Hotel, Motel, and Resort Desk Clerks
Food Preparation & ServiceAI exposure
- Data source: BLSPublished: 2026-08
Very high· relative
LowFour relative bandsVery highGroup-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
- Data source: AnthropicPublished: 2026-03
0.172
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.51
0.09Range of values carried here0.70Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
ISCO-08 unit group — every occupation sharing the code gets this value
Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 17% score at or above this value.
What kind of figure this source publishes
The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.
Task-level exposure
Show 14 hidden tasks| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Answer inquiries pertaining to hotel services, guest registration, and travel directions, or make recommendations regarding shopping, dining, or entertainment.43-4081 | 0.016791.0 | —0 |
Arrange tours, taxis, or restaurant reservations for customers.43-4081 | 0.00179.0 | 0.00286.3 |
Make and confirm reservations. | —0 | 0.028966.6 |
Greet, register, and assign rooms to guests of hotels or motels. | —0 | 0.005713.2 |
Record guest comments or complaints, referring customers to managers as necessary. | —0 | 0.004310.0 |
Keep records of room availability and guests' accounts, manually or using computers. | —0 | 0.00173.9 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Post charges, such as those for rooms, food, liquor, or telephone calls, to ledgers, manually or by using computers.O*NET Task ID 2617 | 1.0 |
Transmit and receive messages, using telephones or telephone switchboards.O*NET Task ID 2618 | 1.0 |
Record guest comments or complaints, referring customers to managers as necessary.O*NET Task ID 2622 | 1.0 |
Date-stamp, sort, and rack incoming mail and messages.O*NET Task ID 2626 | 1.0 |
Greet, register, and assign rooms to guests of hotels or motels.O*NET Task ID 2610 | 0.5 |
Verify customers' credit, and establish how the customer will pay for the accommodation.O*NET Task ID 2611 | 0.5 |
Keep records of room availability and guests' accounts, manually or using computers.O*NET Task ID 2612 | 0.5 |
Compute bills, collect payments, and make change for guests.O*NET Task ID 2613 | 0.5 |
Review accounts and charges with guests during the check out process.O*NET Task ID 2616 | 0.5 |
Contact housekeeping or maintenance staff when guests report problems.O*NET Task ID 2619 | 0.5 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page