Food Service Managers
In short: today's best AI models could do about 50% of this job's working time (projected from METR's April 2026 measurement and Epoch's November 2026 index) if the work were set up for them, and about 52% by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 48% of the time, stays with people.
Plan, direct, or coordinate activities of an organization or department that serves food and beverages.
238,430 people do this job in the US. $69,390 a year.
Reviewed on September 30, 2026: what each task is made of, and what handing it over would ask.
A typical 40-hour week
- AI could take on1 h
- A person checks AI's work19 h
- Desk work AI can't do yet1 h
- Stays with people19 h
Food Service Managers, now: of a 40-hour week, AI could take on 1 hours, 19 hours become checking its work, 1 hours are desk work it can't do yet, and 19 hours stay with people.
The largest pieces: Set service standards (about 4 h, a person checks); Keep required government records (about 2 h, a person checks); Plan menus (about 2 h, a person checks).
Estimates for a typical week of this job, if progress keeps its long-run pace, counting work the best models finish four times in five. Every task is below.
of the working time. Now is projected from METR's April 2026 measurement and Epoch's November 2026 index.
- 38%
- 14%
- 5%
- 23%
- 20%
of the work: Anthropic's measure of what people already bring to Claude at work (2025). It counts help with part of a task, so it can be higher than the share within reach.
On the chosen pace and reliability. Estimate.
Share of the working time within reach, now to 2030
Change assumptionsHide assumptionsLong-run pace, four in five
What each choice means: , , , , . Every pace starts from .
At this date and pace, models finish tasks up to 2.4 work days long four times in five.
What handing it over would take, now
The 50% within reach splits by what a model would need. The dates, pace and reliability follow the controls above. Estimates.
of the working time. The material can be handed to a model and the result handed back.
of the working time. The model has to be connected to the system the work lives in.
is conversation short enough by length. This site leaves conversation with people, so it is not in the total.
How far this has come
With GPT-4, measured in March 2023, 0% of this job's working time was within reach at . With Claude 3.7 Sonnet, measured in February 2025, 0%. With Claude Mythos Preview (early), measured in April 2026, 23%. Today's estimate: 50%.
A backcast: today's task data with the best model METR had measured by each date. It shows how fast the models have moved, not what anyone forecast at the time.
Measured use over time
Of the Claude.ai conversations Anthropic sampled and tied to a work task, 0.14% went to this job's tasks in May 2026, the 133rd largest of the 447 jobs that showed up at all; 2 of its 28 tasks showed up. Within each of Anthropic's methods, never across a change: 0.023% to 0.01% (released February and March 2025); 0.0072% to 0.013% (August 2025 to February 2026); 0.14% to 0.14% (April to May 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.023% | 425 of 708 | 4 of 28 |
| Released March 2025 | 0.01% | 505 of 703 | 2 of 28 |
| The week of Aug 4, 2025 (released September 2025) | 0.0072% | 494 of 655 | 3 of 28 |
| The week of Nov 13, 2025 (released January 2026) | 0.011% | 470 of 672 | 4 of 28 |
| The week of Feb 5, 2026 (released March 2026) | 0.013% | 455 of 677 | 5 of 28 |
| April 2026 (released June 2026) | 0.14% | 136 of 434 | 2 of 28 |
| May 2026 (released June 2026) | 0.14% | 133 of 447 | 2 of 28 |
Measured, not estimated: Anthropic's samples of Claude.ai conversations, each tied by a model to an O*NET task, from the Anthropic Economic Index. It says where Claude's use goes, not how much of this job's working time it covers, so it is not on the same scale as the reach above. The releases differ in their sample, their classifier and the smallest share they report (the June 2026 file rounds each task to a hundredth of a percent, so a smaller task reads as none), which is why a change of method starts a new line. At least 96% of each older release's task-tied use matched this site's tasks, and all of the newest. Claude.ai only, so use through other tools and other companies' models is not in it. Every job ranked.
Robots and the physical work
Anthropic rates 35% of this job's working time as physical work. For 31% of the working time, a robot has been shown doing most of the task somewhere: 25% in a purpose-built cell, 6% in a structured facility. 0.37 of 3, the 356th highest of the 923 jobs.
- Not physical 65%
- No robot shown 4%
- In a purpose-built cell 25%
- In a structured facility 6%
Anthropic's rating, not this site's estimate: Claude Opus 5, searching the web, rated each physical O*NET task by the kind of setting in which a robot it could cite (deployed, sold or demonstrated) does most of the task, weighted by its own estimate of each task's share of the time (What work can robots do?, September 30, 2026, data CC BY 4.0; every job ranked). A robot shown somewhere is not a robot in your building: cost, setup and safety decide that. The share within reach on this page still counts no body work. The robot behind each rated task is under the task below.
What changes for the people in this job
From the task-by-task review, by the end of 2028 on the long-run pace at four in five. It says which parts of the work stay with people and which change, not whether anyone keeps a job: that depends on decisions no data set can see.
What stays with people
48% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Check food preparation and presentation, the in-person work, 6%
- Train, hire and evaluate staff, the in-person work, 5%
- Help cook and serve, the hands-on work, 5%
- Set service standards, the in-person work, 4%
- Create dishes and recipes, the hands-on work, 3%
- Assign kitchen staff, the in-person work, 3%
What becomes checking the machine
47% of the working time: desk work a model could do by then, in tasks where a mistake costs something or a licensed person signs. A person checks it. Another 4% is desk work that is small if wrong, which can be handed over with lighter checks.
- Set service standards, costly if wrong, 9%
- Keep required government records, severe if wrong, 4%
- Plan menus, costly if wrong, 4%
- Set nutritional standards, costly if wrong, 4%
- Improve service and safety, costly if wrong, 3%
- Watch budgets and payroll, costly if wrong, 3%
What to learn
- Reviewing work you did not do yourself. The tasks under What becomes checking the machine, where a mistake costs something, stay a person's to check: spotting what a draft gets wrong is a skill of its own.
- Briefing a model and checking its drafts. About 38% of the time is documents and data, the part models reach first: writing a clear brief, giving the right material, and checking what comes back.
- The conversations. About 28% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- The hands-on craft. About 20% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- Running AI inside the business systems. About 14% of the time is work inside business systems, which a model can do only once it is connected: knowing the systems well enough to set that up and watch it.
Your own version of this job: read it against your job description. The headline is free; the full personal report, every task with its timeline and what to learn, is $19 (a sample).
The tasks
Sorted by . 28 tasks from .
| Task | ||||
|---|---|---|---|---|
| Establish standards for personnel performance and customer service.Standards written and explained to the staff.Done daily by most who do it. | 13% | 2.5 h50 min to 6.7 h | Documents 70%, in person 30% | 67%Desk part half by now |
| Organize and direct worker training programs, resolve personnel problems, hire new staff, and evaluate employee performance in dining and lodging facilities.Mostly in person with the staff, with the paperwork.Done more than weekly by most who do it. | 7% | 3.3 h50 min to 6.7 h | In person 60%, documents 20%, conversation 20% | 18%Desk part half by now |
| Monitor food preparation methods, portion sizes, and garnishing and presentation of food to ensure that food is prepared and presented in an acceptable manner.In the kitchen watching the plates go out.Done daily by most who do it. | 6% | 50 min13 min to 1.7 h | In person 60%, hands-on 40% | Stays with people |
| Establish and enforce nutritional standards for dining establishments, based on accepted industry standards.Standards written and enforced in the kitchen.Done daily by most who do it. | 6% | 5 h1.7 h to 2.1 work days | Documents 70%, in person 30% | 51%Desk part half by now |
| Review work procedures and operational problems to determine ways to improve service, performance, or safety.Watching the operation and writing up the changes.Done daily by most who do it. | 6% | 2.1 h50 min to 4.2 h | Documents 60%, in person 40% | 60%Desk part half by now |
| Create specialty dishes and develop recipes to be used in dining facilities.Cooking trials in the kitchen and the recipe written.Done daily by most who do it. | 6% | 5 h1.7 h to 1 work day | Hands-on 60%, documents 40% | 37%Desk part half by now |
| Maintain food and equipment inventories, and keep inventory records.Counting the stock and entering it.Done more than weekly by most who do it. Robots: in a purpose-built cell, Corvus One (Corvus Robotics) (deployed). Anthropic's rating. | 5% | 1.3 h25 min to 3.3 h | Hands-on 60%, systems 40% | 40%Desk part half by now |
| Perform some food preparation or service tasks, such as cooking, clearing tables, and serving food and drinks when necessary.Cooking, clearing and serving by hand.Done daily by most who do it. Robots: in a purpose-built cell, CA-1 (Circus SE) (deployed). Anthropic's rating. | 5% | 38 min13 min to 2.5 h | Hands-on 100% | Stays with people |
| Keep records required by government agencies regarding sanitation or food subsidies.Entries in the logs.Done daily by most who do it. | 4% | 1.3 h25 min to 2.5 h | Systems 100% | 100%Half by now |
| Plan menus and food utilization, based on anticipated number of guests, nutritional value, palatability, popularity, and costs.Working the menu out at the desk.Done daily by most who do it. | 4% | 3.3 h1.3 h to 6.7 h | Documents 100% | 98%Half by now |
| Monitor compliance with health and fire regulations regarding food preparation and serving, and building maintenance in lodging and dining facilities.Walking the kitchen and the building against the rules.Done daily by most who do it. Robots: no robot shown. Anthropic's rating. | 4% | 1.3 h25 min to 2.5 h | Hands-on 60%, documents 40% | 40%Desk part half by now |
| Schedule staff hours and assign duties.The rota built in the system.Done more than weekly by most who do it. | 4% | 1.3 h25 min to 3.3 h | Systems 60%, documents 40% | 99%Half by now |
Within reach, per task: the share of the task's time whose instances are short enough for a model to finish at the chosen date, pace and reliability. Half by: the date half of that time comes within reach. Made of: how the task's time splits among five kinds of doing. Only the documents and systems parts can count as within reach, so a task that is 30% documents can be at most 30% within reach.
Jobs that share skills with this one
From O*NET's related occupations. Within reach now and by the end of 2028, long-run pace, four in five.
Where this job works
The industry groups that employ the most of it (BLS, May 2025).
Where the most of these jobs are
The metro areas with the most people in it (BLS, May 2025).
Questions about this job
- How much of the work of Food Service Managers is within reach of AI now?
- An estimated 50% of the working time is within reach of AI models now and 52% by the end of 2028, on METR's long-run pace at four in five. Within reach means a model could finish work that long at that reliability. It is not a forecast of jobs lost or of savings.
- Which tasks of Food Service Managers come within reach first?
- Weighted by the time each takes, the tasks with the most within reach now (projected from METR's April 2026 measurement and Epoch's November 2026 index) are: Set service standards (67% of the task's time now); Keep required government records (100% of the task's time now); and Plan menus (98% of the task's time now).
- Which tasks of Food Service Managers stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Check food preparation and presentation; Help cook and serve; and Assign kitchen staff. Together such tasks are about 21% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Food Service Managers learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are reviewing work you did not do yourself; briefing a model and checking its drafts; and the conversations. By the end of 2028 on the long-run pace at four in five, about 48% of the working time stays with people, 47% becomes checking a model's work and 4% could be handed over with lighter checks. It says which parts of the work change, never whether anyone keeps a job.
- Do people already use AI for the work of Food Service Managers?
- In the Claude.ai conversations Anthropic sampled for May 2026 and tied to a work task, 0.14% went to this job's tasks, the 133rd largest share of the 447 jobs that showed up; 2 of its 28 tasks appeared. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 0%. These are measures of use, not of how much of the work AI could finish, and they cover Claude only.
- Can robots do the physical work of Food Service Managers?
- Anthropic's robot exposure rating (September 2026) finds 35% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 31% of the working time, 6% in a structured facility. Its index is 0.37 of 3. One example: Servi Plus ('Jane') for "greet and seat guests". A robot shown somewhere is not one in every workplace; this site's own estimates still count no physical work as within reach.
- How is this estimated?
- From the 28 tasks O*NET lists for the occupation, each with an estimated length checked against GDPval and a share of the working time, reviewed one task at a time on September 30, 2026 for what each is made of (documents, business systems, conversation, in person, hands-on), and METR's measurements of how long a task the best AI models can finish, carried forward to the date from the latest, Claude Mythos Preview (early) in April 2026. Data checked November 10, 2026.
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