Futureproof
Menu

US dataswitch to UK

Credit Counselors

calculating clients' available monthly income to meet debt obligations, disbursing funds from client accounts to creditors and interviewing clients by telephone or in person to gather financial information. If that's your week, this page is about your job.

The honest answer

This job is splitting in two: estimating time for debt repayment is work AI now does quickly and cheaply, and advising clients on housing matters is work it can't touch.

Your move: what you can actually do about this ↓

Which half fills your week decides your exposure. That is more in your control than it sounds.

Your week, as this page understands it

Advise and educate individuals or organizations on acquiring and managing debt. May provide guidance in determining the best type of loan and explain loan requirements or restrictions. May help develop debt management plans or student financial aid packages. May advise on credit issues, or provide budget, mortgage, bankruptcy, or student financial aid counseling. The job title says “credit counselors”. The real job is the part underneath: advising clients on housing matters. 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 counselors is not one task. It is 23 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is advising clients on housing matters, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI
53%
changing shape
39%
staying human
8%

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

Whole-job exposure score 63 out of 100 (58–68 allowing for uncertainty): high exposure, across 23 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 counselors 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

12 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.

  • Calculating clients' available monthly income to meet debt obligations

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

    importance 5 · Core
    Source: “Calculate clients' available monthly income to meet debt obligations.” (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: Working out what is left each month after essentials is arithmetic from documented figures.

    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.

  • Assessing clients' overall financial situations by reviewing income

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

    importance 5 · Core
    Source: “Assess clients' overall financial situations by reviewing income, assets, debts, expenses, credit reports, or other financial information.” (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: Reading income, debts and credit reports to build an overall picture is document work software does well.

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

  • Prioritizing client debt repayment to avoid dire consequences

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

    importance 5 · Core
    Source: “Prioritize client debt repayment to avoid dire consequences, such as bankruptcy or foreclosure or to reduce overall costs, such as by paying high-interest or short-term loans first.” (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: Deciding which debts to clear first is a documented calculation about interest and consequences.

    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.

  • Explaining general financial topics

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

    importance 5 · Core
    Source: “Explain general financial topics to clients, such as credit report ratings, bankruptcy laws, consumer protection laws, wage attachments, or collection actions.” (O*NET task statement)
    How this row was scored

    Exposure score: 70 out of 100 (66–74 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; the value is that a specific person does it.

    The rating behind it: These topics are extensively documented publicly, though an anxious client often wants a person explaining them.

    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 2/4 · how much data exists 4/4.

Changing shape

9 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.

  • Creating debt management plans, spending plans or budgets to assist clients to meet financial goals

    The software now makes the first pass at debt management plans, spending plans or budgets, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.

    importance 5 · Core
    Source: “Create debt management plans, spending plans, or budgets to assist clients to meet financial goals.” (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: Budgets and repayment plans follow set methods and can be drafted from the client’s own numbers.

    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.

  • Recommending strategies for clients to meet their financial goals

    The software now makes the first pass at strategies, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.

    importance 5 · Core
    Source: “Recommend strategies for clients to meet their financial goals, such as borrowing money through loans or loan programs, declaring bankruptcy, making budget adjustments, or enrolling in debt management plans.” (O*NET task statement)
    How this row was scored

    Exposure score: 48 out of 100 (41–55 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; someone qualified has to answer for it.

    The rating behind it: Options can be laid out clearly, but recommending bankruptcy normally requires a certified counselor in the loop.

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

  • Explaining services or policies

    The software now makes the first pass at services, 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 5 · Core
    Source: “Explain services or policies to clients, such as debt management program rules, advantages and disadvantages of using services, or creditor concession policies.” (O*NET task statement)
    How this row was scored

    Exposure score: 53 out of 100 (49–57 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; the value is that a specific person does it.

    The rating behind it: The rules are documented and explainable, but a worried client needs reassurance from a person.

    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 2/4 · how much data exists 3/4.

  • Interviewing clients by telephone or in person to gather financial information

    The software now makes the first pass at clients, 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 5 · Core
    Source: “Interview clients by telephone or in person to gather financial information.” (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: Getting an honest picture of someone’s finances depends on them opening up in conversation.

    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.

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.

  • Advising clients on housing matters

    The rules require a named, qualified person to answer for clients, and that person cannot be a piece of software.

    importance 4 · Core
    Source: “Advise clients on housing matters, such as housing rental, homeownership, mortgage delinquency, or foreclosure prevention.” (O*NET task statement)
    How this row was scored

    Exposure score: 39 out of 100 (32–46 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; someone qualified has to answer for it; the value is that a specific person does it.

    The rating behind it: Housing advice is well documented, but certified housing counselors are normally required for this guidance.

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

  • Negotiating with creditors on behalf of clients to arrange for payment adjustments

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

    importance 4 · Core
    Source: “Negotiate with creditors on behalf of clients to arrange for payment adjustments, interest rate reductions, time extensions, or payment plans.” (O*NET task statement)
    How this row was scored

    Exposure score: 31 out of 100 (24–38 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: Getting a creditor to bend depends on relationships and leverage that are not written down anywhere.

    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 2/4.

Show the other 13 tasks

  • Estimating time for debt repayment

    shifting to AI

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

    importance 5 · Core
    Source: “Estimate time for debt repayment, given amount of debt, interest rates, and available funds.” (O*NET task statement)
    How this row was scored

    Exposure score: 100 out of 100 (96–100 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: Working out how long a debt takes to clear is a standard interest calculation with well-known formulas.

    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 4/4.

  • Maintaining or updating records of client account activity

    shifting to AI

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

    importance 4 · Core
    Source: “Maintain or update records of client account activity, including financial transactions, counseling session notes, correspondence, document images, or client inquiries.” (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: Keeping account activity, notes and correspondence up to date is routine record-keeping inside existing systems.

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

  • Preparing written documents to establish contracts with or communicate financial recommendations to clients

    shifting to AI

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

    importance 4 · Core
    Source: “Prepare written documents to establish contracts with or communicate financial recommendations to clients.” (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: Client agreements and written recommendations follow templates that drafting tools produce very well.

    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.

  • Recommending educational materials or resources to clients on matters

    shifting to AI

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

    importance 4 · Core
    Source: “Recommend educational materials or resources to clients on matters, such as financial planning, budgeting, or credit.” (O*NET task statement)
    How this row was scored

    Exposure score: 70 out of 100 (63–77 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: Matching a client to helpful budgeting or credit materials draws on plenty of published resources.

    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 4/4.

  • Explaining loan information

    shifting to AI

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

    importance 4 · Core
    Source: “Explain loan information to clients, such as available loan types, eligibility requirements, or loan restrictions.” (O*NET task statement)
    How this row was scored

    Exposure score: 70 out of 100 (66–74 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; the value is that a specific person does it.

    The rating behind it: Loan types and eligibility rules are published in detail, though clients often want them explained by a person.

    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 2/4 · how much data exists 4/4.

  • Conducting research to help clients avoid repossessions or foreclosures or removing levies or wage garnishments

    shifting to AI

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

    importance 4 · Core
    Source: “Conduct research to help clients avoid repossessions or foreclosures or remove levies or wage garnishments.” (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: Researching how to stop a repossession or garnishment is documented legal and procedural digging.

    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.

  • Reviewing changes to financial, family or employment situations to determine whether changes to existing debt management plans, spending plans or budgets

    shifting to AI

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

    importance 4 · Core
    Source: “Review changes to financial, family, or employment situations to determine whether changes to existing debt management plans, spending plans, or budgets are needed.” (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: Rechecking a plan when circumstances change is a recalculation against records already on file.

    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.

  • Investigating missing checks, payment histories, held funds, returned checks or other related issues to resolve client or creditor problems

    shifting to AI

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

    importance 3 · Supplemental
    Source: “Investigate missing checks, payment histories, held funds, returned checks, or other related issues to resolve client or creditor problems.” (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: Tracing missing payments through transaction records is exactly the kind of digging software is good at.

    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.

  • Referring clients to social service or community resources for needs beyond those of credit or debt counseling

    changing shape

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

    importance 4 · Core
    Source: “Refer clients to social service or community resources for needs beyond those of credit or debt counseling.” (O*NET task statement)
    How this row was scored

    Exposure score: 57 out of 100 (50–64 allowing for uncertainty): partial exposure, medium confidence.

    Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.

    The rating behind it: Directories can be searched, but knowing which local agency actually helps is local knowledge.

    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 2/4.

  • Creating action plans to assist clients in obtaining permanent housing via rent or mortgage programs

    changing shape

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

    importance 4 · Core
    Source: “Create action plans to assist clients in obtaining permanent housing via rent or mortgage programs.” (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: Housing action plans follow known program rules and can be drafted from the client’s situation.

    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.

  • Disbursing funds from client accounts to creditors

    changing shape

    The software now makes the first pass at funds, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.

    importance 5 · Supplemental
    Source: “Disburse funds from client accounts to creditors.” (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; someone qualified has to answer for it.

    The rating behind it: Payment runs are largely automated, but handling client trust money normally requires an accountable staff member.

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

  • Advising clients or responding to inquiries about financial matters in person or via phone

    changing shape

    The software now makes the first pass at clients, 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: “Advise clients or respond to inquiries about financial matters in person or via phone, email, Web site, or Internet chat.” (O*NET task statement)
    How this row was scored

    Exposure score: 46 out of 100 (42–50 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; the value is that a specific person does it.

    The rating behind it: Many routine queries answer well in writing, but distressed clients still want a person on the line.

    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.

  • Teaching courses or seminars on topics

    changing shape

    The software now makes the first pass at courses, 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: “Teach courses or seminars on topics, such as budgeting, management of personal finances, or financial literacy.” (O*NET task statement)
    How this row was scored

    Exposure score: 43 out of 100 (36–50 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: Course content is easy to prepare, but running a live seminar depends on a person in the room.

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

What this job pays, and how many people do it

Median pay
$52,230a 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
27,770in 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: clients' available monthly income in, a record out. The rows above are exactly that shape: estimating time for debt repayment and calculating clients' available monthly income to meet debt obligations. What it cannot do is be answerable: clients need a named person the rules will accept, and software cannot be that person. 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: estimating time for debt repayment is work today's tools do quickly and cheaply, and that is not coming back.

So, given all that: 53% of this job's task weight sits in rows the software is already learning, 39% in rows that change shape rather than disappear, and 8% in rows it is nowhere near. That is the position, measured across 23 scored tasks. It is not a forecast about you.

What you have that the software does not is advising clients on housing matters, 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 clients' available monthly income 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 clients' available monthly income, 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 estimating time for debt repayment” 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 advising clients on housing matters 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 counselors (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was bill and account collectors: 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: 53% 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. “create debt management plans, spending plans, or budgets to assist clients to…” 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.

  • Bill and Account Collectors

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already estimate time for debt repayment, given amount of debt, interest rates, and available…, and their equivalent is to advise customers of necessary actions and strategies for debt repayment. 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: 47% 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 →

  • Personal Financial Advisors

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already assess clients' overall financial situations by reviewing income, assets, debts, expenses, credit reports…, and their equivalent is to interview clients to determine their current income, expenses, insurance coverage, tax status, financial…. Across both published task lists that is about 8% of the durable work in that job.

    Why I am not recommending it: Almost none of it is work you already do: about 8% of the durable side of that job. That is a different job, not a next step. The pay gap is the market pricing a barrier: $105,070 against your $52,230 is 2.01× (OEWS May 2025 (both)), and you would be crossing it holding about 8% of their durable work. A gap that size with an overlap that small is a wish, not a route.

    Look at that job’s page anyway →

  • Loan Officers

    Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already explain loan information to clients, and their equivalent is to analyze potential loan markets and develop referral networks to locate prospects for loans. 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: 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 →

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: 53% of its task weight, across 23 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: advising clients on housing matters 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 Credit controllers 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 Credit controllers and Counsellors. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.

Your route through this

Where to go next, and what it costs

Free, and complete

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

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

Nothing Collab365 runs is built for credit counselors, 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 53% of the work on this page is already inside what they can do.

Try The AI Authority free →

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

The AI Authority is a general community about working with AI, not a course for credit counselors. 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 credit counselors launches. Nothing else.

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

We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.

No Space for credit counselors yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for credit counselors 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 counselors 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 Counselors?
Not as a job, but it is already doing parts of the work. Across the 23 official task statements scored for Credit Counselors (United States, SOC 13-2071), 53% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 63 out of 100 (range 58–68, 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 “Credit Counselors” can AI already do?
The highest-scoring tasks in release 2026-q4.1 are: “Estimate time for debt repayment, given amount of debt, interest rates, and available funds” (100/100, very high); “Maintain or update records of client account activity, including financial transactions, counseling session notes, correspondence, document images, or client…” (93/100, very high); “Prepare written documents to establish contracts with or communicate financial recommendations to clients” (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 “Credit Counselors” stay human?
About 8% 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: “Negotiate with creditors on behalf of clients to arrange for payment adjustments, interest rate reductions, time extensions, or payment plans” (31/100, low); “Advise clients on housing matters, such as housing rental, homeownership, mortgage delinquency, or foreclosure prevention” (39/100, low); “Interview clients by telephone or in person to gather financial information” (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 Counselors” do about AI?
Start from the ledger rather than the headline: 53% of this job's weighted core work is exposed, and roughly 8% 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 Counselors 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 23 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.

How we score a jobDownload this releaseLook up another job

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.