Will AI replace Highway Maintenance Workers?

Not as a job, but it is already doing parts of the work. Across the 19 official task statements scored for Highway Maintenance Workers (United States, SOC 47-4051), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 0 out of 100 (range 0–5, 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 “Highway Maintenance Workers” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Inspect markers to verify accurate installation” (13/100, minimal); “Flag motorists to warn them of obstacles or repair work ahead” (0/100, minimal); “Set out signs and cones around work areas to divert traffic” (0/100, minimal). 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 “Highway Maintenance Workers” stay human?

About 100% 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: “Drive heavy equipment and vehicles with adjustable attachments to sweep debris from paved surfaces, mow grass and weeds, remove snow and ice, and spread salt…” (0/100, minimal); “Perform preventative maintenance on vehicles and heavy equipment” (0/100, minimal); “Place and remove snow fences used to prevent the accumulation of drifting snow on highways” (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 “Highway Maintenance Workers” do about AI?

Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 100% 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 Highway Maintenance Workers 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 19 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

Highway Maintenance Workers

setting out signs and cones around work areas to divert traffic, hauling and spreading sand and driving heavy equipment and vehicles with adjustable attachments to sweep debris from paved surfaces. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: flagging motorists to warn them of obstacles or repairing work ahead 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: 0%
changing shape: 0%
staying human: 100%

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

Whole-job exposure score 0 out of 100 (0–5 allowing for uncertainty): minimal exposure, across 19 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 highway maintenance workers 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 the routine end of the work: the paper around the work, not the work.

Your week, as this page understands it

Maintain highways, municipal and rural roads, airport runways, and rights-of-way. Duties include patching broken or eroded pavement and repairing guard rails, highway markers, and snow fences. May also mow or clear brush from along road, or plow snow from roadway. The job title says “highway maintenance workers”. The real job is the part underneath: flagging motorists to warn them of obstacles or repairing work ahead. 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 highway maintenance workers is not one task. It is 19 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is flagging motorists to warn them of obstacles or repairing work ahead, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 0%
changing shape: 0%
staying human: 100%

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

How we know this

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

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

Your job, task by task

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

Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

0 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.

Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.

Changing shape

0 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.

Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.

Staying human

19 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.

Flagging motorists to warn them of obstacles or repairing work ahead
This work happens in the physical world: motorists, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Flag motorists to warn them of obstacles or repair work ahead.” (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: Flagging traffic means standing on the roadside where drivers can see you.
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 1/4 · how much data exists 1/4.
Setting out signs and cones around work areas to divert traffic
This work happens in the physical world: signs, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Set out signs and cones around work areas to divert traffic.” (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 out signs and cones means carrying and placing them on the road.
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 1/4 · how much data exists 2/4.
Driving heavy equipment and vehicles with adjustable attachments to sweep debris from paved surfaces
This work happens in the physical world: heavy equipment, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Drive heavy equipment and vehicles with adjustable attachments to sweep debris from paved surfaces, mow grass and weeds, remove snow and ice, and spread salt and sand.” (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: Operating sweepers, mowers and gritters means driving the machines.
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.
Cleaning and clearing debris from culverts
This work happens in the physical world: debris, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Clean and clear debris from culverts, catch basins, drop inlets, ditches, and other drain 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: Clearing drains and culverts is physical outdoor 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.
Driving trucks to transport crews and equipment to work sites
This work happens in the physical world: trucks, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Drive trucks to transport crews and equipment to work sites.” (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: Driving crews and equipment to site is physical work behind the wheel.
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.
Hauling and spreading sand, gravel and clay to fill washouts and repair road shoulders
This work happens in the physical world: sand, gravel and clay, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Haul and spread sand, gravel, and clay to fill washouts and repair road shoulders.” (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: Hauling and spreading sand and gravel 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.
Inspecting, cleaning and repairing drainage systems, bridges, tunnels and other structures
This work happens in the physical world: drainage systems, bridges, tunnels and other structures, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Inspect, clean, and repair drainage systems, bridges, tunnels, and other 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: Checking and repairing drains, bridges and tunnels means being at the structure 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.
Dumping spread and tamp asphalt, using pneumatic tampers, to repair joints and patch broken pavement
This work happens in the physical world: spread, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Dump, spread, and tamp asphalt, using pneumatic tampers, to repair joints and patch broken pavement.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Laying and tamping asphalt is done by hand with 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.
Removing litter and debris from roadways
This work happens in the physical world: litter, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Remove litter and debris from roadways, including debris from rock and mud slides.” (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: Clearing litter and slide debris from the road 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.
Performing preventative maintenance on vehicles and heavy equipment
This work happens in the physical world: preventative maintenance, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Perform preventative maintenance on vehicles and heavy 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: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Servicing trucks and heavy equipment is hands-on mechanical 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 3/4.

Show the other 9 tasks

Inspecting markers to verify accurate installation
staying human
This work happens in the physical world: markers, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Inspect markers to verify accurate installation.” (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: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Checking a marker is correctly installed is mostly about being there to look, with only a short note to write.
The five ratings: output a model can produce 2/4 · 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.
Erecting, installing or repairing guardrails, road shoulders, berms, highway markers, warning signals and highway lighting
staying human
This work happens in the physical world: guardrails, road shoulders, berms, highway markers, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Erect, install, or repair guardrails, road shoulders, berms, highway markers, warning signals, and highway lighting, using hand tools and 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: Fitting guardrails, markers and lighting is done with tools on the roadside.
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 roadside landscaping work, such as clearing weeds and brush and planting and trimming trees
staying human
This work happens in the physical world: roadside landscaping work, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Perform roadside landscaping work, such as clearing weeds and brush, and planting and trimming trees.” (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: Clearing brush and planting or trimming trees is physical outdoor 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.
Painting traffic control lines and placing pavement traffic messages
staying human
This work happens in the physical world: traffic control lines, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Paint traffic control lines and place pavement traffic messages, by hand or using machines.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Painting road lines and markings is done on the road surface itself.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Applying poisons along roadsides and in animal burrows to eliminate unwanted roadside vegetation and rodents
staying human
This work happens in the physical world: poisons along roadsides, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Apply poisons along roadsides and in animal burrows to eliminate unwanted roadside vegetation and rodents.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Applying poisons along the roadside means handling and spreading them on site.
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.
Measuring and marking locations for installation of markers
staying human
This work happens in the physical world: locations, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Measure and mark locations for installation of markers, using tape, string, or chalk.” (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: Measuring and chalking positions on the road is done by hand at the spot.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Applying oil to road surfaces
staying human
This work happens in the physical world: oil, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Apply oil to road surfaces, using sprayers.” (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: Spraying oil onto a road surface is physical work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Blending compounds to form adhesive mixtures used for marker installation
staying human
This work happens in the physical world: compounds, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Blend compounds to form adhesive mixtures used for marker 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: Mixing adhesive compounds is done by hand at the work 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.
Placing and removing snow fences used to prevent the accumulation of drifting snow on highways
staying human
This work happens in the physical world: snow fences, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Place and remove snow fences used to prevent the accumulation of drifting snow on highways.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Putting up and taking down snow fencing is physical outdoor 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.

What this job pays, and how many people do it

Median pay: $50,260a 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: 154,960in 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. Almost none of this job is reading one thing and writing another (the shape today's tools are built for), because the work turns on motorists, which happens with people and things rather than on a screen. The rows above are the evidence rather than the reassurance: flagging motorists to warn them of obstacles or repairing work ahead and setting out signs and cones around work areas to divert traffic. The parts that are changing are the paperwork and the tools around the job, not the middle of it, 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: flagging motorists to warn them of obstacles or repairing work ahead is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 0% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 100% in rows it is nowhere near. That is the position, measured across 19 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. The routine end of the work 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 the routine work, 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 motorists 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 flagging motorists to warn them of obstacles or repairing work ahead. 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 highway maintenance workers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was septic tank servicers and sewer pipe cleaners: only about 10% of its durable work is work you already do and there are far fewer of those jobs than of yours. And on the numbers you do not need one. This job scores 0/100 here, with only 0% of the task list in the top band, and “flag motorists to warn them of obstacles or repair work ahead” 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.

Septic Tank Servicers and Sewer Pipe Cleaners
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already drive trucks to transport crews and equipment to work sites, and their equivalent is to drive trucks to transport crews, materials, and equipment. 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 30,650 of those jobs against 154,960 of yours (OEWS May 2025), 20% as many seats.
Look at that job’s page anyway →
Paving, Surfacing, and Tamping Equipment Operators
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already drive trucks to transport crews and equipment to work sites, and their equivalent is to drive machines onto truck trailers, and drive trucks to transport machines and material…. 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.
Look at that job’s page anyway →
Telecommunications Equipment Installers and Repairers, Except Line Installers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already drive trucks to transport crews and equipment to work sites, and their equivalent is to drive crew trucks to and from work areas. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step.
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: 0% of its task weight, across 19 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 flagging motorists to warn them of obstacles or repairing work ahead 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 Road construction operatives 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:

In UK official statistics this job is counted as Road construction operatives, Groundworkers and Rail construction and maintenance operatives. 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

Why there is no community here

Collab365, who build this site, run paid Spaces for a small number of subjects, and none of them is built for this job. We are not going to point you at the nearest one and call it a fit.

So the free services listed on this page are the whole answer, and it is the same answer we would give a friend.

Noted, and thank you. We’ll email you if a Space for highway maintenance workers launches. Nothing else.

That did not look like an email address, so nothing was saved. Have another go below.

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No Space for highway maintenance workers 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.

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Questions people ask about this job

Will AI replace Highway Maintenance Workers?: Not as a job, but it is already doing parts of the work. Across the 19 official task statements scored for Highway Maintenance Workers (United States, SOC 47-4051), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 0 out of 100 (range 0–5, 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 “Highway Maintenance Workers” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Inspect markers to verify accurate installation” (13/100, minimal); “Flag motorists to warn them of obstacles or repair work ahead” (0/100, minimal); “Set out signs and cones around work areas to divert traffic” (0/100, minimal). 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 “Highway Maintenance Workers” stay human?: About 100% 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: “Drive heavy equipment and vehicles with adjustable attachments to sweep debris from paved surfaces, mow grass and weeds, remove snow and ice, and spread salt…” (0/100, minimal); “Perform preventative maintenance on vehicles and heavy equipment” (0/100, minimal); “Place and remove snow fences used to prevent the accumulation of drifting snow on highways” (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 “Highway Maintenance Workers” do about AI?: Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 100% 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 Highway Maintenance Workers 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 19 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

Worth knowing about these figures

The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

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

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

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

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

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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.