Share and intensity of work current AI systems can materially affect.
SOC 35-3023
Fast Food and Counter Workers AI displacement risk
Kiosk ordering, drive-through voice systems, scheduling tools, and prep automation can reduce routine counter work. Reliability, shift leadership, food safety, and customer recovery remain more resilient.
Likely potential for exposed tasks to move to software after workflow integration.
Physical constraints and franchise economics slow some automation, but wage vulnerability and high turnover make redesign pressure persistent.
Distribution
Where Fast Food and Counter Workers sits across 620 tracked roles
Displacement pressure 62 — higher than 89% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-05-02. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
30 O*NET task statements matched to SOC 35-3023. 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 35-3023.
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 Fast Food and Counter Workers
SOC 35-3023 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 62/100 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 Fast Food and Counter Workers
The current evidence import matched 30 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 tasks30
SOC35-3023
- Core task / ID 23100
Accept payment from customers, and make change as necessary.
- Core task / ID 23110
Serve customers in eating places that specialize in fast service and inexpensive carry-out food.
- Core task / ID 23104
Request and record customer orders, and compute bills, using cash registers, multi-counting machines, or pencil and paper.
- Core task / ID 23103
Balance receipts and payments in cash registers.
- Core task / ID 23099
Communicate with customers regarding orders, comments, and complaints.
- Core task / ID 23105
Serve food, beverages, or desserts to customers in such settings as take-out counters of restaurants or lunchrooms, business or industrial establishments, hotel rooms, and cars.
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 routine orders
Exposure 76, automation 62%, augmentation 16%.
O*NET evidence: Take customers' orders and write ordered items on tickets, giving ticket stubs to custo... (ID 23119)
Prepare standard items
Exposure 42, automation 34%, augmentation 18%.
O*NET evidence: Monitor and order supplies or food items, and restock as necessary to maintain inventory. (ID 23108)
Handle rush exceptions
Exposure 28, automation 14%, augmentation 30%.
Follow food safety steps
Exposure 36, automation 24%, augmentation 34%.
O*NET evidence: Prepare daily food items, and cook simple foods and beverages, such as sandwiches, sala... (ID 23106)
TaskExposureAutomationAugmentation
Take routine orders7662%16%
Prepare standard items4234%18%
Handle rush exceptions2814%30%
Follow food safety steps3624%34%
Transition pathways
Adjacent moves that preserve existing skills
adjacent role
HighShift Supervisor
Training horizon: 1-3 months. Skill overlap 80. Wage preservation signal 120.
- Own shift handoff notes
- Track service bottlenecks
- Practice coaching new hires
industry switch
HighFacilities or Inventory Assistant
Training horizon: 2-5 months. Skill overlap 58. Wage preservation signal 118.
- Document stock movement
- Learn basic maintenance logs
- Build safety checklist habits
Comparison guides
Compare the next move before you commit
1-3 months
Fast Food and Counter Workers to Shift Supervisor
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Fast Food and Counter Workers into Shift Supervisor.
2-5 months
Fast Food and Counter Workers to Facilities or Inventory Assistant
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Fast Food and Counter Workers into Facilities or Inventory Assistant.
What the AI risk score means for Fast Food and Counter Workers
The displacement pressure score for Fast Food and Counter Workers is 62. 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 routine orders carries 62% automation pressure, while Follow food safety steps carries 34% 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: Large service workforce with ordering and scheduling exposure. Typical education: No formal educational credential.
Wage vulnerability is 88, 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.
- Automation pressure in ordering
- High wage vulnerability
- Supervisor bridge remains practical
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Fast Food and Counter Workers, 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.
Shift reliability
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.
Customer 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.
Workflow discipline
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 shift handoff notes.
- By 90 days, compare internal openings and external postings for Shift Supervisor or Facilities or Inventory Assistant and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Fast Food and Counter Workers
Will AI replace Fast Food and Counter Workers?
Kiosk ordering, drive-through voice systems, scheduling tools, and prep automation can reduce routine counter work. Reliability, shift leadership, food safety, and customer recovery remain more resilient. 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 Fast Food and Counter Workers work are most exposed to AI?
Take routine orders and Prepare standard items show the strongest automation pressure in this model. Follow food safety steps and Handle rush exceptions are better treated as AI-augmented work.
What should Fast Food and Counter Workers learn next?
Start with Shift reliability, Food safety, Customer recovery. The most practical adjacent paths in this model are Shift Supervisor and Facilities or Inventory Assistant.
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