Dining Room and Cafeteria Attendants and Bartender Helpers

35-9011.00

Median wage $33,980/yr542,750 employed (US)Rank #721 of 923 scored · top 78% by substitution

Facilitate food service. Clean tables; remove dirty dishes; replace soiled table linens; set tables; replenish supply of clean linens, silverware, glassware, and dishes; supply service bar with food; and serve items such as water, condiments, and coffee to patrons.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution18

Exposure7

Augmentation10

Substitution — the headline: capability discounted by cost, barriers and adoption.

Exposure — technical capability alone, regardless of whether anyone deploys it.

Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.

Tasks on the substitution scale

23 rated tasks, binned by substitution score.

Position among all scored occupations

Distribution of 923 occupation scores; the marker is this occupation.

Tasks with substitution ≥ 70

Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.

Why this score

The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.

Task automatabilityw 35%7

panel mean rating 1.3/5 → substitution pressure 7/100

Technical feasibility todayw 20%6

panel mean rating 1.2/5 → substitution pressure 6/100

Cost vs. human wagew 15%4

panel mean rating 1.2/5 → substitution pressure 4/100

Adoption barriersw 20%inverted — strong barriers lower the score68

panel mean rating 2.3/5 (barrier strength) → substitution pressure 68/100

Sector adoption velocityw 10%4

panel mean rating 1.2/5 → substitution pressure 4/100

Task breakdown (23 tasks)

Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.

Run cash registers.

CI 60–86 · exposure 72 · augmentation 50 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	5/5	Self-checkout and automated POS systems have achieved rapid, deep penetration in food service, retail, and hospitality sectors. Major chains and venues actively deploy these systems; displacement of cashiers is measurable and ongoing in the information and service industries.
Sector adoption velocity	claude-sonnet-5	3/5	Food service and retail have moderate self-checkout adoption in grocery and fast food, but full-service dining and cafeteria settings lag behind due to smaller scale and service-oriented business models.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI-enhanced POS systems assist staff by flagging suspicious transactions, suggesting upsells, and streamlining payment workflows, but the core task is largely automatable rather than augmented. Augmentation is useful at the margins but not transformative for this specific task.
Augmentation potential	claude-sonnet-5	3/5	POS systems with integrated automation (barcode scanning, automatic pricing, digital payment processing) meaningfully speed up cashier work, though the attendant still performs much of the interaction and oversight.
Task automatability	claude-haiku-4-5-20251001	4/5	Cash register operation is highly structured and rule-based: accepting payment, processing transactions, and issuing receipts. Current POS systems and AI-enabled checkout solutions can handle the majority of transactions end-to-end with minimal human intervention, easily meeting the 50% time-saving threshold, though some edge cases (verification of age-restricted items, dispute resolution) may require human oversight.
Task automatability	claude-sonnet-5	3/5	Cash register operations can be handled by self-checkout kiosks and automated POS systems, but full end-to-end replacement requires physical hardware integration, not just software AI.dd Roughly half the task (transaction processing, payment handling) is automatable but customer interaction and cash handling remain manual in many settings.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While customer preference for human interaction and some regulatory requirements around age verification and refunds create minor friction, there are no hard legal barriers preventing cashier automation. Adoption is already widespread, indicating barriers are low.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement exists for cash handling, but cash management, loss prevention, and customer service expectations create some organizational friction against full automation, especially in cafeteria/dining contexts.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	Automated POS and self-checkout infrastructure cost pennies per transaction after amortization, vastly cheaper than paying a human worker (fully loaded wage) to run registers full-time. The cost advantage is at least an order of magnitude.
Cost vs. human wage	claude-sonnet-5	3/5	Self-checkout hardware and software have significant upfront capital and maintenance costs comparable to or sometimes exceeding minimum-wage labor costs for this narrow task, though at scale in high-volume settings it can be cheaper.
Technical feasibility today	claude-haiku-4-5-20251001	5/5	Automated and self-checkout systems are deployed at scale globally in restaurants, cafes, and retail; major POS providers offer AI-enhanced fraud detection and payment processing. These systems reliably perform cash register transactions in production across thousands of establishments daily.
Technical feasibility today	claude-sonnet-5	4/5	Self-checkout and automated POS systems are widely deployed in retail and food service today, reliably processing transactions at scale, though dining/cafeteria contexts still often use human cashiers for smaller-scale or tableside service.

Wash glasses or other serving equipment at bars.

CI 10–35 · exposure 13 · augmentation 13 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Adoption of automated dishwashing in bars and cafeterias remains limited; most small-to-mid-size establishments continue hand-washing or use standard commercial dishwashers, with minimal active displacement or agent-based solutions in this specific task.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and hospitality are low-digitization, physically-oriented sectors with minimal AI/robotics adoption for manual cleaning tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI/robotics offer minimal real-time assistance to a human washing glasses; the task is largely manual and sequential, with little room for algorithmic or ML enhancement of the human's performance in the act.
Augmentation potential	claude-sonnet-5	1/5	AI tools offer essentially no assistance to a human physically washing glasses at a bar.
Task automatability	claude-haiku-4-5-20251001	2/5	While industrial dishwashers can wash glasses at scale, this task requires human judgment about placement, sorting, and handling delicate glassware—current robotics cannot reliably end-to-end perform the task at equal quality with 50% time savings on its own.
Task automatability	claude-sonnet-5	1/5	This is a physical manual task requiring dexterity to handle fragile glassware and equipment; no off-the-shelf AI (software or robotic) can perform this end-to-end today.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Health and food-safety regulations (e.g., NSF standards for dishwashing) create some friction, and bars often prefer human oversight of glassware quality; however, no legal mandate strictly forbids automation, and adoption depends mainly on cost-benefit and customer preference.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human wash glasses, but physical environment constraints (wet, cluttered bar spaces, fragile items) create practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Industrial dishwashers and robotic wash systems have high capital costs and integration expenses; the all-in cost (equipment, installation, maintenance, oversight) typically exceeds the loaded wage of a low-skill bartender helper performing hand-washing.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic substitute deployed at scale, so any hypothetical automation (specialized robotics) would be far more costly than low-wage human labor performing this task.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Specialized robotic dishwashing systems exist for commercial kitchens but are narrowly scoped; no deployed product reliably handles the variability of bar glassware (stemware, tumblers, irregular shapes) and bar-specific workflow integration at production scale.
Technical feasibility today	claude-sonnet-5	1/5	No deployed products perform bar glass-washing autonomously in production; commercial dishwashing machines exist but require human loading/unloading and are not AI-driven automation of this specific task.

Mix and prepare flavors for mixed drinks.

CI 14–29 · exposure 20 · augmentation 25 · importance 3.6/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Hospitality and food service sectors remain heavily resistant to automation in this task; most bars and cafeterias are small operations with low digitization. Adoption of robotic bartenders is experimental and confined to a handful of high-end venues; mainstream penetration remains negligible.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and hospitality are low-digitization, physically-oriented sectors with minimal AI/robotics adoption for manual food and drink preparation tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI offers minimal assistance to human bartenders on this task; current systems cannot reliably suggest flavor adjustments, scale recipes in real time, or enhance sensory decision-making. The task remains primarily human-centered with no transformative augmentation pathway evident today.
Augmentation potential	claude-sonnet-5	2/5	AI could provide recipe suggestions or ratios via an app, but offers little real-time assistance for the physical act of mixing and tasting drinks.
Task automatability	claude-haiku-4-5-20251001	2/5	While liquid measurement and basic sequencing could theoretically be automated, the task requires sensory judgment (taste, aroma, appearance) and real-time adjustments that current AI systems cannot perform reliably end-to-end. Robotic bartenders exist but require significant human oversight and intervention, falling well short of the 50% time-saving threshold.
Task automatability	claude-sonnet-5	2/5	Physical drink-mixing requires manual dexterity, pouring, tasting, and real-time adjustment that current AI systems cannot perform without robotic embodiment, which is not standard or deployed for this role.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Strong barriers include health code compliance, liability for incorrect drink preparation, customer preference for human bartenders, and organizational friction in installing and maintaining robotic systems in hospitality venues. No legal licensing requirement exists, but regulatory oversight of food and beverage handling and customer safety creates meaningful friction.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement specific to mixing drinks for this task, though alcohol service often requires certification (e.g., TIPS) which could apply to human staff, and customer preference for human service adds mild friction.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Current robotic bartending systems are capital-intensive, require specialized infrastructure, maintenance, and human oversight that exceed the loaded wage cost of a bartender helper for years of operation. The labor cost advantage does not materialize given equipment and integration expenses.
Cost vs. human wage	claude-sonnet-5	1/5	Robotic bartending systems capable of this task are far more expensive to install and maintain than paying an entry-level bartender helper's wage.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed product reliably performs flavor mixing and drink preparation autonomously at production scale in real bars. Prototype robotic systems exist but remain narrow in scope, error-prone on custom orders, and heavily dependent on human correction and supervision.
Technical feasibility today	claude-sonnet-5	1/5	No mainstream deployed product performs physical drink mixing and flavor preparation at scale; robotic bartending exists only as novelty installations, not standard practice in dining/cafeteria settings.

Replenish supplies of food or equipment at steam tables or service bars.

CI 15–24 · exposure 8 · augmentation 13 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Foodservice is a fragmented, labor-intensive sector with high turnover and low digitization. Capital investment in specialized robotics remains rare in this domain, and most establishments rely on manual labor for supply replenishment.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and hospitality are low-digitization, physical-labor-heavy sectors with minimal AI/robotic adoption for tasks like restocking supplies.
Augmentation potential	claude-haiku-4-5-20251001	2/5	Automated inventory tracking and alerts could help attendants know when to replenish, but current AI offers minimal real-time assistance during the physical act of restocking itself. Minimal augmentation benefit exists beyond basic monitoring systems.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer no meaningful assistance for the physical act of replenishing food or equipment supplies at service stations.
Task automatability	claude-haiku-4-5-20251001	2/5	Replenishing food and equipment requires physical manipulation in dynamic environments, real-time inventory judgment, and adaptation to varying conditions. Current robotics cannot reliably perform end-to-end restocking of steam tables with the speed and safety margins humans achieve, though automated inventory monitoring could assist.
Task automatability	claude-sonnet-5	1/5	This is a physical manual task requiring locating, carrying, and placing food/equipment items in a dynamic environment; no off-the-shelf AI system can perform this end-to-end today.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Health and safety regulations require safe food handling and sanitation, and there is customer expectation of human oversight in food service contexts, but no strict licensing barrier prevents automation. Liability concerns around food contamination and equipment damage create moderate friction.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human for this task, but practical barriers like kitchen environment complexity, food safety handling, and lack of viable robotic replenishment systems create moderate friction.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized robotic systems capable of safe food handling and restocking would cost tens of thousands of dollars plus integration and maintenance, far exceeding the loaded hourly wage of an attendant performing this repetitive task.
Cost vs. human wage	claude-sonnet-5	1/5	Physical robotic solutions for this task do not exist at commercial scale, so any hypothetical AI/robotic system would be far more expensive than the low-wage human labor currently performing it.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial product reliably performs unstructured food replenishment at steam tables and service bars in production environments. Specialized robotic arms exist in controlled settings but are not integrated into real foodservice operations for this task.
Technical feasibility today	claude-sonnet-5	1/5	There are no deployed products (robotic or otherwise) that reliably replenish steam table or service bar supplies in production food service settings; this remains research-stage robotics at best.

Clean and polish counters, shelves, walls, furniture, or equipment in food service areas or other areas of restaurants and mop or vacuum floors.

CI 10–29 · exposure 13 · augmentation 13 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Adoption of robotic cleaning in food service remains minimal and confined to large chains or experimental pilots; the sector is labor-intensive, low-margin, and has historically relied on human workers, with little production deployment of autonomous cleaning systems.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and janitorial work are low-digitization, physically demanding sectors with minimal AI/robotic adoption; this is a laggard sector for automation.
Augmentation potential	claude-haiku-4-5-20251001	2/5	Powered cleaning tools (e.g., floor buffers, pressure washers) offer some productivity gain, but current AI systems provide minimal assistive value for this primarily manual, physically dexterous task that requires human judgment about surface type and finish quality.
Augmentation potential	claude-sonnet-5	1/5	Current AI offers essentially no meaningful assistance to a human performing manual cleaning and polishing tasks in a restaurant setting.
Task automatability	claude-haiku-4-5-20251001	2/5	While robotic cleaning systems exist in research and limited deployment, they cannot reliably handle the spatial variability, obstacle navigation, and quality control required for counters, shelves, walls, furniture, and floors in real restaurant environments at 50% time savings versus a human. Current systems lack the dexterity and adaptability to polish and clean heterogeneous surfaces in cluttered food service spaces.
Task automatability	claude-sonnet-5	1/5	This is a physical cleaning task requiring manipulation of tools across varied surfaces and environments; no current AI system can perform this end-to-end without robotic embodiment, which is not generally available for this context.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Food service establishments must maintain health and safety standards, and restaurant operators prefer human staff for flexibility and customer-facing presence; however, no explicit licensing requirement or regulatory barrier prevents automation of cleaning tasks themselves.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human for cleaning, but physical environment variability, need for judgment on cleanliness standards, and lack of mature robotics create practical friction.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Purchase, maintenance, and integration of robotic cleaning systems that could partially address this task cost tens of thousands to hundreds of thousands of dollars, far exceeding the all-in wage cost of a part-time or full-time dining room attendant earning $15,000–$30,000 annually.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic substitute performing this full task at scale, so any hypothetical automation would require expensive specialized robotics far costlier than low-wage human labor for this role.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Robotic floor cleaners and vacuums operate in some controlled environments, but deployed products cannot reliably clean and polish counters, shelves, walls, and furniture simultaneously, nor do they integrate well with active food service areas. No production system today performs the full scope of this task reliably at restaurant scale.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product performs restaurant-area cleaning and polishing; commercial cleaning robots exist only for narrow floor-vacuuming in limited settings, not the full task scope described.

Slice and pit fruit used to garnish drinks.

CI 15–24 · exposure 8 · augmentation 13 · importance 3.6/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Food service and hospitality remain low-digitization, labor-intensive sectors with slow AI adoption; most establishments lack the capital investment or operational infrastructure to deploy robotic fruit prep systems.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and hospitality is a low-digitization, physically-oriented sector with minimal AI/robotics adoption for prep tasks like this; robotics adoption in bars is essentially nonexistent.
Augmentation potential	claude-haiku-4-5-20251001	2/5	Current AI offers minimal assistance—perhaps computer vision for ripeness detection or yield planning, but lacks the embodied capability to meaningfully augment a human actually performing the cutting and pitting work itself.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer no meaningful assistance for the physical act of slicing and pitting fruit; this is a manual dexterity task outside typical AI augmentation use cases.
Task automatability	claude-haiku-4-5-20251001	2/5	While computer vision can identify fruit and robotics could theoretically slice and pit, current deployed systems lack the dexterity, speed, and adaptability to handle varied fruit types, ripeness levels, and garnish standards reliably at cost parity with humans. End-to-end automation remains in research/prototype stage.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring dexterity to handle knives, fruit, and precise cutting motions; no off-the-shelf AI system performs this end-to-end today.robotics for this specific task is not commercially deployed.rating reflects near-zero automatability.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few regulatory or licensing barriers exist for automating this task, but food safety oversight, hygiene standards, and practical integration into existing bar workflows create moderate friction to adoption.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but food safety, hygiene standards, and physical workspace constraints in commercial kitchens create some friction against automation beyond simple software substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized robotic systems capable of fruit preparation would cost orders of magnitude more to purchase, integrate, and maintain than paying a helper minimum wage for this task, making AI/automation economically infeasible.
Cost vs. human wage	claude-sonnet-5	1/5	Any hypothetical robotic solution would require expensive specialized hardware (grippers, vision, food-safe design) far exceeding the low wage cost of a human helper performing this simple task.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No commercial products reliably perform fruit slicing and pitting for bartending garnishes in production environments; systems capable of this level of fine manipulation, speed, and quality consistency are not deployed at scale in real bars or cafeterias.
Technical feasibility today	claude-sonnet-5	1/5	No deployed products perform fruit slicing/pitting for drink garnishes in bars or restaurants; this remains a manual kitchen task with no robotic solution in production.

Scrape and stack dirty dishes and carry dishes and other tableware to kitchens for cleaning.

CI 15–15 · exposure 0 · augmentation 0 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Hospitality and food service remain low-digitization, small-firm-dominated sectors with limited AI adoption; physical manipulation tasks in these environments show minimal production-scale automation.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor sector with minimal AI/robotic adoption for manual bussing tasks; this is a laggard sector for automation.
Augmentation potential	claude-haiku-4-5-20251001	1/5	AI systems offer no meaningful assistance for scraping, stacking, and carrying dishes; the task is purely physical and repetitive with no decision-making or information-retrieval component where AI could augment human effort.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no meaningful assistance to a human performing this purely physical, manual clearing and carrying task.
Task automatability	claude-haiku-4-5-20251001	1/5	Scraping, stacking, and transporting physical dishes requires mobile manipulation in unpredictable restaurant environments with fragile items. Current AI robots lack the dexterity, reliability, and cost-effectiveness to perform this end-to-end at 50% time savings compared to human attendants.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring mobile robotics with dexterous manipulation in unstructured environments; no current AI/robotic system performs this end-to-end reliably or cheaply.14 It is not a digital/cognitive task amenable to current AI automation.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While not legally restricted, human contact and immediate on-demand responsiveness are preferred in dining settings; sanitation and food-safety oversight may create mild friction, but no hard licensing barriers exist.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human for this task, but physical environment variability, breakage liability, and lack of viable robotic alternatives create practical friction.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Automated dish handling systems (hardware, integration, maintenance, oversight) are substantially more expensive than employing a minimum-wage dining attendant, even accounting for labor's total loaded cost.
Cost vs. human wage	claude-sonnet-5	1/5	Robotic solutions for this task would require expensive specialized hardware far exceeding the cost of low-wage human labor typically performing this job.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial products reliably perform integrated dish scraping, stacking, and transport in real dining venues. Robotic arms exist for narrow tasks but not for the full workflow at production scale in restaurants.
Technical feasibility today	claude-sonnet-5	1/5	No deployed products in commercial dining settings scrape, stack, and carry dishes; bussing robots remain experimental or extremely limited pilots, not production-reliable systems.

Wipe tables or seats with dampened cloths or replace dirty tablecloths.

CI 15–15 · exposure 0 · augmentation 0 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Dining service remains a low-digitization, physically grounded sector with strong human-contact preferences. Adoption of automation in this specific task is negligible; automation focus is elsewhere (order-taking, cooking prep).
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor sector with minimal AI/robotics adoption for routine cleaning tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	AI offers no meaningful assistance for physical table cleaning or tablecloth replacement; the task is purely manual labor with no decision, information, or communication component that AI could enhance.
Augmentation potential	claude-sonnet-5	1/5	Current AI offers essentially no assistance to a human performing this manual wiping/replacing task.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical dexterity, fine motor control, and navigation of variable table geometry and debris in an unstructured environment. Current AI systems cannot reliably manipulate cloths, navigate physical spaces, or adapt to different table conditions at human speed and quality.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring perception and dexterity to wipe surfaces and replace linens; no off-the-shelf AI system performs this end-to-end today.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While there are no strict legal barriers, health codes and customer expectations for cleanliness create oversight requirements, and customers strongly prefer human service in dining contexts, creating moderate organizational friction.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barriers exist, but the physical environment (varied table sizes, spills, human presence) creates practical friction against automation beyond simple regulation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Robotic systems capable of this task would cost tens of thousands of dollars plus integration and maintenance, far exceeding the loaded wage of a dining attendant ($15–25k annually) and offering no reasonable payback period.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic solution deployed at scale for this task, so any hypothetical robotic system would be far more expensive than a low-wage human worker.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial product reliably performs autonomous table wiping or tablecloth replacement in live dining environments. Robotic manipulation systems exist in research but lack the dexterity, generalization, and cost-effectiveness needed for production restaurant use.
Technical feasibility today	claude-sonnet-5	1/5	No deployed commercial product reliably performs table-wiping or tablecloth-changing in restaurants; this remains a manual task done by humans.

Set tables with clean linens, condiments, or other supplies.

CI 15–15 · exposure 0 · augmentation 0 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Food service and hospitality remain low-digitization sectors with fragmented, small-operator structures; robotic automation of table setting has seen minimal real-world adoption despite decades of potential.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and hospitality are low-digitization, physically-oriented sectors with minimal AI/robotic adoption for manual tasks like table setting.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI offers no meaningful assistance for the physical task of table setting; the task is not amenable to digital augmentation tools or decision support.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no meaningful assistance for this manual, physical task since it involves no cognitive or informational component AI could support.
Task automatability	claude-haiku-4-5-20251001	1/5	Setting tables requires physical manipulation of linens, condiments, and supplies in variable spatial configurations—a task that current robotics cannot reliably perform at human speed and quality without significant environmental constraints and customization.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring picking up and placing physical objects (linens, condiments, utensils) in real space, which current AI systems cannot perform without robotic embodiment far beyond deployed capability.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While there are no strict licensing barriers, the hands-on physical nature and customer-facing presence create modest organizational and customer-preference friction against full automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barrier exists, but practical barriers like physical environment variability, cost of robotics, and lack of mature automation solutions limit substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Even specialized robots capable of limited table-setting tasks cost orders of magnitude more than the hourly wage of a dining attendant, and require significant infrastructure investment and maintenance.
Cost vs. human wage	claude-sonnet-5	1/5	Any robotic solution capable of this would require expensive specialized hardware far exceeding the low wage cost of a human attendant performing this task.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably performs end-to-end table setting in production restaurant or cafeteria environments; this remains a task requiring physical dexterity and adaptability that general-purpose robots lack in real-world settings.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs physical table-setting in restaurants or cafeterias today; robotic manipulation for this remains research-stage or absent entirely.

Clean up spilled food or drink or broken dishes and remove empty bottles and trash.

CI 15–15 · exposure 0 · augmentation 13 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Hospitality and food service sectors show minimal AI/robot adoption for cleanup tasks. Labor remains abundant and cheap in many markets; investment in manipulation robotics is concentrated in manufacturing and logistics, not dining.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and hospitality are low-digitization, physical-labor-heavy sectors with minimal AI/robotics adoption for cleaning tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist marginally (detecting spills via computer vision alerts, optimizing trash collection routes), but the core physical task of cleanup—handling broken glass, wiping surfaces, removing varied debris—offers limited augmentation value without full automation.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer essentially no assistance to a human performing this physical cleanup task.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires mobile manipulation in unstructured environments (varying spill locations, dish fragments, broken glass) with safety-critical hazard detection. Current AI systems lack the embodied dexterity, real-time spatial reasoning, and robust object identification needed to handle fragile items and hazardous materials reliably at scale.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring mobility, dexterity, and perception in unstructured environments; no off-the-shelf AI system performs this end-to-end today.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Health and safety regulations govern food service sanitation, creating some compliance friction, but no hard legal requirement mandates a licensed human perform cleanup. Organizational resistance and customer preference for human service providers add modest barriers.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human, but food-safety and hygiene expectations plus liability for broken glass/spills create some friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized mobile robots capable of even partial cleanup (trash removal, light sweeping) cost tens of thousands of dollars upfront with ongoing maintenance, far exceeding the loaded wage of a dining attendant, especially in high-volume food service.
Cost vs. human wage	claude-sonnet-5	1/5	A general-purpose cleanup robot capable of handling spills, glass shards, and trash removal would cost far more than the low wage paid for this labor-intensive task.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No production systems reliably perform this physical cleanup task autonomously. Robotics prototypes exist but require controlled settings; deployed robots in hospitality remain rare and limited to narrow scenarios like trash removal in structured areas.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product cleans spills, broken dishes, or removes trash and bottles in restaurant/cafeteria settings at commercial scale; relevant robotics remain research or narrow pilot stage.

Maintain adequate supplies of items, such as clean linens, silverware, glassware, dishes, or trays.

CI 15–15 · exposure 0 · augmentation 13 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	This is a low-digitization, physically-rooted service task in hospitality and food service—sectors with historically laggard adoption of automation. Manual supply maintenance remains the norm even in modern establishments.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor-heavy sector with minimal AI/robotics adoption for tasks like restocking supplies.
Augmentation potential	claude-haiku-4-5-20251001	2/5	Simple inventory-tracking software or mobile alerts could help a human attendant prioritize restocking, but AI's role here is narrow and marginal. The task is fundamentally manual and location-dependent, limiting meaningful augmentation potential.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no meaningful assistance for this manual, physical restocking task.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires real-time inventory monitoring, physical movement through spaces, and restocking of diverse items in a service environment. Current AI systems cannot autonomously perform the physical aspects of gathering, organizing, and placing supplies in a dining or bar setting.
Task automatability	claude-sonnet-5	1/5	This is a physical restocking and inventory task requiring manual handling, walking, and stocking of physical items, which current AI systems cannot perform without embodiment (robotics).
Adoption barriers	claude-haiku-4-5-20251001	2/5	Barriers are minimal from a regulatory standpoint, but the physical, embodied nature of the work and practical integration challenges into existing service workflows create moderate friction. Human attendants provide flexibility and adaptability that automated systems cannot yet match.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barriers exist, but the physical, unstructured nature of the environment (kitchens, dining rooms) creates practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The infrastructure cost of robotics capable of handling fragile items (glassware, dishes) and navigating busy service areas would vastly exceed the loaded wage of a dining attendant or helper, making this economically infeasible today.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI-driven substitute for this physical task, so any hypothetical automation (robotics) would be far more expensive than a low-wage human worker performing this task.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably performs end-to-end supply maintenance for dining facilities. While computer vision could theoretically detect low stock levels, the full task—identifying what's needed, retrieving items, and placing them—requires embodied robotics not yet reliable in these dynamic service environments.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product autonomously restocks linens, silverware, or dishware in dining rooms today; this remains purely a human physical labor task.

Fill beverage or ice dispensers.

CI 15–15 · exposure 0 · augmentation 0 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Cafeteria and bar operations are dominated by small, lower-digitization establishments with minimal automation; this task sees no meaningful AI or robotic adoption in real-world settings.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and hospitality are low-digitization, physically-oriented sectors with minimal AI/robotics adoption for manual replenishment tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	AI offers no practical assistance for the physical act of filling dispensers; the task is straightforward manual work with no decision-making or knowledge component to augment.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no productivity assistance for the physical act of filling dispensers with beverages or ice.
Task automatability	claude-haiku-4-5-20251001	1/5	Filling beverage and ice dispensers requires physical manipulation of heavy containers, insertion into machines, and spatial coordination that current AI robotics cannot reliably perform in diverse cafeteria/bar settings at scale.
Task automatability	claude-sonnet-5	1/5	This is a physical manual task requiring locating, lifting, and pouring beverage/ice supplies into dispensers, which current AI systems cannot perform without embodied robotics far beyond off-the-shelf deployment.atika.
Adoption barriers	claude-haiku-4-5-20251001	2/5	No legal or licensing barriers exist, but physical infrastructure design and the need for task-specific robotics create moderate organizational and capital friction.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barrier exists, but physical environment constraints (kitchen/dining layout, handling food-grade materials) create practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The cost of a robotic arm, gripper, vision system, and ongoing maintenance far exceeds the loaded wage of a low-wage service worker performing this simple replenishment task.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI-driven substitute performing this physical task, so any hypothetical robotic solution would be far more expensive than a low-wage human worker.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI systems reliably perform this task end-to-end in production environments; it remains a manual, human-performed job across nearly all food service venues.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs this physical replenishment task in production restaurant or cafeteria settings today; robotic beverage service remains experimental at best.

Perform serving, cleaning, or stocking duties in establishments, such as cafeterias or dining rooms, to facilitate customer service.

CI 15–15 · exposure 0 · augmentation 0 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	The food service sector, particularly cafeterias and small dining establishments, remains characterized by low automation rates, minimal digitization, and high reliance on manual labor. Adoption of robots in this segment has been negligible despite ongoing robotics advances.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and hospitality are low-digitization, physical-labor-heavy sectors with minimal AI/robotic adoption for these specific tasks; automation experiments remain rare and localized.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI offers minimal assistance to dining attendants; the work remains largely manual and contextual, with few tools that meaningfully augment human performance at the core serving, cleaning, and stocking tasks.
Augmentation potential	claude-sonnet-5	1/5	Current AI offers essentially no direct assistance to a person physically bussing tables, serving food, or restocking supplies in a dining room.
Task automatability	claude-haiku-4-5-20251001	1/5	This task fundamentally requires physical presence, dexterity, and real-time responsiveness in a dynamic human environment. Current AI and robotics cannot reliably perform the full range of serving, cleaning, and stocking duties with the mobility, manipulability, and social coordination needed in busy food service settings.
Task automatability	claude-sonnet-5	1/5	This is a physical task requiring mobility, manipulation of dishes and cleaning tools, and navigation of dynamic dining environments—well beyond current AI (software) capabilities and largely beyond affordable robotics deployment today.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Health codes and food safety regulations require human oversight of sanitation, and customer expectations strongly prefer human service in dining settings. However, no strict legal mandate requires a licensed human to perform every task, creating some room for gradual automation of lower-contact duties like stocking.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but customer-facing service expectations, hygiene/safety regulations for food handling, and physical workspace constraints create moderate friction to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Physical service robots capable of this work are extremely expensive to purchase, maintain, and integrate compared to the modest hourly wage of dining attendants, with substantial overhead in infrastructure and continuous operation costs.
Cost vs. human wage	claude-sonnet-5	1/5	Physical robots capable of this multi-task work require expensive hardware, maintenance, and supervision, making them costlier than low-wage human labor performing the same job today.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably performs the integrated job of dining service attendant work. While some specialized warehouse robots handle stocking and autonomous cleaning units exist, none combine serving guests, clearing tables, restocking, and responding to dynamic cafeteria demands at scale in production.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs general bussing, serving, and stocking in real dining rooms at scale; existing food-service robots (e.g., some serving trays or delivery bots) are narrow pilots, not comprehensive replacements.

Carry trays from food counters to tables for cafeteria patrons.

CI 15–15 · exposure 0 · augmentation 0 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Food service remains a highly labor-intensive, low-digitization sector with minimal production-level robot deployment. Adoption remains nearly non-existent outside niche tech-focused venues.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and hospitality are low-digitization, physical-labor sectors with minimal AI/robotic adoption for basic manual tasks like this.
Augmentation potential	claude-haiku-4-5-20251001	1/5	AI provides no meaningful assistance to a human attendant carrying trays; the task is purely physical execution with no information processing, analysis, or decision-making component that AI could augment.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no productivity assistance for the physical act of carrying trays between counter and table.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical navigation of unpredictable environments (crowded dining spaces, variable table layouts), real-time obstacle avoidance, and delicate handling of trays with food/beverages. Current AI robotics cannot reliably perform this end-to-end in uncontrolled restaurant/cafeteria settings at cost parity.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring mobility, dexterity, and navigation through dynamic environments—current AI systems (software-based) cannot perform it, and physical robots for this remain experimental, not deployable.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While there are no strict licensing barriers, customer expectations for human service, safety liability concerns with robots near patrons, and organizational resistance to automation in hospitality create moderate friction to adoption.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human for this task, but physical workspace constraints, safety around food/hot items, and customer interaction norms create some friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized robots capable of tray-carrying would cost tens of thousands to hundreds of thousands of dollars upfront plus integration and maintenance, far exceeding the annual wage of a dining attendant in most jurisdictions.
Cost vs. human wage	claude-sonnet-5	1/5	Any robotic solution capable of this would require expensive specialized hardware, navigation systems, and maintenance far exceeding the low wage cost of a human attendant.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial product reliably carries trays through crowded dining rooms autonomously. Mobile manipulation robots exist in research labs but lack the speed, dexterity, and safety guarantees needed for food service in busy establishments.
Technical feasibility today	claude-sonnet-5	1/5	No commercially deployed product performs unstructured tray-carrying service in cafeterias at scale; robotic serving carts exist only in isolated pilots, not mainstream reliable production use.

Stock cabinets or serving areas with condiments and refill condiment containers.

CI 15–15 · exposure 0 · augmentation 0 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	The hospitality and food service sector shows very low adoption of automation for this type of physical, in-venue task. Most facilities remain entirely manual, with robotics adoption concentrated in kitchen preparation rather than front-of-house service areas.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and hospitality is a low-digitization, physical-labor sector with minimal AI/robotics adoption for such menial stocking tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI systems offer no meaningful assistance to workers stocking condiments or refilling containers. This task does not benefit from AI guidance, computer vision assistance, or decision support in ways that would materially improve a human's productivity.
Augmentation potential	claude-sonnet-5	1/5	AI offers no meaningful assistance for physically stocking and refilling condiment containers, as this is a manual, non-cognitive task.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical manipulation in unstructured environments (identifying correct condiments, physically placing them in cabinets, refilling containers), which current mobile robots and autonomous systems cannot reliably perform at scale. There is no end-to-end automation deployed today that achieves 50% time savings on this task.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring picking up containers, opening/pouring condiments, and placing items in serving areas—no off-the-shelf AI system can perform this physical labor.
Adoption barriers	claude-haiku-4-5-20251001	2/5	There are minimal legal or regulatory barriers to automating this task, and no requirement for human contact. The main barriers are practical (technology immaturity) rather than institutional, though customer preference for human staff in service areas provides some friction.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barriers exist, but the physical, unstructured nature of restocking in variable environments creates practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The hardware cost of a capable mobile manipulator robot, combined with integration, maintenance, and oversight, far exceeds the wage of a low-cost service worker performing this simple restocking task.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute for this task, so any hypothetical robotic solution would be far more expensive than simply having a low-wage worker refill containers.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably performs autonomous condiment stocking and refilling in real restaurant or cafeteria settings. This remains a purely manual task in production environments with no commercial solutions.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs physical restocking of condiment containers in dining rooms; this remains purely a human physical task with no robotic solution in commercial use.

Serve food to customers when waiters or waitresses need assistance.

CI 15–15 · exposure 0 · augmentation 13 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Food service remains highly manual and labor-intensive with minimal AI/robotic adoption; small firms and physical constraints dominate, and few establishments have deployed autonomous serving systems.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor sector with minimal AI/robotic adoption for actual table-side service tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist marginally (e.g., order-tracking displays, kitchen-coordination systems), but cannot meaningfully augment the core act of physically delivering food to tables in real-time.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no assistance to a human physically carrying and serving food to customers in real time.
Task automatability	claude-haiku-4-5-20251001	1/5	Serving food to customers requires physical manipulation of dishes, navigation of dining spaces, reading customer cues, and real-time interaction with people. Current AI cannot reliably perform this embodied, social task end-to-end in uncontrolled restaurant environments.
Task automatability	claude-sonnet-5	1/5	This is a physical task requiring locating tables, carrying trays, and manual dexterity in a dynamic dining room; no current AI system can perform this end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While not legally restricted, direct customer interaction, liability for spills or errors, and organizational preference for human staff provide some friction; however, these are soft barriers rather than hard regulatory ones.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but physical space constraints, customer service expectations, safety concerns around carrying food/drinks, and social preference for human service create moderate friction.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized robotic systems capable of food service are expensive to purchase, maintain, and program, while a cafeteria attendant's wage remains low and requires no upfront capital or bespoke engineering.
Cost vs. human wage	claude-sonnet-5	1/5	Service robots capable of navigating dining rooms and interacting with customers cost far more in capital and maintenance than paying a low-wage attendant.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed production systems perform this task autonomously at scale. Robotic food-serving prototypes exist in labs but lack reliability, speed, and the social fluency needed for real dining service.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product serves food to customers in table service settings; robotic food delivery remains experimental or limited to narrow conveyor/runner applications in a few restaurants.

Carry linens to or from laundry areas.

CI 15–15 · exposure 0 · augmentation 0 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Dining and cafeteria services remain low-digitization, small-operator-dominated sectors with minimal robotics adoption. Budget constraints and workforce availability mean manual linen handling persists as standard practice.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and hospitality is a low-digitization, physically intensive sector with minimal AI/robotics adoption for manual material handling tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	This straightforward physical task offers no meaningful opportunity for AI assistance; it does not involve decision-making, information synthesis, or judgment that an AI could augment.
Augmentation potential	claude-sonnet-5	1/5	AI offers no meaningful assistance for the physical act of carrying linens; there's no cognitive or planning bottleneck here for AI to help with.
Task automatability	claude-haiku-4-5-20251001	1/5	Carrying linens requires physical manipulation and transport in real environments with variable layouts, obstacles, and linen characteristics. Current robotics cannot reliably handle this task end-to-end in typical restaurant/cafeteria settings at scale.
Task automatability	claude-sonnet-5	1/5	This is a physical material-handling task requiring locomotion and manipulation of objects in unstructured environments; no off-the-shelf AI system performs this end-to-end today.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few legal barriers exist to automation, but physical workspace constraints, safety liability in customer-facing areas, and organizational infrastructure (laundry room access, robot maintenance) create moderate friction to adoption.
Adoption barriers	claude-sonnet-5	2/5	No licensing or regulatory barrier exists, but practical barriers like facility layout, safety, and lack of robotic infrastructure make substitution difficult.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The capital and integration costs of a mobile robot system capable of navigating to laundry areas and handling textiles would far exceed the hourly wage of a cafeteria attendant performing this simple labor task.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic solution for this task at any deployable cost, so it is far more expensive than simply having a human do it.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial product reliably performs autonomous linen transport in dining/cafeteria environments. While industrial robots exist in controlled warehouses, adapting them to dynamic restaurant spaces with handling variability remains research-stage.
Technical feasibility today	claude-sonnet-5	1/5	No deployed products (robotic or software) reliably carry linens between areas in commercial dining settings; this remains outside current product capability.

Stock refrigerating units with wines or bottled beer or replace empty beer kegs.

CI 15–15 · exposure 0 · augmentation 0 · importance 3.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Bars and cafeterias are typically small to mid-sized operations with low digitization and limited capital budgets for automation. Adoption of physical robots in this setting remains negligible across the industry.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and hospitality are low-digitization, physically-oriented sectors with minimal robotic automation adoption for stocking tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI and robotics offer minimal assistance for this task; human attendants perform it directly without meaningful tool or software support that would measurably increase their productivity.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no productivity assistance for physically stocking refrigerators or swapping kegs, as this is a purely manual physical task.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical manipulation of heavy items (kegs, bottles) in a real-world environment, navigating confined spaces, and handling fragile goods. Current AI systems cannot perform these embodied actions at scale or with reliability comparable to human workers.
Task automatability	claude-sonnet-5	1/5	This is a physical manual task requiring lifting, carrying, and placement of bottles and kegs, which current AI systems cannot perform end-to-end without a robotic embodiment not in general deployment.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While there are no hard legal barriers preventing automation, the physical and environmental specificity of the task, combined with low margins in food service, creates practical friction. Some venues may prefer human staff for customer interaction and adaptive problem-solving.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barrier prevents automation, but physical handling of heavy kegs and the need for dexterity in tight spaces creates practical organizational and safety friction.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	A general-purpose robotic system capable of performing this task would cost far more than the loaded wage of a dining attendant or bartender helper, both in capital investment and ongoing maintenance and integration.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic solution for this physical task, so any hypothetical automation would require expensive custom robotics far costlier than human labor.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed products reliably perform physical stocking and keg replacement in production settings. Robotic solutions for beverage handling remain research-stage or extremely niche, without meaningful real-world deployment in typical food-service venues.
Technical feasibility today	claude-sonnet-5	1/5	No deployed products perform keg replacement or beverage stocking autonomously in restaurants or bars today; this remains outside commercial robotics deployment.

Greet and seat customers.

CI 5–24 · exposure 8 · augmentation 25 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Food service remains largely low-tech and small-firm-dominated, with slow digitization of front-of-house operations. Adoption of AI seating systems is minimal in production despite pilot interest.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor sector with minimal AI/robotic adoption for front-of-house tasks like seating guests.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist with waitlist management, party-size tracking, or table-readiness alerts, but the greeting and seating task itself—requiring personable human judgment and adaptation—sees limited productivity lift from current AI tools.
Augmentation potential	claude-sonnet-5	2/5	Digital host/waitlist apps and reservation software can assist with queue management, but they provide only marginal support to the core physical greeting and seating task.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI systems can recognize customers and process seating information, greeting and seating requires social presence, real-time negotiation of preferences (parties size, timing, accessibility needs), and physical movement/coordination in a dining space. Current AI falls short of the ≥50% time-saving threshold for end-to-end execution without significant human intervention.
Task automatability	claude-sonnet-5	1/5	Physically greeting and seating customers in a real dining space requires mobility, presence, and interpersonal warmth that current AI systems cannot perform end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Customer experience norms strongly favor human greeters; restaurants rely on hospitality and personal service as competitive differentiators. Health codes and liability concerns around autonomous systems handling customers add organizational friction and regulatory uncertainty.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement exists, but physical presence, customer preference for human interaction, and lack of robotic infrastructure create practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	A robotic or AI-driven seating system (hardware + software + integration + uptime) remains far more expensive than a dining attendant's hourly wage, especially when factoring in maintenance, oversight, and failures.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI product substitute performing this task, so AI cost is effectively infinite relative to a low-wage human worker doing it directly.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably greets and seats customers without human staff. Autonomous systems in restaurants remain rare and experimental; the task requires real-time environmental adaptation, customer interaction, and physical embodiment that exceed current production capabilities.
Technical feasibility today	claude-sonnet-5	1/5	No deployed physical robot or AI system reliably greets and seats customers in restaurants at scale; this remains a human physical/social task.

Garnish foods and position them on tables to make them visible and accessible.

CI 5–24 · exposure 8 · augmentation 13 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Food service remains highly labor-intensive and human-centric; automation adoption has been slow and limited to back-of-house tasks. Dining room and table service are among the slowest sectors to adopt visible automation.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and hospitality are low-digitization, physically-oriented sectors with minimal AI/robotic adoption for tasks like plating and table setup.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI tools offer minimal assistance for this task; there are no mature systems that augment human garnishing or table positioning work in real dining environments.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer no meaningful real-time assistance to a worker physically garnishing and positioning food on tables.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI-powered robotics could theoretically garnish and position food, current systems lack the fine motor control, real-time adaptation to varied plate geometries, and understanding of aesthetic presentation required at production speed. This task requires both precision and judgment that existing deployed systems cannot reliably deliver end-to-end.
Task automatability	claude-sonnet-5	1/5	This requires physical manipulation of food items, plating, and placement in a physical dining environment, which current AI systems cannot perform without robotic embodiment far beyond deployed capability.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Food service establishments operate in health and safety-regulated environments, customer expectations for human-prepared service are strong, and direct food handling automation faces both regulatory scrutiny and consumer preference for human touch.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human specifically, but the physical, dexterous nature of plating and hygiene handling creates practical barriers to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The hardware cost of a robotic arm system capable of this task, plus integration and maintenance, far exceeds the loaded wage of a dining attendant, making AI substitution economically unviable.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic solution for this physical task at any cost, so AI is not cheaper than a human worker performing it directly.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No mainstream deployed product reliably performs food garnishing and table positioning autonomously. Robotic arms exist in research and limited industrial settings but are not production-grade solutions in dining service environments.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs food garnishing and physical table placement; this remains outside the scope of commercial AI or robotics products in food service.

Carry food, dishes, trays, or silverware from kitchens or supply departments to serving counters.

CI 10–15 · exposure 0 · augmentation 0 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Food service remains a low-digitization, physical sector with small-firm dominance and high reliance on in-person labor. Adoption of automation for physical supply tasks in this industry is minimal outside specialized high-volume facilities.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor sector with minimal AI/robotics adoption for material handling tasks like this.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI offers no meaningful augmentation for physically carrying food and dishes; the task is fundamentally about spatial navigation and object manipulation where humans already perform it efficiently without AI assistance.
Augmentation potential	claude-sonnet-5	1/5	Current AI offers essentially no assistance for the physical act of carrying food and dishes between kitchen and counter.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical manipulation, navigation of dynamic kitchen environments, and handling fragile items—capabilities that current deployed robotic systems cannot reliably perform end-to-end. While robotics research explores these challenges, no commercially available AI system achieves 50% time savings at equal quality for this physical supply-chain task.
Task automatability	claude-sonnet-5	1/5	This is a physical manual material-handling task requiring mobility and dexterity in unstructured environments; no off-the-shelf AI/robotic system performs this reliably today.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Health and safety regulations govern food service environments, and some jurisdictions may require human oversight of food handling. However, these are not absolute legal bars to task automation—organizational friction and customer preference for human service represent the primary barriers rather than hard licensing requirements.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barrier exists, but physical environment variability, cost, and lack of mature robotics create practical friction against substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	A robot capable of reliable kitchen-to-counter transport, including hardware, integration, maintenance, and liability insurance, would cost far more per task-equivalent than the wage of a low-wage attendant, especially given the physical capital required.
Cost vs. human wage	claude-sonnet-5	1/5	Robotic solutions for this task would require expensive hardware, maintenance, and navigation infrastructure, making them far costlier than low-wage human labor currently performing this work.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No mature deployed products perform this task reliably in production at scale. Industrial robots exist in controlled settings, but autonomous systems that navigate crowded kitchens, handle variable dishware, and transport items safely are not in routine operational use.
Technical feasibility today	claude-sonnet-5	1/5	No deployed commercial products autonomously carry food and dishware between kitchen and serving counters in restaurants at scale; any robotics for this remains experimental or extremely narrow.

Serve ice water, coffee, rolls, or butter to patrons.

CI 5–15 · exposure 0 · augmentation 0 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Hospitality and food service sectors show minimal AI/robotic adoption for table service; most venues remain dependent on human staff for these tasks. Adoption is concentrated in niche applications (delivery robots in kitchens), not patron-facing service.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor sector with minimal AI/robotic adoption for basic serving tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	AI offers no meaningful assistance to a human performing this task; there is no decision-support, predictive, or analytical component that an AI system could enhance in real-time beverage and food serving.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no assistance to a human performing this manual serving task.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical presence, navigation in unstructured spaces, handling fragile items, and real-time responsiveness to patron needs—capabilities far beyond current AI. No autonomous system can reliably serve water, coffee, rolls, or butter to seated patrons at scale today.
Task automatability	claude-sonnet-5	1/5	This requires physical manipulation of objects and mobility in a dining environment, which current AI systems cannot perform end-to-end without embodied robotics that are not generally available.},
Adoption barriers	claude-haiku-4-5-20251001	4/5	Strong barriers exist: customer preference for human service in hospitality, liability concerns around automated food/beverage handling, health and safety regulations, and the expectation of personal interaction in dining contexts create substantial friction against automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but physical workspace constraints, safety around hot liquids, and customer service expectations create some friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The capital cost of robotics capable of this task (hardware, integration, maintenance) far exceeds the loaded wage of a dining attendant or bartender helper, making automation economically infeasible for most venues.
Cost vs. human wage	claude-sonnet-5	1/5	Robotic solutions capable of this task are far more expensive to acquire, install, and maintain than paying a low-wage attendant.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product performs full end-to-end service of beverages and food items to patrons. Robotic arms exist in labs but lack the dexterity, safety guarantees, and environmental adaptation needed for reliable production deployment in dining settings.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs physical food/beverage serving in restaurants at scale; robotic serving remains experimental or niche novelty deployments.

Locate items requested by customers.

CI 5–15 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Food service and hospitality remain low-digitization, small-operator-dominated sectors with minimal AI/robotic adoption; this task occurs in environments where human presence is culturally expected and economically entrenched.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and hospitality are low-digitization, physical-labor sectors with minimal AI/robotic adoption for this kind of task.
Augmentation potential	claude-haiku-4-5-20251001	2/5	While AI-assisted inventory systems could help locate items more efficiently, the actual retrieval and delivery to customers requires human presence; modest augmentation is possible through better mapping tools, but gains are limited.
Augmentation potential	claude-sonnet-5	2/5	AI could marginally help via inventory or POS lookup systems to identify item locations, but it offers little direct assistance to the physical act of locating and delivering items.
Task automatability	claude-haiku-4-5-20251001	1/5	Locating items in a dining or bar environment requires spatial navigation, physical retrieval, and real-time adaptation to dynamic environments that current AI systems cannot perform end-to-end without human intervention.
Task automatability	claude-sonnet-5	1/5	This is a physical task requiring locating and retrieving items in a real-world dining/cafeteria environment, which current AI systems cannot perform end-to-end.'
Adoption barriers	claude-haiku-4-5-20251001	4/5	Direct customer service interactions create strong expectations for human contact, and safety/liability concerns in food service environments with complex spatial constraints present meaningful adoption barriers.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barriers exist, but the physical nature of navigating a dining space and identifying items is a practical barrier to any non-physical AI system.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The labor cost of a dining attendant or bartender helper is very low (minimum wage), making AI-based robotic systems prohibitively expensive per task instance compared to human performance.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute performing this physical retrieval task, so AI cost per task-equivalent is effectively infinite or requires robotics far more expensive than human labor.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI product reliably performs autonomous item location and retrieval in restaurant or bar settings; the task requires embodied robotics with strong perception that is not yet in production at scale in these contexts.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product physically locates and retrieves items for customers in a dining room setting; this remains outside AI's current physical capabilities.

Related occupations — Food Preparation & Serving

How to read this

A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.

What would change this score

New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.