Elementary occupations · code 9411

Fast food preparers

A kiosk can take the order. Someone still has to make the burger.

Taking and serving orders is mostly out of AI's reach. It barely touches the other eight tasks, including reheating food and cleaning the cooking area.

What they actually do (and what else they get called)

Fast food preparers prepare and cook to order a limited range of foods or beverages that involve simple preparation processes and a small number of ingredients. They may take orders from customers and serve at counters or tables. Tasks include - (a) preparing simple or pre-prepared foods and beverages such as sandwiches, hamburgers, pizzas, fish and chips, salads and coffee; (b) washing, cutting, measuring and mixing foods for cooking; (c) operating large-volume single-process cooking equipment such as grills, deep-fat fryers or griddles; (d) re-heating pre-prepared food; (e) cleaning food preparation areas, cooking surfaces and utensils; (f) taking and serving food and beverage orders in eating places that specialize in fast service and carry-out food; (g) ordering and taking delivery of fast food ingredients; (h) maintaining sanitation, health and safety standards in work areas; (i) verifying that prepared food meets requirements for quality and quantity. Examples of the occupations classified here: - Fast food cook - Hamburger maker - Pizza maker - Short order cook Some related occupations classified elsewhere: - Chef - 3434 - Cook - 5120 - Food service counter attendant - 5246 Note Jobs that combine preparation of simple food items with taking orders, serving clients and calculating or receiving payment should be classified in Unit Group 5246: Food Service Counter Attendants, unless client service is an incidental component of the work.

pizzaiolo
quick service restaurant crew member

Job titles from the EU's ESCO list. Search for any of them on the jobs page and you'll end up here.

82 / 100

Strongly holds up against AI

Most of this job still needs an actual human. AI would mostly be making the tea.

Not the odds you keep your job. It's how much of the everyday work still needs a person, out of 100.

← Robot's turf Human's turf →

The ILO's official verdict: Not Exposed

123 of 427 for holding up against AI

Why this score?

The ILO rated each of this job's 9 tasks for how much of it AI could do. They average 18.3 out of 100, so it holds up at 100 − 18.3 = 81.7.

That's the whole sum. No black box, no secret sauce: you can check it against the ILO's study. ILO group “Not Exposed”: None of this job's tasks scored high enough to count.

This job rates 41.5 out of 100 for how hands-on it is. The score above only looks at AI tools, not robots, but for work this far from the physical that matters less.

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Man v Bot · compare

Who holds up better?

Fast food preparers 82

Go on, try your partner's job. Or your sibling's. We won't tell.

Man v Bot

Where the bot helps, and where the human wins

Bot

What AI takes

The routine stuff. You may not miss it.

Orders through kiosks and apps.
Stock ordering and deliveries.
Food safety records.

Man

What people still do better

Judgement, trust and skilled hands. Hard to download.

Cooking and assembling food fast.
Keeping the kitchen clean and safe.
Handling customers and problems at the counter.

Both

What's arriving

New work that only exists because the AI does.

Working with kiosks and delivery apps.
Cooking robots in some kitchens.
Delivery-only kitchens.

Written by a human, from the ILO's task ratings below. Yes, we see the irony. Read it as one person's take.

Meet your two rivals

They're not the same machine. AI software can only reach the parts of a job done on a screen or in words. A chatbot can't climb a ladder. A robot might, one day, if it doesn't fall off.

The AI

Lives in a screen. Never had to fix a boiler.

Threat level: Low

27 could do without you

It could take over very little of the work on its own. It could help with 56 of 100.

The robot

The wildcard. Has hands, still working out stairs.

How much it matters here: Some

42 how hands-on

A fair part of this job is hands-on, so robots would be needed for that part, not just software.

So could a robot do the physical part?

Less than half of this job is hands-on. Robots still matter, but for a smaller part of the work, so we don't sort it any further.

Robots hate fiddly work in awkward spaces. On that measure, this job scores 35 out of 100. The higher it is, the longer the robot waits.

42 How
hands-on

35 How fiddly
and cramped

Kind of hands-on work Less than half hands-on

The small print

They aren't a robot score, and nothing on this page is. Nobody measures how far robots have got well enough to put a number on it. These are the closest thing we have: how hands-on the job is, and how much fiddly, cramped work it takes, which is what Frey and Osborne found hardest for machines.

From O*NET, averaged across 2 US jobs .

finger skill 34.9 · hand skill 41.1 · cramped spaces 29.3 · averaged · rule: hands-on under 50

estimated O*NET 31.0 BLS occupational crosswalk chain ISCO-08 to 2010 SOC (Aug 2012, rev. Jun 2015); 2010 to 2018 SOC (Nov 2017); O*NET-SOC 2019 taxonomy data 0.8-observed-use measures chosen by Frey & Osborne (2017) · the grouping is a sorting, not a measurement

The job, task by task

Every job is really a bundle of tasks. Here's this one unpacked, with how much of each AI could do, out of 100. The score is the average, and averages can hide a lot.

AI's favourite task here scores 25, against an average of 18. The bigger the gap, the more lopsided the job: some of it wide open to AI, some of it barely touched.

25
Taking and serving food and beverage orders in eating places that specialize in fast service and carry-out food

Low
24
Ordering and taking delivery of fast food ingredients

Very Low
23
Maintaining sanitation, health, and safety standards in work areas

Very Low
21
Verifying that prepared food meets requirements for quality and quantity.

Very Low
17
Operating large-volume single-process cooking equipment such as grills, deep-fat fryers or griddles

Very Low
16
Preparing simple or pre-prepared foods and beverages such as sandwiches, hamburgers, pizzas, fish and chips, salads and coffee

Very Low
14
Washing, cutting, measuring and mixing foods for cooking

Very Low
14
Cleaning food preparation areas, cooking surfaces and utensils

Very Low
13
Re-heating pre-prepared food

Very Low

9 tasks · the High to Very Low labels are the ILO's

from the source Working Paper 140, Generative AI and Jobs 2025 index data 0.8-observed-use

Sidekick or replacement?

A tool that makes you faster is a very different thing from one that does the task while you watch. Jobs and Skills Australia measured the two separately, which is handy.

AI helps you: 56
AI does it for you: 27

Sidekick wins, by 29 points.

How good a match is this?

One Australian job matches this one closely, so this is about as reliable as it gets here.

estimated Our Gen AI Transition (Gen AI Capacity Study) September 2025 detailed data release data 0.8-observed-use matched to Australian jobs 8511

So, should fast food workers be worried?

Short answer: not really. Put the kettle on.

Parts of it are moving. Taking and serving orders is the most exposed task, rated Low; cooking, cleaning and checking food rate Very Low. Self-service kiosks and apps are taking orders, and some kitchens are trialling cooking robots, so the counter changes faster than the kitchen.

Worth remembering: we score tasks, not jobs. When AI takes on a task, the job usually reshuffles around it rather than vanishing.

Where this job could lead

None of the new jobs we've written up start from here yet. Could be you're the first. See the jobs AI is creating →

How are the neighbours doing?

There are 33 jobs in the “Elementary occupations” family, averaging 85. This one is below the family average.

Pit this against any other job →