Will AI replace Waiters and Waitresses?

Not as a job, but it is already doing parts of the work. Across the 25 official task statements scored for Waiters and Waitresses (United States, SOC 35-3031), 5% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 8 out of 100 (range 5–14, 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 “Waiters and Waitresses” can AI already do?

The highest-scoring tasks in release 2026-q4.1 are: “Prepare checks that itemize and total meal costs and sales taxes” (69/100, high); “Assist host or hostess by answering phones to take reservations or to-go orders, and by greeting, seating, and thanking guests” (26/100, low); “Provide guests with information about local areas, including directions” (21/100, low). 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 “Waiters and Waitresses” stay human?

About 95% 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: “Perform cleaning duties, such as sweeping and mopping floors, vacuuming carpet, tidying up server station, taking out trash, or checking and cleaning bathroom” (0/100, minimal); “Remove dishes and glasses from tables or counters, and take them to kitchen for cleaning” (0/100, minimal); “Roll silverware, set up food stations, or set up dining areas to prepare for the next shift or for large parties” (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 “Waiters and Waitresses” do about AI?

Start from the ledger rather than the headline: 5% of this job's weighted core work is exposed, and roughly 95% 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 Waiters and Waitresses 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 25 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

US dataswitch to UK

Waiters and Waitresses

collecting payments from customers, serving food or beverages and performing cleaning duties. If that's your week, this page is about your job.

The honest answer

AI changes the edges of this job, not the middle: collecting payments from customers is work software can't reach.

Your move: what you can actually do about this ↓

Where the work sits, by task weight

shifting to AI: 5%
changing shape: 0%
staying human: 95%

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

Whole-job exposure score 8 out of 100 (5–14 allowing for uncertainty): minimal exposure, across 25 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 waiters and waitresses is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.

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

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

What shifts is preparing checks that itemize and total meal costs and sales taxes: the overhead at the edges, not the middle you trained for.

Your week, as this page understands it

Take orders and serve food and beverages to patrons at tables in dining establishment. The job title says “waiters” or “waitresses”: officially one job, two names. The real job is the part underneath: collecting payments from customers. 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 waiters and waitresses is not one task. It is 25 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is collecting payments from customers, and the ledger below shows exactly why.

Where the work sits, by task weight

shifting to AI: 5%
changing shape: 0%
staying human: 95%

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

How we know this

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

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

Your job, task by task

These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.

2 tasks scored differently between repeat runs, so their range on this page is wider. We would rather show the wobble than hide it.
4 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.

Shifting to AI

1 task

Tasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.

Preparing checks that itemize and total meal costs and sales taxes
This is reading one thing and writing another: checks in, a record out. That is the shape today's tools are built for.
importance 5 · Core
Source: “Prepare checks that itemize and total meal costs and sales taxes.” (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: Itemizing a bill and adding sales tax is arithmetic that registers and software handle.
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.

Changing shape

0 tasks

Tasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.

Nothing in this job’s scored task list landed in this group. That is the measurement, not an editorial choice, and it is worth knowing either way.

Staying human

24 tasks

Tasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.

Writing patrons' food orders on order slips
This work happens in the physical world: patrons' food orders, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Write patrons' food orders on order slips, memorize orders, or enter orders into computers for transmittal to kitchen staff.” (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: Writing up an order is simple recording, but it starts with a diner talking to someone standing at the table.
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.
Checking with customers to ensure that they are enjoying their meals
This work happens in the physical world: customers, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Check with customers to ensure that they are enjoying their meals, and take action to correct any problems.” (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: Checking in on diners mid-meal and putting things right depends on reading the table in person.
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.
Taking orders from patrons for food or beverages
This work happens in the physical world: orders, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Take orders from patrons for food or beverages.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (2–16 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Taking a food or drink order means being at the table listening to the people there.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Collecting payments from customers
This work happens in the physical world: payments, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Collect payments from customers.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–7 allowing for uncertainty): minimal exposure, high confidence, and it moved between repeat runs, so the range is widened.
Why it sits in this group: the same decision, made over and over; work that happens in the physical world.
The rating behind it: Taking payment at a counter is hands-on till work.
The five ratings: output a model can produce 1/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Removing dishes and glasses from tables or counters
This work happens in the physical world: dishes, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Remove dishes and glasses from tables or counters, and take them to kitchen for cleaning.” (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: Clearing dishes and glasses to the kitchen 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 1/4.
Cleaning tables or counters after patrons have finished dining
This work happens in the physical world: tables, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Clean tables or counters after patrons have finished dining.” (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: Clearing and wiping tables and counters 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 1/4.
Presenting menus to patrons and answering questions about menu items
This work happens in the physical world: menus, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Present menus to patrons and answer questions about menu items, making recommendations upon request.” (O*NET task statement)
How this row was scored
Exposure score: 9 out of 100 (2–16 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: work that happens in the physical world; the value is that a specific person does it.
The rating behind it: Menu questions and recommendations are well-documented information, but they are asked and answered at the table.
The five ratings: output a model can produce 2/4 · needs a body in a room 3/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Performing cleaning duties, such as sweeping and mopping floors, vacuuming carpet
This work happens in the physical world: duties, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Perform cleaning duties, such as sweeping and mopping floors, vacuuming carpet, tidying up server station, taking out trash, or checking and cleaning bathroom.” (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: Sweeping, mopping, vacuuming, taking out trash and checking restrooms 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 1/4.
Stocking service areas with supplies
This work happens in the physical world: service areas, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Stock service areas with supplies such as coffee, food, tableware, and linens.” (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: Restocking service areas with supplies means carrying and placing them.
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 15 tasks

Assisting host or hostess by answering phones to take reservations or to-go orders
staying human
This work happens in the physical world: host, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Assist host or hostess by answering phones to take reservations or to-go orders, and by greeting, seating, and thanking guests.” (O*NET task statement)
How this row was scored
Exposure score: 26 out of 100 (19–33 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the 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: Phone reservations and to-go orders are easily handled remotely, but greeting and seating guests happens in person.
The five ratings: output a model can produce 3/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Providing guests with information about local areas
staying human
This work happens in the physical world: guests, in a real place. Software cannot follow it there.
importance 3 · Core
Source: “Provide guests with information about local areas, including directions.” (O*NET task statement)
How this row was scored
Exposure score: 21 out of 100 (14–28 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: Local directions and tips are exactly the sort of public information software gives well, though guests ask in person.
The five ratings: output a model can produce 4/4 · 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 4/4.
Explaining how various menu items
staying human
This work happens in the physical world: how various menu items, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Explain how various menu items are prepared, describing ingredients and cooking methods.” (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: Explaining ingredients and cooking methods is well-documented information, though it is delivered face to face.
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.
Describing and recommending wines to customers
staying human
This work happens in the physical world: wines, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Describe and recommend wines to customers.” (O*NET task statement)
How this row was scored
Exposure score: 13 out of 100 (6–20 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: Wine knowledge is widely documented, but the recommendation is made in conversation at the table.
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 2/4 · how much data exists 3/4.
Informing customers of daily specials
staying human
This work happens in the physical world: customers of daily specials, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Inform customers of daily specials.” (O*NET task statement)
How this row was scored
Exposure score: 11 out of 100 (1–21 allowing for uncertainty): minimal exposure, medium confidence, and it moved between repeat runs, so the range is widened.
Why it sits in this group: mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Telling diners about the day's specials is simple information, delivered in person at the table.
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.
Checking patrons' identification to ensure that they meet minimum age requirements for consumption of alcoholic beverages
staying human
This work happens in the physical world: patrons' identification, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Check patrons' identification to ensure that they meet minimum age requirements for consumption of alcoholic beverages.” (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; someone qualified has to answer for it.
The rating behind it: Checking a photo ID against the person holding it has to happen at the table.
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 2/4.
Serving food or beverages
staying human
This work happens in the physical world: food, in a real place. Software cannot follow it there.
importance 5 · Core
Source: “Serve food or beverages to patrons, and prepare or serve specialty dishes at tables as required.” (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 and drink to a table is physical service.
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 1/4.
Preparing hot, cold and mixed drinks for patrons and chill bottles of wine
staying human
This work happens in the physical world: hot, cold and mixed drinks, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Prepare hot, cold, and mixed drinks for patrons, and chill bottles of wine.” (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: Making hot, cold and mixed drinks and chilling wine is hands-on bar work.
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 0/4 · how much data exists 2/4.
Preparing tables for meals, including setting up items, linens, silverware and glassware
staying human
This work happens in the physical world: tables, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Prepare tables for meals, including setting up items such as linens, silverware, and glassware.” (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: Laying tables with linen, silverware and glassware is manual setup.
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.
Performing food preparation duties, such as preparing salads, appetizers and cold dishes, portioning desserts and brewing coffee
staying human
This work happens in the physical world: food preparation duties, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Perform food preparation duties, such as preparing salads, appetizers, and cold dishes, portioning desserts, and brewing coffee.” (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: Preparing salads, appetizers, desserts and coffee 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 2/4.
Garnishing and decorating dishes in preparation for serving
staying human
This work happens in the physical world: dishes, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Garnish and decorate dishes in preparation for serving.” (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: Garnishing and plating dishes means handling food 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 1/4.
Filling salt, pepper, sugar, cream, condiment and napkin containers
staying human
This work happens in the physical world: salt, pepper, sugar, cream, condiment and napkin containers, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Fill salt, pepper, sugar, cream, condiment, and napkin containers.” (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: Refilling condiment, sugar and napkin containers is manual 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 1/4.
Escorting customers to their tables
staying human
This work happens in the physical world: customers, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Escort customers to their 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: Walking guests to their table is done in person.
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 1/4.
Bring wine selections to tables with appropriate glasses
staying human
This work happens in the physical world: wine selections, in a real place. Software cannot follow it there.
importance 4 · Supplemental
Source: “Bring wine selections to tables with appropriate glasses, and pour the wines for customers.” (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: Bringing wine and glasses to the table and pouring is physical service.
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.
Rolling silverware
staying human
This work happens in the physical world: silverware, in a real place. Software cannot follow it there.
importance 4 · Core
Source: “Roll silverware, set up food stations, or set up dining areas to prepare for the next shift or for large parties.” (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: Rolling silverware and setting up stations and dining areas is manual 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 1/4.

What this job pays, and how many people do it

Median pay: $35,230a 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: 2,270,910in 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: checks in, a record out. The rows above are exactly that shape: preparing checks that itemize and total meal costs and sales taxes. What it cannot do is be there in the room, and that is still where payments 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: collecting payments from customers is the middle of this job, and the evidence on this page says it stays with a person.

So, given all that: 5% of this job's task weight sits in rows the software is already learning, 0% in rows that change shape rather than disappear, and 95% in rows it is nowhere near. That is the position, measured across 25 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. Preparing checks that itemize and total meal costs and sales taxes 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 checks, 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 patrons' food orders 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 writing patrons' food orders on order slips. 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 waiters and waitresses (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was hosts and hostesses, restaurant, lounge, and coffee shop: only about 12% of its durable work is work you already do, it pays 11.4% less and there are far fewer of those jobs than of yours. And on the numbers you do not need one. This job scores 8/100 here, with only 5% of the task list in the top band, and “write patrons' food orders on order slips, memorize orders, or enter orders…” 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.

Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already present menus to patrons and answer questions about menu items, making recommendations upon…, and their equivalent is to provide guests with menus. Across both published task lists that is about 12% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 12% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. It is a pay cut, in those words: $31,200 against your $35,230, 11.4% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice. And it is a narrow door: about 432,690 of those jobs against 2,270,910 of yours (OEWS May 2025), 19% as many seats.
Look at that job’s page anyway →
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 remove dishes and glasses from tables or counters, and take them to kitchen…, and their equivalent is to carry food, dishes, trays, or silverware from kitchens or supply departments to serving…. Across both published task lists that is about 12% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 12% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. And it is a narrow door: about 542,750 of those jobs against 2,270,910 of yours (OEWS May 2025), 24% as many seats.
Look at that job’s page anyway →
Food Servers, Nonrestaurant
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already remove dishes and glasses from tables or counters, and take them to kitchen…, and their equivalent is to clean or sterilize dishes, kitchen utensils, equipment, or facilities. Across both published task lists that is about 11% of the durable work in that job.
Why I am not recommending it: You would be starting most of it from nothing: about 11% of the durable work in that job is work you do today, and the rest you would be learning while the bills carried on. And it is a narrow door: about 293,900 of those jobs against 2,270,910 of yours (OEWS May 2025), 13% as many seats.
Look at that job’s page anyway →

What I’d stop worrying about

A friend tells you what not to spend fear on. This is that list.

The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 5% of its task weight, across 25 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 collecting payments from customers 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 Waiters and waitresses 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 Waiters and waitresses. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.

Your route through this

Where to go next, and what it costs

Free, and complete

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

Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
American Job Center locator
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
- CareerOneStop local help hub
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
CareerOneStop
Free

Why there is no community here

Collab365, who build this site, run paid Spaces for a small number of subjects, and none of them is built for this job. We are not going to point you at the nearest one and call it a fit.

So the free services listed on this page are the whole answer, and it is the same answer we would give a friend.

Noted, and thank you. We’ll email you if a Space for waiters / waitresses launches. Nothing else.

That did not look like an email address, so nothing was saved. Have another go below.

We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.

No Space for waiters / waitresses yet. Should there be one?

Collab365 Spaces is built by the same people as this site. We find the problems that AI and automation are creating inside one kind of work, then solve them as short courses, briefings and Blueprints. Each Space is the community too, so the research and the people doing your job are in the same place.

What a Space actually is, in full

Collab365 launches new communities where the need is real. If one for waiters / waitresses existed, with researched problems, courses and people in the same boat, would you want in?

Your email

We use your email address for one thing: to tell you if a Space for waiters / waitresses launches. We never sell it, never use it for unrelated marketing, and every email has a one-click unsubscribe. Our privacy policy has the full version.

This unlocks nothing. Every figure, every row and every step on this page is already yours, whether you fill this in or not.

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 Waiters and Waitresses?: Not as a job, but it is already doing parts of the work. Across the 25 official task statements scored for Waiters and Waitresses (United States, SOC 35-3031), 5% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 8 out of 100 (range 5–14, 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 “Waiters and Waitresses” can AI already do?: The highest-scoring tasks in release 2026-q4.1 are: “Prepare checks that itemize and total meal costs and sales taxes” (69/100, high); “Assist host or hostess by answering phones to take reservations or to-go orders, and by greeting, seating, and thanking guests” (26/100, low); “Provide guests with information about local areas, including directions” (21/100, low). 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 “Waiters and Waitresses” stay human?: About 95% 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: “Perform cleaning duties, such as sweeping and mopping floors, vacuuming carpet, tidying up server station, taking out trash, or checking and cleaning bathroom” (0/100, minimal); “Remove dishes and glasses from tables or counters, and take them to kitchen for cleaning” (0/100, minimal); “Roll silverware, set up food stations, or set up dining areas to prepare for the next shift or for large parties” (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 “Waiters and Waitresses” do about AI?: Start from the ledger rather than the headline: 5% of this job's weighted core work is exposed, and roughly 95% 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 Waiters and Waitresses 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 25 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.

Where these numbers come from

About the data on this page

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

Task statements: onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
Task weights: onet-db (im-rt)
Scores: Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
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.

How we score a job Download this release Look up another job

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}
}

Permalink

https://futureproof.collab365.com/data/2026-q4.1

Data as of release 2026-q4.1, published 2026-08-05. Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.