Hotel, Motel, and Resort Desk Clerks
In short: today's best AI models could do about 45% 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 the same by the end of 2028 if progress keeps its long-run pace. The conversations, in-person and hands-on work, about 55% of the time, stays with people.
Accommodate hotel, motel, and resort patrons by registering and assigning rooms to guests, issuing room keys or cards, transmitting and receiving messages, keeping records of occupied rooms and guests' accounts, making and confirming reservations, and presenting statements to and collecting payments from departing guests.
261,420 people do this job in the US. $35,070 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 on9 h
- A person checks AI's work9 h
- Desk work AI can't do yet0 h
- Stays with people22 h
Hotel, Motel, and Resort Desk Clerks, now: of a 40-hour week, AI could take on 9 hours, 9 hours become checking its work, 0 hours are desk work it can't do yet, and 22 hours stay with people.
The largest pieces: Run the night audit (about 7 h, a person checks); Keep room and account records (about 2 h); Make reservations (about 2 h).
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.
- 7%
- 38%
- 10%
- 22%
- 24%
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 45% 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, 13%. With Claude Mythos Preview (early), measured in April 2026, 42%. Today's estimate: 45%.
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, no reportable share went to this job's tasks in May 2026; 0 of its 20 tasks showed up. Within each of Anthropic's methods, never across a change: 0.022% to 0.015% (released February and March 2025); 0.02% to 0.033% (August 2025 to February 2026).
Each release
| Conversations | Share | Rank | Tasks seen |
|---|---|---|---|
| Released February 2025 | 0.022% | 432 of 708 | 4 of 20 |
| Released March 2025 | 0.015% | 458 of 703 | 4 of 20 |
| The week of Aug 4, 2025 (released September 2025) | 0.02% | 370 of 655 | 2 of 20 |
| The week of Nov 13, 2025 (released January 2026) | 0.028% | 334 of 672 | 4 of 20 |
| The week of Feb 5, 2026 (released March 2026) | 0.033% | 313 of 677 | 3 of 20 |
| April 2026 (released June 2026) | none | n/a | 0 of 20 |
| May 2026 (released June 2026) | none | n/a | 0 of 20 |
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 7% of this job's working time as physical work. For 6% of the working time, a robot has been shown doing most of the task somewhere: 4% in a purpose-built cell, 1% in a structured facility. 0.07 of 3, the 544th highest of the 923 jobs.
- Not physical 93%
- No robot shown 1%
- In a purpose-built cell 4%
- In a structured facility 1%
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
55% of the working time: the conversation, in-person and hands-on parts of the tasks.
- Tidy the lobby, the hands-on work, 13%
- Set up breakfast service, the hands-on work, 7%
- Check guests in, the in-person work, 5%
- Answer guests' questions, the in-person work, 4%
- Schedule and supervise desk staff, the in-person work, 4%
- Check guests out, the in-person work, 4%
What becomes checking the machine
23% 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 21% is desk work that is small if wrong, which can be handed over with lighter checks.
- Run the night audit, costly if wrong, 17%
- Check guests out, costly if wrong, 2%
- Log guest complaints, costly if wrong, 2%
- Verify payment at check in, costly if wrong, 2%
What to learn
- Running AI inside the business systems. About 38% 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.
- The conversations. About 31% of the time is conversation and in-person work, which this site leaves with people: listening, explaining, persuading, calming.
- 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.
- The hands-on craft. About 24% of the time is hands-on work, which no model here does; robots are rated separately on this page.
- Dealing with people outside the organization. Customers, patients, suppliers or the public: trust with them is built by a person.
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 . 20 tasks from .
| Task | ||||
|---|---|---|---|---|
| Perform bookkeeping activities, such as balancing accounts and conducting nightly audits.Balancing the day's accounts in the system.Done daily by most who do it. | 17% | 1.3 h38 min to 2.5 h | Systems 60%, documents 40% | 100%Half by now |
| Clean and maintain lobby and common areas, such as restocking supplies and watering plants.Cleaning and restocking by hand.Done several times a day by most who do it. Robots: in a purpose-built cell, DYNA-1 (Dyna Robotics) (sold). Anthropic's rating. | 13% | 25 min8 min to 50 min | Hands-on 100% | Stays with people |
| Greet, register, and assign rooms to guests of hotels or motels.The welcome at the desk and the registration entered.Done hourly or more by most who do it. | 9% | 8 min4 min to 17 min | In person 60%, systems 40% | 40%Desk part half by now |
| Prepare for basic food service, such as setting up continental breakfast or coffee and tea supplies.Setting out the breakfast by hand.Done daily by most who do it.Robots: no robot shown. Anthropic's rating. | 7% | 25 min13 min to 50 min | Hands-on 100% | Stays with people |
| Make and confirm reservations.Bookings entered, with the guest on the phone.Done several times a day by most who do it. AI observed on this task. | 6% | 8 min4 min to 21 min | Systems 60%, conversation 40% | 60%Desk part half by now |
| Keep records of room availability and guests' accounts, manually or using computers.Entries in the property system.Done daily by most who do it. | 6% | 13 min4 min to 25 min | Systems 100% | 100%Half by now |
| Plan, schedule or supervise the work of other employees.The rota built in the system and the shift led in person.Done daily by most who do it. | 6% | 38 min17 min to 1.7 h | In person 60%, systems 40% | 40%Desk part half by now |
| Review accounts and charges with guests during the check out process.Going over the bill with the guest at the desk.Done hourly or more by most who do it. | 6% | 8 min3 min to 21 min | In person 60%, systems 40% | 40%Desk part half by now |
| Compute bills, collect payments, and make change for guests.The bill in the system and the payment taken at the desk.Done hourly or more by most who do it. | 6% | 7 min3 min to 13 min | Systems 50%, in person 30%, hands-on 20% | 50%Desk part half by now |
| Record guest comments or complaints, referring customers to managers as necessary.Hearing the guest out and entering the note.Done daily by most who do it. | 4% | 8 min4 min to 21 min | Systems 50%, in person 50% | 50%Desk part half by now |
| Answer inquiries pertaining to hotel services, guest registration, and travel directions, or make recommendations regarding shopping, dining, or entertainment.At the desk and on the phone.Done daily by most who do it. AI observed on this task. | 4% | 7 min3 min to 13 min | In person 70%, conversation 30% | Stays with people |
| Transmit and receive messages, using telephones or telephone switchboards.Live calls.Done hourly or more by most who do it. | 3% | 4 min2 min to 8 min | Conversation 100% | Stays with people |
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 Hotel, Motel, and Resort Desk Clerks is within reach of AI now?
- An estimated 45% of the working time is within reach of AI models now and 45% 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 Hotel, Motel, and Resort Desk Clerks 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: Run the night audit (100% of the task's time now); Keep room and account records (100% of the task's time now); and Make reservations (60% of the task's time now).
- Which tasks of Hotel, Motel, and Resort Desk Clerks stay with people?
- Tasks that are people work or body work are not counted as within reach at any date. The largest: Tidy the lobby; Set up breakfast service; and Answer guests' questions. Together such tasks are about 34% of the working time, and the people and body parts of the other tasks stay with people too.
- What should Hotel, Motel, and Resort Desk Clerks learn as AI takes on more of the work?
- By the job's own task-by-task review, the first things to learn are running AI inside the business systems; the conversations; and reviewing work you did not do yourself. By the end of 2028 on the long-run pace at four in five, about 55% of the working time stays with people, 23% becomes checking a model's work and 21% 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 Hotel, Motel, and Resort Desk Clerks?
- In the Claude.ai conversations Anthropic sampled for May 2026, no reportable share went to this job's tasks. Anthropic's labor market measure puts the share of the job's tasks seen in real Claude use at work at 17%. 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 Hotel, Motel, and Resort Desk Clerks?
- Anthropic's robot exposure rating (September 2026) finds 7% of the working time is physical work, and that a robot has been shown doing most of the task, somewhere, for 6% of the working time, 1% in a structured facility. Its index is 0.07 of 3. One example: KIOSK Information Systems hotel guest check-in kiosk for "issue keys". 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 20 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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