US dataswitch to UK
Eligibility Interviewers, Government Programs
computing and authorizing amounts of assistance, scheduling benefits claimants for adjudication interviews to address questions of eligibility and referring applicants to job openings or to interviews with other staff. 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: compiling, recording. The tasks, though, are not you.
It would be a lie to soften that; interviewing benefits recipients at specified intervals to certify their eligibility for continuing benefits is what this work rebuilds around. Your move starts there.
Your week, as this page understands it
Determine eligibility of persons applying to receive assistance from government programs and agency resources, such as welfare, unemployment benefits, social security, and public housing. The job title says “eligibility interviewers” or “government programs”: officially one job, two names. The real job is the part underneath: interviewing benefits recipients at specified intervals to certify their eligibility for continuing benefits. 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 eligibility interviewers, government programs is not one task. It is 17 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is interviewing benefits recipients at specified intervals to certify their eligibility for continuing benefits, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 71%
- changing shape
- 19%
- staying human
- 10%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 62 out of 100 (58–68 allowing for uncertainty): high exposure, across 17 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 eligibility interviewers, government programs 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.
- 3 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
11 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.
Interpreting and explaining information, eligibility requirements, application details, payment methods and applicants' legal rights
This is reading one thing and writing another: information, eligibility requirements, application details in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Interpret and explain information such as eligibility requirements, application details, payment methods, and applicants' legal rights.” (O*NET task statement)
How this row was scored
Exposure score: 61 out of 100 (57–65 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: Benefit rules are published in detail, so explaining them accurately is well within what current tools do.
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 4/4.
Initiating procedures to grant, modify, deny or terminating assistance or referring applicants to other agencies for assistance
This is reading one thing and writing another: procedures in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Initiate procedures to grant, modify, deny, or terminate assistance, or refer applicants to other agencies for assistance.” (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: Starting, changing or ending a payment follows written rules and happens inside the case system.
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.
Checking with employers or other references to verify answers and obtain further information
This is reading one thing and writing another: employers in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Check with employers or other references to verify answers and obtain further information.” (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: Verification requests to employers are standard letters and data checks that software sends and matches.
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.
Compiling, recording
This is reading one thing and writing another: personal and financial data in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Compile, record, and evaluate personal and financial data to verify completeness and accuracy, and to determine eligibility status.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Checking submitted details against eligibility rules is rule-following comparison work 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.
Changing shape
4 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.
Interviewing and investigating applicants for public assistance to gather information pertinent to their applications
The software now makes the first pass at applicants, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Interview and investigate applicants for public assistance to gather information pertinent to their applications.” (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: Online applications and structured questions already gather most of this, though some cases still need 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 1/4 · how much data exists 3/4.
Providing applicants with assistance in completing application forms
The software now makes the first pass at applicants, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Provide applicants with assistance in completing application forms, such as those for job referrals or unemployment compensation claims.” (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; mistakes that are cheap to catch.
The rating behind it: Guided online forms help most people, but some applicants need someone sitting with them.
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 1/4 · how much data exists 3/4.
Preparing applications and forms for applicants for such purposes as school enrollment
The software now makes the first pass at applications, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Prepare applications and forms for applicants for such purposes as school enrollment, employment, and medical services.” (O*NET task statement)
How this row was scored
Exposure score: 59 out of 100 (52–66 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: Filling in standard applications from information already held is routine form work, often done alongside the applicant.
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 1/4 · how much data exists 3/4.
Investigating claimants for the possibility of fraud or abuse
The software now makes the first pass at claimants, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Investigate claimants for the possibility of fraud or abuse.” (O*NET task statement)
How this row was scored
Exposure score: 49 out of 100 (42–56 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Fraud checks are largely data-matching now, though following up a suspect case can need legwork.
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 0/4 · how much data exists 3/4.
Staying human
2 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.
Interviewing benefits recipients at specified intervals to certify their eligibility for continuing benefits
The rules require a named, qualified person to answer for benefits recipients, and that person cannot be a piece of software.
importance 4 · CoreSource: “Interview benefits recipients at specified intervals to certify their eligibility for continuing benefits.” (O*NET task statement)
How this row was scored
Exposure score: 20 out of 100 (13–27 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Certifying that someone still qualifies rests on a real interview and an accountable decision.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Conducting annual, interim and special housing reviews and home visits to ensure conformance to regulations
This work happens in the physical world: annual, interim and special housing reviews and home, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Conduct annual, interim, and special housing reviews and home visits to ensure conformance to regulations.” (O*NET task statement)
How this row was scored
Exposure score: 11 out of 100 (7–15 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Housing reviews mean visiting the home and seeing the conditions for yourself.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Show the other 7 tasks
Keeping records of assigned cases
shifting to AIThis is reading one thing and writing another: records of assigned cases in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Keep records of assigned cases, and prepare required reports.” (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: Case records and routine reports are generated straight from the case system.
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.
Computing and authorizing amounts of assistance
shifting to AIThis is reading one thing and writing another: amounts of assistance in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Compute and authorize amounts of assistance for programs, such as grants, monetary payments, and food stamps.” (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: Benefit amounts follow written rules applied to recorded figures, which is exactly what software calculates reliably.
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.
Monitoring the payments of benefits throughout the duration of a claim
shifting to AIThis is reading one thing and writing another: the payments of benefits throughout the duration in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Monitor the payments of benefits throughout the duration of a claim.” (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: Watching payments run correctly across a claim is exactly the kind of checking software does continuously.
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.
Scheduling benefits claimants for adjudication interviews to address questions of eligibility
shifting to AIThis is reading one thing and writing another: benefits claimants in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Schedule benefits claimants for adjudication interviews to address questions of eligibility.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (75–83 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: Booking interview slots against a diary is routine scheduling that software already handles.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Answering applicants' questions about benefits and claim procedures
shifting to AIThis is reading one thing and writing another: applicants' questions in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Answer applicants' questions about benefits and claim procedures.” (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: Benefit questions are answered from published rules, though people with complicated cases still want 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 1/4 · how much data exists 4/4.
Providing social workers with pertinent information gathered during applicant interviews
shifting to AIThis is reading one thing and writing another: social workers in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Provide social workers with pertinent information gathered during applicant interviews.” (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: Turning interview notes into a useful handover for a social worker is drafting 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 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Referring applicants to job openings or to interviews with other staff
shifting to AIThis is reading one thing and writing another: applicants in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Refer applicants to job openings or to interviews with other staff, in accordance with administrative guidelines or office procedures.” (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: Matching applicants to job openings and booking them in follows office rules that software applies well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
What this job pays, and how many people do it
- Median pay
- $54,210a 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
- 154,800in 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: personal and financial data in, a record out. The rows above are exactly that shape: compiling, recording and interpreting and explaining information, eligibility requirements, application details, payment methods and applicants' legal rights. What it cannot do is be answerable: benefits recipients 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: compiling, recording is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 71% of this job's task weight sits in rows the software is already learning, 19% in rows that change shape rather than disappear, and 10% in rows it is nowhere near. That is the position, measured across 17 scored tasks. It is not a forecast about you.
What you have that the software does not is interviewing benefits recipients at specified intervals to certify their eligibility for continuing benefits, 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 personal and financial data 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 personal and financial data, 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 compiling, recording” 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 interviewing benefits recipients at specified intervals to certify their eligibility for continuing benefits 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 eligibility interviewers, government programs (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 5% 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: 71% 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. “interview benefits recipients at specified intervals to certify their eligibility for continuing…” 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 answer applicants' questions about benefits and claim procedures, and their equivalent is to meet with applicants to obtain information for loan applications and to answer questions…. 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.
Human Resources Assistants, Except Payroll and Timekeeping
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already provide social workers with pertinent information gathered during applicant interviews, and their equivalent is to interview job applicants to obtain and verify information used to screen and evaluate…. 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.
Loan Interviewers and Clerks
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already check with employers or other references to verify answers and obtain further information, and their equivalent is to contact credit bureaus, employers, and other sources to check applicants' credit and personal…. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step.
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: 71% of its task weight, across 17 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: interviewing benefits recipients at specified intervals to certify their eligibility for continuing benefits 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 National government administrative occupations 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 National government administrative occupations. 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 eligibility interviewers / government programs, 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 71% 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 eligibility interviewers / government programs. 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 eligibility interviewers / government programs launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
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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 Eligibility Interviewers, Government Programs?
- Not as a job, but it is already doing parts of the work. Across the 17 official task statements scored for Eligibility Interviewers, Government Programs (United States, SOC 43-4061), 71% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 62 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 “Eligibility Interviewers, Government Programs” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Keep records of assigned cases, and prepare required reports” (93/100, very high); “Compile, record, and evaluate personal and financial data to verify completeness and accuracy, and to determine eligibility status” (81/100, very high); “Compute and authorize amounts of assistance for programs, such as grants, monetary payments, and food stamps” (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 “Eligibility Interviewers, Government Programs” stay human?
- About 10% 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: “Conduct annual, interim, and special housing reviews and home visits to ensure conformance to regulations” (11/100, minimal); “Interview benefits recipients at specified intervals to certify their eligibility for continuing benefits” (20/100, low); “Interview and investigate applicants for public assistance to gather information pertinent to their applications” (42/100, 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 “Eligibility Interviewers, Government Programs” do about AI?
- Start from the ledger rather than the headline: 71% of this job's weighted core work is exposed, and roughly 10% 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 Eligibility Interviewers, Government Programs 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 17 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.
- 3 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.