US dataswitch to UK
Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop
providing guests with menus, operating cash registers to accept payments for food and beverages, inspecting restrooms for cleanliness and availability of supplies and directing patrons to coatrooms and waiting areas. If that's your week, this page is about your job.
The honest answer
AI changes the edges of this job, not the middle: providing guests with menus is work software can't reach.
What shifts is answering telephone calls and responding to inquiries or transferring calls: the paper around the work, not the work.
Your week, as this page understands it
Welcome patrons, seat them at tables or in lounge, and help ensure quality of facilities and service. The job title says “hosts”, “hostesses”, “restaurant”, “lounge” or “coffee shop”: officially one job, several names. The real job is the part underneath: providing guests with menus. 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 hosts and hostesses, restaurant, lounge, and coffee shop 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 providing guests with menus, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 8%
- changing shape
- 7%
- staying human
- 85%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 16 out of 100 (13–22 allowing for uncertainty): minimal 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 hosts and hostesses, restaurant, lounge, and coffee shop 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.
- 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 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.
Answering telephone calls and responding to inquiries or transferring calls
This is reading one thing and writing another: telephone calls in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Answer telephone calls and respond to inquiries or transfer calls.” (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: Answering calls and giving standard information is something automated phone systems already handle well for restaurants.
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.
Performing marketing and advertising services
This is reading one thing and writing another: services in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Perform marketing and advertising services.” (O*NET task statement)
How this row was scored
Exposure score: 75 out of 100 (68–82 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing promotions, social posts and adverts for a restaurant is exactly the kind of copy software drafts well.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 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.
Receiving and recording patrons' dining reservations
The software now makes the first pass at patrons' dining reservations, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Receive and record patrons' dining reservations.” (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: Taking and recording a reservation is data entry that online booking systems already do.
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.
Ordering or requisitioning supplies and equipment for tables and serving stations
The software now makes the first pass at supplies, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · SupplementalSource: “Order or requisition supplies and equipment for tables and serving stations.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 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: Working out what to reorder from usage records is routine, though someone must check the stockroom.
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.
Staying human
16 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.
Providing guests with menus
This work happens in the physical world: guests, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Provide guests with menus.” (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: Handing a menu to someone sitting at a table needs a person physically there.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Greeting guests and seat them at tables or in waiting areas
This work happens in the physical world: guests, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Greet guests and seat them at tables or in waiting areas.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Walking guests to a table is physical work in the room; software cannot show anyone to their seat.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Maintaining contact with kitchen staff
This work happens in the physical world: contact, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Maintain contact with kitchen staff, management, serving staff, and customers to ensure that dining details are handled properly and customers' concerns are addressed.” (O*NET task statement)
How this row was scored
Exposure score: 3 out of 100 (0–10 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: Most of this is talking with kitchen and floor staff while on the restaurant floor, reading the room.
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 3/4 · how much data exists 1/4.
Assigning patrons to tables suitable for their needs and according to rotation so that servers receive an appropriate number of seatings
This work happens in the physical world: patrons, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Assign patrons to tables suitable for their needs and according to rotation so that servers receive an appropriate number of seatings.” (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: Table systems already handle rotation and seating maths well, though someone still checks which tables are genuinely free.
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.
Speaking with patrons to ensure satisfaction with food and service
This work happens in the physical world: patrons, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Speak with patrons to ensure satisfaction with food and service, to respond to complaints, or to make conversation.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Checking in at the table is face to face; the point is a person stopping by to ask.
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 3/4 · how much data exists 1/4.
Inspecting dining and serving areas to ensure cleanliness and proper setup
This work happens in the physical world: areas, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Inspect dining and serving areas to ensure cleanliness and proper setup.” (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: Someone has to walk the room and look at the tables, so this stays hands on.
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.
Show the other 10 tasks
Preparing cash receipts after establishments close
staying humanThis work happens in the physical world: cash receipts after establishments close, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Prepare cash receipts after establishments close, and make bank deposits.” (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: Adding up the day's takings is straightforward paperwork, but the trip to the bank still needs a 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 0/4 · how much data exists 3/4.
Conferring with other staff to help plan establishments' menus
staying humanThe ratings behind this row put other staff well outside what today's tools can do on their own.
importance 3 · SupplementalSource: “Confer with other staff to help plan establishments' menus.” (O*NET task statement)
How this row was scored
Exposure score: 37 out of 100 (30–44 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.
The rating behind it: Menu ideas and costings can be drafted well, but final choices come from the kitchen team's own taste.
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 1/4 · how much data exists 3/4.
Informing patrons of establishment specialties and features
staying humanThis work happens in the physical world: patrons of establishment specialties, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Inform patrons of establishment specialties and features.” (O*NET task statement)
How this row was scored
Exposure score: 16 out of 100 (9–23 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: The words describing specials are easy to generate, but they are usually spoken to guests standing in front of you.
The five ratings: output a model can produce 3/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Operating cash registers to accept payments for food and beverages
staying humanThis work happens in the physical world: cash registers, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Operate cash registers to accept payments for food and beverages.” (O*NET task statement)
How this row was scored
Exposure score: 12 out of 100 (5–19 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Someone has to work the till with the customer present, even though self-service payment already exists.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Taking and preparing to-go orders
staying humanThis work happens in the physical world: to-go orders, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Take and prepare to-go orders.” (O*NET task statement)
How this row was scored
Exposure score: 12 out of 100 (5–19 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Taking the order is easy to automate, but someone still has to put the food together and bag it.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Directing patrons to coatrooms and waiting areas
staying humanThis work happens in the physical world: patrons, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Direct patrons to coatrooms and waiting areas, such as lounges.” (O*NET task statement)
How this row was scored
Exposure score: 11 out of 100 (4–18 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Pointing people towards the cloakroom or lounge happens in person, though signs can do part of it.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Hiring, training and supervising food and beverage service staff
staying humanThis work happens in the physical world: food, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Hire, train, and supervise food and beverage service staff.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (5–13 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Hiring and training staff depends on judging people and showing them the job in person.
The five ratings: output a model can produce 1/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Supervising and coordinating activities of dining room staff to ensure that patrons receive prompt and courteous service
staying humanThis work happens in the physical world: activities of dining room staff, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Supervise and coordinate activities of dining room staff to ensure that patrons receive prompt and courteous service.” (O*NET task statement)
How this row was scored
Exposure score: 3 out of 100 (0–10 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: Directing staff during service happens live on the floor and depends on trust built with the team.
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 3/4 · how much data exists 1/4.
Inspecting restrooms for cleanliness and availability of supplies
staying humanThis work happens in the physical world: restrooms, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Inspect restrooms for cleanliness and availability of supplies, and clean restrooms when necessary.” (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 and cleaning restrooms is hands-on work in the room itself.
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.
Assisting other restaurant workers by serving food and beverages
staying humanThis work happens in the physical world: other restaurant workers, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Assist other restaurant workers by serving food and beverages, or by bussing tables.” (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: Carrying plates and clearing tables is physical work that needs hands in the dining room.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
What this job pays, and how many people do it
- Median pay
- $31,200a 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
- 432,690in 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: telephone calls in, a record out. The rows above are exactly that shape: answering telephone calls and responding to inquiries or transferring calls and performing marketing and advertising services. What it cannot do is be there in the room, and that is still where guests get done. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
Start with what does not change: providing guests with menus is the middle of this job, and the evidence on this page says it stays with a person.
So, given all that: 8% of this job's task weight sits in rows the software is already learning, 7% in rows that change shape rather than disappear, and 85% in rows it is nowhere near. That is the position, measured across 20 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 telephone calls and responding to inquiries or transferring calls 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 telephone calls, 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 guests 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 receiving and recording patrons' dining reservations. 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 hosts and hostesses, restaurant, lounge, and coffee shop (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was waiters and waitresses: only about 10% of its durable work is work you already do. And on the numbers you do not need one. This job scores 16/100 here, with only 8% of the task list in the top band, and “provide guests with menus” 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.
Waiters and Waitresses
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already receive and record patrons' dining reservations, and their equivalent is to clean tables or counters after patrons have finished dining. Across both published task lists that is about 10% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 10% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on.
First-Line Supervisors of Food Preparation and Serving Workers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already provide guests with menus, and their equivalent is to greet and seat guests, and present menus and wine lists. Across both published task lists that is about 8% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 8% of the durable side of that job. That is a different job, not a next step.
Concierges
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already provide guests with menus, and their equivalent is to provide directions to guests. 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 49,240 of those jobs against 432,690 of yours (OEWS May 2025), 11% 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: 8% of its task weight, across 20 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The headlines about your trade disappearing
They are usually about the technology, not the timetable. Changes to work like providing guests with menus 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 Sports and leisure assistants 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 Sports and leisure assistants. 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 hosts / hostesses / restaurant / lounge / coffee shop, 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 8% 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 hosts / hostesses / restaurant / lounge / coffee shop. 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 hosts / hostesses / restaurant / lounge / coffee shop 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 Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop?
- Not as a job, but it is already doing parts of the work. Across the 20 official task statements scored for Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop (United States, SOC 35-9031), 8% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 16 out of 100 (range 13–22, band: minimal). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
- Which tasks in “Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Perform marketing and advertising services” (75/100, high); “Answer telephone calls and respond to inquiries or transfer calls” (64/100, high); “Receive and record patrons' dining reservations” (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 “Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop” stay human?
- About 85% 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: “Assist other restaurant workers by serving food and beverages, or by bussing tables” (0/100, minimal); “Inspect restrooms for cleanliness and availability of supplies, and clean restrooms when necessary” (0/100, minimal); “Inspect dining and serving areas to ensure cleanliness and proper setup” (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 “Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop” do about AI?
- Start from the ledger rather than the headline: 8% of this job's weighted core work is exposed, and roughly 85% 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 Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop 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.
- 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.
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.