Will AI replace Demonstrators and Product Promoters?

Not as a job, but it is already doing parts of the work. Across the 21 official task statements scored for Demonstrators and Product Promoters (United States, SOC 41-9011), 19% 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 16–25, 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 “Demonstrators and Product Promoters” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Prepare or alter presentation contents to target specific audiences” (93/100, very high); “Research or investigate products to be presented to prepare for demonstrations” (75/100, high); “Record and report demonstration-related information, such as the number of questions asked by the audience or the number of coupons distributed” (69/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.

Which tasks in “Demonstrators and Product Promoters” stay human?

About 79% 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: “Stock shelves with products” (0/100, minimal); “Wear costumes or sign boards and walk in public to promote merchandise, services, or events” (0/100, minimal); “Visit trade shows, stores, community organizations, or other venues to demonstrate products or services or to answer questions from potential customers” (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 “Demonstrators and Product Promoters” do about AI?

Start from the ledger rather than the headline: 19% of this job's weighted core work is exposed, and roughly 79% 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 Demonstrators and Product Promoters 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 21 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.

US dataswitch to UK

Demonstrators and Product Promoters

providing product samples, coupons, setting up and arranging displays or demonstration areas to attract the attention of prospective customers and identifying interested and qualified customers to provide them with additional information. If that's your week, this page is about your job.

The honest answer

AI is already taking a real slice of the routine work here: recording and reporting demonstration-related information. That is a slice of tasks, not of you.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 19%
changing shape: 3%
staying human: 79%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

Whole-job exposure score 19 out of 100 (16–25 allowing for uncertainty): minimal exposure, across 21 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 demonstrators and product promoters 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.

That slice is not coming back; the core of the job, providing product samples, coupons, informational brochures or other incentives to persuade people to buy products, stays yours. The tools change, the responsibility doesn't.

Your week, as this page understands it

Demonstrate merchandise and answer questions for the purpose of creating public interest in buying the product. May sell demonstrated merchandise. The job title says “demonstrators” or “product promoters”: officially one job, two names. The real job is the part underneath: providing product samples, coupons, informational brochures or other incentives to persuade people to buy products. 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 demonstrators and product promoters is not one task. It is 21 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is providing product samples, coupons, informational brochures or other incentives to persuade people to buy products, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 19%
changing shape: 3%
staying human: 79%

These bars are tasks changing hands, not people being counted out. The ledger below shows which.

How we know this

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.

4 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

4 tasks

Tasks 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 and reporting demonstration-related information
This is reading one thing and writing another: demonstration-related information in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Record and report demonstration-related information, such as the number of questions asked by the audience or the number of coupons distributed.” (O*NET task statement)
How this row was scored
Exposure score: 69 out of 100 (65–73 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: Counting coupons handed out and questions asked, then reporting it, is straightforward record-keeping.
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.
Researching or investigating products to be presented to prepare for demonstrations
This is reading one thing and writing another: products in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Research or investigate products to be presented to prepare for demonstrations.” (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: Researching a product before demonstrating it is desk reading that software does quickly.
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.
Learning about competitors' products or consumers' interests or concerns to answer questions or provide more complete information
This is reading one thing and writing another: competitors' products in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Learn about competitors' products or consumers' interests or concerns to answer questions or provide more complete information.” (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: Reading up on competitors and customer concerns is research software does 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 1/4 · how much data exists 3/4.
Preparing or altering presentation contents to target specific audiences
This is reading one thing and writing another: presentation contents in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Prepare or alter presentation contents to target specific audiences.” (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: Rewriting presentation material for a particular audience is writing work software does at least as 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 0/4 · how much data exists 3/4.

Changing shape

1 task

Tasks 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.

Recommending product or service improvements to employers
The software now makes the first pass at product, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Supplemental
Source: “Recommend product or service improvements to employers.” (O*NET task statement)
How this row was scored
Exposure score: 57 out of 100 (50–64 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; mistakes that are cheap to catch.
The rating behind it: Writing up improvement suggestions is straightforward, but the useful detail comes from what you saw customers do.
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 2/4.

Staying human

16 tasks

Tasks 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.

Providing product samples, coupons, informational brochures or other incentives to persuade people to buy products
This work happens in the physical world: product samples, coupons, informational brochures or other incentives, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Provide product samples, coupons, informational brochures, or other incentives to persuade people to buy products.” (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 value is that a specific person does it.
The rating behind it: Handing samples and coupons to shoppers is physical work in front of real people.
The five ratings: output a model can produce 1/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Demonstrating or explaining products, methods or services to persuade customers to purchase products or using services
This work happens in the physical world: products, methods or services, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Demonstrate or explain products, methods, or services to persuade customers to purchase products or use services.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal 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: A live product demonstration is a person performing in front of customers who can ask anything.
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 2/4 · how much data exists 3/4.
Keeping areas neat while working and returning items to correct locations following demonstrations
This work happens in the physical world: areas neat, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Keep areas neat while working and return items to correct locations following demonstrations.” (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: Tidying the stand and putting stock back in place is physical work.
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.
Setting up and arranging displays or demonstration areas to attract the attention of prospective customers
This work happens in the physical world: and arranging displays, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Set up and arrange displays or demonstration areas to attract the attention of prospective customers.” (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: Building and arranging a display or demonstration area is physical work.
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.
Selling products being promoted and keeping records of sales
This work happens in the physical world: products, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Sell products being promoted and keep records of sales.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal 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: Selling to shoppers at the stand happens face to face, even if the sales record is digital.
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 2/4 · how much data exists 3/4.

Show the other 11 tasks

Training demonstrators to present a company's products or services
staying human
This work happens in the physical world: demonstrators, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Train demonstrators to present a company's products or services.” (O*NET task statement)
How this row was scored
Exposure score: 20 out of 100 (13–27 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: Training other demonstrators mixes written material with live coaching and practice.
The five ratings: output a model can produce 2/4 · 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.
Suggesting specific product purchases to meet customers' needs
staying human
This work happens in the physical world: specific product purchases, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Suggest specific product purchases to meet customers' needs.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (6–20 allowing for uncertainty): minimal 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: Suggesting the right product happens at the stand, reading the shopper standing in front of you.
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 2/4 · how much data exists 3/4.
Providing product information, using lectures, films, charts or slide shows
staying human
This work happens in the physical world: product information, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Provide product information, using lectures, films, charts, or slide shows.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal 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: Delivering the talk or slideshow to an audience is a person presenting in the room.
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 2/4 · how much data exists 3/4.
Identifying interested and qualified customers to provide them with additional information
staying human
This work happens in the physical world: interested, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Identify interested and qualified customers to provide them with additional information.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (2–16 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Spotting genuinely interested shoppers in a crowd depends on being there and reading people.
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 2/4 · how much data exists 2/4.
Instructing customers in alteration of products
staying human
This work happens in the physical world: customers, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Instruct customers in alteration of products.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (2–16 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Showing a customer how to alter the product means demonstrating it with your hands.
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 2/4 · how much data exists 2/4.
Transporting, assembling and disassemble materials used in presentations
staying human
This work happens in the physical world: disassemble materials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Transport, assemble, and disassemble materials used in presentations.” (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: Carrying, building and taking down presentation kit is physical work.
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.
Practicing demonstrations to ensure that they will run smoothly
staying human
This work happens in the physical world: demonstrations, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Practice demonstrations to ensure that they will run smoothly.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–7 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Rehearsing a demonstration is a person practicing a performance.
The five ratings: output a model can produce 1/4 · 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.
Working as part of a team of demonstrators to accommodate large crowds
staying human
This work happens in the physical world: part of a team of demonstrators, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Work as part of a team of demonstrators to accommodate large crowds.” (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 value is that a specific person does it.
The rating behind it: Working a large crowd alongside other demonstrators is physical presence.
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 2/4 · how much data exists 2/4.
Visiting trade shows, stores, community organizations or other venues to demonstrate products or services or to answer questions from potential customers
staying human
This work happens in the physical world: trade shows, stores, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Visit trade shows, stores, community organizations, or other venues to demonstrate products or services or to answer questions from potential customers.” (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 value is that a specific person does it.
The rating behind it: Traveling to shows and stores to demonstrate products is being physically present in a place.
The five ratings: output a model can produce 1/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Wearing costumes or signing boards and walk in public to promote merchandise
staying human
This work happens in the physical world: costumes, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Wear costumes or sign boards and walk in public to promote merchandise, services, or 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: Wearing a costume and walking the streets is entirely about a person being physically 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.
Stocking shelves with products
staying human
This work happens in the physical world: shelves, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Stock shelves with products.” (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: Filling shelves is physical work.
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: $39,320a 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: 64,520in 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: demonstration-related information in, a record out. The rows above are exactly that shape: recording and reporting demonstration-related information and researching or investigating products to be presented to prepare for demonstrations. What it cannot do is be there in the room, and that is still where product samples, coupons, informational brochures or other incentives 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: providing product samples, coupons, informational brochures or other incentives to persuade people to buy products is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 19% of this job's task weight sits in rows the software is already learning, 3% in rows that change shape rather than disappear, and 79% in rows it is nowhere near. That is the position, measured across 21 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 and reporting demonstration-related information 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 demonstration-related information, 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 product samples, coupons, informational brochures or other incentives 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 recommending product or service improvements to employers. 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 demonstrators and product promoters (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was door-to-door sales workers, news and street vendors, and related workers: only about 11% of its durable work is work you already do and there are far fewer of those jobs than of yours. And on the numbers you do not need one. This job scores 19/100 here, with only 19% of the task list in the top band, and “provide product samples, coupons, informational brochures, or other incentives to persuade people…” 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.

Door-to-Door Sales Workers, News and Street Vendors, and Related Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already demonstrate or explain products, methods, or services to persuade customers to purchase products…, and their equivalent is to persuade customers to purchase merchandise or services. Across both published task lists that is about 11% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 11% 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 2,760 of those jobs against 64,520 of yours (OEWS May 2025), 4% as many seats.
Look at that job’s page anyway →
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 demonstrate or explain products, methods, or services to persuade customers to purchase products…, and their equivalent is to contact regular and prospective customers to demonstrate products, explain product features, and solicit…. Across both published task lists that is about 5% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 5% of the durable side of that job. That is a different job, not a next step.
Look at that job’s page anyway →
Telemarketers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already demonstrate or explain products, methods, or services to persuade customers to purchase products…, and their equivalent is to explain products or services and prices, and answer questions from customers. 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: 70% of its own task list already scores in the top exposure band (70/100 in this release), so the same software is eating it.
Look at that job’s page anyway →

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: 19% of its task weight, across 21 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 providing product samples, coupons, informational brochures or other incentives to persuade people to buy products 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 Merchandisers 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 Merchandisers. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

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No Space for this job, but one for what is happening to it

Nothing Collab365 runs is built for demonstrators / product promoters, and we are not going to point you at the nearest one and call it a fit.

There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 19% of the work on this page is already inside what they can do.

Try The AI Authority free →

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 demonstrators / product promoters. 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 demonstrators / product promoters launches. Nothing else.

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No Space for demonstrators / product promoters yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for demonstrators / product promoters existed, with researched problems, courses and people in the same boat, would you want in?

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This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

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 Demonstrators and Product Promoters?: Not as a job, but it is already doing parts of the work. Across the 21 official task statements scored for Demonstrators and Product Promoters (United States, SOC 41-9011), 19% 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 16–25, 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 “Demonstrators and Product Promoters” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Prepare or alter presentation contents to target specific audiences” (93/100, very high); “Research or investigate products to be presented to prepare for demonstrations” (75/100, high); “Record and report demonstration-related information, such as the number of questions asked by the audience or the number of coupons distributed” (69/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
Which tasks in “Demonstrators and Product Promoters” stay human?: About 79% 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: “Stock shelves with products” (0/100, minimal); “Wear costumes or sign boards and walk in public to promote merchandise, services, or events” (0/100, minimal); “Visit trade shows, stores, community organizations, or other venues to demonstrate products or services or to answer questions from potential customers” (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 “Demonstrators and Product Promoters” do about AI?: Start from the ledger rather than the headline: 19% of this job's weighted core work is exposed, and roughly 79% 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 Demonstrators and Product Promoters 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 21 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.
4 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.

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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}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. 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.