Share and intensity of work current AI systems can materially affect.
SOC 35-3041
Food Servers, Nonrestaurant AI displacement risk
Cafeteria lines, tray delivery, and counter service face self-service kiosks and tray-return automation. Institutional settings — hospitals, schools — keep human servers for special diets, patient needs, and service quality.
Likely potential for exposed tasks to move to software after workflow integration.
Self-service absorbs simple counter transactions fastest. The durable work is institutional service where recipients need assistance: hospital trays, school lunch lines, and care-facility dining.
Distribution
Where Food Servers, Nonrestaurant sits across 620 tracked roles
Displacement pressure 34 — higher than 56% 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.
14 O*NET task statements matched to SOC 35-3041. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $35,360 (May 2025, US national). The latest BLS row matched SOC 35-3041.
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 Food Servers, Nonrestaurant
SOC 35-3041 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 34/100 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 Food Servers, Nonrestaurant
The current evidence import matched 14 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 tasks14
SOC35-3041
- Core task / ID 11152
Place food servings on plates or trays according to orders or instructions.
- Core task / ID 11150
Clean or sterilize dishes, kitchen utensils, equipment, or facilities.
- Core task / ID 11149
Monitor food distribution, ensuring that meals are delivered to the correct recipients and that guidelines, such as those for special diets, are followed.
- Core task / ID 11151
Examine trays to ensure that they contain required items.
- Core task / ID 11153
Load trays with accessories, such as eating utensils, napkins, or condiments.
- Core task / ID 11154
Take food orders and relay orders to kitchens or serving counters so they can be filled.
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
Serve food and beverages to patrons
Exposure 34, automation 22%, augmentation 20%.
O*NET evidence: Prepare food items, such as sandwiches, salads, soups, or beverages. (ID 11157)
Take and relay food orders
Exposure 62, automation 48%, augmentation 24%.
O*NET evidence: Take food orders and relay orders to kitchens or serving counters so they can be filled. (ID 11154)
Monitor special diet delivery
Exposure 36, automation 18%, augmentation 46%.
O*NET evidence: Monitor food distribution, ensuring that meals are delivered to the correct recipients ... (ID 11149)
Clean and stock service areas
Exposure 30, automation 18%, augmentation 24%.
O*NET evidence: Stock service stations with items, such as ice, napkins, or straws. (ID 11155)
TaskExposureAutomationAugmentation
Serve food and beverages to patrons3422%20%
Take and relay food orders6248%24%
Monitor special diet delivery3618%46%
Clean and stock service areas3018%24%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
ModerateDietary Aide Supervisor
Training horizon: 1-3 months. Skill overlap 70. Wage preservation signal 114.
- Own tray-line quality
- Track diet compliance
- Train service staff
adjacent role
ModerateBarista or Counter Lead
Training horizon: 1-2 months. Skill overlap 72. Wage preservation signal 100.
- Build beverage craft skills
- Own customer flow
- Manage station inventory
Comparison guides
Compare the next move before you commit
1-3 months
Food Servers, Nonrestaurant to Dietary Aide Supervisor
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Food Servers, Nonrestaurant into Dietary Aide Supervisor.
1-2 months
Food Servers, Nonrestaurant to Barista or Counter Lead
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Food Servers, Nonrestaurant into Barista or Counter Lead.
What the AI risk score means for Food Servers, Nonrestaurant
The displacement pressure score for Food Servers, Nonrestaurant is 34. 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 relay food orders carries 48% automation pressure, while Monitor special diet delivery 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: $35,360 (May 2025, US national). Employment context: Institutional and counter service role with self-service pressure. Typical education: No formal educational credential.
Wage vulnerability is 76, while transition feasibility is 62. 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
- Self-service absorbs counter volume
- Institutional service persists
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Food Servers, Nonrestaurant, 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.
Customer 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.
Food safety
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.
Dietary compliance awareness
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 Dietary Aide Supervisor, such as own tray-line quality.
- By 90 days, compare internal openings and external postings for Dietary Aide Supervisor or Barista or Counter Lead and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Food Servers, Nonrestaurant
Will AI replace Food Servers, Nonrestaurant?
Cafeteria lines, tray delivery, and counter service face self-service kiosks and tray-return automation. Institutional settings — hospitals, schools — keep human servers for special diets, patient needs, and service quality. 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 Food Servers, Nonrestaurant work are most exposed to AI?
Take and relay food orders and Serve food and beverages to patrons show the strongest automation pressure in this model. Monitor special diet delivery and Take and relay food orders are better treated as AI-augmented work.
What should Food Servers, Nonrestaurant learn next?
Start with Customer service, Food safety, Physical stamina. The most practical adjacent paths in this model are Dietary Aide Supervisor and Barista or Counter Lead.
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