US dataswitch to UK
Bill and Account Collectors
recording information about financial status of customers and status of collection efforts, answering customer questions regarding problems with their accounts and persuading customers to pay amounts due on credit accounts. If that's your week, this page is about your job.
The honest answer
This job is splitting in two: recording information about financial status of customers and status of collection efforts is work AI now does quickly and cheaply, and conferring with customers by telephone or in person to determine reasons for overdue payments and to review the terms of sales is work it can't touch.
Which half fills your week decides your exposure. Moving toward the second half is a real, doable plan.
Your week, as this page understands it
Locate and notify customers of delinquent accounts by mail, telephone, or personal visit to solicit payment. Duties include receiving payment and posting amount to customer's account, preparing statements to credit department if customer fails to respond, initiating repossession proceedings or service disconnection, and keeping records of collection and status of accounts. The job title says “bill” or “account collectors”: officially one job, two names. The real job is the part underneath: conferring with customers by telephone or in person to determine reasons for overdue payments and to review the terms of sales. 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 bill and account collectors is not one task. It is 15 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is conferring with customers by telephone or in person to determine reasons for overdue payments and to review the terms of sales, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 47%
- changing shape
- 35%
- staying human
- 18%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 60 out of 100 (55–65 allowing for uncertainty): high exposure, across 15 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 bill and account collectors 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.
- 1 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
7 tasksTasks 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.
Recording information about financial status of customers and status of collection efforts
This is reading one thing and writing another: information in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Record information about financial status of customers and status of collection efforts.” (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: Logging a customer financial position and what has been chased is straightforward record-keeping that collection 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 4/4.
Answering customer questions regarding problems with their accounts
This is reading one thing and writing another: customer questions regarding problems in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Answer customer questions regarding problems with their accounts.” (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: Account queries are answered from records and set rules, which software already does at scale.
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.
Locating and monitoring overdue accounts
This is reading one thing and writing another: overdue accounts in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Locate and monitor overdue accounts, using computers and a variety of automated systems.” (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: Finding and tracking overdue accounts is exactly what automated account systems are built to do.
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.
Receiving payments and posting amounts paid to customer accounts
This is reading one thing and writing another: payments in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Receive payments and post amounts paid to customer accounts.” (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: Taking a payment and posting it to the right account is fully handled by payment systems.
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
5 tasksTasks 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.
Locating and notifying customers of delinquent accounts
The software now makes the first pass at customers of delinquent accounts, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Locate and notify customers of delinquent accounts by mail, telephone, or personal visits to solicit payment.” (O*NET task statement)
How this row was scored
Exposure score: 42 out of 100 (38–46 allowing for uncertainty): partial 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: Automated letters, texts and calls already handle most chasing, though doorstep visits still need a person.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Arranging for debt repayment or establishing repayment schedules
The software now makes the first pass at debt repayment, 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 · CoreSource: “Arrange for debt repayment or establish repayment schedules, based on customers' financial situations.” (O*NET task statement)
How this row was scored
Exposure score: 46 out of 100 (39–53 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; the value is that a specific person does it.
The rating behind it: Repayment plans follow clear affordability rules, but agreeing one usually means talking a worried customer through it.
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 2/4 · how much data exists 3/4.
Advising customers of necessary actions and strategies for debt repayment
The software now makes the first pass at customers of necessary actions, 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 · CoreSource: “Advise customers of necessary actions and strategies for debt repayment.” (O*NET task statement)
How this row was scored
Exposure score: 46 out of 100 (39–53 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; the value is that a specific person does it.
The rating behind it: Repayment advice follows documented rules, though customers weigh it partly on whether they trust the person saying it.
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 2/4 · how much data exists 3/4.
Staying human
3 tasksTasks 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 customers by telephone or in person to determine reasons for overdue payments and to review the terms of sales
The value here is that a specific person handles customers and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Confer with customers by telephone or in person to determine reasons for overdue payments and to review the terms of sales, service, or credit contracts.” (O*NET task statement)
How this row was scored
Exposure score: 34 out of 100 (27–41 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: The questions are standard, but finding out why someone has not paid depends on a real conversation.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Persuading customers to pay amounts due on credit accounts
The value here is that a specific person handles customers and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Persuade customers to pay amounts due on credit accounts, damage claims, or nonpayable checks, or to return merchandise.” (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: Scripts help, but getting someone who has not paid to pay usually turns on a live back-and-forth.
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.
Negotiating credit extensions
The value here is that a specific person handles credit extensions and stands behind it. That is earned, not computed.
importance 4 · SupplementalSource: “Negotiate credit extensions when necessary.” (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: The rules for extending credit are clear, but agreeing terms with a customer is a live negotiation.
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 5 tasks
Performing various administrative functions for assigned accounts
shifting to AIThis is reading one thing and writing another: various administrative functions in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Perform various administrative functions for assigned accounts, such as recording address changes and purging the records of deceased customers.” (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: Address changes and record clean-ups are routine database housekeeping.
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.
Sorting and filing correspondence and performing miscellaneous clerical duties
shifting to AIThis is reading one thing and writing another: correspondence in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Sort and file correspondence and perform miscellaneous clerical duties, such as answering correspondence and writing reports.” (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: Correspondence, filing and routine reports are standard office work, apart from handling any paper.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Notifying credit departments
shifting to AIThis is reading one thing and writing another: credit departments in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Notify credit departments, order merchandise repossession or service disconnection, and turn over account records to attorneys when customers fail to respond to collection attempts.” (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: Sending notifications and handing files onward is rule-driven office work triggered straight from the account record.
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.
Contacting insurance companies to check on status of claims payments and write appeal letters for denial on claims
changing shapeThe software now makes the first pass at insurance companies, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Contact insurance companies to check on status of claims payments and write appeal letters for denial on claims.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (52–60 allowing for uncertainty): partial 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: Chasing claim status and writing appeal letters follows set formats that software drafts well.
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.
Tracing delinquent customers to new addresses by inquiring at post offices
changing shapeThe software now makes the first pass at delinquent customers, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Trace delinquent customers to new addresses by inquiring at post offices, telephone companies, credit bureaus, or through the questioning of neighbors.” (O*NET task statement)
How this row was scored
Exposure score: 42 out of 100 (35–49 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: Address tracing is mostly database searching now, though occasional legwork still needs a person.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · 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
- $47,030a 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
- 158,830in 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: customer questions regarding problems in, a record out. The rows above are exactly that shape: recording information about financial status of customers and status of collection efforts and answering customer questions regarding problems with their accounts. What it cannot do is be trusted in person, which is what customers run 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: recording information about financial status of customers and status of collection efforts is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 47% of this job's task weight sits in rows the software is already learning, 35% in rows that change shape rather than disappear, and 18% in rows it is nowhere near. That is the position, measured across 15 scored tasks. It is not a forecast about you.
What you have that the software does not is conferring with customers by telephone or in person to determine reasons for overdue payments and to review the terms of sales, 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 customer questions regarding problems 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 customer questions regarding problems, 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 recording information about financial status of customers and status of collection efforts” 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 customers by telephone or in person to determine reasons for overdue payments and to review the terms of sales 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 bill and account collectors (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was loan interviewers and clerks: only about 14% of its durable work is work you already do. I am not going to pretend that is comfortable news: 47% 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. “locate and notify customers of delinquent accounts by mail, telephone, or personal…” 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 Interviewers and Clerks
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already answer customer questions regarding problems with their accounts, and their equivalent is to answer questions and advise customers regarding loans and transactions. Across both published task lists that is about 14% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 14% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Telemarketers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already answer customer questions regarding problems with their accounts, and their equivalent is to explain products or services and prices, and answer questions from customers. Across both published task lists that is about 5% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 5% of the durable side of that job. That is a different job, not a next step. I will not move you off one melting floe onto another: 70% of its own task list already scores in the top exposure band (70/100 in this release), so the same software is eating it. It is a pay cut, in those words: $35,450 against your $47,030, 24.6% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Credit Analysts
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already locate and notify customers of delinquent accounts by mail, telephone, or personal visits…, and their equivalent is to contact customers to collect payments on delinquent accounts. 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. I will not move you off one melting floe onto another: 78% of its own task list already scores in the top exposure band (70/100 in this release), so the same software is eating it.
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: 47% of its task weight, across 15 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 customers by telephone or in person to determine reasons for overdue payments and to review the terms of sales 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 Debt, rent and other cash collectors 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 Debt, rent and other cash collectors. 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.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for bill / account collectors, 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 47% of the work on this page is already inside what they can do.
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 bill / account collectors. 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 bill / account collectors 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 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 Bill and Account Collectors?
- Not as a job, but it is already doing parts of the work. Across the 15 official task statements scored for Bill and Account Collectors (United States, SOC 43-3011), 47% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 60 out of 100 (range 55–65, 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 “Bill and Account Collectors” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Receive payments and post amounts paid to customer accounts” (88/100, very high); “Locate and monitor overdue accounts, using computers and a variety of automated systems” (88/100, very high); “Record information about financial status of customers and status of collection efforts” (88/100, 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 “Bill and Account Collectors” stay human?
- About 18% 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: “Confer with customers by telephone or in person to determine reasons for overdue payments and to review the terms of sales, service, or credit contracts” (34/100, low); “Negotiate credit extensions when necessary” (35/100, low); “Persuade customers to pay amounts due on credit accounts, damage claims, or nonpayable checks, or to return merchandise” (35/100, low). 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 “Bill and Account Collectors” do about AI?
- Start from the ledger rather than the headline: 47% of this job's weighted core work is exposed, and roughly 18% 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 Bill and Account Collectors 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 15 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.
Where these numbers come from
Worth knowing about these figures
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 1 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
- Task statements
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
- Pay and employment
- bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))
Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.
The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.
The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.
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.