Share and intensity of work current AI systems can materially affect.
SOC 35-3031
Waiters and Waitresses AI displacement risk
Tablets and QR ordering handle order capture, and robotic runners appear in some chains. Carrying meals, reading tables, upselling, dietary judgment, and turning a meal into an experience remain physical and social work robots do not do.
Likely potential for exposed tasks to move to software after workflow integration.
Counter-service formats automate ordering fastest. Full-service dining keeps servers because hospitality is the product, though technology shrinks section sizes and changes the job mix.
Distribution
Where Waiters and Waitresses sits across 620 tracked roles
Displacement pressure 30 — higher than 48% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
25 O*NET task statements matched to SOC 35-3031. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $35,230 (May 2025, US national). The latest BLS row matched SOC 35-3031.
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 Waiters and Waitresses
SOC 35-3031 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 30/100 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 Waiters and Waitresses
The current evidence import matched 25 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 tasks25
SOC35-3031
- Core task / ID 2276
Collect payments from customers.
- Core task / ID 2275
Check patrons' identification to ensure that they meet minimum age requirements for consumption of alcoholic beverages.
- Core task / ID 2277
Write patrons' food orders on order slips, memorize orders, or enter orders into computers for transmittal to kitchen staff.
- Core task / ID 2279
Check with customers to ensure that they are enjoying their meals, and take action to correct any problems.
- Core task / ID 2278
Take orders from patrons for food or beverages.
- Core task / ID 2281
Prepare checks that itemize and total meal costs and sales taxes.
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
Take and transmit orders
Exposure 68, automation 50%, augmentation 20%.
O*NET evidence: Take orders from patrons for food or beverages. (ID 2278)
Serve food and beverages
Exposure 18, automation 8%, augmentation 16%.
O*NET evidence: Serve food or beverages to patrons, and prepare or serve specialty dishes at tables as ... (ID 2280)
Check on diners and fix problems
Exposure 26, automation 8%, augmentation 36%.
Recommend menu items
Exposure 34, automation 12%, augmentation 44%.
O*NET evidence: Present menus to patrons and answer questions about menu items, making recommendations ... (ID 2283)
TaskExposureAutomationAugmentation
Take and transmit orders6850%20%
Serve food and beverages188%16%
Check on diners and fix problems268%36%
Recommend menu items3412%44%
Transition pathways
Adjacent moves that preserve existing skills
adjacent role
ModerateShift Supervisor
Training horizon: 1-3 months. Skill overlap 80. Wage preservation signal 122.
- Own floor coordination
- Train new servers
- Handle escalated guest issues
adjacent role
ModerateCatering Coordinator
Training horizon: 3-6 months. Skill overlap 62. Wage preservation signal 130.
- Plan event service logistics
- Build client proposals
- Coordinate staffing for events
Comparison guides
Compare the next move before you commit
1-3 months
Waiters and Waitresses to Shift Supervisor
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Waiters and Waitresses into Shift Supervisor.
3-6 months
Waiters and Waitresses to Catering Coordinator
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Waiters and Waitresses into Catering Coordinator.
What the AI risk score means for Waiters and Waitresses
The displacement pressure score for Waiters and Waitresses is 30. 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. Take and transmit orders carries 50% automation pressure, while Recommend menu items carries 44% 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: $35,230 (May 2025, US national). Employment context: Very large in-person service role. 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
- Ordering technology shrinks sections
- Full-service hospitality stays human
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Waiters and Waitresses, 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.
Service recovery
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.
Menu knowledge
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.
Upselling
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.
Physical stamina
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 Shift Supervisor, such as own floor coordination.
- By 90 days, compare internal openings and external postings for Shift Supervisor or Catering Coordinator and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Waiters and Waitresses
Will AI replace Waiters and Waitresses?
Tablets and QR ordering handle order capture, and robotic runners appear in some chains. Carrying meals, reading tables, upselling, dietary judgment, and turning a meal into an experience remain physical and social work robots do not do. 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 Waiters and Waitresses work are most exposed to AI?
Take and transmit orders and Recommend menu items show the strongest automation pressure in this model. Recommend menu items and Check on diners and fix problems are better treated as AI-augmented work.
What should Waiters and Waitresses learn next?
Start with Service recovery, Menu knowledge, Upselling. The most practical adjacent paths in this model are Shift Supervisor and Catering Coordinator.
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