US dataswitch to UK
Shipping, Receiving, and Inventory Clerks
examining shipment contents and comparing with records, packing, sealing, labelling or affix postage to prepare materials and conferring or corresponding with establishment representatives to rectify problems. If that's your week, this page is about your job.
The honest answer
This job is splitting in two: recording shipment data, such as weight, charges, space availability, damages or discrepancies, for reporting, accounting or recordkeeping purposes is work AI now does quickly and cheaply, and examining shipment contents and comparing with records is work it can't touch.
Which half fills your week decides your exposure. The ledger below shows which rows you can move toward.
Your week, as this page understands it
Verify and maintain records on incoming and outgoing shipments involving inventory. Duties include verifying and recording incoming merchandise or material and arranging for the transportation of products. May prepare items for shipment. The job title says “shipping”, “receiving” or “inventory clerks”: officially one job, several names. The real job is the part underneath: examining shipment contents and comparing with records. 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 shipping, receiving, and inventory clerks is not one task. It is 11 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is examining shipment contents and comparing with records, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 49%
- changing shape
- 11%
- staying human
- 40%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 53 out of 100 (49–58 allowing for uncertainty): partial exposure, across 11 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 shipping, receiving, and inventory clerks 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
6 tasksTasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.
Recording shipment data, such as weight, charges, space availability, damages or discrepancies, for reporting, accounting or recordkeeping purposes
This is reading one thing and writing another: shipment data in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Record shipment data, such as weight, charges, space availability, damages, or discrepancies, for reporting, accounting, or recordkeeping purposes.” (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: Recording weights, charges and discrepancies is data entry that systems capture automatically.
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 documents, such as work orders, bills of lading or shipping orders, to route materials
This is reading one thing and writing another: documents in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Prepare documents, such as work orders, bills of lading, or shipping orders, to route materials.” (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: Bills of lading and shipping orders are generated straight from order data by shipping software.
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.
Contacting carrier representatives to make arrangements or to issue instructions for shipping and delivery of materials
This is reading one thing and writing another: carrier representatives in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Contact carrier representatives to make arrangements or to issue instructions for shipping and delivery of materials.” (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: Booking collections and giving instructions to carriers happens by email and through carrier systems.
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.
Determining shipping methods, routes or rates for materials
This is reading one thing and writing another: methods, routes or rates in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Determine shipping methods, routes, or rates for materials to be shipped.” (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: Comparing carriers, routes and rates is exactly the calculation shipping software already does.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Computing amounts, such as space available, shipping, storage or demurrage charges, using computer or price list
This is reading one thing and writing another: amounts in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Compute amounts, such as space available, shipping, storage, or demurrage charges, using computer or price list.” (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: Working out shipping, storage and demurrage charges from price lists is straightforward calculation.
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
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.
Conferring or corresponding with establishment representatives to rectify problems
The software now makes the first pass at establishment representatives, 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: “Confer or correspond with establishment representatives to rectify problems, such as damages, shortages, or nonconformance to specifications.” (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: Chasing damages and shortages can be drafted automatically, but suppliers respond better to someone they deal with.
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.
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.
Examining shipment contents and comparing with records
This work happens in the physical world: shipment contents, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Examine shipment contents and compare with records, such as manifests, invoices, or orders, to verify accuracy.” (O*NET 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: Comparing paperwork is easy, but someone has to physically open and check what actually arrived.
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.
Requisitioning and storing shipping materials and supplies to maintain inventory of stock
This work happens in the physical world: materials, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Requisition and store shipping materials and supplies to maintain inventory of stock.” (O*NET task statement)
How this row was scored
Exposure score: 38 out of 100 (31–45 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: Raising the order is simple desk work; putting the stock away is done by hand.
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 0/4 · how much data exists 3/4.
Packing, sealing, labelling or affix postage to prepare materials
This work happens in the physical world: affix postage, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Pack, seal, label, or affix postage to prepare materials for shipping, using hand tools, power tools, or postage meter.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 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: Packing, sealing, labelling and franking parcels is hands-on work at the bench.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Delivering or routing materials to departments
This work happens in the physical world: materials, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Deliver or route materials to departments using handtruck, conveyor, or sorting bins.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Wheeling materials to departments is a physical delivery round.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Show the other 1 task
Comparing shipping routes or methods to determine which have the least environmental impact
shifting to AIThis is reading one thing and writing another: routes in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Compare shipping routes or methods to determine which have the least environmental impact.” (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: Emissions comparisons use published calculation methods, but real figures for particular ships and routes are patchy.
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.
What this job pays, and how many people do it
- Median pay
- $45,260a 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
- 816,870in 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: shipment data in, a record out. The rows above are exactly that shape: recording shipment data, such as weight, charges and preparing documents, such as work orders. What it cannot do is be there in the room, and that is still where shipment contents get 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: recording shipment data, such as weight, charges, space availability, damages or discrepancies, for reporting, accounting or recordkeeping purposes is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 49% of this job's task weight sits in rows the software is already learning, 11% in rows that change shape rather than disappear, and 40% in rows it is nowhere near. That is the position, measured across 11 scored tasks. It is not a forecast about you.
What you have that the software does not is examining shipment contents and comparing with records, 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 shipment 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 shipment 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 recording shipment data, such as weight, charges, space availability, damages or discrepancies, for reporting, accounting or recordkeeping purposes” 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 examining shipment contents and comparing with records 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 shipping, receiving, and inventory clerks (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was couriers and messengers: only about 6% of its durable work is work you already do, it pays 13.4% less and there are far fewer of those jobs than of yours. Your own job splits about 49/51: 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 “examine shipment contents and compare with records”, 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.
Couriers and Messengers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already deliver or route materials to departments using handtruck, conveyor, or sorting bins, and their equivalent is to sort items to be delivered according to the delivery route. Across both published task lists that is about 6% of the durable work in that job.
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. It is a pay cut, in those words: $39,200 against your $45,260, 13.4% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 68,640 of those jobs against 816,870 of yours (OEWS May 2025), 8% as many seats.
Craft Artists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already determine shipping methods, routes, or rates for materials to be shipped, and their equivalent is to pack products for shipping. 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. And it is a narrow door: about 4,580 of those jobs against 816,870 of yours (OEWS May 2025), 1% as many seats.
Weighers, Measurers, Checkers, and Samplers, Recordkeeping
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already examine shipment contents and compare with records, and their equivalent is to compare product labels, tags, or tickets, shipping manifests, purchase orders, and bills of…. 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. And it is a narrow door: about 53,300 of those jobs against 816,870 of yours (OEWS May 2025), 7% as many seats.
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: 49% of its task weight, across 11 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: examining shipment contents and comparing with records 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 Stock control clerks and assistants is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
The other groups this work is counted across:
In UK official statistics this job is counted as Stock control clerks and assistants and Importers and exporters. 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.
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 shipping, receiving and inventory clerks, and we are not going to point you at the nearest one and call it a fit.
The working behind that
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 49% 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 shipping, receiving and inventory clerks. 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 shipping, receiving and inventory clerks launches. Nothing else.
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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 Shipping, Receiving, and Inventory Clerks?
- Not as a job, but it is already doing parts of the work. Across the 11 official task statements scored for Shipping, Receiving, and Inventory Clerks (United States, SOC 43-5071), 49% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 53 out of 100 (range 49–58, 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 “Shipping, Receiving, and Inventory Clerks” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Prepare documents, such as work orders, bills of lading, or shipping orders, to route materials” (93/100, very high); “Record shipment data, such as weight, charges, space availability, damages, or discrepancies, for reporting, accounting, or recordkeeping purposes” (93/100, very high); “Compute amounts, such as space available, shipping, storage, or demurrage charges, using computer or price list” (93/100, 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 “Shipping, Receiving, and Inventory Clerks” stay human?
- About 40% 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: “Deliver or route materials to departments using handtruck, conveyor, or sorting bins” (0/100, minimal); “Pack, seal, label, or affix postage to prepare materials for shipping, using hand tools, power tools, or postage meter” (0/100, minimal); “Examine shipment contents and compare with records, such as manifests, invoices, or orders, to verify accuracy” (10/100, minimal). 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 “Shipping, Receiving, and Inventory Clerks” do about AI?
- Start from the ledger rather than the headline: 49% of this job's weighted core work is exposed, and roughly 40% 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 Shipping, Receiving, and Inventory Clerks 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 11 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.