Will AI replace Maintenance Workers, Machinery?

Not as a job, but it is already doing parts of the work. Across the 18 official task statements scored for Maintenance Workers, Machinery (United States, SOC 49-9043), 6% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 11 out of 100 (range 9–15, 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 “Maintenance Workers, Machinery” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Record production, repair, and machine maintenance information” (62/100, high); “Read work orders and specifications to determine machines and equipment requiring repair or maintenance” (56/100, partial); “Inventory and requisition machine parts, equipment, and other supplies so that stock can be maintained and replenished” (56/100, partial). 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 “Maintenance Workers, Machinery” stay human?

About 83% 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: “Replace, empty, or replenish machine and equipment containers such as gas tanks or boxes” (0/100, minimal); “Measure, mix, prepare, and test chemical solutions used to clean or repair machinery and equipment” (0/100, minimal); “Remove hardened material from machines or machine parts, using abrasives, power and hand tools, jackhammers, sledgehammers, or other equipment” (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 “Maintenance Workers, Machinery” do about AI?

Start from the ledger rather than the headline: 6% of this job's weighted core work is exposed, and roughly 83% 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 Maintenance Workers, Machinery 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 18 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

Maintenance Workers, Machinery

dismantling machines and removing parts, collaborating with other workers to repair or move machines and reading work orders and specifications to determine machines and equipment requiring repair or maintenance. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: reassembling machines after the completion of repair or maintenance work 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: 6%
changing shape: 11%
staying human: 83%

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

Whole-job exposure score 11 out of 100 (9–15 allowing for uncertainty): minimal exposure, across 18 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 maintenance workers, machinery 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.

What shifts is recording production, repair and machine maintenance information: the paper around the work, not the work.

Your week, as this page understands it

Lubricate machinery, change parts, or perform other routine machinery maintenance. The job title says “maintenance workers” or “machinery”: officially one job, two names. The real job is the part underneath: reassembling machines after the completion of repair or maintenance work. 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 maintenance workers, machinery is not one task. It is 18 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is reassembling machines after the completion of repair or maintenance work, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 6%
changing shape: 11%
staying human: 83%

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.

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.

Recording production, repair and machine maintenance information
This is reading one thing and writing another: production, repair and machine maintenance information in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Record production, repair, and machine maintenance information.” (O*NET task statement)
How this row was scored
Exposure score: 62 out of 100 (58–66 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: Writing up repair and maintenance records is routine paperwork software already handles.
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 4/4.

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.

Reading work orders and specifications to determine machines and equipment requiring repair or maintenance
The software now makes the first pass at work orders, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Read work orders and specifications to determine machines and equipment requiring repair or maintenance.” (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: Reading work orders to decide what needs attention is document work 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 3/4.
Inventorying and requisitioning machine parts
The software now makes the first pass at machine parts, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Inventory and requisition machine parts, equipment, and other supplies so that stock can be maintained and replenished.” (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: Counting stock and raising requisitions is ordinary record and ordering work.
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

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.

Reassembling machines after the completion of repair or maintenance work
This work happens in the physical world: machines after the completion of repair, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Reassemble machines after the completion of repair or maintenance work.” (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 a machine back together is 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.
Dismantling machines and removing parts
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Dismantle machines and remove parts for repair, using hand tools, chain falls, jacks, cranes, or hoists.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Taking a machine apart with tools and hoists 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.
Lubricating, or applying adhesivesing or other materials
This work happens in the physical world: other materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Lubricate or apply adhesives or other materials to machines, machine parts, or other equipment according to specified procedures.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Greasing and applying materials to machine parts 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.
Installing, replacing or change machine parts and attachments, according to production specifications
This work happens in the physical world: change machine parts, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Install, replace, or change machine parts and attachments, according to production specifications.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Fitting parts and attachments to a machine requires being at it 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.
Inspecting or testing damaged machine parts
This work happens in the physical world: damaged machine parts, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Inspect or test damaged machine parts, and mark defective areas or advise supervisors of repair needs.” (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: Software can help describe a fault, but spotting and marking damage means handling the parts.
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.
Starting machines and observing mechanical operation to determine efficiency and to detect problems
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Start machines and observe mechanical operation to determine efficiency and to detect problems.” (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: Listening to and watching a running machine requires standing next to it.
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.
Collaborating with other workers to repair or move machines
This work happens in the physical world: other workers, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Collaborate with other workers to repair or move machines, machine parts, or 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: Repairing or moving heavy machines with workmates is physical teamwork.
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.

Show the other 8 tasks

Setting up and operating machines
staying human
This work happens in the physical world: and operating machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Set up and operate machines, and adjust controls to regulate operations.” (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: Setting up and running the machine happens 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.
Transporting machine parts, tools, equipment and other material between work areas and storage, using cranes, hoists or dollies
staying human
This work happens in the physical world: machine parts, tools, equipment and other material, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Transport machine parts, tools, equipment, and other material between work areas and storage, using cranes, hoists, or dollies.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Moving parts and tools around with hoists and dollies is manual work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Collecting and discarding worn machine parts and other refuse to maintain machinery and work areas
staying human
This work happens in the physical world: worn machine parts, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Collect and discard worn machine parts and other refuse to maintain machinery and work areas.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Collecting and disposing of worn parts 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.
Cleaning machines and machine parts
staying human
This work happens in the physical world: machines, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Clean machines and machine parts, using cleaning solvents, cloths, air guns, hoses, vacuums, or other equipment.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Cleaning machines with solvents and air guns 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.
Replacing or repairing metal, wood, leather, glass or other lining in machines or in equipment compartments or containers
staying human
This work happens in the physical world: metal, wood, leather, glass, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Replace or repair metal, wood, leather, glass, or other lining in machines, or in equipment compartments or containers.” (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: Replacing linings inside machines needs hands and 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.
Removing hardened material from machines or machine parts
staying human
This work happens in the physical world: hardened material, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Remove hardened material from machines or machine parts, using abrasives, power and hand tools, jackhammers, sledgehammers, or other equipment.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Breaking hardened material off machinery with power tools is heavy 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.
Measuring, mixing, preparing and testing chemical solutions used to clean or repair machinery and equipment
staying human
This work happens in the physical world: chemical solutions, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Measure, mix, prepare, and test chemical solutions used to clean or repair machinery and 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: Mixing and testing cleaning chemicals means handling 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.
Replacing empty or replenish machine and equipment containers, gas tanks or boxes
staying human
This work happens in the physical world: empty, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Replace, empty, or replenish machine and equipment containers such as gas tanks or boxes.” (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: Refilling and swapping tanks and containers 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.

What this job pays, and how many people do it

Median pay: $60,850a 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: 60,020in 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: production, repair and machine maintenance information in, a record out. The rows above are exactly that shape: recording production, repair and machine maintenance information and reading work orders and specifications to determine machines and equipment requiring repair or maintenance. What it cannot do is be there in the room, and that is still where machines after the completion of repair 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

Start with what does not change: reassembling machines after the completion of repair or maintenance work is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 6% of this job's task weight sits in rows the software is already learning, 11% in rows that change shape rather than disappear, and 83% in rows it is nowhere near. That is the position, measured across 18 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. Recording production, repair and machine maintenance information 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 production, repair and machine maintenance information, 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 machines after the completion of repair is 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 reading work orders and specifications to determine machines and equipment requiring repair or maintenance. 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 maintenance workers, machinery (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was industrial machinery mechanics: only about 11% of its durable work is work you already do. And on the numbers you do not need one. This job scores 11/100 here, with only 6% of the task list in the top band, and “reassemble machines after the completion of repair or maintenance work” 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.

Industrial Machinery Mechanics
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already reassemble machines after the completion of repair or maintenance work, and their equivalent is to reassemble equipment after completion of inspections, testing, or repairs. Across both published task lists that is about 11% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 11% 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 →
Mobile Heavy Equipment Mechanics, Except Engines
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already dismantle machines and remove parts for repair, using hand tools, chain falls, jacks…, and their equivalent is to dismantle and reassemble heavy equipment using hoists and hand tools. Across both published task lists that is about 10% of the durable work in that job.
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.
Look at that job’s page anyway →
Farm Equipment Mechanics and Service Technicians
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already reassemble machines after the completion of repair or maintenance work, and their equivalent is to reassemble machines and equipment following repair, testing operation and making adjustments, as necessary. Across both published task lists that is about 10% of the durable work in that job.
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.
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: 6% of its task weight, across 18 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 reassembling machines after the completion of repair or maintenance work 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 Rail and rolling stock builders and repairers is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

The other groups this work is counted across:

Metal working production and maintenance fitters and technicians

In UK official statistics this job is counted as Rail and rolling stock builders and repairers and Metal working production and maintenance fitters and technicians. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

Your route through this

Where to go next, and what it costs

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 maintenance workers / machinery, 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 6% 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 maintenance workers / machinery. 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 maintenance workers / machinery launches. Nothing else.

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No Space for maintenance workers / machinery 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 maintenance workers / machinery existed, with researched problems, courses and people in the same boat, would you want in?

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Questions people ask about this job

Will AI replace Maintenance Workers, Machinery?: Not as a job, but it is already doing parts of the work. Across the 18 official task statements scored for Maintenance Workers, Machinery (United States, SOC 49-9043), 6% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 11 out of 100 (range 9–15, 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 “Maintenance Workers, Machinery” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Record production, repair, and machine maintenance information” (62/100, high); “Read work orders and specifications to determine machines and equipment requiring repair or maintenance” (56/100, partial); “Inventory and requisition machine parts, equipment, and other supplies so that stock can be maintained and replenished” (56/100, partial). 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 “Maintenance Workers, Machinery” stay human?: About 83% 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: “Replace, empty, or replenish machine and equipment containers such as gas tanks or boxes” (0/100, minimal); “Measure, mix, prepare, and test chemical solutions used to clean or repair machinery and equipment” (0/100, minimal); “Remove hardened material from machines or machine parts, using abrasives, power and hand tools, jackhammers, sledgehammers, or other equipment” (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 “Maintenance Workers, Machinery” do about AI?: Start from the ledger rather than the headline: 6% of this job's weighted core work is exposed, and roughly 83% 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 Maintenance Workers, Machinery 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 18 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 2026-08-05.
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.