US dataswitch to UK
Bartenders
cleaning glasses, utensils and bar equipment, taking beverage orders from serving staff or directly from patrons and serving wine and bottled or drafting beer. If that's your week, this page is about your job.
The honest answer
AI changes the edges of this job, not the middle: checking identification of customers to verify age requirements for purchase of alcohol is work software can't reach.
What shifts is ordering or requisitioning liquors and supplies. This page scores what today's tools actually do, not headlines.
Your week, as this page understands it
Mix and serve drinks to patrons, directly or through waitstaff. The job title says “bartenders”. The real job is the part underneath: checking identification of customers to verify age requirements for purchase of alcohol. 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 bartenders 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 checking identification of customers to verify age requirements for purchase of alcohol, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 10%
- changing shape
- 6%
- staying human
- 85%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 12 out of 100 (9–17 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 bartenders 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.
- 4 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
3 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.
Ordering or requisitioning liquors and supplies
This is reading one thing and writing another: liquors in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Order or requisition liquors and supplies.” (O*NET task statement)
How this row was scored
Exposure score: 81 out of 100 (74–88 allowing for uncertainty): very 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: Reordering drinks and supplies against stock levels is routine ordering that software already handles.
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.
Creating drink recipes
This is reading one thing and writing another: drink recipes in, a record out. That is the shape today's tools are built for.
importance 3 · CoreSource: “Create drink recipes.” (O*NET task statement)
How this row was scored
Exposure score: 62 out of 100 (55–69 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: Inventing drink recipes draws on a huge published body of recipes, though the result still needs tasting.
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 4/4.
Planning bar menus
This is reading one thing and writing another: bar menus in, a record out. That is the shape today's tools are built for.
importance 4 · SupplementalSource: “Plan bar menus.” (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: Designing a bar menu is planning and writing built on widely published drinks knowledge.
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
1 taskTasks 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.
Balancing cash receipts
The software now makes the first pass at cash receipts, but part of it still happens in the physical world. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Balance cash receipts.” (O*NET task statement)
How this row was scored
Exposure score: 40 out of 100 (33–47 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Totting up takings is arithmetic software does instantly, although the cash itself still needs counting.
The five ratings: output a model can produce 4/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · 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.
Checking identification of customers to verify age requirements for purchase of alcohol
This work happens in the physical world: identification of customers, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Check identification of customers to verify age requirements for purchase of alcohol.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; someone qualified has to answer for it.
The rating behind it: Checking a customer's ID means looking at the person and the document in front of you.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Cleaning glasses, utensils and bar equipment
This work happens in the physical world: glasses, utensils and bar equipment, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Clean glasses, utensils, and bar equipment.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Washing glasses, utensils and bar equipment is done by hand behind the bar.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Cleaning bars, work areas and tables
This work happens in the physical world: bars, work areas and tables, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Clean bars, work areas, and 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: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Wiping down the bar, work areas and tables is physical cleaning.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Attempting to limit problems and liability related to customers' excessive drinking by taking steps
This work happens in the physical world: limit problems, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Attempt to limit problems and liability related to customers' excessive drinking by taking steps such as persuading customers to stop drinking, or ordering taxis or other transportation for intoxicated patrons.” (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: Judging when someone has had enough and talking them down happens face to face in the room.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Taking beverage orders from serving staff or directly from patrons
This work happens in the physical world: beverage orders, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Take beverage orders from serving staff or directly from patrons.” (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: Apps can take orders, but bar service still means hearing them across a busy counter.
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.
Collecting money for drinks
This work happens in the physical world: money, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Collect money for drinks served.” (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: Card machines handle the money, but taking payment at the bar happens face to face.
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.
Show the other 10 tasks
Planning, organizing and controlling the operations of a cocktail lounge or bar
staying humanThis work happens in the physical world: the operations of a cocktail lounge, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Plan, organize, and control the operations of a cocktail lounge or bar.” (O*NET task statement)
How this row was scored
Exposure score: 18 out of 100 (11–25 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Running a bar involves planning software can support, but the day-to-day is people and premises.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 1/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Supervising the work of bar staff and other bartenders
staying humanThis work happens in the physical world: the work of bar staff, in a real place. Software cannot follow it there.
importance 4 · SupplementalSource: “Supervise the work of bar staff and other bartenders.” (O*NET task statement)
How this row was scored
Exposure score: 6 out of 100 (0–13 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: Supervising bar staff happens on the floor, alongside the people working the shift.
The five ratings: output a model can produce 1/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Serving wine and bottled or drafting beer
staying humanThis work happens in the physical world: wine, in a real place. Software cannot follow it there.
importance 5 · CoreSource: “Serve wine, and bottled or draft beer.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Pouring and serving beer and wine is hands-on.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Mixing ingredients, such as liquor, soda, water, sugar and bitters, to prepare cocktails and other drinks
staying humanThis work happens in the physical world: ingredients, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Mix ingredients, such as liquor, soda, water, sugar, and bitters, to prepare cocktails and other drinks.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Mixing a cocktail means physically handling bottles, ice and glassware.
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 3/4.
Serving snacks or food items to customers seated at the bar
staying humanThis work happens in the physical world: snacks, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Serve snacks or food items to customers seated at the bar.” (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 food to someone sitting at the bar needs hands and presence.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 1/4 · needs to be trusted in the moment 1/4 · how much data exists 2/4.
Asking customers who become loud and obnoxious
staying humanThis work happens in the physical world: customers who become loud, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Ask customers who become loud and obnoxious to leave, or physically remove them.” (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: Asking a difficult customer to leave, or removing them, requires being physically present.
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.
Slicing and pitting fruit for garnishing drinks
staying humanThis work happens in the physical world: fruit, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Slice and pit fruit for garnishing drinks.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Cutting and preparing fruit garnishes is manual prep work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Arranging bottles and glasses to make attractive displays
staying humanThis work happens in the physical world: bottles, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Arrange bottles and glasses to make attractive displays.” (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: Arranging bottles and glassware into a display is done by hand.
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.
Preparing appetizers, pickles, cheese and cold meats
staying humanThis work happens in the physical world: appetizers, pickles, cheese and cold meats, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Prepare appetizers such as pickles, cheese, and cold meats.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Preparing cold food platters is hands-on kitchen work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Stocking bar with beer, wine, liquor and related supplies, ice, glassware, napkins or straws
staying humanThis work happens in the physical world: bar, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Stock bar with beer, wine, liquor, and related supplies such as ice, glassware, napkins, or straws.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Carrying and shelving stock, ice and glassware is physical work.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
What this job pays, and how many people do it
- Median pay
- $34,340a 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
- 756,390in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)
What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.
Why this is shifting
The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: drink recipes in, a record out. The rows above are exactly that shape: ordering or requisitioning liquors and supplies and creating drink recipes. What it cannot do is be answerable: identification of customers needs a named person the rules will accept, and software cannot be that person. 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: checking identification of customers to verify age requirements for purchase of alcohol is the middle of this job, and the evidence on this page says it stays with a person.
So, given all that: 10% of this job's task weight sits in rows the software is already learning, 6% 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. Ordering or requisitioning liquors and supplies 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 drink recipes, 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 identification of customers is 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 balancing cash receipts. 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 bartenders (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was dishwashers: only about 8% of its durable work is work you already do. And on the numbers you do not need one. This job scores 12/100 here, with only 10% of the task list in the top band, and “check identification of customers to verify age requirements for purchase of alcohol” 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.
Dishwashers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already clean glasses, utensils, and bar equipment, and their equivalent is to maintain kitchen work areas, equipment, or utensils in clean and orderly condition. 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.
Dining Room and Cafeteria Attendants and Bartender Helpers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already slice and pit fruit for garnishing drinks, and their equivalent is to slice and pit fruit used to garnish drinks. 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.
Food Service Managers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already collect money for drinks served, and their equivalent is to perform some food preparation or service tasks. 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. The pay gap is the market pricing a barrier: $69,390 against your $34,340 is 2.02× (OEWS May 2025 (both)), and you would be crossing it holding about 5% of their durable work. A gap that size with an overlap that small is a wish, not a route.
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: 10% 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 checking identification of customers to verify age requirements for purchase of alcohol 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 Bar staff is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
The other groups this work is counted across:
In UK official statistics this job is counted as Bar staff and Coffee shop workers. Pay is shown separately for each of those groups (medians cannot be averaged together), while the task list and the scores on this page are for this group only.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
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 bartenders, 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 10% 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 bartenders. 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 bartenders 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 Bartenders?
- Not as a job, but it is already doing parts of the work. Across the 20 official task statements scored for Bartenders (United States, SOC 35-3011), 10% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 12 out of 100 (range 9–17, 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 “Bartenders” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Order or requisition liquors and supplies” (81/100, very high); “Plan bar menus” (75/100, high); “Create drink recipes” (62/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Bartenders” 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: “Stock bar with beer, wine, liquor, and related supplies such as ice, glassware, napkins, or straws” (0/100, minimal); “Prepare appetizers such as pickles, cheese, and cold meats” (0/100, minimal); “Arrange bottles and glasses to make attractive displays” (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 “Bartenders” do about AI?
- Start from the ledger rather than the headline: 10% 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 Bartenders 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.
- 4 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
- Task statements
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-04.
- Pay and employment
- bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))
Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.
The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.
The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.
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.