Will AI replace Maintenance and Repair Workers, General?

Not as a job, but it is already doing parts of the work. Across the 27 official task statements scored for Maintenance and Repair Workers, General (United States, SOC 49-9071), 8% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 15 out of 100 (range 13–20, 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 and Repair Workers, General” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Order parts, supplies, or equipment from catalogs or suppliers” (93/100, very high); “Record type and cost of maintenance or repair work” (85/100, very high); “Estimate costs to repair machinery, equipment, or building structures” (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 and Repair Workers, General” stay human?

About 84% 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: “Provide groundskeeping services, such as landscaping or snow removal” (0/100, minimal); “Perform general cleaning of buildings or properties” (0/100, minimal); “Maintain or repair specialized equipment or machinery located in cafeterias, laundries, hospitals, stores, offices, or factories” (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 and Repair Workers, General” do about AI?

Start from the ledger rather than the headline: 8% of this job's weighted core work is exposed, and roughly 84% 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 and Repair Workers, General 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 27 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 and Repair Workers, General

performing routine maintenance, cleaning or lubricating shafts, bearings, gears or other parts of machinery and providing groundskeeping services, such as landscaping or snow removal. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: adjusting functional parts of devices or controlling instruments 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: 8%
changing shape: 8%
staying human: 84%

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

Whole-job exposure score 15 out of 100 (13–20 allowing for uncertainty): minimal exposure, across 27 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 and repair workers, general 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 ordering parts, supplies or equipment from catalogs or suppliers: the overhead at the edges, not the middle you trained for.

Your week, as this page understands it

Perform work involving the skills of two or more maintenance or craft occupations to keep machines, mechanical equipment, or the structure of a building in repair. Duties may involve pipe fitting; HVAC maintenance; insulating; welding; machining; carpentry; repairing electrical or mechanical equipment; installing, aligning, and balancing new equipment; and repairing buildings, floors, or stairs. The job title says “maintenance”, “repair workers” or “general”: officially one job, several names. The real job is the part underneath: adjusting functional parts of devices or controlling instruments. 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 and repair workers, general is not one task. It is 27 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is adjusting functional parts of devices or controlling instruments, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 8%
changing shape: 8%
staying human: 84%

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

How we know this

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

1 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

2 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.

Ordering parts, supplies or equipment from catalogs or suppliers
This is reading one thing and writing another: parts, supplies or equipment in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Order parts, supplies, or equipment from catalogs or suppliers.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (86–100 allowing for uncertainty): very 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: Matching a needed part to a supplier catalogue and placing the order is desk work ordering systems already do.
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.
Recording type and cost of maintenance or repairing work
This is reading one thing and writing another: type in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Record type and cost of maintenance or repair work.” (O*NET task statement)
How this row was scored
Exposure score: 85 out of 100 (78–92 allowing for uncertainty): very 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: Logging what was repaired and what it cost is straightforward record-keeping that software can capture and file automatically.
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 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.

Planning and laying out repair work
The software now makes the first pass at repair work, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Plan and lay out repair work, using diagrams, drawings, blueprints, maintenance manuals, or schematic diagrams.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial 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: Laying out a repair from drawings and manuals is planning work software supports, though you still need to see the job.
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 3/4.
Estimating costs to repair machinery
The software now makes the first pass at costs, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Estimate costs to repair machinery, equipment, or building structures.” (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: Costing a repair is a calculation from parts prices and labour rates, though someone usually has to see the damage.
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

23 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

Inspecting, operating or testing machinery or equipment to diagnose machine malfunctions
This work happens in the physical world: machinery, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Inspect, operate, or test machinery or equipment to diagnose machine malfunctions.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (6–14 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Diagnosing a fault this way means physically inspecting, running and testing the machine in front of you.
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 3/4.
Adjusting functional parts of devices or controlling instruments
This work happens in the physical world: functional parts of devices, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Adjust functional parts of devices or control instruments, using hand tools, levels, plumb bobs, or straightedges.” (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: Adjusting parts with hand tools and levels is physical work done directly on the device.
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.
Repairing machines, equipment or structures, using tools, hammers, hoists, saws, drills, wrenches or equipment
This work happens in the physical world: machines, equipment or structures, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Repair machines, equipment, or structures, using tools such as hammers, hoists, saws, drills, wrenches, or equipment such as precision measuring instruments or electrical or electronic testing devices.” (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 machines and structures with hand tools and test instruments is physical work at the fault.
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.
Performing routine maintenance, such as inspecting drives, motors or belts
This work happens in the physical world: routine maintenance, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Perform routine maintenance, such as inspecting drives, motors, or belts, checking fluid levels, replacing filters, or doing other preventive maintenance actions.” (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: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Checking belts and fluids and swapping filters means being at the machine with tools in hand.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Assembling, installing or repairing wiring electrical or electronic components, pipe systems, plumbing, machinery or equipment
This work happens in the physical world: electrical or electronic components, pipe systems, plumbing, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Assemble, install, or repair wiring, electrical or electronic components, pipe systems, plumbing, machinery, 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: Wiring, pipes and machinery have to be physically assembled and fitted, so this is hands-on work throughout.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Dismantling machines, equipment or devices to access and removing defective parts, using hoists, cranes, hand tools or power tools
This work happens in the physical world: machines, equipment or devices, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Dismantle machines, equipment, or devices to access and remove defective parts, using hoists, cranes, hand tools, or power tools.” (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 machines down with hoists and power tools to reach broken parts is hands-on work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.

Show the other 17 tasks

Designing new equipment to aid in the repair or maintenance of machines
staying human
The ratings behind this row put new equipment well outside what today's tools can do on their own.
importance 4 · Core
Source: “Design new equipment to aid in the repair or maintenance of machines, mechanical equipment, or building structures.” (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: Designing a new tool or fixture is creative engineering where AI gives useful starting points but not finished designs.
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.
Diagnosing mechanical problems and determining how to correct them
staying human
This work happens in the physical world: mechanical problems, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Diagnose mechanical problems and determine how to correct them, checking blueprints, repair manuals, or parts catalogs, as necessary.” (O*NET task statement)
How this row was scored
Exposure score: 29 out of 100 (22–36 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: Working out what is wrong involves reading manuals and blueprints, which software helps with, but also being at the machine.
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 3/4.
Training or managing maintenance personnel or subcontractors
staying human
The value here is that a specific person handles maintenance personnel and stands behind it. That is earned, not computed.
importance 3 · Core
Source: “Train or manage maintenance personnel or subcontractors.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (6–20 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Training and managing maintenance staff and subcontractors depends on working relationships built in person on the job.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Cleaning or lubricating shafts, bearings, gears or other parts of machinery
staying human
This work happens in the physical world: shafts, bearings, gears or other parts of machinery, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Clean or lubricate shafts, bearings, gears, or other parts of machinery.” (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 and lubricating machine parts is hands-on work that has to be done 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.
Painting or repairing roofs, windows, doors, floors, woodwork, plaster, drywall or other parts of building structures
staying human
This work happens in the physical world: roofs, windows, doors, floors, woodwork, plaster, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Paint or repair roofs, windows, doors, floors, woodwork, plaster, drywall, or other parts of building structures.” (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: Painting and repairing roofs, floors and walls is manual building work carried out 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.
Operating cutting torches or welding equipment to cut or join metal parts
staying human
This work happens in the physical world: torches, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Operate cutting torches or welding equipment to cut or join metal parts.” (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 and welding metal is skilled physical work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Aligning and balancing new equipment after installation
staying human
This work happens in the physical world: new equipment after installation, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Align and balance new equipment after installation.” (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: Aligning and balancing installed equipment is physical adjustment that has to happen 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.
Inspecting used parts to determine changes in dimensional requirements
staying human
This work happens in the physical world: used parts, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Inspect used parts to determine changes in dimensional requirements, using rules, calipers, micrometers, or other measuring instruments.” (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: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Measuring worn parts with calipers and micrometers means physically handling each part.
The five ratings: output a model can produce 1/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Setting up and operating machine tools to repair or fabricate machine parts
staying human
This work happens in the physical world: and operating machine tools, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Set up and operate machine tools to repair or fabricate machine parts, jigs, fixtures, or tools.” (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 machine tools to make or mend parts is hands-on workshop 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.
Fabricating or repairing counters, benches, partitions or other wooden structures, such as sheds or outbuildings
staying human
This work happens in the physical world: counters, benches, partitions or other wooden structures, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Fabricate or repair counters, benches, partitions, or other wooden structures, such as sheds or outbuildings.” (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: Building or repairing wooden counters, benches and sheds is manual carpentry done 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.
Assembling boilers at installation sites
staying human
This work happens in the physical world: boilers, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Assemble boilers at installation sites, using tools such as levels, plumb bobs, hammers, torches, or other hand tools.” (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: Boilers are assembled physically on site with hand tools, so this cannot be done from a screen.
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 equipment to improve the energy or operational efficiency of residential or commercial buildings
staying human
This work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Install equipment to improve the energy or operational efficiency of residential or commercial buildings.” (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: Installing efficiency equipment in buildings is physical work carried out at the property.
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.
Performing routine maintenance on boilers
staying human
This work happens in the physical world: routine maintenance, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Perform routine maintenance on boilers, such as replacing burners or hoses, installing replacement parts, or reinforcing structural weaknesses to ensure optimal boiler efficiency.” (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 burners, hoses and parts on a boiler is hands-on maintenance at the equipment.
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.
Positioning, attaching or blow insulating materials to prevent energy losses from buildings, pipes or other structures or objects
staying human
This work happens in the physical world: blow insulating materials, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Position, attach, or blow insulating materials to prevent energy losses from buildings, pipes, or other structures or objects.” (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: Positioning and blowing insulation into buildings and pipework is physical work done 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.
Maintaining or repairing specialized equipment or machinery located in cafeterias
staying human
This work happens in the physical world: specialized equipment, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Maintain or repair specialized equipment or machinery located in cafeterias, laundries, hospitals, stores, offices, or factories.” (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: Maintaining equipment in kitchens, laundries and shops means travelling to it and working on it directly.
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.
Performing general cleaning of buildings or properties
staying human
This work happens in the physical world: general cleaning of buildings, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Perform general cleaning of buildings or properties.” (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: General cleaning of buildings and grounds is physical work done on the premises.
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.
Providing groundskeeping services, such as landscaping or snow removal
staying human
This work happens in the physical world: services, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Provide groundskeeping services, such as landscaping or snow removal.” (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: Landscaping and snow clearing are outdoor physical jobs done at the property.
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: $49,590a 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: 1,529,700in 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: parts, supplies or equipment in, a record out. The rows above are exactly that shape: ordering parts, supplies or equipment from catalogs or suppliers and recording type and cost of maintenance or repairing work. What it cannot do is be there in the room, and that is still where functional parts of devices 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: adjusting functional parts of devices or controlling instruments is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 8% of this job's task weight sits in rows the software is already learning, 8% in rows that change shape rather than disappear, and 84% in rows it is nowhere near. That is the position, measured across 27 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. Ordering parts, supplies or equipment from catalogs or suppliers 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 parts, supplies or equipment, 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 functional parts of devices 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 planning and laying out repair work. 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 and repair workers, general (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 12% of its durable work is work you already do. And on the numbers you do not need one. This job scores 15/100 here, with only 8% of the task list in the top band, and “inspect, operate, or test machinery or equipment to diagnose machine malfunctions” 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 inspect, operate, or test machinery or equipment to diagnose machine malfunctions, and their equivalent is to observe and test the operation of machinery or equipment to diagnose malfunctions, using…. Across both published task lists that is about 12% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 12% 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 →
Machinists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already inspect, operate, or test machinery or equipment to diagnose machine malfunctions, and their equivalent is to diagnose machine tool malfunctions to determine need for adjustments or repairs. 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. And it is a narrow door: about 287,050 of those jobs against 1,529,700 of yours (OEWS May 2025), 19% as many seats.
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 dismantle machines, equipment, or devices to access and remove defective parts, using hoists…, and their equivalent is to dismantle defective machines for repair, using 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. And it is a narrow door: about 37,870 of those jobs against 1,529,700 of yours (OEWS May 2025), 2% 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: 8% of its task weight, across 27 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 adjusting functional parts of devices or controlling instruments 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 Construction operatives n.e.c. 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:

Bar staff

In UK official statistics this job is counted as Construction operatives n.e.c. and Bar staff. 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 / repair workers / general, 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 8% 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 / repair workers / general. 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 / repair workers / general 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 maintenance / repair workers / general 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 / repair workers / general 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 maintenance / repair workers / general 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 Maintenance and Repair Workers, General?: Not as a job, but it is already doing parts of the work. Across the 27 official task statements scored for Maintenance and Repair Workers, General (United States, SOC 49-9071), 8% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 15 out of 100 (range 13–20, 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 and Repair Workers, General” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Order parts, supplies, or equipment from catalogs or suppliers” (93/100, very high); “Record type and cost of maintenance or repair work” (85/100, very high); “Estimate costs to repair machinery, equipment, or building structures” (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 and Repair Workers, General” stay human?: About 84% 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: “Provide groundskeeping services, such as landscaping or snow removal” (0/100, minimal); “Perform general cleaning of buildings or properties” (0/100, minimal); “Maintain or repair specialized equipment or machinery located in cafeterias, laundries, hospitals, stores, offices, or factories” (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 and Repair Workers, General” do about AI?: Start from the ledger rather than the headline: 8% of this job's weighted core work is exposed, and roughly 84% 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 and Repair Workers, General 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 27 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

Worth knowing about these figures

The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
1 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

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

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

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

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Using these figures?

Cite this

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

Plain text

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

BibTeX

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

Permalink

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

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