Will AI replace Counter and Rental Clerks?

Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Counter and Rental Clerks (United States, SOC 41-2021), 14% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 39 out of 100 (range 35–44, band: low). 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 “Counter and Rental Clerks” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Reserve items for requested times and keep records of items rented” (79/100, high); “Answer telephones to provide information and receive orders” (64/100, high); “Prepare rental forms, obtaining customer signature and other information, such as required licenses” (59/100, partial). 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 “Counter and Rental Clerks” stay human?

About 55% 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: “Inspect and adjust rental items to meet needs of customer” (0/100, minimal); “Prepare merchandise for display or for purchase or rental” (0/100, minimal); “Allocate equipment to participants in sporting events or recreational activities” (14/100, 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 “Counter and Rental Clerks” do about AI?

Start from the ledger rather than the headline: 14% of this job's weighted core work is exposed, and roughly 55% 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 Counter and Rental Clerks calculated?

Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 16 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

Counter and Rental Clerks

computing charges for merchandise or services and receiving payments, greeting customers and discussing the type and answering telephones to provide information and receive orders. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: inspecting and adjusting rental items to meet needs of customer is work software can't reach.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 14%
changing shape: 31%
staying human: 55%

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

Whole-job exposure score 39 out of 100 (35–44 allowing for uncertainty): low exposure, across 16 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 counter and rental clerks is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.

Release: 2026-q4.1, scores computed 2026-08-04. Read the full method.

What shifts is answering telephones to provide information and receive orders. This page scores what today's tools actually do, not headlines.

Your week, as this page understands it

Receive orders, generally in person, for repairs, rentals, and services. May describe available options, compute cost, and accept payment. The job title says “counter” or “rental clerks”: officially one job, two names. The real job is the part underneath: inspecting and adjusting rental items to meet needs of customer. 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 counter and rental clerks is not one task. It is 16 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is inspecting and adjusting rental items to meet needs of customer, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 14%
changing shape: 31%
staying human: 55%

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.

1 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

2 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.

Answering telephones to provide information and receive orders
This is reading one thing and writing another: telephones in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Answer telephones to provide information and receive orders.” (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: Phone enquiries and order taking follow set information, and automated phone and chat systems already handle much of it.
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.
Reserving items for requested times and keeping records of items
This is reading one thing and writing another: items in, a record out. That is the shape today's tools are built for.
importance 4 · Core
Source: “Reserve items for requested times and keep records of items rented.” (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: Reservations and rental records are exactly what booking systems do, and customers often make them themselves online.
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.

Changing shape

5 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.

Advising customers on use and care of merchandise
The software now makes the first pass at customers, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Advise customers on use and care of merchandise.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (44–52 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: Care and use guidance is documented for most products, so it can be written out or delivered by an assistant tool.
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 1/4 · how much data exists 3/4.
Providing information about rental items
The software now makes the first pass at information, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Provide information about rental items, such as availability, operation, or description.” (O*NET task statement)
How this row was scored
Exposure score: 59 out of 100 (55–63 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: Availability and product details sit in a computer system that customers and staff can look up directly.
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 1/4 · how much data exists 3/4.
Explaining rental fees, policies and procedures
The software now makes the first pass at rental fees, policies and procedures, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Explain rental fees, policies, and procedures.” (O*NET task statement)
How this row was scored
Exposure score: 48 out of 100 (44–52 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: Rental terms and fees are set wording that can be written out or shown on screen before the customer arrives.
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 1/4 · how much data exists 3/4.
Preparing rental forms, obtaining customer signature and other information, such as required licenses
The software now makes the first pass at rental forms, obtaining customer signature and other information, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Prepare rental forms, obtaining customer signature and other information, such as required licenses.” (O*NET task statement)
How this row was scored
Exposure score: 59 out of 100 (52–66 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; mistakes that are cheap to catch.
The rating behind it: Rental agreements are standard forms filled from customer details, and many are now completed online or at a kiosk.
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 1/4 · 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.

Computing charges for merchandise or services and receiving payments
This work happens in the physical world: charges, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Compute charges for merchandise or services and receive payments.” (O*NET task statement)
How this row was scored
Exposure score: 39 out of 100 (35–43 allowing for uncertainty): low 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: Tills work out the charge automatically, but taking payment across a counter still involves the customer being there.
The five ratings: output a model can produce 4/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Greeting customers and discussing the type
This work happens in the physical world: customers, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Greet customers and discuss the type, quality, and quantity of merchandise sought for rental.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (22–30 allowing for uncertainty): low 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: Greeting someone and working out what they actually want is a face-to-face conversation at the counter.
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.
Receiving orders for services, such as rentals, repairs, dry cleaning and storage
This work happens in the physical world: orders, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Receive orders for services, such as rentals, repairs, dry cleaning, and storage.” (O*NET task statement)
How this row was scored
Exposure score: 32 out of 100 (25–39 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: Online booking systems already take rental, repair and cleaning orders, though counter customers hand items and details over 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 1/4 · how much data exists 3/4.
Recommending and providing advice on a wide variety of products and services
The value here is that a specific person handles advice and stands behind it. That is earned, not computed.
importance 4 · Core
Source: “Recommend and provide advice on a wide variety of products and services.” (O*NET task statement)
How this row was scored
Exposure score: 39 out of 100 (32–46 allowing for uncertainty): low 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: Product suggestions can be generated from catalogue and purchase data, though a persuasive recommendation often lands better in person.
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 2/4 · how much data exists 3/4.

Show the other 6 tasks

Keeping records of transactions and of the number of customers entering an establishment
changing shape
The software now makes the first pass at records of transactions, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · Core
Source: “Keep records of transactions and of the number of customers entering an establishment.” (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: Transaction and visitor-count records come straight from till and counting systems rather than being written up by hand.
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.
Renting items, arrange for provision of services to customers and accept returns
staying human
This work happens in the physical world: items, arrange, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Rent items, arrange for provision of services to customers, and accept returns.” (O*NET task statement)
How this row was scored
Exposure score: 16 out of 100 (12–20 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: Handing items out and taking them back happens over the counter, even though the paperwork behind it is simple.
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 1/4 · how much data exists 3/4.
Receiving, examining and tag articles
staying human
This work happens in the physical world: tag articles, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Receive, examine, and tag articles to be altered, cleaned, stored, or repaired.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (10–18 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 in, checking and tagging garments or goods means handling each item.
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.
Allocating equipment to participants in sporting events or recreational activities
staying human
This work happens in the physical world: equipment, in a real place. Software cannot follow it there.
importance 3 · Supplemental
Source: “Allocate equipment to participants in sporting events or recreational activities.” (O*NET task statement)
How this row was scored
Exposure score: 14 out of 100 (10–18 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: Handing out kit to players happens at the equipment store, even if tracking who has what is easy.
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.
Preparing merchandise for display or for purchase or rental
staying human
This work happens in the physical world: merchandise, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Prepare merchandise for display or for purchase or rental.” (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: Getting stock ready for display or hire is physical preparation of the goods.
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.
Inspecting and adjusting rental items to meet needs of customer
staying human
This work happens in the physical world: rental items, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Inspect and adjust rental items to meet needs of customer.” (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: Adjusting a rental item to fit the customer means handling the equipment.
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: $41,300a 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: 400,810in 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: telephones in, a record out. The rows above are exactly that shape: answering telephones to provide information and receive orders and reserving items for requested times and keeping records of items. What it cannot do is be there in the room, and that is still where rental items 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: inspecting and adjusting rental items to meet needs of customer is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 14% of this job's task weight sits in rows the software is already learning, 31% in rows that change shape rather than disappear, and 55% in rows it is nowhere near. That is the position, measured across 16 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. Answering telephones to provide information and receive orders 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 telephones, 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 rental items 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 advising customers on use and care of merchandise. 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 counter and rental clerks (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was retail salespersons: only about 21% of its durable work is work you already do and it pays 14.3% less. And on the numbers you do not need one. This job scores 39/100 here, with only 14% of the task list in the top band, and “compute charges for merchandise or services and receive payments” 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.

Retail Salespersons
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already prepare merchandise for display or for purchase or rental, and their equivalent is to prepare merchandise for purchase or rental. Across both published task lists that is about 21% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 21% 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: $35,410 against your $41,300, 14.3% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Look at that job’s page anyway →
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 compute charges for merchandise or services and receive payments, and their equivalent is to deliver merchandise and collect payment. Across both published task lists that is about 7% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 2,760 of those jobs against 400,810 of yours (OEWS May 2025), 1% as many seats.
Look at that job’s page anyway →
Order Clerks
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already advise customers on use and care of merchandise, and their equivalent is to recommend merchandise or services that will meet customers' needs. 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. 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 400,810 of yours (OEWS May 2025), 19% as many seats.
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: 14% of its task weight, across 16 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 inspecting and adjusting rental items to meet needs of customer 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 Sales related occupations n.e.c. 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 Sales related occupations n.e.c.. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

Your route through this

Where to go next, and what it costs

Free, and complete

The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.

Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
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 counter / rental clerks, 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 14% 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 counter / rental clerks. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.

Noted, and thank you. We’ll email you if a Space for counter / rental clerks launches. Nothing else.

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No Space for counter / rental clerks yet. Should there be one?

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What a Space actually is, in full

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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 Counter and Rental Clerks?: Not as a job, but it is already doing parts of the work. Across the 16 official task statements scored for Counter and Rental Clerks (United States, SOC 41-2021), 14% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 39 out of 100 (range 35–44, band: low). 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 “Counter and Rental Clerks” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Reserve items for requested times and keep records of items rented” (79/100, high); “Answer telephones to provide information and receive orders” (64/100, high); “Prepare rental forms, obtaining customer signature and other information, such as required licenses” (59/100, partial). 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 “Counter and Rental Clerks” stay human?: About 55% 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: “Inspect and adjust rental items to meet needs of customer” (0/100, minimal); “Prepare merchandise for display or for purchase or rental” (0/100, minimal); “Allocate equipment to participants in sporting events or recreational activities” (14/100, 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 “Counter and Rental Clerks” do about AI?: Start from the ledger rather than the headline: 14% of this job's weighted core work is exposed, and roughly 55% 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 Counter and Rental Clerks calculated?: Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 16 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

Worth knowing about these figures

The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
1 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
Pay and employment: bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))

Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.

The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.

The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.

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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.