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Budget Analysts

analyzing monthly department budgeting and accounting reports to maintain expenditure controls, examining budget estimates and summarizing budgets and submitting recommendations for the approval or disapproval of funds requests. 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: examining budget estimates. The tasks, though, are not you.

Your move: three real directions from here ↓

It would be a lie to soften that; testifying before examining and fund-granting authorities is what this work rebuilds around. The plan below starts there.

Your week, as this page understands it

Examine budget estimates for completeness, accuracy, and conformance with procedures and regulations. Analyze budgeting and accounting reports. The job title says “budget analysts”. The real job is the part underneath: testifying before examining and fund-granting authorities. 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 budget analysts is not one task. It is 13 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is testifying before examining and fund-granting authorities, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI
64%
changing shape
32%
staying human
5%

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

Whole-job exposure score 64 out of 100 (59–68 allowing for uncertainty): high exposure, across 13 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 budget analysts is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

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

Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.

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

8 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.

  • Analyzing monthly department budgeting and accounting reports to maintain expenditure controls

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

    importance 4 · Core
    Source: “Analyze monthly department budgeting and accounting reports to maintain expenditure controls.” (O*NET task statement)
    How this row was scored

    Exposure score: 75 out of 100 (71–79 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: Monthly variance analysis runs on accounting data that is already in the finance system and follows a repeatable method.

    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.

  • Examining budget estimates

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

    importance 4 · Core
    Source: “Examine budget estimates for completeness, accuracy, and conformance with procedures and regulations.” (O*NET task statement)
    How this row was scored

    Exposure score: 81 out of 100 (77–85 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: Checking estimates for gaps, arithmetic errors and rule breaches is mechanical comparison against written procedures.

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

  • Reviewing operating budgets to analyze trends affecting budget needs

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

    importance 4 · Core
    Source: “Review operating budgets to analyze trends affecting budget needs.” (O*NET task statement)
    How this row was scored

    Exposure score: 75 out of 100 (71–79 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: Spotting spending trends in operating budgets is exactly the pattern-finding software does well on existing data.

    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.

  • Directing the preparation of regular and special budget reports

    This is reading one thing and writing another: the preparation of regular and special budget reports in, a record out. That is the shape today's tools are built for.

    importance 4 · Core
    Source: “Direct the preparation of regular and special budget reports.” (O*NET task statement)
    How this row was scored

    Exposure score: 64 out of 100 (60–68 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: Regular budget reports are built from structured data on a fixed cycle, with people to be chased for inputs.

    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.

  • Summarizing budgets and submitting recommendations for the approval or disapproval of funds requests

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

    importance 4 · Core
    Source: “Summarize budgets and submit recommendations for the approval or disapproval of funds requests.” (O*NET task statement)
    How this row was scored

    Exposure score: 66 out of 100 (62–70 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: Summarizing budgets and drafting a recommendation is document work; a manager still approves or rejects the request.

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

Changing shape

4 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.

  • Providing advice and technical assistance with cost analysis

    The software now makes the first pass at advice, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 4 · Core
    Source: “Provide advice and technical assistance with cost analysis, fiscal allocation, and budget preparation.” (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: Software can do the cost analysis and draft the budget, though colleagues want someone to talk it through.

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

  • Consulting with managers to ensure that budget adjustments are made in accordance with program changes

    The software now makes the first pass at managers, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.

    importance 4 · Core
    Source: “Consult with managers to ensure that budget adjustments are made in accordance with program changes.” (O*NET task statement)
    How this row was scored

    Exposure score: 40 out of 100 (36–44 allowing for uncertainty): partial exposure, high confidence.

    Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.

    The rating behind it: The adjustment itself is simple, but agreeing it with the manager running the program is a negotiation.

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

  • Seeking new ways to improve efficiency and increase profits

    The software now makes the first pass at new ways, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 3 · Core
    Source: “Seek new ways to improve efficiency and increase profits.” (O*NET task statement)
    How this row was scored

    Exposure score: 49 out of 100 (42–56 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: AI suggests plenty of efficiency ideas, but knowing which would actually work here depends on local knowledge.

    The five ratings: output a model can produce 2/4 · 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.

  • Interpreting budget directives and establishing policies for carrying out directives

    The software now makes the first pass at budget directives, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 4 · Core
    Source: “Interpret budget directives and establish policies for carrying out directives.” (O*NET task statement)
    How this row was scored

    Exposure score: 49 out of 100 (42–56 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: Reading a directive is easy, but setting the policy that follows from it is an authority call inside the organization.

    The five ratings: output a model can produce 2/4 · 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.

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.

  • Testifying before examining and fund-granting authorities

    This work happens in the physical world: before examining and fund-granting authorities, in a real place. Software cannot follow it there.

    importance 4 · Supplemental
    Source: “Testify before examining and fund-granting authorities, clarifying and promoting the proposed budgets.” (O*NET task statement)
    How this row was scored

    Exposure score: 9 out of 100 (5–13 allowing for uncertainty): minimal exposure, high confidence.

    Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.

    The rating behind it: Testifying before a funding panel is a person answering questions live and being believed.

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

Show the other 3 tasks

  • Matching appropriations for specific programs with appropriations for broader programs

    shifting to AI

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

    importance 3 · Core
    Source: “Match appropriations for specific programs with appropriations for broader programs, including items for emergency funds.” (O*NET task statement)
    How this row was scored

    Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.

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

    The rating behind it: Matching program appropriations against broader ones is a records-matching job that software handles reliably.

    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.

  • Performing cost-benefit analyses to compare operating programs

    shifting to AI

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

    importance 4 · Core
    Source: “Perform cost-benefit analyses to compare operating programs, review financial requests, or explore alternative financing methods.” (O*NET task statement)
    How this row was scored

    Exposure score: 75 out of 100 (71–79 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: Cost-benefit comparison is a defined modelling method applied to figures already held in finance systems.

    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.

  • Compiling and analyzing accounting records and other data to determine the financial resources required to implement a program

    shifting to AI

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

    importance 4 · Core
    Source: “Compile and analyze accounting records and other data to determine the financial resources required to implement a program.” (O*NET task statement)
    How this row was scored

    Exposure score: 75 out of 100 (71–79 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: Working out what a program will cost draws on accounting records already held digitally.

    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.

What this job pays, and how many people do it

Median pay
$91,640a 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
47,160in 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: budget estimates in, a record out. The rows above are exactly that shape: examining budget estimates and analyzing monthly department budgeting and accounting reports to maintain expenditure controls. What it cannot do is be there in the room, and that is still where before examining and fund-granting authorities 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: examining budget estimates is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 64% of this job's task weight sits in rows the software is already learning, 32% in rows that change shape rather than disappear, and 5% in rows it is nowhere near. That is the position, measured across 13 scored tasks. It is not a forecast about you.

What you have that the software does not is testifying before examining and fund-granting authorities, 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 budget estimates 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 budget estimates, 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 examining budget estimates” 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 testifying before examining and fund-granting authorities 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 budget analysts (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was accountants and auditors: only about 2% of its durable work is work you already do. I am not going to pretend that is comfortable news: 64% 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. “provide advice and technical assistance with cost analysis, fiscal allocation, and budget…” 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.

  • Accountants and Auditors

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already examine budget estimates for completeness, accuracy, and conformance with procedures and regulations, and their equivalent is to prepare, examine, or analyze accounting records, financial statements, or other financial reports to…. 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 →

  • Financial Managers

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already direct the preparation of regular and special budget reports, and their equivalent is to prepare or direct preparation of financial statements, business activity reports, financial position forecasts…. Across both published task lists that is about 1% of the durable work in that job.

    Why I am not recommending it: Almost none of it is work you already do: about 1% of the durable side of that job. That is a different job, not a next step.

    Look at that job’s page anyway →

  • Credit Counselors

    Why it looked obvious: It came up as a near neighbour on the overall shape of the two task lists, but nothing in your day matched a specific piece of theirs closely enough to name.

    Why I am not recommending it: The two task lists look alike from a distance and share almost nothing close up: no single piece of their work matched a piece of yours. That is a resemblance, not a route. I will not move you off one melting floe onto another: 53% of its own task list already scores in the top exposure band (63/100 in this release), so the same software is eating it. It is a pay cut, in those words: $52,230 against your $91,640, 43.0% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.

    Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

  • The headline number you read somewhere

    The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 64% of its task weight, across 13 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: testifying before examining and fund-granting authorities 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 Chartered and certified accountants 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 Chartered and certified accountants and Taxation experts. 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

Two honest options, and no deadline on either

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.

A nearby route

There's no Space built for budget analysts yet.

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

The closest match is Microsoft 365 Report Builders, a community for people who build business reports in Excel, Power Query and Power BI without a data team behind them. It overlaps with the part of your job that is growing: building the budget pack so it refreshes and reconciles, rather than rebuilding it every cycle. If that overlap isn't you, the free route below covers the same ground.

Try Microsoft 365 Report Builders free →

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

After the trial it is a paid community, and you get identical data either way. If the overlap above is not your job, the moves above cost nothing and stand on their own.

Noted, and thank you. We’ll email you if a Space for budget analysts launches. Nothing else.

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No Space for budget analysts yet. Should there be one?

Collab365 launches new communities where the need is real. If one for budget analysts 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 budget analysts 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 Budget Analysts?
Not as a job, but it is already doing parts of the work. Across the 13 official task statements scored for Budget Analysts (United States, SOC 13-2031), 64% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 64 out of 100 (range 59–68, 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 “Budget Analysts” can AI already do?
The highest-scoring tasks in release 2026-q4.1 are: “Match appropriations for specific programs with appropriations for broader programs, including items for emergency funds” (93/100, very high); “Examine budget estimates for completeness, accuracy, and conformance with procedures and regulations” (81/100, very high); “Analyze monthly department budgeting and accounting reports to maintain expenditure controls” (75/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Budget Analysts” stay human?
About 5% 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: “Testify before examining and fund-granting authorities, clarifying and promoting the proposed budgets” (9/100, minimal); “Consult with managers to ensure that budget adjustments are made in accordance with program changes” (40/100, partial); “Interpret budget directives and establish policies for carrying out directives” (49/100, partial). 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 “Budget Analysts” do about AI?
Start from the ledger rather than the headline: 64% of this job's weighted core work is exposed, and roughly 5% 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 Budget Analysts 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 13 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

Worth knowing about these figures

  • The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
  • 1 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
  • Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Task statements
onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights
onet-db (im-rt)
Scores
Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
Pay and employment
bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

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

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

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

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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 . 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.