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Credit Analysts

analyzing credit data and financial statements to determine the degree of risk involved in extending credit or lending money, preparing reports that include the degree of risk involved in extending credit or lending money and analyzing financial data. 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: generating financial ratios, using computer programs, to evaluate customers' financial status. 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 credit association is real, and the moves below are built from it. The first step is down this page.

Your week, as this page understands it

Analyze credit data and financial statements of individuals or firms to determine the degree of risk involved in extending credit or lending money. Prepare reports with credit information for use in decisionmaking. The job title says “credit analysts”. The real job is the part underneath: conferring with credit association and other business representatives to exchange credit information. 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 credit analysts is not one task. It is 11 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is conferring with credit association and other business representatives to exchange credit information, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI
78%
changing shape
13%
staying human
9%

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

Whole-job exposure score 70 out of 100 (65–75 allowing for uncertainty): high exposure, across 11 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 credit 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 2026-08-04. 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

7 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 credit data and financial statements to determine the degree of risk involved in extending credit or lending money

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

    importance 5 · Core
    Source: “Analyze credit data and financial statements to determine the degree of risk involved in extending credit or lending money.” (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: Reading financial statements to judge lending risk is number work software does well, with a lender still signing off.

    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.

  • Generating financial ratios, using computer programs, to evaluate customers' financial status

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

    importance 5 · Core
    Source: “Generate financial ratios, using computer programs, to evaluate customers' financial status.” (O*NET task statement)
    How this row was scored

    Exposure score: 88 out of 100 (84–92 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: Financial ratios are formulas run over figures already sitting in the system.

    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 4/4.

  • Preparing reports that include the degree of risk involved in extending credit or lending money

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

    importance 5 · Core
    Source: “Prepare reports that include the degree of risk involved in extending credit or lending money.” (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: Risk write-ups follow a standard shape and come from figures already gathered.

    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.

  • Analyzing financial data, such as income growth

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

    importance 5 · Core
    Source: “Analyze financial data, such as income growth, quality of management, and market share to determine expected profitability of loans.” (O*NET task statement)
    How this row was scored

    Exposure score: 66 out of 100 (59–73 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: Most of this is analyzing published financial data, though judging management quality still needs a human read.

    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.

  • Comparing liquidity, profitability and crediting histories of establishments being evaluated with those of similar establishments in the same industries and geographic locations

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

    importance 4 · Core
    Source: “Compare liquidity, profitability, and credit histories of establishments being evaluated with those of similar establishments in the same industries and geographic locations.” (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: Comparing a company against similar firms in its industry is exactly the benchmarking software does quickly.

    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.

  • Completing loan applications

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

    importance 5 · Core
    Source: “Complete loan applications, including credit analyses and summaries of loan requests, and submit to loan committees for approval.” (O*NET task statement)
    How this row was scored

    Exposure score: 88 out of 100 (84–92 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: Loan application packs follow fixed formats and pull straight from financial data already held.

    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 4/4.

Changing shape

2 tasks

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

  • Consulting with customers to resolve complaints and verify financial and credit transactions

    The software now makes the first pass at customers, 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 3 · Core
    Source: “Consult with customers to resolve complaints and verify financial and credit transactions.” (O*NET task statement)
    How this row was scored

    Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium 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: Sorting out a complaint about someone’s account depends on a conversation where they feel heard.

    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.

  • Evaluating customer records and recommending payment plans

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

    importance 4 · Supplemental
    Source: “Evaluate customer records and recommend payment plans, based on earnings, savings data, payment history, and purchase activity.” (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: Working out an affordable payment plan is calculation from records, though the customer’s situation still needs checking.

    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.

Staying human

2 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.

  • Conferring with credit association and other business representatives to exchange credit information

    The value here is that a specific person handles credit association and stands behind it. That is earned, not computed.

    importance 3 · Supplemental
    Source: “Confer with credit association and other business representatives to exchange credit information.” (O*NET task statement)
    How this row was scored

    Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium confidence.

    Why it sits in this group: the value is that a specific person does it.

    The rating behind it: Swapping credit information with trade contacts runs on professional relationships and things people say only person to person.

    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 2/4.

  • Contacting customers to collect payments on delinquent accounts

    The value here is that a specific person handles customers and stands behind it. That is earned, not computed.

    importance 4 · Supplemental
    Source: “Contact customers to collect payments on delinquent accounts.” (O*NET task statement)
    How this row was scored

    Exposure score: 35 out of 100 (28–42 allowing for uncertainty): low exposure, medium 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: Chasing an overdue payment is a sensitive conversation with the person who owes the money.

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

Show the other 1 task

  • Reviewing individual or commercial customer files to identify and select delinquent accounts for collection

    shifting to AI

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

    importance 3 · Supplemental
    Source: “Review individual or commercial customer files to identify and select delinquent accounts for collection.” (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: Picking out overdue accounts from customer files is rule-based screening software already does.

    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.

What this job pays, and how many people do it

Median pay
$83,510a 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
64,390in 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: financial ratios in, a record out. The rows above are exactly that shape: generating financial ratios, using computer programs and analyzing credit data and financial statements to determine the degree of risk involved in extending credit or lending money. What it cannot do is be trusted in person, which is what credit association runs on: someone specific doing it and standing behind 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

The exposed part of your job is the biggest part, and I am not going to dress that up: generating financial ratios, using computer programs, to evaluate customers' financial status is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 78% of this job's task weight sits in rows the software is already learning, 13% in rows that change shape rather than disappear, and 9% in rows it is nowhere near. That is the position, measured across 11 scored tasks. It is not a forecast about you.

What you have that the software does not is conferring with credit association and other business representatives to exchange credit information, 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 financial ratios 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 financial ratios, 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 generating financial ratios, using computer programs, to evaluate customers' financial status” 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 conferring with credit association and other business representatives to exchange credit information 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 credit analysts (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was loan officers: only about 9% of its durable work is work you already do and it is under the same pressure this job is. I am not going to pretend that is comfortable news: 78% 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. “consult with customers to resolve complaints and verify financial and credit transactions” 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.

  • Loan Officers

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already consult with customers to resolve complaints and verify financial and credit transactions, and their equivalent is to handle customer complaints and take appropriate action to resolve them. Across both published task lists that is about 9% of the durable work in that job.

    Why I am not recommending it: Almost none of it is work you already do: about 9% of the durable side of that job. That is a different job, not a next step. I will not move you off one melting floe onto another: 58% of its own task list already scores in the top exposure band (60/100 in this release), so the same software is eating it.

    Look at that job’s page anyway →

  • Credit Authorizers, Checkers, and Clerks

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already consult with customers to resolve complaints and verify financial and credit transactions, and their equivalent is to consult with customers to resolve complaints or verify financial or credit transactions. Across both published task lists that is about 9% of the durable work in that job.

    Why I am not recommending it: Almost none of it is work you already do: about 9% of the durable side of that job. That is a different job, not a next step. I will not move you off one melting floe onto another: 76% of its own task list already scores in the top exposure band (73/100 in this release), so the same software is eating it. It is a pay cut, in those words: $50,080 against your $83,510, 40.0% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 12,030 of those jobs against 64,390 of yours (OEWS May 2025), 19% as many seats.

    Look at that job’s page anyway →

  • Loan Interviewers and Clerks

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already contact customers to collect payments on delinquent accounts, and their equivalent is to accept payment on accounts. Across both published task lists that is about 6% of the durable work in that job.

    Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step. It is a pay cut, in those words: $50,020 against your $83,510, 40.1% 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: 78% of its task weight, across 11 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: conferring with credit association and other business representatives to exchange credit information 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 Finance and investment analysts and advisers 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 Finance and investment analysts and advisers and Financial accounts managers. 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 credit 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: the data and model side of credit assessment. It does not cover credit policy or lending judgement. 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 credit analysts 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 credit analysts yet. Should there be one?

Collab365 launches new communities where the need is real. If one for credit 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 credit 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 Credit Analysts?
Not as a job, but it is already doing parts of the work. Across the 11 official task statements scored for Credit Analysts (United States, SOC 13-2041), 78% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 70 out of 100 (range 65–75, 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 “Credit Analysts” can AI already do?
The highest-scoring tasks in release 2026-q4.1 are: “Complete loan applications, including credit analyses and summaries of loan requests, and submit to loan committees for approval” (88/100, very high); “Generate financial ratios, using computer programs, to evaluate customers' financial status” (88/100, very high); “Prepare reports that include the degree of risk involved in extending credit or lending money” (81/100, 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 “Credit Analysts” stay human?
About 9% 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: “Contact customers to collect payments on delinquent accounts” (35/100, low); “Confer with credit association and other business representatives to exchange credit information” (35/100, low); “Consult with customers to resolve complaints and verify financial and credit transactions” (40/100, 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 “Credit Analysts” do about AI?
Start from the ledger rather than the headline: 78% of this job's weighted core work is exposed, and roughly 9% 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 Credit 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 11 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.
  • 2 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.