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Craft and related trades workers · code 7122

Floor layers and tile setters

AI can work out how many tiles you need. It can't get the grout lines straight.

AI barely touches any of the four tasks: preparing floors and walls, and laying carpet, tiles and mosaics.

What they actually do (and what else they get called)

Floor layers and tile setters install, maintain and repair flooring, and cover floors, walls and other surfaces with carpets, tiles or mosaic panels for decorative or other purposes. Tasks include - (a) preparing floor areas for covering with a variety of materials; (b) assembling carpet, tiles or other materials and laying them on floors according to design and other specifications; (c) preparing wall areas for covering with tiles or other materials for decorative or other purposes such as acoustic insulation; (d) setting tiles and constructing and laying mosaic panels to walls, floors and other surfaces. Examples of the occupations classified here: - Carpet layer - Marble setter - Parquetry worker - Tile setter

  • carpet fitter
  • hardwood floor layer
  • tile fitter
  • resilient floor layer

Job titles from the EU's ESCO list. Search for any of them on the jobs page and you'll end up here.

90 / 100

Strongly holds up against AI

Most of this job still needs an actual human. AI would mostly be making the tea.

Not the odds you keep your job. It's how much of the everyday work still needs a person, out of 100.

← Robot's turf Human's turf →

The ILO's official verdict: Not Exposed

13 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 4 tasks for how much of it AI could do. They average 10.2 out of 100, so it holds up at 100 − 10.2 = 89.8.

That's the whole sum. No black box, no secret sauce: you can check it against the ILO's study. ILO group “Not Exposed”: None of this job's tasks scored high enough to count.

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Pinch of salt: this number is probably too rosy

It only asks what AI software could do, and this job is 66 out of 100 hands-on. A chatbot can't do that part. A robot might, one day, and nobody measures how close that is.

Man v Bot · compare

Who holds up better?

Floor layers and tile setters 90

v

Go on, try your partner's job. Or your sibling's. We won't tell.

Man v Bot

Where the bot helps, and where the human wins

Bot

What AI takes

The routine stuff. You may not miss it.

  • Measuring rooms and calculating materials from plans.
  • Quotes and ordering.
  • Showing customers a design before it's laid.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

  • Preparing uneven floors and walls properly.
  • Cutting and setting tiles neatly around awkward shapes.
  • A finish customers look at every day.

Both

What's arriving

New work that only exists because the AI does.

  • Customers arriving with digital mock-ups of what they want.
  • New materials and underfloor heating systems.
  • Retrofit work in older homes.

Written by a human, from the ILO's task ratings below. Yes, we see the irony. Read it as one person's take.

Meet your two rivals

They're not the same machine. AI software can only reach the parts of a job done on a screen or in words. A chatbot can't climb a ladder. A robot might, one day, if it doesn't fall off.

The AI

Lives in a screen. Never had to fix a boiler.

Threat level: Low

18 could do without you

It could take over very little of the work on its own. It could help with 57 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Higher

66 how hands-on

Most of this job needs hands, a body or turning up. That's robot territory, and nobody measures how far robots have got well enough to give you a number. We won't make one up.

So could a robot do the physical part?

More fine handwork, and more time in cramped or awkward spots, than a typical hands-on job. That is exactly what machines find hardest: they do best in tidy, predictable places.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 48 out of 100. The higher it is, the longer the robot waits.

66 How
hands-on
48 How fiddly
and cramped

Kind of hands-on work Hands-on, fiddly work in tight spaces

The small print

They aren't a robot score, and nothing on this page is. Nobody measures how far robots have got well enough to put a number on it. These are the closest thing we have: how hands-on the job is, and how much fiddly, cramped work it takes, which is what Frey and Osborne found hardest for machines.

From O*NET, averaged across 4 US jobs .

finger skill 41.5 · hand skill 42.5 · cramped spaces 60.5 · averaged · rule: hands-on 50+ · fiddly 39.4+

estimated O*NET 31.0 BLS occupational crosswalk chain ISCO-08 to 2010 SOC (Aug 2012, rev. Jun 2015); 2010 to 2018 SOC (Nov 2017); O*NET-SOC 2019 taxonomy data 0.8-observed-use measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

The job, task by task

Every job is really a bundle of tasks. Here's this one unpacked, with how much of each AI could do, out of 100. The score is the average, and averages can hide a lot.

AI's favourite task here scores 13, against an average of 10. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

  1. 13

    Assembling carpet, tiles or other materials and laying them on floors according to design and other specifications

    Very Low
  2. 10

    Setting tiles and constructing and laying mosaic panels to walls, floors and other surfaces.

    Very Low
  3. 10

    Preparing floor areas for covering with a variety of materials

    Very Low
  4. 9

    Preparing wall areas for covering with tiles or other materials for decorative or other purposes such as acoustic insulation

    Very Low

4 tasks · the High to Very Low labels are the ILO's

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Sidekick or replacement?

A tool that makes you faster is a very different thing from one that does the task while you watch. Jobs and Skills Australia measured the two separately, which is handy.

AI helps you
57
AI does it for you
18

Sidekick wins, by 39 points.

How good a match is this?

2 Australian jobs match this one, and we've simply averaged them. Treat the numbers as a rough guide.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.8-observed-use matched to Australian jobs 3321, 3334

Meanwhile, in real life…

Everything else here is about what AI could do. This is what people actually did. When they asked Claude for help with this kind of work, did they hand the whole task over, or roll their sleeves up and work through it together?

Tiny sample alert This job makes up under 0.01% of all the conversations: a tiny slice, mostly the paperwork rather than the work. People using AI for a job isn't the same as AI being able to do it.

Out of every 100 conversations

50
50

worked through it together handed it over

The small print

Anthropic publishes this about its own product, and it doesn't feed into any score here. These are people who use Claude, not workers in general, so little use here doesn't mean AI can't reach the job.

The data is sorted by US job, and this one matches 2 of them. We've combined them, counting each by how often it comes up in the conversations, so the busiest one counts most.

These two add up to 100. The bars elsewhere on the page don't, because each is scored on its own, so we don't put them side by side.

observed use Anthropic Economic Index release 2026-06-26, period beginning 2026-05-01 data 0.8-observed-use 2026-05-01 · Anthropic, about its own product

So, should floor layers and tilers be worried?

Short answer: not really. Put the kettle on.

No. Laying carpet and tiles and preparing surfaces all rate Very Low. Quoting and layout planning are where AI helps, and customers can now preview a floor before choosing. The fitting itself needs a skilled pair of hands.

Worth remembering: we score tasks, not jobs. When AI takes on a task, the job usually reshuffles around it rather than vanishing.

Where this job could lead

None of the new jobs we've written up start from here yet. Could be you're the first. See the jobs AI is creating →

How are the neighbours doing?

There are 65 jobs in the “Craft and related trades workers” family, averaging 83. This one is above the family average.

Pit this against any other job →