencils, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Cut stencils and brush or spray lettering or decorations on 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: Cutting stencils and spraying lettering is physical work on the surface itself.
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: $49,400a 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: 225,190in 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. Most of what is shifting here is reading one thing and writing another: amounts of required materials in, a record out. The rows above are exactly that shape: calculating amounts of required materials and estimating costs and selecting and purchasing tools or finishes for surfaces. What it cannot do is be there in the room, and that is still where surfaces get done. 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: covering surfaces with dropcloths or masking tape and paper to protect surfaces during painting is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 5% of this job's task weight sits in rows the software is already learning, 4% in rows that change shape rather than disappear, and 91% in rows it is nowhere near. That is the position, measured across 17 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. Calculating amounts of required materials and estimating costs 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 amounts of required materials, 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 cracks, holes or joints 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 selecting and purchasing tools or finishes for surfaces. 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 painters, construction and maintenance (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was helpers--painters, paperhangers, plasterers, and stucco masons: only about 10% of its durable work is work you already do, it pays 18.1% less and there are far fewer of those jobs than of yours. And on the numbers you do not need one. This job scores 8/100 here, with only 5% of the task list in the top band, and “cover surfaces with dropcloths or masking tape and paper to protect surfaces…” 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.

Helpers--Painters, Paperhangers, Plasterers, and Stucco Masons
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “erect scaffolding or swing gates, or set up ladders, to work above ground level”. Across the whole of both lists that adds up to about 10% of the work in that job the software is not taking.
Why I am not recommending it: You would be starting most of it from nothing: about 10% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. It is a pay cut, in those words: $40,470 against your $49,400, 18.1% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 7,490 of those jobs against 225,190 of yours (OEWS May 2025), 3% as many seats.
Look at that job’s page anyway →
Floor Sanders and Finishers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already smooth surfaces, using sandpaper, scrapers, brushes, steel wool, or sanding machines, and their equivalent is to attach sandpaper to rollers of sanding machines. Across both published task lists that is about 9% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 9% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 3,720 of those jobs against 225,190 of yours (OEWS May 2025), 2% as many seats.
Look at that job’s page anyway →
Furniture Finishers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already smooth surfaces, using sandpaper, scrapers, brushes, steel wool, or sanding machines, and their equivalent is to smooth, shape, and touch up surfaces to prepare them for finishing, using sandpaper…. 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 14,480 of those jobs against 225,190 of yours (OEWS May 2025), 6% 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: 5% of its task weight, across 17 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 covering surfaces with dropcloths or masking tape and paper to protect surfaces during painting 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 Painters and decorators 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 Painters and decorators. 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 painters / construction / maintenance 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 painters / construction / maintenance 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 painters / construction / maintenance 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 painters / construction / maintenance 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 Painters, Construction and Maintenance?: Not as a job, but it is already doing parts of the work. Across the 17 official task statements scored for Painters, Construction and Maintenance (United States, SOC 47-2141), 5% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 8 out of 100 (range 7–13, 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 “Painters, Construction and Maintenance” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Calculate amounts of required materials and estimate costs, based on surface measurements or work orders” (69/100, high); “Select and purchase tools or finishes for surfaces to be covered, considering durability, ease of handling, methods of application, and customers' wishes” (48/100, partial); “Read work orders or receive instructions from supervisors or homeowners to determine work requirements” (37/100, low). 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 “Painters, Construction and Maintenance” stay human?: About 91% 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: “Cut stencils and brush or spray lettering or decorations on surfaces” (0/100, minimal); “Waterproof buildings, using waterproofers or caulking” (0/100, minimal); “Use special finishing techniques such as sponging, ragging, layering, or faux finishing” (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 “Painters, Construction and Maintenance” do about AI?: Start from the ledger rather than the headline: 5% of this job's weighted core work is exposed, and roughly 91% 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 Painters, Construction and Maintenance 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 17 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.

How we score a job Download this release Look 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 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.