Will AI replace First-Line Supervisors of Non-Retail Sales Workers?

Not as a job, but it is already doing parts of the work. Across the 18 official task statements scored for First-Line Supervisors of Non-Retail Sales Workers (United States, SOC 41-1012), 38% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 46 out of 100 (range 41–52, 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.

What should someone working in “First-Line Supervisors of Non-Retail Sales Workers” do about AI?

Start from the ledger rather than the headline: 38% of this job's weighted core work is exposed, and roughly 47% 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 First-Line Supervisors of Non-Retail 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 18 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

First-Line Supervisors of Non-Retail Sales Workers

monitoring sales staff performance to ensure that goals are met, examining products purchased for resale or received for storage to determine product condition and examining merchandise to ensure correct pricing and display and that it functions as advertised. If that's your week, this page is about your job.

The honest answer

This job is splitting in two: keeping records pertaining is work AI now does quickly and cheaply, and providing staff with assistance in performing difficult or complicated duties is work it can't touch.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 38%
changing shape: 15%
staying human: 47%

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

Whole-job exposure score 46 out of 100 (41–52 allowing for uncertainty): partial exposure, across 18 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 first-line supervisors of non-retail 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.

Which half fills your week decides your exposure. That is more in your control than it sounds.

Your week, as this page understands it

Directly supervise and coordinate activities of sales workers other than retail sales workers. May perform duties such as budgeting, accounting, and personnel work, in addition to supervisory duties. The job title says “first-line supervisors of non-retail sales workers”. The real job is the part underneath: providing staff with assistance in performing difficult or complicated duties. 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 first-line supervisors of non-retail sales workers is not one task. It is 18 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is providing staff with assistance in performing difficult or complicated duties, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 38%
changing shape: 15%
staying human: 47%

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.

One task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide 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

7 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.

Planning and preparing work schedules
This is reading one thing and writing another: work schedules in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Plan and prepare work schedules, and assign employees to specific duties.” (O*NET task statement)
How this row was scored
Exposure score: 79 out of 100 (75–83 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: Rostering staff against shifts and skills is exactly what scheduling software already does.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Monitoring sales staff performance to ensure that goals are met
This is reading one thing and writing another: sales staff performance in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Monitor sales staff performance to ensure that goals are met.” (O*NET task statement)
How this row was scored
Exposure score: 64 out of 100 (60–68 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Sales figures against targets are already tracked by software, which can flag who is falling behind and why.
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.
Keeping records pertaining
This is reading one thing and writing another: records in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Keep records pertaining to purchases, sales, and requisitions.” (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 purchase, sales and requisition records is routine data work that software does end to end.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Preparing sales and inventory reports for management and budget departments
This is reading one thing and writing another: sales in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Prepare sales and inventory reports for management and budget departments.” (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 and stock reports are built straight from till data, which reporting software already produces.
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.

Changing shape

2 tasks

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.

Hiring, training and evaluating personnel
The software now makes the first pass at personnel, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Hire, train, and evaluate personnel.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Applications can be screened automatically, but hiring, training and judging staff is done by a person.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Listening to and resolving customer complaints regarding services
The software now makes the first pass at and resolving customer complaints regarding services, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Listen to and resolve customer complaints regarding services, products, or personnel.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Automated service handles most complaints well, though an upset customer often wants a person to take it seriously.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

Staying human

9 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 staff with assistance in performing difficult or complicated duties
This work happens in the physical world: staff, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Provide staff with assistance in performing difficult or complicated duties.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (0–16 allowing for uncertainty): minimal exposure, medium confidence, and it moved between repeat runs, so the range is widened.
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: Stepping in to help with a difficult job means being there to do it.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Attending company meetings to exchange product information and coordinating work activities with other departments
The value here is that a specific person handles company meetings and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Attend company meetings to exchange product information and coordinate work activities with other departments.” (O*NET task statement)
How this row was scored
Exposure score: 28 out of 100 (21–35 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: The task is taking part in the meeting, so summaries help but someone still has to be there.
The five ratings: output a model can produce 1/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Conferring with company officials to develop methods and procedures to increase sales
The value here is that a specific person handles company officials and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Confer with company officials to develop methods and procedures to increase sales, expand markets, and promote business.” (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: Working out growth plans with company officials happens in discussions where reading the room matters.
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.
Directing and supervising employees engaged in sales
The value here is that a specific person handles employees and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Direct and supervise employees engaged in sales, inventory-taking, reconciling cash receipts, or performing specific services.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Some direction can be pushed through systems, but supervising a working team means being present and answerable.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.

Show the other 8 tasks

Preparing rental or lease agreements
shifting to AI
This is reading one thing and writing another: rental in, a record out. That is the shape today's tools are built for.
importance 3 · Supplemental
Source: “Prepare rental or lease agreements, specifying charges and payment procedures for use of machinery, tools, or other items.” (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: Rental and lease agreements come from standard templates with the charges filled in, 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 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Formulating pricing policies on merchandise according to profitability requirements
shifting to AI
This is reading one thing and writing another: policies in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Formulate pricing policies on merchandise according to profitability requirements.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 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: Setting prices from cost and margin targets is calculation against documented rules.
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.
Analyzing details of sales territories to assess their growth potential and to set quotas
shifting to AI
This is reading one thing and writing another: details of sales territories in, a record out. That is the shape today's tools are built for.
importance 4 · Supplemental
Source: “Analyze details of sales territories to assess their growth potential and to set quotas.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (71–79 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: Territory analysis and quota setting use sales data the company already holds, and software models it 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.
Coordinating sales promotion activities, such as preparing merchandise displays and advertising copy
staying human
This work happens in the physical world: sales promotion activities, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Coordinate sales promotion activities, such as preparing merchandise displays and advertising copy.” (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: Advertising copy and promotion plans draft easily; building the merchandise display is physical work.
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.
Inventorying stock and reorder when inventories drop to specified levels
staying human
This work happens in the physical world: stock, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Inventory stock and reorder when inventories drop to specified levels.” (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: Reordering triggers automatically from stock levels, but counting what is actually on the shelves is hands-on.
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.
Visiting retailers and sales representatives to promote products and gather information
staying human
This work happens in the physical world: retailers, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Visit retailers and sales representatives to promote products and gather 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: Calling on retailers in person is the point of the visit, even though the notes could be typed anywhere.
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.
Examining merchandise to ensure correct pricing and display and that it functions as advertised
staying human
This work happens in the physical world: merchandise, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Examine merchandise to ensure correct pricing and display, and that it functions as advertised.” (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: Checking displays, price tickets and whether an item works means walking the floor and picking things up.
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 0/4 · how much data exists 2/4.
Examining products purchased for resale or received for storage to determine product condition
staying human
This work happens in the physical world: products, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Examine products purchased for resale or received for storage to determine product condition.” (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: Judging the condition of goods means handling and looking at them.
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 0/4 · how much data exists 2/4.

What this job pays, and how many people do it

Median pay: $87,520a 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: 214,390in 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: work schedules in, a record out. The rows above are exactly that shape: keeping records pertaining and planning and preparing work schedules. What it cannot do is be there in the room, and that is still where staff gets done. Which is why this page talks about your tasks changing, not your job ending.

Your move

Over a pint: what I’d tell you if you were my friend

Your week is splitting in two, and which half fills it is the whole question. Keeping records pertaining is going; providing staff with assistance in performing difficult or complicated duties is not.

So, given all that: 38% of this job's task weight sits in rows the software is already learning, 15% in rows that change shape rather than disappear, and 47% in rows it is nowhere near. That is the position, measured across 18 scored tasks. It is not a forecast about you.

The occupation is an average and you are not, so the first job below is finding out where your own week actually sits.

This week: one thing

Print the task ledger above and put your own hours next to each row. Roughly, in pen, in one sitting. Then look at which group your hours actually pile up in. Twenty minutes, this week.

What you end up holding: your own week, on one page, sorted into what is shifting and what is not
How long it takes: about twenty minutes

If printing it isn’t practical: Read the rows off this page and write the same list on the back of an envelope. Same twenty minutes, same page, and it works just as well said out loud to someone who knows the job. The point is your hours next to the rows, not the paper it is on.

Over the next 90 days

Volunteer toward the durable end, visibly. Over the next ninety days put your hand up for the work in the bottom group (providing staff with assistance in performing difficult or complicated duties) and let people see you doing it. Not a new project: the same job, with your mix deliberately tilted. The point is that when the rota or the reorganisation comes, the version of you people picture is the one doing the part that stays.

Over the next 12 months

Claim a specialism at the durable end and let the other end go. Over a year, deliberately become the person who handles providing staff with assistance in performing difficult or complicated duties, and deliberately stop being the first choice for the rows in the top group. That trade costs something, because the exposed work is often the comfortable work. Decide it on purpose rather than by drift. 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 first-line supervisors of non-retail 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 retail sales workers: it pays 44.6% less. Your own job splits about 38/62: 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 “provide staff with assistance in performing difficult or complicated duties”, 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.

First-Line Supervisors of Retail Sales Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already direct and supervise employees engaged in sales, inventory-taking, reconciling cash receipts, or performing…, and their equivalent is to direct and supervise employees engaged in sales, inventory-taking, reconciling cash receipts, or in…. Across both published task lists that is about 39% of the durable work in that job.
Why I am not recommending it: It is a pay cut, in those words: $48,520 against your $87,520, 44.6% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services
Why it looked obvious: It came up as a near neighbour because one of your tasks is on their list in the same words: “provide staff with assistance in performing difficult or complicated duties”. Across the whole of both lists that adds up to about 13% 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 13% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. It is a pay cut, in those words: $48,560 against your $87,520, 44.5% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
Driver/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 customer complaints regarding services, products, or personnel, and their equivalent is to listen to and resolve customers' complaints regarding products or services. 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. It is a pay cut, in those words: $38,770 against your $87,520, 55.7% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
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: 38% of its task weight, across 18 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The whole-job doom story
Nothing on this page says this job ends. It says the mix inside it moves. Half the rows above are unchanged or changing shape, and the plan is about which half your week sits in. That is a very different problem, and a solvable one.
Panic-buying a course
Do the twenty-minute sorting exercise first. Most people who buy a course before they have done it buy the wrong one, and the free services listed below will tell you the same thing without charging for it.
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.

If you run a team doing this job

If you run a team doing this job, the conversation you owe them is the one on this page, and sooner than feels comfortable. Show them the ledger rather than a reassurance: the rows moving toward the software are keeping records pertaining, and the rows that are not are where you want your people visible. Ask each of them to do the this-week move and bring the list to your next one-to-one. It turns a rumour into a piece of work, and it tells you which parts of your team's week are actually at stake. And say the thing out loud that a team lead usually leaves unsaid: a shrinking team is your exposure too, so do the move yourself as well.

You are reading the United States figures

The United Kingdom splits this work across more than one official group, of which Travel agents 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 Travel agents, Estate agents and auctioneers, Business sales executives, Brokers and Insurance underwriters. 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:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

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

Nothing Collab365 runs is built for first-line supervisors of non-retail sales workers, 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 38% 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 first-line supervisors of non-retail sales workers. 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 first-line supervisors of non-retail sales workers launches. Nothing else.

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No Space for first-line supervisors of non-retail sales workers yet. Should there be one?

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Questions people ask about this job

Will AI replace First-Line Supervisors of Non-Retail Sales Workers?: Not as a job, but it is already doing parts of the work. Across the 18 official task statements scored for First-Line Supervisors of Non-Retail Sales Workers (United States, SOC 41-1012), 38% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 46 out of 100 (range 41–52, 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 “First-Line Supervisors of Non-Retail Sales Workers” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Keep records pertaining to purchases, sales, and requisitions” (93/100, very high); “Prepare sales and inventory reports for management and budget departments” (81/100, very high); “Prepare rental or lease agreements, specifying charges and payment procedures for use of machinery, tools, or other items” (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 “First-Line Supervisors of Non-Retail Sales Workers” stay human?: About 47% 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: “Examine products purchased for resale or received for storage to determine product condition” (0/100, minimal); “Examine merchandise to ensure correct pricing and display, and that it functions as advertised” (0/100, minimal); “Provide staff with assistance in performing difficult or complicated duties” (6/100, 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 “First-Line Supervisors of Non-Retail Sales Workers” do about AI?: Start from the ledger rather than the headline: 38% of this job's weighted core work is exposed, and roughly 47% 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 First-Line Supervisors of Non-Retail 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 18 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

One task scored differently between repeat runs, so its range on this page is wider. We would rather show the wobble than hide it.
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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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.