The Task Exposure Indexv2026.Q3
Occupation · SOC 35-3031.00 · Job Zone 2

AI exposure: Waiters and Waitresses

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

Reading this score

computed

At 23.2% of weighted task load, Waiters and Waitresses sits at the 44th percentile, below the point where a job's centre of gravity has moved. 67.7% of what this role does is untouched, meaning current systems cannot produce that work at all, whatever the commercial incentive.

What holds the line here is embodiment. Across this occupation's 25 tasks it averages 2.28 out of 3, the highest of the five friction dimensions. In plain terms, the work has to happen in physical space. A language model cannot move matter. Until the robotics to do this work is both good enough and cheap enough to deploy widely, capability in software does not reach it.

The most exposed thing this job does is Provide guests with information about local areas, at 86.7%. The least is Bring wine selections to tables with appropriate glasses, and pour the wines for customers, at 0.0%. A gap of 86.7% between two parts of the same job is the reason this index publishes at task level. An occupation-wide number would have hidden both.

Within food preparation and serving occupations, this one is more exposed than most. The median across the 16 roles in the group is 16.1%, and only 4 of them score higher than this. Occupational families are not uniform, and the spread inside them is often wider than the gap between them.

What would move this score. Of 25 tasks, 7 are currently banded exposed, 0 assisted and 18 untouched. For that distribution to shift materially would take robotics cheap and reliable enough to deploy at scale, not a better language model. The score is re-computed every quarter against a fresh capability reference, and the change is published rather than quietly applied.

Where the score comes from

judged

Every task is scored through the standardised work activities it maps to. These are this occupation’s averages on the six rubric dimensions. Capability is what AI can do; the other five are what stands in the way.

DimensionMeanScale
Capability1.180-4
Embodiment2.280-3
Presence1.220-3
Accountability0.320-3
Context0.940-3
Verification cost0.360-3

Task by task

25 tasks, O*NET 31.0
TaskExposedAssistedUntouchedImportanceBand
Provide guests with information about local areas, including directions.86.7%13.3%0.0%3.45exposed
Collect payments from customers.80.0%20.0%0.0%4.89exposed
Prepare checks that itemize and total meal costs and sales taxes.80.0%20.0%0.0%4.71exposed
Write patrons' food orders on order slips, memorize orders, or enter orders into computers for transmittal to kitchen staff.55.0%20.0%25.0%4.85exposed
Take orders from patrons for food or beverages.55.0%20.0%25.0%4.77exposed
Check with customers to ensure that they are enjoying their meals, and take action to correct any problems.50.0%25.0%25.0%4.79exposed
Assist host or hostess by answering phones to take reservations or to-go orders, and by greeting, seating, and thanking guests.45.8%16.7%37.5%4.12exposed
Present menus to patrons and answer questions about menu items, making recommendations upon request.13.3%11.7%75.0%4.52untouched
Inform customers of daily specials.13.3%11.7%75.0%4.32untouched
Explain how various menu items are prepared, describing ingredients and cooking methods.13.3%11.7%75.0%4.25untouched
Escort customers to their tables.13.3%11.7%75.0%3.78untouched
Describe and recommend wines to customers.13.3%11.7%75.0%3.76untouched
Check patrons' identification to ensure that they meet minimum age requirements for consumption of alcoholic beverages.6.7%18.3%75.0%4.86untouched
Remove dishes and glasses from tables or counters, and take them to kitchen for cleaning.0.0%0.0%100.0%4.70untouched
Clean tables or counters after patrons have finished dining.0.0%0.0%100.0%4.68untouched
Serve food or beverages to patrons, and prepare or serve specialty dishes at tables as required.0.0%0.0%100.0%4.60untouched
Perform cleaning duties, such as sweeping and mopping floors, vacuuming carpet, tidying up server station, taking out trash, or checking and cleaning bathroom.0.0%0.0%100.0%4.54untouched
Prepare tables for meals, including setting up items such as linens, silverware, and glassware.0.0%0.0%100.0%4.37untouched
Stock service areas with supplies such as coffee, food, tableware, and linens.0.0%0.0%100.0%4.37untouched
Roll silverware, set up food stations, or set up dining areas to prepare for the next shift or for large parties.0.0%0.0%100.0%4.36untouched
Fill salt, pepper, sugar, cream, condiment, and napkin containers.0.0%0.0%100.0%4.08untouched
Perform food preparation duties, such as preparing salads, appetizers, and cold dishes, portioning desserts, and brewing coffee.0.0%0.0%100.0%3.99untouched
Prepare hot, cold, and mixed drinks for patrons, and chill bottles of wine.0.0%0.0%100.0%3.99untouched
Garnish and decorate dishes in preparation for serving.0.0%0.0%100.0%4.02untouched
Bring wine selections to tables with appropriate glasses, and pour the wines for customers.0.0%0.0%100.0%3.77untouched

Task text and importance ratings sourced from O*NET 31.0. Shares computed. The occupation score is the importance-weighted mean.

Occupations either side of this one

The four closest scores in the same occupational family, then the four closest anywhere in the index.

Read this carefully. Exposure is not displacement. A high score means current AI systems can produce this work, not that anyone will stop paying a person to do it. Adoption depends on economics, regulation and inertia that this index deliberately does not model. How the score is built.

What this means in practice

Most of this work is not reachable by current systems, so the immediate pressure is on the administrative edges of the role rather than its core: the scheduling, the reporting, the written records. That is where time is recovered.