US dataswitch to UK
Driver/Sales Workers
driving trucks to deliver such items as food, informing regular customers of new products or services and price changes and recording sales or delivery information on daily sales or delivery record. If that's your week, this page is about your job.
The honest answer
AI changes the edges of this job, not the middle: driving trucks to deliver such items as food is work software can't reach.
What shifts is recording sales or delivery information on daily sales or delivery record: the overhead at the edges, not the middle you trained for.
Your week, as this page understands it
Drive truck or other vehicle over established routes or within an established territory and sell or deliver goods, such as food products, including restaurant take-out items, or pick up or deliver items such as commercial laundry. May also take orders, collect payment, or stock merchandise at point of delivery. The job title says “driver/sales workers”. The real job is the part underneath: driving trucks to deliver such items as food. 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 driver/sales workers 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 driving trucks to deliver such items as food, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 0%
- changing shape
- 17%
- staying human
- 83%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 19 out of 100 (16–24 allowing for uncertainty): minimal 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 driver/sales workers 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.
- 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
0 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.
Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.
Changing shape
2 tasksTasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.
Recording sales or delivery information on daily sales or delivery record
The software now makes the first pass at sales, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Record sales or delivery information on daily sales or delivery record.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (52–60 allowing for uncertainty): partial 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 and delivery details are typed into a system, and handheld devices already capture most of this information automatically.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Writing customer orders and sales contracts according to company guidelines
The software now makes the first pass at customer orders, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Write customer orders and sales contracts according to company guidelines.” (O*NET task statement)
How this row was scored
Exposure score: 52 out of 100 (48–56 allowing for uncertainty): partial 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: Orders and standard contracts follow set company wording that software already fills in reliably, with a manager approving the result.
The five ratings: output a model can produce 4/4 · needs a body in a room 1/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Staying human
9 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.
Driving trucks to deliver such items as food
This work happens in the physical world: trucks, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Drive trucks to deliver such items as food, medical supplies, or newspapers.” (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: Delivering goods means someone has to drive the route and hand the items over at the door.
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.
Listening to and resolving customers' complaints regarding products or services
This work happens in the physical world: and resolving customers' complaints regarding products, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Listen to and resolve customers' complaints regarding products or services.” (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 same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Standard answers to complaints are easy to produce, but customers on a route expect the driver to sort it out in person.
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 2/4 · how much data exists 3/4.
Informing regular customers of new products or services and price changes
This work happens in the physical world: regular customers of new products, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Inform regular customers of new products or services and price changes.” (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 same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Product and price updates are easy to write, but on a delivery round the news is usually given face to face.
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 2/4 · how much data exists 3/4.
Collecting money from customers, make change and recording transactions on customer receipts
This work happens in the physical world: money, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Collect money from customers, make change, and record transactions on customer receipts.” (O*NET task statement)
How this row was scored
Exposure score: 19 out of 100 (15–23 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Taking cash and giving change happens hand to hand; only the receipt record is straightforward to automate.
The five ratings: output a model can produce 3/4 · 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.
Maintaining trucks and food-dispensing equipment and cleaning inside of machines that dispense food or beverages
This work happens in the physical world: trucks, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Maintain trucks and food-dispensing equipment and clean inside of machines that dispense food or beverages.” (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: Cleaning trucks and food machines is hands-on work that has to be done at the vehicle.
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.
Arranging merchandise and sales promotion displays or issuing sales promotion materials to customers
This work happens in the physical world: merchandise, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Arrange merchandise and sales promotion displays or issue sales promotion materials to customers.” (O*NET task statement)
How this row was scored
Exposure score: 11 out of 100 (7–15 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Software can suggest a display layout, but somebody still has to build the display and hand out the leaflets.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Collecting coins from vending machines
This work happens in the physical world: coins, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Collect coins from vending machines, refill machines, and remove aged merchandise.” (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: Emptying coin boxes and restocking machines requires being at the machine with your hands.
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.
Reviewing lists of dealers, customers or station drops and loading trucks
This work happens in the physical world: lists of dealers, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Review lists of dealers, customers, or station drops and load trucks.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (7–21 allowing for uncertainty): minimal 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: Checking the drop list is desk work, but loading the truck is lifting that happens at the depot.
The five ratings: output a model can produce 2/4 · 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 1 task
Selling food specialties, such as sandwiches and beverages, to office workers and patrons of sports events
staying humanThis work happens in the physical world: food specialties, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Sell food specialties, such as sandwiches and beverages, to office workers and patrons of sports events.” (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: Selling sandwiches and drinks to a crowd means being there to serve people.
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.
What this job pays, and how many people do it
- Median pay
- $38,770a 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
- 409,180in 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: sales in, a record out. The rows above are exactly that shape: recording sales or delivery information on daily sales or delivery record. What it cannot do is be there in the room, and that is still where trucks 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
Start with what does not change: driving trucks to deliver such items as food is the middle of this job, and the evidence on this page says it stays with a person.
So, given all that: 0% of this job's task weight sits in rows the software is already learning, 17% in rows that change shape rather than disappear, and 83% in rows it is nowhere near. That is the position, measured across 11 scored tasks. It is not a forecast about you.
So the thing worth your attention is not the job going away. It is the layer around it. Recording sales or delivery information on daily sales or delivery record is the part turning into software, and being the person who understands that layer is worth money.
This week: one thing
Ask the one question. Find whoever is bringing new software into your workplace (the manager, the office, whoever runs the system) and ask them what it is meant to do to sales, and what it is not meant to touch. Ten minutes, this week, before anyone decides it for you.
- What you end up holding
- a straight answer about what is actually being rolled out, and when
- How long it takes
- ten minutes
If there’s nobody obvious to ask, or you’d rather not ask your manager: Put the same question to your union rep, your shift lead or the person who has been there longest, in person, over a break. Same ten minutes, same answer, and you will usually get a straighter one. Write down what they say. The note is the artifact, and it tells you whether and resolving customers' complaints regarding products are in scope or not. Nothing to log into, no license needed.
Over the next 90 days
Get inside the tool rollout rather than waiting for it. Over the next ninety days, ask to be in the group that tests, checks or signs off whatever new system arrives near recording sales or delivery information on daily sales or delivery record. It is usually an unglamorous seat that nobody fights for, and it is the one that decides how the software is used on your job rather than to it.
Over the next 12 months
On this evidence I would not retrain out of this job, and I will say that plainly rather than hedge it. The task list here is dominated by work that stays with a person. What I would do with a year is get formally recognised for the layer around it (the systems, the compliance, the planning), so you are the one who understands the software instead of the one it is done to. 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 driver/sales workers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was first-line supervisors of non-retail sales workers: only about 9% of its durable work is work you already do and the 2.3× pay gap is the market pricing a barrier. And on the numbers you do not need one. This job scores 19/100 here, with only 0% of the task list in the top band, and “drive trucks to deliver such items as food, medical supplies, or newspapers” is not work that hands over cleanly. None of them beats deepening what you already have.
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.
First-Line Supervisors of Non-Retail Sales Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already listen to and resolve customers' complaints regarding products or services, and their equivalent is to listen to and resolve customer complaints regarding services, products, or personnel. Across both published task lists that is about 9% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 9% 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: $87,520 against your $38,770 is 2.26× (OEWS May 2025 (both)), and you would be crossing it holding about 9% of their durable work. A gap that size with an overlap that small is a wish, not a route.
Order Clerks
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already collect money from customers, make change, and record transactions on customer receipts, and their equivalent is to collect payment for merchandise, record transactions, and send items. Across both published task lists that is about 4% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 4% of the durable side of that job. That is a different job, not a next step. I will not move you off one melting floe onto another: 68% of its own task list already scores in the top exposure band (72/100 in this release), so the same software is eating it. And it is a narrow door: about 75,200 of those jobs against 409,180 of yours (OEWS May 2025), 18% as many seats.
Sales Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already listen to and resolve customers' complaints regarding products or services, and their equivalent is to resolve customer complaints regarding sales and service. 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. The pay gap is the market pricing a barrier: $148,270 against your $38,770 is 3.82× (OEWS May 2025 (both)), and you would be crossing it holding about 4% of their durable work. A gap that size with an overlap that small is a wish, not a route.
What I’d stop worrying about
A friend tells you what not to spend fear on. This is that list.
The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 0% of its task weight, across 11 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like driving trucks to deliver such items as food arrive through rules, insurance and money, slowly and visibly. This page tracks the task evidence and will move when it moves.
Retraining out of a job that is holding up
On this evidence I would not spend money leaving. Spend it on the layer around the job instead: the tools, the paperwork, the planning. That is where the change actually is.
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 Taxi and cab drivers and chauffeurs 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 Taxi and cab drivers and chauffeurs, Road transport drivers n.e.c. and Delivery drivers and couriers. 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
Why there is no community here
Collab365, who build this site, run paid Spaces for a small number of subjects, and none of them is built for this job. We are not going to point you at the nearest one and call it a fit.
So the free services listed on this page are the whole answer, and it is the same answer we would give a friend.
Noted, and thank you. We’ll email you if a Space for driver/sales workers launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
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No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.
Questions people ask about this job
- Will AI replace Driver/Sales Workers?
- Not as a job, but it is already doing parts of the work. Across the 11 official task statements scored for Driver/Sales Workers (United States, SOC 53-3031), 0% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 19 out of 100 (range 16–24, band: minimal). 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 “Driver/Sales Workers” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Record sales or delivery information on daily sales or delivery record” (56/100, partial); “Write customer orders and sales contracts according to company guidelines” (52/100, partial); “Listen to and resolve customers' complaints regarding products or services” (26/100, low). 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 “Driver/Sales Workers” stay human?
- About 83% 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: “Sell food specialties, such as sandwiches and beverages, to office workers and patrons of sports events” (0/100, minimal); “Maintain trucks and food-dispensing equipment and clean inside of machines that dispense food or beverages” (0/100, minimal); “Collect coins from vending machines, refill machines, and remove aged merchandise” (0/100, 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 “Driver/Sales Workers” do about AI?
- Start from the ledger rather than the headline: 0% of this job's weighted core work is exposed, and roughly 83% 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 Driver/Sales Workers 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.
- 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.