Will AI replace Fabric and Apparel Patternmakers?

Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Fabric and Apparel Patternmakers (United States, SOC 51-6092), 23% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 37 out of 100 (range 32–44, band: low). 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 “Fabric and Apparel Patternmakers” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Input specifications into computers to assist with pattern design and pattern cutting” (93/100, very high); “Determine the best layout of pattern pieces to minimize waste of material, and mark fabric accordingly” (69/100, high); “Compute dimensions of patterns according to sizes, considering stretching of material” (68/100, high). 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 “Fabric and Apparel Patternmakers” stay human?

About 65% 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: “Trace outlines of specified patterns onto material, and cut fabric, using scissors” (0/100, minimal); “Trace outlines of paper onto cardboard patterns, and cut patterns into parts to make templates” (0/100, minimal); “Test patterns by making and fitting sample garments” (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 “Fabric and Apparel Patternmakers” do about AI?

Start from the ledger rather than the headline: 23% of this job's weighted core work is exposed, and roughly 65% 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 Fabric and Apparel Patternmakers 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 16 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

Fabric and Apparel Patternmakers

creating a master pattern for each size within a range of garment sizes, marking samples and finished patterns with information and drawing outlines of pattern parts by adapting or copying existing patterns. If that's your week, this page is about your job.

The honest answer

AI is already taking a real slice of the routine work here: inputting specifications into computers to assist with pattern design and pattern cutting. That is a slice of tasks, not of you.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 23%
changing shape: 12%
staying human: 65%

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

Whole-job exposure score 37 out of 100 (32–44 allowing for uncertainty): low exposure, across 16 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 fabric and apparel patternmakers 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 2026-08-05. Read the full method.

That slice is not coming back; the core of the job, positioning and cutting out master or sampling patterns, stays yours. The tools change, the responsibility doesn't.

Your week, as this page understands it

Draw and construct sets of precision master fabric patterns or layouts. May also mark and cut fabrics and apparel. The job title says “fabric” or “apparel patternmakers”: officially one job, two names. The real job is the part underneath: positioning and cutting out master or sampling patterns. 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 fabric and apparel patternmakers is not one task. It is 16 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is positioning and cutting out master or sampling patterns, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 23%
changing shape: 12%
staying human: 65%

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 2026-08-05. 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

4 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.

Determining the best layout of pattern pieces to minimize waste of material
This is reading one thing and writing another: the best layout of pattern pieces in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Determine the best layout of pattern pieces to minimize waste of material, and mark fabric accordingly.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 allowing for uncertainty): high exposure, high 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: Laying out pattern pieces to waste the least fabric is an optimization problem computers already solve better than people.
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.
Computing dimensions of patterns
This is reading one thing and writing another: dimensions of patterns in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Compute dimensions of patterns according to sizes, considering stretching of material.” (O*NET task statement)
How this row was scored
Exposure score: 68 out of 100 (61–75 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Working out pattern dimensions is arithmetic, although how a particular fabric stretches is knowledge held in the workroom.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Inputting specifications into computers to assist with pattern design and pattern cutting
This is reading one thing and writing another: specifications in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Input specifications into computers to assist with pattern design and pattern cutting.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high 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: Typing specifications into pattern software is straightforward data entry that computers handle routinely.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Creating design specifications to provide instructions on garment sewing and assembly
This is reading one thing and writing another: design specifications in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Create design specifications to provide instructions on garment sewing and assembly.” (O*NET task statement)
How this row was scored
Exposure score: 68 out of 100 (61–75 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Writing sewing and assembly instructions is documentation, though the construction know-how behind it sits with the maker.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.

Changing shape

2 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.

Examining sketches, sample articles and designing specifications to determine quantities
The software now makes the first pass at sketches, sample articles and designing specifications, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Examine sketches, sample articles, and design specifications to determine quantities, shapes, and sizes of pattern parts, and to determine the amount of material or fabric required to make a product.” (O*NET task statement)
How this row was scored
Exposure score: 51 out of 100 (44–58 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Reading sketches and specs to work out part shapes and fabric quantities is analysis software does well.
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 0/4 · how much data exists 2/4.
Creating a master pattern for each size within a range of garment sizes
The software now makes the first pass at a master pattern, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · Core
Source: “Create a master pattern for each size within a range of garment sizes, using charts, drafting instruments, computers, or grading devices.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 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: Sizing a pattern across a size range follows documented grading rules that pattern software already applies well.
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 0/4 · how much data exists 3/4.

Staying human

10 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.

Marking samples and finished patterns with information
This work happens in the physical world: samples, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Mark samples and finished patterns with information, such as garment size, section, style, identification, and sewing instructions.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Labeling patterns with size, style and sewing notes is routine, though the marking itself happens on physical pieces.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Drawing outlines of pattern parts by adapting or copying existing patterns
The ratings behind this row put outlines of pattern parts well outside what today's tools can do on their own.
importance 4 · Core
Source: “Draw outlines of pattern parts by adapting or copying existing patterns, or by drafting new patterns.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Drafting or adapting pattern outlines can start in software, but a skilled patternmaker reworks most of it.
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 0/4 · how much data exists 2/4.
Making adjustments to patterns after fittings
This work happens in the physical world: adjustments, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Make adjustments to patterns after fittings.” (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: Fixing a pattern after a fitting depends on what someone saw and felt on the fitting model.
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.
Positioning and cutting out master or sampling patterns
This work happens in the physical world: master, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Position and cut out master or sample patterns, using scissors and knives, or print out copies of patterns, using computers.” (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: Cutting master patterns is hands-on work with scissors and knives, even when copies can be printed.
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.

Show the other 6 tasks

Drawing details on outlined parts to indicate where parts
staying human
The ratings behind this row put details well outside what today's tools can do on their own.
importance 5 · Core
Source: “Draw details on outlined parts to indicate where parts are to be joined, as well as the positions of pleats, pockets, buttonholes, and other features, using computers or drafting instruments.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Marking joins, pleats and pockets can be drafted digitally, but placement judgment still comes from an experienced patternmaker.
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 0/4 · how much data exists 2/4.
Discussing design specifications with designers
staying human
The ratings behind this row put design specifications well outside what today's tools can do on their own.
importance 4 · Core
Source: “Discuss design specifications with designers, and convert their original models of garments into patterns of separate parts that can be laid out on a length of fabric.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch.
The rating behind it: Converting a designer’s model into pattern parts needs back-and-forth with the designer and experienced interpretation.
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 2/4.
Creating a paper pattern from which to mass-produce a design concept
staying human
This work happens in the physical world: a paper pattern, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Create a paper pattern from which to mass-produce a design concept.” (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: Turning a design concept into a production paper pattern still needs hands and craft judgment in the workroom.
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.
Testing patterns by making and fitting sample garments
staying human
This work happens in the physical world: patterns, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Test patterns by making and fitting sample garments.” (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: Making and fitting a sample garment is physical sewing and fitting 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.
Tracing outlines of paper onto cardboard patterns
staying human
This work happens in the physical world: outlines of paper, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Trace outlines of paper onto cardboard patterns, and cut patterns into parts to make templates.” (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: Tracing onto cardboard and cutting templates 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 1/4.
Tracing outlines of specified patterns onto material
staying human
This work happens in the physical world: outlines of specified patterns, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Trace outlines of specified patterns onto material, and cut fabric, using scissors.” (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: Tracing onto fabric and cutting with scissors is physical work at the table.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.

What this job pays, and how many people do it

Median pay: $62,750a 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: 2,950in 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: the best layout of pattern pieces in, a record out. The rows above are exactly that shape: inputting specifications into computers to assist with pattern design and pattern cutting and determining the best layout of pattern pieces to minimize waste of material. What it cannot do is be there in the room, and that is still where master gets 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

Your week is splitting in two, and which half fills it is the whole question. Inputting specifications into computers to assist with pattern design and pattern cutting is going; positioning and cutting out master or sampling patterns is not.

So, given all that: 23% of this job's task weight sits in rows the software is already learning, 12% in rows that change shape rather than disappear, and 65% in rows it is nowhere near. That is the position, measured across 16 scored tasks. It is not a forecast about you.

The occupation is an average and you are not, so the first job below is finding out where your own week actually sits.

This week: one thing

Print the task ledger above and put your own hours next to each row. Roughly, in pen, in one sitting. Then look at which group your hours actually pile up in. Twenty minutes, this week.

What you end up holding: your own week, on one page, sorted into what is shifting and what is not
How long it takes: about twenty minutes

If printing it isn’t practical: Read the rows off this page and write the same list on the back of an envelope. Same twenty minutes, same page, and it works just as well said out loud to someone who knows the job. The point is your hours next to the rows, not the paper it is on.

Over the next 90 days

Volunteer toward the durable end, visibly. Over the next ninety days put your hand up for the work in the bottom group (positioning and cutting out master or sampling patterns) and let people see you doing it. Not a new project: the same job, with your mix deliberately tilted. The point is that when the rota or the reorganisation comes, the version of you people picture is the one doing the part that stays.

Over the next 12 months

Claim a specialism at the durable end and let the other end go. Over a year, deliberately become the person who handles positioning and cutting out master or sampling patterns, and deliberately stop being the first choice for the rows in the top group. That trade costs something, because the exposed work is often the comfortable work. Decide it on purpose rather than by drift. 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 fabric and apparel patternmakers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was fashion designers: only about 13% of its durable work is work you already do. And on the numbers you do not need one. This job scores 37/100 here, with only 23% of the task list in the top band, and “mark samples and finished patterns with information” 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.

Fashion Designers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already test patterns by making and fitting sample garments, and their equivalent is to provide sample garments to agents and sales representatives, and arrange for showings of…. Across both published task lists that is about 13% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 13% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Look at that job’s page anyway →
Tailors, Dressmakers, and Custom Sewers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already create design specifications to provide instructions on garment sewing and assembly, and their equivalent is to sew garments, using needles and thread or sewing machines. 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. It is a pay cut, in those words: $41,640 against your $62,750, 33.6% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Shoe and Leather Workers and Repairers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already trace outlines of specified patterns onto material, and cut fabric, using scissors, and their equivalent is to cut out parts, following patterns or outlines, using knives, shears, scissors, or machine…. 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. It is a pay cut, in those words: $37,800 against your $62,750, 39.8% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
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: 23% of its task weight, across 16 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The whole-job doom story
Nothing on this page says this job ends. It says the mix inside it moves. Half the rows above are unchanged or changing shape, and the plan is about which half your week sits in. That is a very different problem, and a solvable one.
Panic-buying a course
Do the twenty-minute sorting exercise first. Most people who buy a course before they have done it buy the wrong one, and the free services listed below will tell you the same thing without charging for it.
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 Tailors and dressmakers 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 Tailors and dressmakers. 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

No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for fabric / apparel patternmakers, and we are not going to point you at the nearest one and call it a fit.

There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 23% of the work on this page is already inside what they can do.

Try The AI Authority free →

7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.

The AI Authority is a general community about working with AI, not a course for fabric / apparel patternmakers. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.

Noted, and thank you. We’ll email you if a Space for fabric / apparel patternmakers launches. Nothing else.

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No Space for fabric / apparel patternmakers 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 fabric / apparel patternmakers 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 fabric / apparel patternmakers 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 Fabric and Apparel Patternmakers?: Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Fabric and Apparel Patternmakers (United States, SOC 51-6092), 23% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 37 out of 100 (range 32–44, band: low). 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 “Fabric and Apparel Patternmakers” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Input specifications into computers to assist with pattern design and pattern cutting” (93/100, very high); “Determine the best layout of pattern pieces to minimize waste of material, and mark fabric accordingly” (69/100, high); “Compute dimensions of patterns according to sizes, considering stretching of material” (68/100, high). 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 “Fabric and Apparel Patternmakers” stay human?: About 65% 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: “Trace outlines of specified patterns onto material, and cut fabric, using scissors” (0/100, minimal); “Trace outlines of paper onto cardboard patterns, and cut patterns into parts to make templates” (0/100, minimal); “Test patterns by making and fitting sample garments” (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 “Fabric and Apparel Patternmakers” do about AI?: Start from the ledger rather than the headline: 23% of this job's weighted core work is exposed, and roughly 65% 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 Fabric and Apparel Patternmakers 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 16 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 2026-08-05.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

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

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

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

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

Plain text

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

BibTeX

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

Permalink

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

Data as of release 2026-q4.1, published 2026-08-05. Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.