Futureproof

US dataswitch to UK

Dispatchers, Except Police, Fire, and Ambulance

scheduling or dispatching workers, work crews, relaying work orders, recording and maintaining files or records of customer requests and arranging for necessary repairs to restore service and schedules. If that's your week, this page is about your job.

The honest answer

Most tasks in this job are the kind AI has learned to do: preparing daily work and run schedules. The tasks, though, are not you.

Your move: three real directions from here ↓

It would be a lie to soften that, and another lie to promise a rebuilt version of this job inside this job.

So the hope here is what you already carry: the judgment you bring to supplies is real, and the moves below are built from it. The first step is down this page.

Your week, as this page understands it

Schedule and dispatch workers, work crews, equipment, or service vehicles for conveyance of materials, freight, or passengers, or for normal installation, service, or emergency repairs rendered outside the place of business. Duties may include using radio, telephone, or computer to transmit assignments and compiling statistics and reports on work progress. The job title says “dispatchers”, “except police”, “fire” or “ambulance”: officially one job, several names. The real job is the part underneath: ordering supplies or equipment and issuing them to personnel. 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 dispatchers, except police, fire, and ambulance is not one task. It is 12 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is ordering supplies or equipment and issuing them to personnel, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI
96%
changing shape
0%
staying human
4%

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

Whole-job exposure score 76 out of 100 (7281 allowing for uncertainty): high exposure, across 12 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 dispatchers, except police, fire, and ambulance 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.

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.

Shifting to AI

11 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.

  • Scheduling or dispatching workers, work crews, equipment or service vehicles to appropriate locations

    This is reading one thing and writing another: workers, work crews, equipment or service vehicles in, a record out. That is the shape today's tools are built for.

    importance 5 · Core
    Source:Schedule or dispatch workers, work crews, equipment, or service vehicles to appropriate locations, according to customer requests, specifications, or needs, using radios or telephones.” (O*NET task statement)
    How this row was scored

    Exposure score: 64 out of 100 (6068 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: Routing crews and vehicles against jobs and locations is scheduling work software already does well.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

  • Preparing daily work and run schedules

    This is reading one thing and writing another: work in, a record out. That is the shape today's tools are built for.

    importance 5 · Core
    Source:Prepare daily work and run schedules.” (O*NET task statement)
    How this row was scored

    Exposure score: 93 out of 100 (8997 allowing for uncertainty): very high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Building daily work and run schedules from jobs and availability is exactly what scheduling software produces.

    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 and maintaining files or records of customer requests

    This is reading one thing and writing another: files in, a record out. That is the shape today's tools are built for.

    importance 4 · Core
    Source:Record and maintain files or records of customer requests, work or services performed, charges, expenses, inventory, or other dispatch information.” (O*NET task statement)
    How this row was scored

    Exposure score: 93 out of 100 (8997 allowing for uncertainty): very high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Keeping records of requests, work done and charges is routine record-keeping done by systems.

    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.

  • Relaying work orders, messages or information to or from work crews, supervisors or field inspectors

    This is reading one thing and writing another: work orders, messages or information in, a record out. That is the shape today's tools are built for.

    importance 5 · Core
    Source:Relay work orders, messages, or information to or from work crews, supervisors, or field inspectors, using telephones or two-way radios.” (O*NET task statement)
    How this row was scored

    Exposure score: 79 out of 100 (7583 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: Passing work orders and messages between office and field is a routing job systems do instantly.

    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 1/4 · how much data exists 3/4.

  • Conferring with customers or supervising personnel to address questions

    This is reading one thing and writing another: customers in, a record out. That is the shape today's tools are built for.

    importance 5 · Core
    Source:Confer with customers or supervising personnel to address questions, problems, or requests for service or equipment.” (O*NET task statement)
    How this row was scored

    Exposure score: 64 out of 100 (6068 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: Answering customer and supervisor questions about service is largely handled by automated tools now.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

  • Monitoring personnel or equipment locations and utilization to coordinate service and schedules

    This is reading one thing and writing another: personnel in, a record out. That is the shape today's tools are built for.

    importance 4 · Core
    Source:Monitor personnel or equipment locations and utilization to coordinate service and schedules.” (O*NET task statement)
    How this row was scored

    Exposure score: 75 out of 100 (7179 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: Vehicle and crew tracking already runs on software, which flags where capacity is being wasted.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Advising personnel about traffic problems

    This is reading one thing and writing another: personnel in, a record out. That is the shape today's tools are built for.

    importance 4 · Core
    Source:Advise personnel about traffic problems, such as construction areas, accidents, congestion, weather conditions, or other hazards.” (O*NET task statement)
    How this row was scored

    Exposure score: 85 out of 100 (8189 allowing for uncertainty): very high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Traffic, roadworks and weather data are public and constantly updated, so alerts can be sent 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 1/4 · how much data exists 4/4.

  • Arranging for necessary repairs to restore service and schedules

    This is reading one thing and writing another: necessary repairs in, a record out. That is the shape today's tools are built for.

    importance 4 · Core
    Source:Arrange for necessary repairs to restore service and schedules.” (O*NET task statement)
    How this row was scored

    Exposure score: 64 out of 100 (6068 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: Arranging repairs to restore service means matching a fault to available crews and parts, which software does.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

  • Receiving or preparing work orders

    This is reading one thing and writing another: work orders in, a record out. That is the shape today's tools are built for.

    importance 4 · Core
    Source:Receive or prepare work orders.” (O*NET task statement)
    How this row was scored

    Exposure score: 93 out of 100 (8997 allowing for uncertainty): very high exposure, high confidence.

    Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.

    The rating behind it: Taking in and writing up work orders is structured data entry software handles end to end.

    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.

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

1 task

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.

  • Ordering supplies or equipment and issuing them to personnel

    This work happens in the physical world: supplies, in a real place. Software cannot follow it there.

    importance 4 · Supplemental
    Source:Order supplies or equipment and issue them to personnel.” (O*NET task statement)
    How this row was scored

    Exposure score: 32 out of 100 (2539 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: Ordering supplies is straightforward system work; handing them out to staff means being there.

    The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

Show the other 2 tasks
  • Determining types or amounts of equipment

    shifting to AI

    This is reading one thing and writing another: types in, a record out. That is the shape today's tools are built for.

    importance 4 · Core
    Source:Determine types or amounts of equipment, vehicles, materials, or personnel required, according to work orders or specifications.” (O*NET task statement)
    How this row was scored

    Exposure score: 75 out of 100 (6882 allowing for uncertainty): 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: Working out what equipment and people a job needs follows the work order and documented standards.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.

  • Overseeing all communications within specifically assigned territories

    shifting to AI

    This is reading one thing and writing another: all communications within specifically assigned territories in, a record out. That is the shape today's tools are built for.

    importance 5 · Supplemental
    Source:Oversee all communications within specifically assigned territories.” (O*NET task statement)
    How this row was scored

    Exposure score: 64 out of 100 (5771 allowing for uncertainty): 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: Overseeing communications across a territory is monitoring and routing, which systems handle with someone watching.

    The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.

What this job pays, and how many people do it

Median pay
$50,340a 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
202,810in 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: workers, work crews, equipment or service vehicles in, a record out. The rows above are exactly that shape: preparing daily work and run schedules and scheduling or dispatching workers, work crews, equipment or service vehicles to appropriate locations. What it cannot do is be there in the room, and that is still where supplies 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

The exposed part of your job is the biggest part, and I am not going to dress that up: preparing daily work and run schedules is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 96% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 4% in rows it is nowhere near. That is the position, measured across 12 scored tasks. It is not a forecast about you.

What you have that the software does not is ordering supplies or equipment and issuing them to personnel, plus the years of knowing when something looks wrong before you can say why. That is the raw material for everything below.

This week: one thing

Sit on the machine's side of the desk. Pick one real piece of workers, work crews, equipment or service vehicles you would normally do yourself, let whatever software you already have take the first pass at it, and then go through what it produced and write down every single thing it got wrong. One evening this week. Do not fix anything yet. Just catch it.

What you end up holding
a written list of the machine’s mistakes, in your handwriting
How long it takes
an evening, or an hour if you pick one job rather than one client

If you can’t run software on your employer’s or your clients’ data: Do the same hunt on paper. Take one printed piece of workers, work crews, equipment or service vehicles, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.

Over the next 90 days

Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is preparing daily work and run schedules” to “I check what the software does and tell you what it means.” Your error list from this week is what makes that sentence true instead of a claim, so use it: show it once, to one person, inside the next ninety days. Same skills, priced as judgment rather than as typing.

Over the next 12 months

Walk toward the end of this job that answers for things, and get it recognised. Pick the one part of ordering supplies or equipment and issuing them to personnel you are already best at, and spend the year making it formal: a qualification, a named responsibility, a specialism people ask for by name. Price it honestly: that is evenings, it is months rather than weeks, and the seats are competitive because everyone in your position is looking at the same door. 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 dispatchers, except police, fire, and ambulance (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was first-line supervisors of housekeeping and janitorial workers: only about 4% of its durable work is work you already do. I am not going to pretend that is comfortable news: 96% of your own task list is already in the top exposure band. But the answer on this evidence is not a sideways jump into a job with the same problem. It is to walk toward the end of this one that answers for things. “order supplies or equipment and issue them to personnel” is the part that stays with a person, and spending a year getting formally recognised for it beats a standing start somewhere else.

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.

  • First-Line Supervisors of Housekeeping and Janitorial Workers

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already order supplies or equipment and issue them to personnel, and their equivalent is to issue supplies and equipment to workers. 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 →

  • Fast Food and Counter Workers

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already receive or prepare work orders, and their equivalent is to receive and process customer payments. Across both published task lists that is about 3% of the durable work in that job.

    Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $31,200 against your $50,340, 38.0% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.

    Look at that job’s page anyway →

  • Public Safety Telecommunicators

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already schedule or dispatch workers, work crews, equipment, or service vehicles to appropriate locations…, and their equivalent is to operate and maintain mobile dispatch vehicles and equipment. Across both published task lists that is about 2% of the durable work in that job.

    Why I am not recommending it: Almost none of it is work you already do: about 2% 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: 96% of its task weight, across 12 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.

  • “It’s too late for me to become something else”

    You are not starting from zero, and the page shows why: ordering supplies or equipment and issuing them to personnel is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.

  • “I should learn to code”

    Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.

  • 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 Transport and distribution clerks and assistants is the closest. The pay and employment figures are not directly comparable, and we do not average them together.

Switch to the United Kingdom page →partial match

In UK official statistics this job is counted as Transport and distribution clerks and assistants. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

Your route through this

Where to go next, and what it costs

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

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

Nothing Collab365 runs is built for dispatchers / except police / fire / ambulance, 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 96% 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 dispatchers / except police / fire / ambulance. 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 dispatchers / except police / fire / ambulance 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 dispatchers / except police / fire / ambulance 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 dispatchers / except police / fire / ambulance existed, with researched problems, courses and people in the same boat, would you want in?

We use your email address for one thing: to tell you if a Space for dispatchers / except police / fire / ambulance 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 Dispatchers, Except Police, Fire, and Ambulance?
Not as a job, but it is already doing parts of the work. Across the 12 official task statements scored for Dispatchers, Except Police, Fire, and Ambulance (United States, SOC 43-5032), 96% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 76 out of 100 (range 72–81, band: high). 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 “Dispatchers, Except Police, Fire, and Ambulance” can AI already do?
The highest-scoring tasks in release 2026-q4.1 are: “Prepare daily work and run schedules” (93/100, very high); “Receive or prepare work orders” (93/100, very high); “Record and maintain files or records of customer requests, work or services performed, charges, expenses, inventory, or other dispatch information” (93/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Dispatchers, Except Police, Fire, and Ambulance” stay human?
About 4% 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: “Order supplies or equipment and issue them to personnel” (32/100, low); “Oversee all communications within specifically assigned territories” (64/100, high); “Confer with customers or supervising personnel to address questions, problems, or requests for service or equipment” (64/100, high). 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 “Dispatchers, Except Police, Fire, and Ambulance” do about AI?
Start from the ledger rather than the headline: 96% of this job's weighted core work is exposed, and roughly 4% 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 Dispatchers, Except Police, Fire, and Ambulance 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 12 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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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}
}

Data as of release 2026-q4.1, published . 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.