Share and intensity of work current AI systems can materially affect.
SOC 35-9031
Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop AI displacement risk
Reservation apps and waitlist software automate the booking ledger that hosts once managed. Greeting, table-read judgment during rushes, guest recovery, and managing the door's human flow keep the role present in full-service dining.
Likely potential for exposed tasks to move to software after workflow integration.
The app took the clipboard, not the greeting. High-volume restaurants keep hosts because seating judgment — balancing sections, reading walk-in flow, handling unhappy waits — is real-time human coordination.
Distribution
Where Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop sits across 620 tracked roles
Displacement pressure 44 — higher than 73% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
20 O*NET task statements matched to SOC 35-9031. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $31,200 (May 2025, US national). The latest BLS row matched SOC 35-9031.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop
SOC 35-9031 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 44/100 role score and are not an occupation forecast.
+1.1% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +1.6% GDP and 3.9% unemployment.
+5.9% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +8.3% GDP and 4.6% unemployment.
+33.6% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop
The current evidence import matched 20 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks20
SOC35-9031
- Core task / ID 2296
Provide guests with menus.
- Core task / ID 2297
Greet guests and seat them at tables or in waiting areas.
- Core task / ID 2302
Maintain contact with kitchen staff, management, serving staff, and customers to ensure that dining details are handled properly and customers' concerns are addressed.
- Core task / ID 18749
Assign patrons to tables suitable for their needs and according to rotation so that servers receive an appropriate number of seatings.
- Core task / ID 18750
Speak with patrons to ensure satisfaction with food and service, to respond to complaints, or to make conversation.
- Core task / ID 18752
Inspect dining and serving areas to ensure cleanliness and proper setup.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Manage reservations and waitlists
Exposure 72, automation 56%, augmentation 30%.
Greet and seat guests
Exposure 28, automation 12%, augmentation 30%.
O*NET evidence: Greet guests and seat them at tables or in waiting areas. (ID 2297)
Coordinate with kitchen and servers
Exposure 34, automation 15%, augmentation 46%.
Handle guest concerns at the door
Exposure 26, automation 9%, augmentation 42%.
TaskExposureAutomationAugmentation
Manage reservations and waitlists7256%30%
Greet and seat guests2812%30%
Coordinate with kitchen and servers3415%46%
Handle guest concerns at the door269%42%
Transition pathways
Adjacent moves that preserve existing skills
adjacent role
ModerateServer
Training horizon: 1-2 months. Skill overlap 72. Wage preservation signal 108.
- Learn menu and service steps
- Practice upselling
- Handle full table sections
role redesign
ModerateFront-of-House Supervisor
Training horizon: 2-4 months. Skill overlap 76. Wage preservation signal 124.
- Own door and floor coordination
- Track seating metrics
- Train hosts and servers
Comparison guides
Compare the next move before you commit
1-2 months
Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop to Server
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop into Server.
2-4 months
Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop to Front-of-House Supervisor
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop into Front-of-House Supervisor.
What the AI risk score means for Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop
The displacement pressure score for Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop is 44. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Manage reservations and waitlists carries 56% automation pressure, while Coordinate with kitchen and servers carries 46% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $31,200 (May 2025, US national). Employment context: Front-of-house role with reservation-app pressure. Typical education: No formal educational credential.
Wage vulnerability is 78, while transition feasibility is 64. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Moderate displacement pressure
- Apps absorb the booking ledger
- Rush-time floor judgment persists
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Guest service
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Seating judgment
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Reservation systems
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Communication
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Server, such as learn menu and service steps.
- By 90 days, compare internal openings and external postings for Server or Front-of-House Supervisor and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop
Will AI replace Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop?
Reservation apps and waitlist software automate the booking ledger that hosts once managed. Greeting, table-read judgment during rushes, guest recovery, and managing the door's human flow keep the role present in full-service dining. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop work are most exposed to AI?
Manage reservations and waitlists and Coordinate with kitchen and servers show the strongest automation pressure in this model. Coordinate with kitchen and servers and Handle guest concerns at the door are better treated as AI-augmented work.
What should Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop learn next?
Start with Guest service, Seating judgment, Reservation systems. The most practical adjacent paths in this model are Server and Front-of-House Supervisor.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics