US dataswitch to UK
Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel
computing and comparing costs of services, quoting prices, credit terms, contract terms or fulfillment dates, identifying prospective customers using business directories and answering customers' questions about services. 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: computing and comparing costs of services. The tasks, though, are not you.
It would be a lie to soften that; distributing promotional materials at meetings is what this work rebuilds around. The routes below start from it.
Your week, as this page understands it
Sell services to individuals or businesses. May describe options or resolve client problems. The job title says “sales representatives of services”, “except advertising”, “insurance”, “financial services” or “travel”: officially one job, several names. The real job is the part underneath: distributing promotional materials at meetings. 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 sales representatives of services, except advertising, insurance, financial services, and travel 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 distributing promotional materials at meetings, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 60%
- changing shape
- 7%
- staying human
- 33%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 56 out of 100 (51–62 allowing for uncertainty): partial 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 sales representatives of services, except advertising, insurance, financial services, and travel 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.
- O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
- 2 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
9 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.
Computing and comparing costs of services
This is reading one thing and writing another: costs of services in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Compute and compare costs of services.” (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 and comparing service costs is arithmetic on known figures, which software does reliably.
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.
Maintaining customer records using automated systems
This is reading one thing and writing another: customer records in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Maintain customer records using automated systems.” (O*NET task statement)
How this row was scored
Exposure score: 93 out of 100 (89–97 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Keeping customer records up to date in a system is straightforward data entry that software handles reliably.
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.
Creating forms or agreements to complete sales
This is reading one thing and writing another: forms in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Create forms or agreements to complete sales.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (77–85 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Sales forms and agreements follow set templates, so software can produce them and someone signs off later.
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.
Quoting prices, credit terms, contract terms or fulfillment dates for services
This is reading one thing and writing another: prices, credit terms, contract terms or fulfillment dates in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Quote prices, credit terms, contract terms, or fulfillment dates for services.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (72–86 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: Producing a quote from a price book and delivery calendar is a rules-based job software does well.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Developing sales presentations or proposals to explain service specifications
This is reading one thing and writing another: sales presentations in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Develop sales presentations or proposals to explain service specifications.” (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: Building a proposal or presentation from service specifications is exactly the kind of writing 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.
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.
Emphasizing or recommending service features based on knowledge of customers' needs and vendor capabilities and limitations
The software now makes the first pass at service features, 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 not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Emphasize or recommend service features based on knowledge of customers' needs and vendor capabilities and limitations.” (O*NET task statement)
How this row was scored
Exposure score: 47 out of 100 (40–54 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the value is that a specific person does it.
The rating behind it: Matching features to a customer works from documented capabilities, but the pitch still lands better from a person.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Staying human
5 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.
Attending sales or trade meetings or reading related publications to obtain information about market conditions
This work happens in the physical world: sales, in a real place. Software cannot follow it there.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Attend sales or trade meetings or read related publications to obtain information about market conditions, business trends, regulations, or industry developments.” (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: Reading trade publications and pulling out what matters suits software, but attending the meetings does not.
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.
Contacting prospective or existing customers to discuss how services can meet their needs
The value here is that a specific person handles prospective and stands behind it. That is earned, not computed.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Contact prospective or existing customers to discuss how services can meet their needs.” (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: Talking through what a customer actually needs depends on reading the person during the conversation.
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.
Consulting with clients after sales or contract signings to resolve problems and provide ongoing support
The value here is that a specific person handles clients after sales and stands behind it. That is earned, not computed.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Consult with clients after sales or contract signings to resolve problems and provide ongoing support.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (17–25 allowing for uncertainty): low exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Sorting out problems after a sale rests on the customer trusting the person they already know.
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 3/4 · how much data exists 2/4.
Negotiating prices or terms of sales or service agreements
The value here is that a specific person handles prices and stands behind it. That is earned, not computed.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Negotiate prices or terms of sales or service agreements.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (17–25 allowing for uncertainty): low exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Haggling over price and terms turns on the working relationship and reading the other side live.
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 3/4 · how much data exists 2/4.
Show the other 5 tasks
Monitoring market conditions, innovations and competitors' services, prices and sales
shifting to AIThis is reading one thing and writing another: market conditions, innovations and competitors' services in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Monitor market conditions, innovations, and competitors' services, prices, and sales.” (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: Tracking competitor prices and market movements is reading and comparing published information, a strong fit for software.
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.
Identifying prospective customers using business directories
shifting to AIThis is reading one thing and writing another: prospective customers in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Identify prospective customers using business directories, leads from clients, or information from conferences or trade shows.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (62–76 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: Building prospect lists from directories and leads is routine data work that software does quickly and well.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Answering customers' questions about services
shifting to AIThis is reading one thing and writing another: customers' questions in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Answer customers' questions about services, prices, availability, or credit terms.” (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: Answering questions about price, availability and terms draws on documented information and is mostly handled well by software.
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.
Informing customers of contracts or other information pertaining to purchased services
shifting to AIThis is reading one thing and writing another: customers of contracts in, a record out. That is the shape today's tools are built for.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Inform customers of contracts or other information pertaining to purchased services.” (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: Telling customers what their contract covers means relaying documented information, which software does accurately.
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.
Distributing promotional materials at meetings
staying humanThis work happens in the physical world: promotional materials, in a real place. Software cannot follow it there.
importance not publishedO*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
Source: “Distribute promotional materials at meetings, conferences, or trade shows.” (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: Handing out materials at meetings and trade shows needs someone there.
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 1/4 · how much data exists 2/4.
What this job pays, and how many people do it
- Median pay
- $69,990a 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
- 1,256,010in 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: costs of services in, a record out. The rows above are exactly that shape: computing and comparing costs of services and maintaining customer records using automated systems. What it cannot do is be there in the room, and that is still where promotional materials 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: computing and comparing costs of services is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 60% of this job's task weight sits in rows the software is already learning, 7% in rows that change shape rather than disappear, and 33% 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 distributing promotional materials at meetings, 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 costs of services 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 costs of services, 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 computing and comparing costs of services” 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 distributing promotional materials at meetings 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 sales representatives of services, except advertising, insurance, financial services, and travel (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was sales representatives, wholesale and manufacturing, except technical and scientific products: only about 23% of its durable work is work you already do. Your own job splits about 60/40: 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 “attend sales or trade meetings or read related publications to obtain 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.
Sales Representatives, Wholesale and Manufacturing, Except Technical and Scientific Products
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already consult with clients after sales or contract signings to resolve problems and provide…, and their equivalent is to consult with clients after sales or contract signings to resolve problems and to…. Across both published task lists that is about 23% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 23% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “negotiate prices or terms of sales or service agreements”. Across the whole of both lists that adds up to about 19% of the work in that job the software is not taking.
Why I am not recommending it: You would be starting most of it from nothing: about 19% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. And it is a narrow door: about 284,800 of those jobs against 1,256,010 of yours (OEWS May 2025), 23% as many seats.
Marketing Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already distribute promotional materials at meetings, conferences, or trade shows, and their equivalent is to coordinate or participate in promotional activities or trade shows, working with developers, advertisers…. 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. The pay gap is the market pricing a barrier: $166,790 against your $69,990 is 2.38× (OEWS May 2025 (both)), and you would be crossing it holding about 6% 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: 60% 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: distributing promotional materials at meetings 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 Business sales executives 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 Business sales executives. 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 sales representatives, 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 60% 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 sales representatives. 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 sales representatives 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 Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel?
- Not as a job, but it is already doing parts of the work. Across the 15 official task statements scored for Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel (United States, SOC 41-3091), 60% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 56 out of 100 (range 51–62, 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 “Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Compute and compare costs of services” (93/100, very high); “Maintain customer records using automated systems” (93/100, very high); “Create forms or agreements to complete sales” (81/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel” stay human?
- About 33% 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: “Distribute promotional materials at meetings, conferences, or trade shows” (0/100, minimal); “Negotiate prices or terms of sales or service agreements” (21/100, low); “Consult with clients after sales or contract signings to resolve problems and provide ongoing support” (21/100, 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 “Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel” do about AI?
- Start from the ledger rather than the headline: 60% of this job's weighted core work is exposed, and roughly 33% 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 Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel 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
- O*NET hasn't yet published importance ratings for this occupation's tasks, so the ordering here is ours, not theirs.
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 2 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-with-imputed)
- 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.