US dataswitch to UK
Hotel, Motel, and Resort Desk Clerks
greeting, registering, posting charges and reviewing accounts and charges with guests during the check out process. If that's your week, this page is about your job.
The honest answer
This job is splitting in two: keeping records of room availability and guests' accounts is work AI now does quickly and cheaply, and issuing room keys and escorting instructions to bellhops is work it can't touch.
Which half fills your week decides your exposure. The ledger below shows which rows you can move toward.
Your week, as this page understands it
Accommodate hotel, motel, and resort patrons by registering and assigning rooms to guests, issuing room keys or cards, transmitting and receiving messages, keeping records of occupied rooms and guests' accounts, making and confirming reservations, and presenting statements to and collecting payments from departing guests. The job title says “hotel”, “motel” or “resort desk clerks”: officially one job, several names. The real job is the part underneath: issuing room keys and escorting instructions to bellhops. 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 hotel, motel, and resort desk clerks is not one task. It is 20 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is issuing room keys and escorting instructions to bellhops, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 47%
- changing shape
- 11%
- staying human
- 41%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 53 out of 100 (49–58 allowing for uncertainty): partial exposure, across 20 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 hotel, motel, and resort desk 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.
Your job, task by task
These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
9 tasksTasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.
Contacting housekeeping or maintenance staff when guests report problems
This is reading one thing and writing another: maintenance staff in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Contact housekeeping or maintenance staff when guests report problems.” (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: Passing a guest's reported problem to housekeeping or maintenance is a routing job software handles instantly.
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.
Making and confirming reservations
This is reading one thing and writing another: reservations in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Make and confirm reservations.” (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: Making and confirming bookings is standard system work already done online without staff involvement.
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.
Keeping records of room availability and guests' accounts
This is reading one thing and writing another: records of room availability in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Keep records of room availability and guests' accounts, manually or using computers.” (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 room availability and guest account records accurate is exactly what a hotel booking system 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 0/4 · how much data exists 3/4.
Posting charges, such as those for rooms, food, liquor or telephone calls
This is reading one thing and writing another: charges in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Post charges, such as those for rooms, food, liquor, or telephone calls, to ledgers, manually or by using computers.” (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: Posting room, food and other charges to a guest's account is routine bookkeeping software does automatically.
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
2 tasksTasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.
Recording guest comments or complaints
The software now makes the first pass at guest comments, 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 5 · CoreSource: “Record guest comments or complaints, referring customers to managers as necessary.” (O*NET task statement)
How this row was scored
Exposure score: 49 out of 100 (42–56 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: Complaints are easy to log and route, though an unhappy guest usually wants a person to acknowledge the problem.
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 2/4 · how much data exists 3/4.
Verifying customers' credit and establishing how the customer will pay for the accommodation
The software now makes the first pass at customers' credit, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Verify customers' credit, and establish how the customer will pay for the accommodation.” (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: Card checks and payment arrangements run through payment systems that need little human input.
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 tasksTasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.
Greeting, registering and assigning rooms to guests of hotels or motels
This work happens in the physical world: rooms, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Greet, register, and assign rooms to guests of hotels or motels.” (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: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Registration and room assignment are already automated at kiosks, but a welcome at the desk is the part guests notice.
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.
Issuing room keys and escorting instructions to bellhops
This work happens in the physical world: room keys, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Issue room keys and escort instructions to bellhops.” (O*NET task statement)
How this row was scored
Exposure score: 19 out of 100 (15–23 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Programming and handing over a physical key at the desk requires someone there.
The five ratings: output a model can produce 3/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Reviewing accounts and charges with guests during the check out process
This work happens in the physical world: accounts, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Review accounts and charges with guests during the check out process.” (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: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: The bill itself is produced by the system, but going through it with a departing guest happens at the desk.
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.
Computing bills, collect payments and making change for guests
This work happens in the physical world: bills, collect payments and making change, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Compute bills, collect payments, and make change for guests.” (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: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Bill calculation is automatic, but taking cash and giving change means being at the desk with the guest.
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.
Show the other 10 tasks
Advising housekeeping staff when rooms have been vacated and are ready for cleaning
shifting to AIThis is reading one thing and writing another: staff in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Advise housekeeping staff when rooms have been vacated and are ready for cleaning.” (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: Telling housekeeping which rooms are free is a status update the booking system can send by itself.
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.
Performing bookkeeping activities, such as balancing accounts and conducting nightly audits
shifting to AIThis is reading one thing and writing another: activities in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Perform bookkeeping activities, such as balancing accounts and conducting nightly audits.” (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: Balancing accounts and running the night audit follows fixed rules on system data that software applies consistently.
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.
Transmiting and receiving messages, using telephones or telephone switchboards
shifting to AIThis is reading one thing and writing another: messages in, a record out. That is the shape today's tools are built for.
importance 5 · CoreSource: “Transmit and receive messages, using telephones or telephone switchboards.” (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: Taking and passing on messages is routine communication handling that software manages reliably.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Arranging tours, taxis or restaurant reservations for customers
shifting to AIThis is reading one thing and writing another: tours, taxis or restaurant reservations in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Arrange tours, taxis, or restaurant reservations for customers.” (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: Booking taxis, tours and restaurant tables is straightforward arranging that apps and software already do well.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Answering inquiries pertaining to hotel services
shifting to AIThis is reading one thing and writing another: inquiries in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Answer inquiries pertaining to hotel services, guest registration, and travel directions, or make recommendations regarding shopping, dining, or entertainment.” (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: Hotel information and local recommendations are abundantly documented, so software gives accurate answers to most guest questions.
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 4/4.
Planning, scheduling or supervising the work of other employees
staying humanThe value here is that a specific person handles the work of other employees and stands behind it. That is earned, not computed.
importance 4 · SupplementalSource: “Plan, schedule or supervise the work of other employees.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (17–25 allowing for uncertainty): low exposure, high confidence.
Why it sits in this group: the value is that a specific person does it.
The rating behind it: Planning shifts is easy to automate, but supervising colleagues depends on daily contact and their trust.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Datestamping sort and rack incoming mail and messages
staying humanThis work happens in the physical world: sort, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Date-stamp, sort, and rack incoming mail and messages.” (O*NET task statement)
How this row was scored
Exposure score: 19 out of 100 (15–23 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Sorting and racking physical post means handling it, even though electronic messages are easy to route.
The five ratings: output a model can produce 3/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Depositing guests' valuables in hotel safes or safe-deposit boxes
staying humanThis work happens in the physical world: guests' valuables, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Deposit guests' valuables in hotel safes or safe-deposit boxes.” (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: Putting guests' valuables into a safe or deposit box is a physical task at the desk.
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.
Cleaning and maintaining lobby and common areas
staying humanThis work happens in the physical world: lobby, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Clean and maintain lobby and common areas, such as restocking supplies and watering plants.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Cleaning the lobby, restocking supplies and watering plants is physical work around the building.
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 1/4.
Preparing for basic food service
staying humanThis work happens in the physical world: basic food service, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Prepare for basic food service, such as setting up continental breakfast or coffee and tea supplies.” (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: Setting out breakfast and hot drinks is entirely hands-on preparation.
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
- $35,070a 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
- 261,420in 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: records of room availability in, a record out. The rows above are exactly that shape: keeping records of room availability and guests' accounts and contacting housekeeping or maintenance staff when guests report problems. What it cannot do is be there in the room, and that is still where room keys get done. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
The exposed part of your job is the biggest part, and I am not going to dress that up: keeping records of room availability and guests' accounts is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 47% of this job's task weight sits in rows the software is already learning, 11% in rows that change shape rather than disappear, and 41% in rows it is nowhere near. That is the position, measured across 20 scored tasks. It is not a forecast about you.
What you have that the software does not is issuing room keys and escorting instructions to bellhops, plus the years of knowing when something looks wrong before you can say why. That is the raw material for everything below.
This week: one thing
Sit on the machine's side of the desk. Pick one real piece of records of room availability you would normally do yourself, let whatever software you already have take the first pass at it, and then go through what it produced and write down every single thing it got wrong. One evening this week. Do not fix anything yet. Just catch it.
- What you end up holding
- a written list of the machine’s mistakes, in your handwriting
- How long it takes
- an evening, or an hour if you pick one job rather than one client
If you can’t run software on your employer’s or your clients’ data: Do the same hunt on paper. Take one printed piece of records of room availability, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.
Over the next 90 days
Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is keeping records of room availability and guests' accounts” to “I check what the software does and tell you what it means.” Your error list from this week is what makes that sentence true instead of a claim, so use it: show it once, to one person, inside the next ninety days. Same skills, priced as judgment rather than as typing.
Over the next 12 months
Walk toward the end of this job that answers for things, and get it recognised. Pick the one part of issuing room keys and escorting instructions to bellhops you are already best at, and spend the year making it formal: a qualification, a named responsibility, a specialism people ask for by name. Price it honestly: that is evenings, it is months rather than weeks, and the seats are competitive because everyone in your position is looking at the same door. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.
The roads out of here, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to hotel, motel, and resort desk clerks (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was lodging managers: only about 9% of its durable work is work you already do and there are far fewer of those jobs than of yours. Your own job splits about 47/53: 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 “greet, register, and assign rooms to guests of hotels or motels”, 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.
Lodging Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already greet, register, and assign rooms to guests of hotels or motels, and their equivalent is to greet and register guests. 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. And it is a narrow door: about 42,620 of those jobs against 261,420 of yours (OEWS May 2025), 16% as many seats.
Baggage Porters and Bellhops
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already greet, register, and assign rooms to guests of hotels or motels, and their equivalent is to greet incoming guests and escort them to their rooms. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step. And it is a narrow door: about 28,510 of those jobs against 261,420 of yours (OEWS May 2025), 11% as many seats.
Credit Analysts
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already verify customers' credit, and establish how the customer will pay for the accommodation, and their equivalent is to consult with customers to resolve complaints and verify financial and credit transactions. 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: 78% of its own task list already scores in the top exposure band (70/100 in this release), so the same software is eating it. The pay gap is the market pricing a barrier: $83,510 against your $35,070 is 2.38× (OEWS May 2025 (both)), and you would be crossing it holding about 4% of their durable work. A gap that size with an overlap that small is a wish, not a route. And it is a narrow door: about 64,390 of those jobs against 261,420 of yours (OEWS May 2025), 25% as many seats.
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: 47% of its task weight, across 20 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
“It’s too late for me to become something else”
You are not starting from zero, and the page shows why: issuing room keys and escorting instructions to bellhops is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.
“I should learn to code”
Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Receptionists 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 Receptionists. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for hotel / motel / resort desk 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 47% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for hotel / motel / resort desk 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 hotel / motel / resort desk clerks launches. Nothing else.
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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 Hotel, Motel, and Resort Desk Clerks?
- Not as a job, but it is already doing parts of the work. Across the 20 official task statements scored for Hotel, Motel, and Resort Desk Clerks (United States, SOC 43-4081), 47% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 53 out of 100 (range 49–58, 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 “Hotel, Motel, and Resort Desk Clerks” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Keep records of room availability and guests' accounts, manually or using computers” (93/100, very high); “Post charges, such as those for rooms, food, liquor, or telephone calls, to ledgers, manually or by using computers” (93/100, very high); “Advise housekeeping staff when rooms have been vacated and are ready for cleaning” (93/100, 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 “Hotel, Motel, and Resort Desk Clerks” stay human?
- About 41% 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: “Prepare for basic food service, such as setting up continental breakfast or coffee and tea supplies” (0/100, minimal); “Clean and maintain lobby and common areas, such as restocking supplies and watering plants” (0/100, minimal); “Deposit guests' valuables in hotel safes or safe-deposit boxes” (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 “Hotel, Motel, and Resort Desk Clerks” do about AI?
- Start from the ledger rather than the headline: 47% of this job's weighted core work is exposed, and roughly 41% 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 Hotel, Motel, and Resort Desk 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 20 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.
Where these numbers come from
Worth knowing about these figures
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
- Task statements
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
- Pay and employment
- bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))
Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.
The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.
The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.
Using these figures?
Cite this
Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.
Plain text
Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).
BibTeX
@misc{collab365futureproof2026q41,
title = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
author = {{Collab365}},
year = {2026},
url = {https://futureproof.collab365.com/data/2026-q4.1},
note = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}Data as of release 2026-q4.1, published . Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.