UK dataswitch to US
Professional/chartered company secretaries
monitoring changes in legislation and industry developments relevant to the field, negotiating terms and corresponding with parties on behalf of clients and completing necessary paperwork to support operational activities. 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: monitoring changes in legislation and industry developments relevant to the field. The tasks, though, are not you.
It would be a lie to soften that; posting legal correspondence is what this work rebuilds around. The plan below starts there.
Your week, as this page understands it
Chartered company secretaries and governance professionals ensure companies conform to relevant legal, statutory and financial requirements and monitors standards of corporate governance. The job title says “professional/chartered company secretaries”. The real job is the part underneath: posting legal correspondence. 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 professional/chartered company secretaries is not one task. It is 15 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is posting legal correspondence, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 67%
- changing shape
- 6%
- staying human
- 26%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 61 out of 100 (56–67 allowing for uncertainty): high exposure, across 15 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.
How we know this
What is measured: Every published task statement for professional/chartered company secretaries 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.
- 5 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.
- ASHE suppresses or does not publish a median for this unit group. We leave it empty rather than interpolating one from sibling groups, which would be a fabricated number.
- 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
10 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.
Monitoring changes in legislation and industry developments relevant to the field
This is reading one thing and writing another: changes in, a record out. That is the shape today's tools are built for.
importance 85 · 2435/00Source: “Monitor changes in legislation and industry developments relevant to the field.” (UK task statement)
How this row was scored
Exposure score: 83 out of 100 (76–90 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: Legislation and industry news are published and searchable, so tracking changes suits automation 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 4/4.
Preparing legal documents for corporate governance
This is reading one thing and writing another: legal documents in, a record out. That is the shape today's tools are built for.
importance 85 · 2435/00Source: “Prepare legal documents for corporate governance.” (UK task statement)
How this row was scored
Exposure score: 62 out of 100 (55–69 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; someone qualified has to answer for it.
The rating behind it: Minutes, resolutions and registers follow standard forms, but a qualified company secretary must stand behind what is filed.
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 4/4.
Drafting confidential letters
This is reading one thing and writing another: confidential letters in, a record out. That is the shape today's tools are built for.
importance 80 · 2435/00Source: “Draft confidential letters.” (UK 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: Drafting a letter from known facts is one of the things software does most 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.
Analysing, researching and summarising legal information
This is reading one thing and writing another: legal information in, a record out. That is the shape today's tools are built for.
importance 80 · 2435/00Source: “Analyse, research and summarise legal information.” (UK task statement)
How this row was scored
Exposure score: 72 out of 100 (68–76 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: Legal research and summarising is well-documented desk work, checked by the person who signs the advice.
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 4/4.
Researching information for the preparation of legal documents
This is reading one thing and writing another: information in, a record out. That is the shape today's tools are built for.
importance 80 · 2435/00Source: “Research information for the preparation of legal documents.” (UK 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: Legal research is reading and summarising published material, which software does quickly, though a person checks it before use.
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.
Changing shape
1 taskTasks 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.
Contacting professionals, mortgage lenders and planning officers on behalf of clients
The software now makes the first pass at professionals, mortgage lenders and planning officers, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 70 · 2435/00Source: “Contact professionals such as mortgage lenders and planning officers on behalf of clients.” (UK task statement)
How this row was scored
Exposure score: 50 out of 100 (43–57 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: Contacting lenders and planning officers is largely routine correspondence, though useful answers often come from knowing the person.
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 2/4.
Staying human
4 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.
Negotiating terms and corresponding with parties on behalf of clients
The value here is that a specific person handles terms and stands behind it. That is earned, not computed.
importance 75 · 2435/00Source: “Negotiate terms and correspond with parties on behalf of clients.” (UK task statement)
How this row was scored
Exposure score: 24 out of 100 (17–31 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: Correspondence drafts easily, but negotiating on a client's behalf depends on judgement and a relationship with the other side.
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 3/4 · how much data exists 2/4.
Supervising the work of junior staff to ensure quality and efficiency
The value here is that a specific person handles the work of junior staff and stands behind it. That is earned, not computed.
importance 70 · 2435/00Source: “Supervise the work of junior staff to ensure quality and efficiency.” (UK 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: Supervising junior staff depends on knowing the individuals and their work.
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.
Reaching agreements with clients through effective communication
The value here is that a specific person handles agreements and stands behind it. That is earned, not computed.
importance 70 · 2435/00Source: “Reach agreements with clients through effective communication.” (UK task statement)
How this row was scored
Exposure score: 28 out of 100 (21–35 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: Reaching agreement with a client depends on trust built in the conversation itself.
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 3/4 · how much data exists 2/4.
Posting legal correspondence
This work happens in the physical world: legal correspondence, in a real place. Software cannot follow it there.
importance 70 · 2435/00Source: “Post legal correspondence to clients, witnesses, and court officials.” (UK task statement)
How this row was scored
Exposure score: 10 out of 100 (6–14 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Getting envelopes into the post is physical work in the office.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Show the other 5 tasks
Completing necessary paperwork to support operational activities
shifting to AIThis is reading one thing and writing another: necessary paperwork in, a record out. That is the shape today's tools are built for.
importance 70 · 2435/00Source: “Complete necessary paperwork to support operational activities.” (UK 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: Routine operational paperwork is standard form-filling drawn from records the business already holds, which software completes quickly.
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 and distributing invoices to bill clients or pay account expenses
shifting to AIThis is reading one thing and writing another: invoices in, a record out. That is the shape today's tools are built for.
importance 70 · 2435/00Source: “Prepare and distribute invoices to bill clients or pay account expenses.” (UK 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: Invoicing and expense billing is routine, rule-based paperwork that software already produces, with someone authorising payment.
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.
Arranging appointments for clients and staff across various sectors
shifting to AIThis is reading one thing and writing another: appointments in, a record out. That is the shape today's tools are built for.
importance 60 · 2435/00Source: “Arrange appointments for clients and staff across various sectors.” (UK task statement)
How this row was scored
Exposure score: 85 out of 100 (81–89 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: Booking appointments is scheduling work that online systems already do from start to finish.
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 4/4.
Filling out accident reports to document workplace incidents
shifting to AIThis is reading one thing and writing another: accident reports in, a record out. That is the shape today's tools are built for.
importance 60 · 2435/00Source: “Fill out accident reports to document workplace incidents.” (UK task statement)
How this row was scored
Exposure score: 81 out of 100 (74–88 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: Once the facts of an incident are known, filling in an accident report is standard form work software completes 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 3/4.
Answering telephone calls and directing them appropriately
shifting to AIThis is reading one thing and writing another: telephone calls in, a record out. That is the shape today's tools are built for.
importance 60 · 2435/00Source: “Answer telephone calls and direct them appropriately.” (UK 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: Taking calls and routing them is transactional, and automated systems already do much of it.
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
- No median pay figure is published for this exact group, so there is none here. We would rather show you the gap than a number borrowed from somewhere else.The ONS doesn't publish a reliable pay figure for this exact job (too few people in its survey sample), so none is shown here.
- People doing this job
- 1,700in the UK, 2026.nomis-aps · Apr 2025-Mar 2026 (latest APS 12-month period)This headcount comes from a survey, not a census, so treat it as a good estimate rather than an exact count.
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: legal documents in, a record out. The rows above are exactly that shape: monitoring changes in legislation and industry developments relevant to the field and preparing legal documents for corporate governance. What it cannot do is be there in the room, and that is still where legal correspondence gets done. 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: monitoring changes in legislation and industry developments relevant to the field is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 67% of this job's task weight sits in rows the software is already learning, 6% in rows that change shape rather than disappear, and 26% in rows it is nowhere near. That is the position, measured across 15 scored tasks. It is not a forecast about you.
What you have that the software does not is posting legal correspondence, 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 legal documents 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 legal documents, 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 monitoring changes in legislation and industry developments relevant to the field” 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 posting legal correspondence 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, spend an hour with National Careers Service. It is free and government-funded, 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 UK occupations to professional/chartered company secretaries (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was legal secretaries: only about 6% 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: 67% 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. “negotiate terms and correspond with parties on behalf of clients” 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 412 UK 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.
Legal secretaries
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “post legal correspondence to clients, witnesses, and court officials”. Across the whole of both lists that adds up to about 6% of the work in that job the software is not taking.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step. I will not move you off one melting floe onto another: 65% of its own task list already scores in the top exposure band (63/100 in this release), so the same software is eating it. This release publishes no median pay for that job, so I cannot show you what the move costs or pays. I do not recommend a move I cannot price.
Solicitors and lawyers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already post legal correspondence to clients, witnesses, and court officials, and their equivalent is to interview clients and witnesses. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step. This release publishes no median pay for that job, so I cannot show you what the move costs or pays. I do not recommend a move I cannot price.
Barristers and judges
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already post legal correspondence to clients, witnesses, and court officials, and their equivalent is to interrogate witnesses in court. Across both published task lists that is about 2% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 2% of the durable side of that job. That is a different job, not a next step. This release publishes no median pay for that job, so I cannot show you what the move costs or pays. I do not recommend a move I cannot price.
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: 67% of its task weight, across 15 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
“It’s too late for me to become something else”
You are not starting from zero, and the page shows why: posting legal correspondence 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 Kingdom figures
The United States splits this work across more than one official group, of which Financial Managers is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United States page →partial match
The other groups this work is counted across:
In US official statistics this job is counted as Financial Managers and Business Operations Specialists, All Other. 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.
In England:
A free hour with a government-funded careers adviser is worth more than another evening of reading. In England that's the National Careers Service.
Free, government-funded
In England:
National Careers Service - Find a course
Search what's actually running near you before you spend anything.
Free to search; individual courses vary
In England:
Free courses for jobs (Level 3 qualifications)
A free Level 3 qualification you already qualify for beats a paid course you don't need.
Free for eligible adults
In England:
Free, up to 16 weeks, and you get a job interview at the end. In England these are Skills Bootcamps - search what's running near you.
Free for eligible adults in England
In Scotland:
My World of Work (Skills Development Scotland)
In Scotland it's My World of Work, from Skills Development Scotland.
Free, publicly funded
In Wales:
In Wales it's Careers Wales.
Free, Welsh Government-funded
In Northern Ireland:
Careers Service Northern Ireland
In Northern Ireland it's the Careers Service on nidirect.
Free, Department for the Economy-funded
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for professional/chartered company secretaries, 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 67% 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 professional/chartered company secretaries. 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 professional/chartered company secretaries 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 Professional/chartered company secretaries?
- Not as a job, but it is already doing parts of the work. Across the 15 official task statements scored for Professional/chartered company secretaries (United Kingdom, SOC 2435), 67% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 61 out of 100 (range 56–67, 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 “Professional/chartered company secretaries” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Complete necessary paperwork to support operational activities” (93/100, very high); “Prepare and distribute invoices to bill clients or pay account expenses” (88/100, very high); “Arrange appointments for clients and staff across various sectors” (85/100, 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 “Professional/chartered company secretaries” stay human?
- About 26% 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: “Post legal correspondence to clients, witnesses, and court officials” (10/100, minimal); “Negotiate terms and correspond with parties on behalf of clients” (24/100, low); “Reach agreements with clients through effective communication” (28/100, 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 “Professional/chartered company secretaries” do about AI?
- Start from the ledger rather than the headline: 67% of this job's weighted core work is exposed, and roughly 26% 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 Professional/chartered company secretaries calculated?
- Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 15 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.
Where these numbers come from
About the data on this page
- The ONS doesn't publish a reliable pay figure for this exact job (too few people in its survey sample), so none is shown here.
- This headcount comes from a survey, not a census, so treat it as a good estimate rather than an exact count.
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 5 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.
- ASHE suppresses or does not publish a median for this unit group. We leave it empty rather than interpolating one from sibling groups, which would be a fabricated number.
- 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
- gaisi-indexProcessing: catalogue-bridge → ssc-relatedness-weighting → task-scoring → score-aggregation
- Task weights
- gaisi-index (relatedness)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
- Pay and employment
- no pay figure published for this groupnomis-aps (Apr 2025-Mar 2026 (latest APS 12-month period))
Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.
The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.
The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.
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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.