# Waiters and Waitresses

> Take orders and serve food and beverages to patrons at tables in dining establishment.

- **SOC code:** 35-3031.00
- **Canonical URL:** https://singulariki.com/roles/role-35-3031-00
- **Also known as:** Food Server, Server, Waiter, Waitress, Banquet Server, Busser, Cocktail Server, Food Runner
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Collect payments from customers.
- Check patrons' identification to ensure that they meet minimum age requirements for consumption of alcoholic beverages.
- Write patrons' food orders on order slips, memorize orders, or enter orders into computers for transmittal to kitchen staff.
- Check with customers to ensure that they are enjoying their meals, and take action to correct any problems.
- Take orders from patrons for food or beverages.
- Prepare checks that itemize and total meal costs and sales taxes.
- Remove dishes and glasses from tables or counters, and take them to kitchen for cleaning.
- Clean tables or counters after patrons have finished dining.
- Serve food or beverages to patrons, and prepare or serve specialty dishes at tables as required.
- Perform cleaning duties, such as sweeping and mopping floors, vacuuming carpet, tidying up server station, taking out trash, or checking and cleaning bathroom.
- Present menus to patrons and answer questions about menu items, making recommendations upon request.
- Prepare tables for meals, including setting up items such as linens, silverware, and glassware.

**Emerging tasks** (O*NET):
- Check with customers to see if they want to apply any rewards to their purchase.
- Perform routine tasks, such as refilling syrups, sanitizer bottles, and other essential supplies.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Customer and Personal Service _(knowledge)_
- Service Orientation _(transferable_skill)_
- Oral Comprehension _(ability)_
- Oral Expression _(ability)_
- Speech Recognition _(ability)_
- Speech Clarity _(ability)_
- English Language _(knowledge)_
- Active Listening _(essential_skill)_
- Speaking _(essential_skill)_
- Social Perceptiveness _(transferable_skill)_
- Sales and Marketing _(knowledge)_
- Time Sharing _(ability)_

**Skills in demand:**
- Speech Recognition _(Specialized Skill)_
- English Language _(Common Skill)_
- Active Listening _(Common Skill)_
- Social Perceptiveness _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Facebook _(Specialized Skill)_
- Deductive Reasoning _(Common Skill)_
- Finger Dexterity _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Time Management _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Inductive Reasoning _(Common Skill)_

**Tools & technology:**
- Facebook _(hot technology)_
- Blink
- Compris Advanced Manager's Workstation
- Compris software
- Hospitality Control Solutions Aloha Point-of-Sale
- Intuit QuickBooks Point of Sale
- MICROS Systems HSI Profits Series
- NCR Advanced Checkout Solution
- NCR NeighborhoodPOS
- The General Store

## AI exposure & outlook

- **AI task-overlap index:** 44th percentile (Moderate) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 20th percentile (Low) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 31st percentile (Low) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 83rd percentile (High) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 86th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** no — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** -0.7% growth (Declining); 456.7k annual openings; 2,329.7k → 2,313.5k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $33,760; 2,302,690 employed.

## How people actually use AI here

Anthropic Economic Index — measured AI conversations mapped to this occupation's tasks:

- **Automation vs augmentation:** 43% automation, 43% augmentation (usage-weighted).
- **Autonomy median:** 3.5 (higher = AI acts more independently).
- **Dominant collaboration mode:** directive.

**Tasks most handed to AI here:**
- Describe and recommend wines to customers. _(1.4% of measured AI use; directive)_
- Provide guests with information about local areas, including giving directions. _(0.5% of measured AI use; task iteration)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me describe and recommend wines to customers.
- Help me provide guests with information about local areas, including giving directions.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-35-3031-00_