US dataswitch to UK
Interviewers, Except Eligibility and Loan
asking questions in accordance with instructions to obtain various specified information, reviewing data obtained from interview for completeness and accuracy and compiling, recording. If that's your week, this page is about your job.
The honest answer
AI is already taking a real slice of the routine work here: identifying and reporting problems in obtaining valid data. That is a slice of tasks, not of you.
That slice is not coming back; the core of the job, meeting with supervisor daily to submit completed assignments and discuss progress, stays yours. New tools, same person answering for it.
Your week, as this page understands it
Interview persons by telephone, mail, in person, or by other means for the purpose of completing forms, applications, or questionnaires. Ask specific questions, record answers, and assist persons with completing form. May sort, classify, and file forms. The job title says “interviewers”, “except eligibility” or “loan”: officially one job, several names. The real job is the part underneath: meeting with supervisor daily to submit completed assignments and discuss progress. 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 interviewers, except eligibility and loan is not one task. It is 16 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is meeting with supervisor daily to submit completed assignments and discuss progress, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 33%
- changing shape
- 25%
- staying human
- 42%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 50 out of 100 (44–57 allowing for uncertainty): partial exposure, across 16 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 interviewers, except eligibility and loan 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
5 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.
Identifying and reporting problems in obtaining valid data
This is reading one thing and writing another: problems in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Identify and report problems in obtaining valid data.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Spotting and flagging where data collection went wrong is a checking job software handles 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.
Reviewing data obtained from interview for completeness and accuracy
This is reading one thing and writing another: data in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Review data obtained from interview for completeness and accuracy.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 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: Checking answers for gaps and errors is routine, though some accuracy checks mean going back to the 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 0/4 · how much data exists 3/4.
Performing office duties, such as telemarketing or customer service inquiries
This is reading one thing and writing another: office duties in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Perform office duties, such as telemarketing or customer service inquiries, maintaining staff records, billing patients, or receiving payments.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (57–71 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: Billing, records and routine customer queries are standard office tasks software already covers.
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 and coding results or data from interview or survey, using computer or specified form
This is reading one thing and writing another: results in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Compile, record, and code results or data from interview or survey, using computer or specified form.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (86–100 allowing for uncertainty): very 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: Coding and recording survey answers against a set codebook is exactly what software is good at.
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.
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.
Asking questions in accordance with instructions to obtain various specified information
The software now makes the first pass at questions, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Ask questions in accordance with instructions to obtain various specified information, such as person's name, address, age, religious preference, or state of residency.” (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: Asking a fixed list of questions and capturing the answers is something automated surveys already do.
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.
Ensuring payment for services by verifying benefits with the person's insurance provider or working out financing options
The software now makes the first pass at payment, 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: “Ensure payment for services by verifying benefits with the person's insurance provider or working out financing options.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 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: Insurance checks are largely automated, though talking someone through payment options still needs reassurance.
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.
Locating and listing addresses and households
The software now makes the first pass at addresses, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Locate and list addresses and households.” (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: Building address and household lists is mostly map and records work done at a screen.
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 0/4 · how much data exists 3/4.
Staying human
7 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.
Performing patient services, such as answering the telephone or assisting patients with financial or medical questions
The value here is that a specific person handles patient services and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Perform patient services, such as answering the telephone or assisting patients with financial or medical questions.” (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; the value is that a specific person does it.
The rating behind it: Phone and desk answers can be automated, but anxious patients often want a person in front of 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 2/4 · how much data exists 3/4.
Meeting with supervisor daily to submit completed assignments and discuss progress
The value here is that a specific person handles supervisor and stands behind it. That is earned, not computed.
importance 3 · CoreSource: “Meet with supervisor daily to submit completed assignments and discuss progress.” (O*NET task statement)
How this row was scored
Exposure score: 17 out of 100 (10–24 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: A daily catch-up with your supervisor is a working relationship, not a document to produce.
The five ratings: output a model can produce 1/4 · 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 2/4.
Supervising or training other staff members
The value here is that a specific person handles other staff members and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Supervise or train other staff members.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (19–33 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: Supervising and training staff depends on watching people work and building their confidence.
The five ratings: output a model can produce 2/4 · 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 2/4.
Show the other 6 tasks
Preparing reports to provide answers in response to specific problems
shifting to AIThis is reading one thing and writing another: reports in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Prepare reports to provide answers in response to specific problems.” (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: Writing a report that answers a specific question from collected data is standard document work.
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.
Collecting and analyzing data, such as studying old records
changing shapeThe software now makes the first pass at data, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Collect and analyze data, such as studying old records, tallying the number of outpatients entering each day or week, or participating in federal, state, or local population surveys as a Census Enumerator.” (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: Studying records and tallying counts is desk work, though enumerator duties sometimes mean going out.
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.
Explaining survey objectives and procedures to interviewees and interpreting survey questions to help interviewees' comprehension
staying humanThe value here is that a specific person handles survey objectives and stands behind it. That is earned, not computed.
importance 4 · SupplementalSource: “Explain survey objectives and procedures to interviewees and interpret survey questions to help interviewees' comprehension.” (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; the value is that a specific person does it.
The rating behind it: Explaining a survey can be scripted, but helping a confused respondent understand takes live judgment.
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 3/4.
Contacting individuals to be interviewed at home
staying humanThis work happens in the physical world: individuals, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Contact individuals to be interviewed at home, place of business, or field location, by telephone, mail, or in person.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 allowing for uncertainty): low exposure, medium 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: Phone and mail contact automates easily, but doorstep and workplace approaches still need someone to turn up.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Assisting individuals in filling out applications or questionnaires
staying humanThe value here is that a specific person handles individuals and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Assist individuals in filling out applications or questionnaires.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (23–37 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: Guided forms help a lot, but sitting with someone struggling to complete paperwork needs patience and presence.
The five ratings: output a model can produce 2/4 · 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 3/4.
Identifying and resolving inconsistencies in interviewees' responses by means of appropriate questioning or explanation
staying humanThe value here is that a specific person handles inconsistencies and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Identify and resolve inconsistencies in interviewees' responses by means of appropriate questioning or explanation.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (19–33 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: Following up an odd answer means reading the person and rephrasing on the spot.
The five ratings: output a model can produce 2/4 · 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 2/4.
What this job pays, and how many people do it
- Median pay
- $45,920a 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
- 148,060in 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: problems in, a record out. The rows above are exactly that shape: identifying and reporting problems in obtaining valid data and reviewing data obtained from interview for completeness and accuracy. What it cannot do is be trusted in person, which is what supervisor runs on: someone specific doing it and standing behind it. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
Your week is splitting in two, and which half fills it is the whole question. Identifying and reporting problems in obtaining valid data is going; meeting with supervisor daily to submit completed assignments and discuss progress is not.
So, given all that: 33% of this job's task weight sits in rows the software is already learning, 25% in rows that change shape rather than disappear, and 42% in rows it is nowhere near. That is the position, measured across 16 scored tasks. It is not a forecast about you.
The occupation is an average and you are not, so the first job below is finding out where your own week actually sits.
This week: one thing
Print the task ledger above and put your own hours next to each row. Roughly, in pen, in one sitting. Then look at which group your hours actually pile up in. Twenty minutes, this week.
- What you end up holding
- your own week, on one page, sorted into what is shifting and what is not
- How long it takes
- about twenty minutes
If printing it isn’t practical: Read the rows off this page and write the same list on the back of an envelope. Same twenty minutes, same page, and it works just as well said out loud to someone who knows the job. The point is your hours next to the rows, not the paper it is on.
Over the next 90 days
Volunteer toward the durable end, visibly. Over the next ninety days put your hand up for the work in the bottom group (meeting with supervisor daily to submit completed assignments and discuss progress) and let people see you doing it. Not a new project: the same job, with your mix deliberately tilted. The point is that when the rota or the reorganisation comes, the version of you people picture is the one doing the part that stays.
Over the next 12 months
Claim a specialism at the durable end and let the other end go. Over a year, deliberately become the person who handles meeting with supervisor daily to submit completed assignments and discuss progress, and deliberately stop being the first choice for the rows in the top group. That trade costs something, because the exposed work is often the comfortable work. Decide it on purpose rather than by drift. 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 interviewers, except eligibility and loan (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was lodging managers: only about 8% of its durable work is work you already do. Your own job splits about 33/67: that share of the list sits in the top exposure band and the rest does not. On this evidence the honest move is inside the job rather than out of it. Become the person who owns “ask questions in accordance with instructions to obtain various specified information”, and let the exposed end go.
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.
Lodging Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already supervise or train other staff members, and their equivalent is to train staff members. 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.
Database Administrators
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already perform patient services, such as answering the telephone or assisting patients with financial…, and their equivalent is to train users and 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: 82% of its own task list already scores in the top exposure band (67/100 in this release), so the same software is eating it. The pay gap is the market pricing a barrier: $104,620 against your $45,920 is 2.28× (OEWS May 2025 (both)), and you would be crossing it holding about 5% of their durable work. A gap that size with an overlap that small is a wish, not a route.
Data Scientists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already supervise or train other staff members, and their equivalent is to supervise the work of data management project staff. 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: 84% of its own task list already scores in the top exposure band (75/100 in this release), so the same software is eating it. The pay gap is the market pricing a barrier: $120,230 against your $45,920 is 2.62× (OEWS May 2025 (both)), and you would be crossing it holding about 4% of their durable work. A gap that size with an overlap that small is a wish, not a route.
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: 33% of its task weight, across 16 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The whole-job doom story
Nothing on this page says this job ends. It says the mix inside it moves. Half the rows above are unchanged or changing shape, and the plan is about which half your week sits in. That is a very different problem, and a solvable one.
Panic-buying a course
Do the twenty-minute sorting exercise first. Most people who buy a course before they have done it buy the wrong one, and the free services listed below will tell you the same thing without charging for it.
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 Market research interviewers 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 Market research interviewers. 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 interviewers / except eligibility / loan, 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 33% 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 interviewers / except eligibility / loan. 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 interviewers / except eligibility / loan 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 Interviewers, Except Eligibility and Loan?
- Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Interviewers, Except Eligibility and Loan (United States, SOC 43-4111), 33% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 50 out of 100 (range 44–57, band: partial). 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 “Interviewers, Except Eligibility and Loan” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Compile, record, and code results or data from interview or survey, using computer or specified form” (93/100, very high); “Review data obtained from interview for completeness and accuracy” (75/100, high); “Identify and report problems in obtaining valid data” (75/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Interviewers, Except Eligibility and Loan” stay human?
- About 42% 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: “Meet with supervisor daily to submit completed assignments and discuss progress” (17/100, minimal); “Supervise or train other staff members” (26/100, low); “Identify and resolve inconsistencies in interviewees' responses by means of appropriate questioning or explanation” (26/100, 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 “Interviewers, Except Eligibility and Loan” do about AI?
- Start from the ledger rather than the headline: 33% of this job's weighted core work is exposed, and roughly 42% 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 Interviewers, Except Eligibility and Loan 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 16 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.