Food Preparation Workers

35-2021.00

Median wage $35,320/yr893,600 employed (US)Rank #486 of 923 scored · top 53% by substitution

Perform a variety of food preparation duties other than cooking, such as preparing cold foods and shellfish, slicing meat, and brewing coffee or tea.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution28

Exposure19

Augmentation22

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

31 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%21

panel mean rating 1.8/5 → substitution pressure 21/100

Technical feasibility todayw 20%16

panel mean rating 1.7/5 → substitution pressure 16/100

Cost vs. human wagew 15%16

panel mean rating 1.6/5 → substitution pressure 16/100

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%11

panel mean rating 1.4/5 → substitution pressure 11/100

Task breakdown (31 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.

Operate cash register, handle money, and give correct change.

CI 80–86 · exposure 84 · augmentation 63 · 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	5/5	Food service and retail have rapidly adopted automated POS systems; digital payment processing and self-checkout solutions are now standard in fast-casual, quick-service, and many full-service establishments, showing deep penetration in information-heavy sectors.
Sector adoption velocity	claude-sonnet-5	4/5	Fast food and retail food service have widely adopted self-service kiosks, mobile ordering, and automated payment systems over the past decade, though full replacement of cashiers remains uneven across smaller establishments.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI-powered POS systems significantly augment worker productivity by handling calculation, payment validation, and receipt generation automatically, allowing staff to focus on customer service and order accuracy while reducing mental load and errors.
Augmentation potential	claude-sonnet-5	3/5	POS systems assist workers by calculating change and processing transactions faster, but this is more full task substitution than augmentation for the remaining human tasks in food prep roles.
Task automatability	claude-haiku-4-5-20251001	4/5	End-to-end automation is feasible with current AI systems. AI-powered point-of-sale systems can calculate change instantly, process payments, and manage transactions at scale with >50% time savings compared to manual entry, though human oversight of payment reconciliation may still be needed.
Task automatability	claude-sonnet-5	4/5	Cash register operation and payment handling is largely automated already via POS/self-checkout kiosks and automated payment terminals that compute change automatically, saving significant time versus manual handling.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While some jurisdictions have minimal cash-handling regulation and most businesses accept digital POS systems, customer preference for human interaction and occasional audit/security oversight create modest friction rather than hard legal barriers to full automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement exists for handling cash or operating registers; some friction comes from customer preference for human interaction and cash-handling trust/security policies, but no hard legal barrier blocks automation.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	Modern POS systems cost a fraction of a full-time wage and handle multiple registers/transactions simultaneously. The per-task cost of automated payment processing and change calculation is orders of magnitude cheaper than hourly human labor.
Cost vs. human wage	claude-sonnet-5	4/5	Kiosk/terminal hardware plus minimal maintenance is far cheaper over time than paying a worker's wage for the cashier function, though upfront capital and maintenance costs keep it from a full order-of-magnitude edge in all settings.
Technical feasibility today	claude-haiku-4-5-20251001	5/5	Deployed POS systems with integrated payment processors and automated calculation reliably handle cash transactions at scale across thousands of restaurants, cafes, and food service establishments daily in production environments.
Technical feasibility today	claude-sonnet-5	5/5	Self-checkout kiosks, card/contactless payment systems, and automated POS terminals are deployed at massive scale in restaurants and food service today, reliably handling transactions.

Keep records of the quantities of food used.

CI 67–80 · exposure 75 · augmentation 63 · 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	4/5	Food service and food manufacturing sectors have steadily adopted inventory management systems for cost control and compliance; large chains and commercial kitchens deploy these routinely, though smaller operations lag. Overall trend is toward digitized inventory tracking.
Sector adoption velocity	claude-sonnet-5	3/5	Food service is a lower-digitization sector overall, but inventory management software adoption is moderately common in larger chains and growing steadily, while small independent kitchens still rely on manual methods.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI-assisted systems can help workers quickly identify stock levels, suggest reordering thresholds, and flag waste patterns, improving efficiency in the recording process. However, the worker must still verify counts and interpret contextual decisions about stock management.
Augmentation potential	claude-sonnet-5	4/5	AI-enabled inventory systems significantly reduce manual counting and paperwork burdens, letting workers verify and adjust rather than perform full manual tracking.
Task automatability	claude-haiku-4-5-20251001	4/5	Tracking food quantities can be largely automated through weighing scales, barcode scanning, inventory management systems, and image recognition of stock levels. Most workflows can achieve substantial time savings with existing point-of-sale and inventory software, though some manual verification may remain for accuracy.
Task automatability	claude-sonnet-5	4/5	Recording quantities of food used is a structured data-entry task easily handled by inventory software, POS integrations, or AI-assisted logging with minimal human input beyond initial data capture.time saved could exceed 50% with off-the-shelf inventory management tools.
Adoption barriers	claude-haiku-4-5-20251001	3/5	While no legal licensing barrier exists, some adoption friction remains due to organizational inertia, need for staff training, and integration complexity with existing kitchen workflows. Small establishments and informal food preparation operations may resist digitization.
Adoption barriers	claude-sonnet-5	1/5	No licensing, regulatory, or human-judgment requirement blocks automated record-keeping of food quantities; it's a purely administrative task.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	Once initial hardware (scales, scanners) and software systems are in place, the per-task marginal cost of recording quantities is very low, representing a fraction of a food preparation worker's hourly wage. The upfront capital investment is typically amortized across many transactions.
Cost vs. human wage	claude-sonnet-5	4/5	Software subscription costs for inventory tracking are far lower than paying a worker's time to manually tally and record usage, especially at scale across multiple locations.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Mature inventory management systems are widely deployed in food service operations today, with barcode scanning and automated stock-tracking reliably performing quantity recording at scale. Integration with POS systems is standard practice in many restaurants and food prep facilities.
Technical feasibility today	claude-sonnet-5	4/5	Restaurant and food service inventory management systems (e.g., MarketMan, Toast, Craftable) are widely deployed and reliably track ingredient usage in production kitchens today.

Take and record temperature of food and food storage areas, such as refrigerators and freezers.

CI 34–84 · exposure 55 · 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	4/5	Large food service chains, hospitals, and institutional food facilities have rapidly adopted automated temperature monitoring systems over the past 5–10 years. Smaller independent food operations still rely partly on manual checks, but adoption among digitized sectors is deep and accelerating.
Sector adoption velocity	claude-sonnet-5	2/5	Food service is a low-digitization, physical-labor-heavy sector with slow technology adoption for routine compliance tasks like this, though some larger chains have begun adopting IoT monitoring.
Augmentation potential	claude-haiku-4-5-20251001	3/5	Automated systems assist workers by eliminating manual recording burden and flagging anomalies in real time, raising overall safety oversight. However, workers may still visually inspect equipment or investigate alerts, so augmentation is meaningful but partial rather than transformative.
Augmentation potential	claude-sonnet-5	3/5	Digital thermometers and automated logging apps/sensors can assist workers by reducing manual recording errors and providing alerts, offering moderate productivity gains on this specific task.
Task automatability	claude-haiku-4-5-20251001	4/5	Temperature recording from food storage areas is highly automatable using deployed IoT sensors and thermometers that integrate with logging systems. Current AI can reliably read, record, and flag out-of-range temperatures, achieving well over 50% time savings compared to manual spot checks, though occasional human verification of sensor accuracy maintains quality parity.
Task automatability	claude-sonnet-5	2/5	Physical temperature measurement and manual logging requires physical sensing and human presence; while IoT sensors can automate the data capture, the task as described (manual taking and recording) is not something general AI systems perform end-to-end without hardware infrastructure.dishwasher... not applicable here.'
Adoption barriers	claude-haiku-4-5-20251001	2/5	While food safety regulations (HACCP, FDA) require temperature documentation, they do not mandate human manual checking—automated sensor records are widely accepted for compliance. Some facilities may prefer human verification for liability reasons, but there is no legal barrier to automation itself.
Adoption barriers	claude-sonnet-5	3/5	Health code regulations often require documented temperature logs and may specify verification procedures, creating some compliance-related friction, though the actual measurement itself isn't legally required to be human-performed.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	Automated IoT temperature sensors cost pennies per reading when amortized and require minimal human oversight, making them an order of magnitude cheaper than paying a worker to manually check and record temperatures multiple times per day.
Cost vs. human wage	claude-sonnet-5	3/5	Automated sensor systems can be cheaper long-term than manual checks but require upfront hardware investment and integration, making the cost comparison mixed depending on scale and existing infrastructure.
Technical feasibility today	claude-haiku-4-5-20251001	5/5	Temperature monitoring solutions are mature and widely deployed in production food service operations today. Digital thermometers, continuous IoT monitoring systems, and automated alert platforms (e.g., from Ecolab, Haas, SafeChain) are reliably used at scale in restaurants, hospitals, and food facilities to record and log temperatures.
Technical feasibility today	claude-sonnet-5	2/5	IoT temperature monitoring systems exist and are deployed in some commercial kitchens, but they are specialized hardware/software solutions rather than general AI products, and many food service establishments still do this manually.

Cut, slice or grind meat, poultry, and seafood to prepare for cooking.

CI 10–87 · exposure 45 · augmentation 25 · 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	4/5	Meat processing and commercial food preparation sectors have been rapidly adopting automated cutting and grinding equipment for decades; industrial facilities show high penetration of robotic systems, though small independent restaurants lag.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor-intensive sector with minimal AI/robotics penetration into hands-on prep tasks; adoption of automation for this specific task is essentially nonexistent outside large-scale meat processing plants.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI-assisted systems (vision-guided cutting, automated portion control) can help human food-prep workers improve precision and consistency, though the task itself is dominated by mechanical automation rather than AI-based augmentation.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer no meaningful real-time assistance to a worker physically cutting or slicing meat; there's no interactive AI layer that enhances this manual task's productivity today.
Task automatability	claude-haiku-4-5-20251001	5/5	Cutting, slicing, and grinding meat, poultry, and seafood are highly repetitive, standardized physical tasks that can be performed end-to-end by automated systems (e.g., industrial meat-cutting robots, grinders) with consistent quality and significant time savings compared to manual labor.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring dexterity, force control, and judgment about texture/cuts; current AI (software/LLMs) cannot perform it, and robotics for this remain research/niche-stage, not deployable at equal quality with time savings.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Food safety regulations (HACCP, sanitation standards) and worker safety laws create modest friction, but they apply equally to human and automated processes; no licensing requirement mandates human labor for this specific task, and automation is already widespread in commercial settings.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement for a human to do this specific task, but food safety regulations, equipment sanitation standards, and liability for contamination or injury create meaningful organizational friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	Automated meat-cutting and grinding systems operate continuously at low per-unit cost (electricity, maintenance) and are orders of magnitude cheaper than loaded human wages when amortized across high-volume operations.
Cost vs. human wage	claude-sonnet-5	1/5	Robotic cutting systems capable of this task would require expensive specialized hardware, maintenance, and food-safety compliance, making them far costlier than a low-wage food prep worker for the low-volume, variable-task setting typical of this occupation.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed industrial automation systems (robotic arms, automated slicers, and grinders) reliably perform these tasks in production at scale in meat processing facilities and commercial kitchens, though integration complexity and product variability occasionally require human oversight.
Technical feasibility today	claude-sonnet-5	1/5	No general-purpose deployed product reliably cuts, slices, or grinds diverse meat/poultry/seafood in commercial kitchens; existing food-processing automation is limited to industrial-scale, single-product lines, not the flexible task described.

Weigh or measure ingredients.

CI 31–62 · exposure 53 · augmentation 38 · 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	Food preparation remains heavily manual and concentrated in small, fragmented establishments with limited digitization. Adoption of automation in this sector lags far behind information and professional services, with capital and regulatory barriers slowing uptake.
Sector adoption velocity	claude-sonnet-5	2/5	Food service is a physically-oriented, low-digitization sector with slow overall AI/automation adoption outside large-scale manufacturing or chain operations.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI-powered measuring tools could provide guidance or verification, but the task is simple and routine enough that augmentation offers modest productivity gains. Current assistance (smart scales, recipe apps) exists but is not transformative for the core measurement task.
Augmentation potential	claude-sonnet-5	3/5	Digital scales, recipe apps, and portion-control tools already assist workers in measuring more accurately and quickly, though the human still performs the physical action.
Task automatability	claude-haiku-4-5-20251001	3/5	Weighing and measuring ingredients can be partially automated with vision systems and robotic arms in controlled kitchen environments, but integration with existing workflows and handling diverse container types requires significant setup. Current systems can reliably measure standard volumes or weights, but real-world kitchens with varied equipment and ingredients present material friction.
Task automatability	claude-sonnet-5	4/5	Weighing/measuring is a simple, well-defined physical task that smart scales, portioning equipment, and robotic dispensers can already perform with high accuracy given proper setup.But full end-to-end automation requires hardware integration into the kitchen line, not just software.rating reflects strong feasibility of automation potential
Adoption barriers	claude-haiku-4-5-20251001	4/5	Food safety regulations, hygiene standards, and health codes impose stringent requirements on food handling; human sign-off or oversight is typically mandated. Liability concerns around measurement errors affecting food safety and customer allergies create strong regulatory and organizational friction.
Adoption barriers	claude-sonnet-5	1/5	No licensing, regulatory, or human-judgment requirement exists for measuring ingredients; it's a purely mechanical task with no legal barrier to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Robotic systems capable of measuring and weighing ingredients remain capital-intensive and require integration costs that typically exceed the hourly wage of food preparation workers in many settings. Amortized cost per task is not yet competitive for small-to-medium food service operations.
Cost vs. human wage	claude-sonnet-5	2/5	Automated weighing/dispensing hardware requires significant capital investment, installation, and maintenance, which is not clearly cheaper than low-wage food prep labor for most small-scale operations.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While specialized food-service robots exist, few deployed systems reliably perform ingredient measurement end-to-end in production kitchens at scale. Lab demos and research prototypes show promise, but widespread reliable deployment in real commercial or institutional kitchens remains limited.
Technical feasibility today	claude-sonnet-5	3/5	Automated portioning and dispensing systems exist and are deployed in some food manufacturing and QSR settings, but adoption in general food prep (restaurants, cafeterias) remains limited and often still manual.

Inform supervisors when equipment is not working properly and when food and supplies are getting low, and order needed items.

CI 35–49 · exposure 30 · augmentation 50 · 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	2/5	Food service remains fragmented across small independent operators and larger chains with limited digitization; while large QSR and corporate kitchens do use monitoring systems, the sector overall lags information-intensive industries in AI adoption for this workflow.
Sector adoption velocity	claude-sonnet-5	2/5	Food service is a low-digitization, high-turnover sector with slow technology adoption at the line-worker level, though some chains use inventory management software.
Augmentation potential	claude-haiku-4-5-20251001	3/5	Automated alerts and inventory dashboards meaningfully assist food prep supervisors by surfacing issues without the need for manual rounds, reducing cognitive load and response time, though humans remain the decision-makers on ordering and escalation.
Augmentation potential	claude-sonnet-5	3/5	Inventory management apps and automated reordering systems can meaningfully assist workers in tracking supplies and flagging equipment issues, though physical inspection remains manual.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI could monitor inventory levels via sensors or data feeds and flag low supplies, the task requires judgment about when to escalate (severity assessment), which equipment failure warrants immediate action, and supplier relationships that remain human-dependent. Current systems cannot reliably replace the full task end-to-end with 50% time savings.
Task automatability	claude-sonnet-5	2/5	Detecting equipment malfunction and observing physical stock levels requires sensory/physical presence in a kitchen environment, though the reporting and ordering communication itself could be automated with sensors and inventory software.5:5 not applicable here since core sensing is manual. ratio remains low. ratio 2. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio. ratio.
Adoption barriers	claude-haiku-4-5-20251001	2/5	No legal licensing requirement exists, but operational friction is moderate: supervisors typically prefer direct human communication about urgent failures, and integration with existing supply chains and procurement systems creates friction. Staff resistance is low but organizational adoption is not universal.
Adoption barriers	claude-sonnet-5	1/5	No licensing or regulatory requirement mandates a human perform this simple observational/communication task; it's a low-stakes operational function.
Cost vs. human wage	claude-haiku-4-5-20251001	3/5	Sensor networks and monitoring software have non-trivial upfront and maintenance costs comparable to periodic manual checking and communication in small to mid-sized operations, though large chains may achieve better economies of scale.
Cost vs. human wage	claude-sonnet-5	2/5	IoT sensors and inventory software have upfront and maintenance costs that may not be cheaper than a low-wage worker simply noticing and reporting issues verbally.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Inventory management systems and IoT sensors exist in many commercial kitchens and can alert supervisors to low stock or equipment failures, but adoption is uneven, integration is incomplete, and human verification of alerts remains standard practice. Deployed solutions work for parts of this task but lack full autonomy.
Technical feasibility today	claude-sonnet-5	2/5	Smart inventory management and IoT sensor systems exist in some commercial kitchens for stock tracking and equipment monitoring, but widespread deployment specifically automating this worker-level reporting task is limited.

Prepare and serve a variety of beverages, such as coffee, tea, and soft drinks.

CI 28–35 · exposure 25 · augmentation 38 · 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	2/5	Adoption is limited to high-volume, standardized settings (fast food chains, airports); most food service venues continue manual beverage prep due to variety demands and customer expectations.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor-heavy sector with slow, limited robotic/automation adoption for beverage prep tasks.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI-assisted ordering systems and pre-programmed dispensing can help workers manage high-volume, repetitive orders, but the task remains largely human-directed with modest productivity gains.
Augmentation potential	claude-sonnet-5	2/5	AI can assist with order-taking, inventory, or recipe/timing guidance, but offers little direct enhancement to the physical act of preparing and serving beverages.
Task automatability	claude-haiku-4-5-20251001	2/5	While beverage dispensing hardware exists, the task requires varied item selection, custom orders, and quality judgment. Current AI can automate simple dispensing but cannot reliably handle the full scope (custom temperatures, milk options, customer preferences) at 50% time savings without significant human oversight.
Task automatability	claude-sonnet-5	2/5	Physical preparation and serving of beverages requires manipulation, pouring, and handling of physical objects in unstructured environments, which current AI cannot do end-to-end; only narrow automated dispensing exists.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Health and safety regulations govern food and beverage preparation, and customer preference for human service in cafes and restaurants creates moderate friction, though no absolute legal prohibition on machine service exists.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but customer-facing service expectations, hygiene regulations, and physical infrastructure changes create moderate friction to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Automated beverage systems require substantial capital investment and ongoing maintenance; labor costs for a food prep worker are relatively low, making full automation economically unattractive for most venues.
Cost vs. human wage	claude-sonnet-5	2/5	Robotic beverage-making systems require significant capital investment, maintenance, and space, generally costing more than a low-wage human worker for equivalent flexible output.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Vending machines and automated dispensers exist but serve only standardized beverages; they fail on customization and quality control. No deployed AI system reliably handles the full range of beverage prep and service requests in a live food-service setting.
Technical feasibility today	claude-sonnet-5	2/5	Some automated kiosks and robotic baristas exist (e.g., automated coffee machines), but they are niche, expensive, and not widely deployed as reliable substitutes for human workers across food service settings.

Use manual or electric appliances to clean, peel, slice, and trim foods.

CI 10–49 · exposure 25 · augmentation 25 · 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	2/5	Adoption is concentrated in large industrialized food processing and chain restaurants with standardized menus and high volumes. Most food service (small cafes, restaurants, catering) relies on manual prep; digitization is low, and capital constraints limit automation uptake in fragmented small-operator sectors.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor sector with minimal AI/robotics adoption for hands-on food prep tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI-based visual sorting or robotic slicing assists production-line environments, but most food prep workers operate in kitchens where manual tools remain primary and human inspection/judgment on texture, ripeness, and quality is irreplaceable. Augmentation is limited to industrial, standardized contexts.
Augmentation potential	claude-sonnet-5	2/5	Electric appliances (already standard) provide some efficiency, but AI-specific augmentation (e.g., smart cutting guidance) is minimal and not widely used in this task.
Task automatability	claude-haiku-4-5-20251001	3/5	Slicing, peeling, and trimming repetitive items (potatoes, vegetables) can be partially automated with existing food-processing equipment, but variable food sizes, shapes, and quality require human judgment and dexterity. Current systems handle standardized items at ~50% time savings, but mixed preparation still requires significant manual work.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring dexterity, force control, and adaptation to irregular food items; no general-purpose AI system can perform this end-to-end today.'
Adoption barriers	claude-haiku-4-5-20251001	3/5	Food safety regulations (HACCP, FDA) and liability for contamination create oversight requirements and record-keeping friction. Kitchen layout, labor affordability, and worker preference for flexibility present organizational barriers, but no licensing requirement prevents automation of the manual task itself.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but food safety standards, kitchen workflow variability, and equipment investment create moderate organizational friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Industrial food-processing equipment has high capital and maintenance costs that are economical only at large volume. For small-to-medium food service operations (the majority of food prep workers), the per-task cost of deploying and maintaining automation remains higher than minimum-wage labor, even at scale.
Cost vs. human wage	claude-sonnet-5	1/5	Specialized food-prep robotics require expensive custom hardware, integration, and maintenance that exceed the cost of low-wage kitchen labor for this task.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Commercial food-processing robots exist for specific tasks (peeling, slicing uniform items), but they operate in controlled factory settings with high-consistency inputs. In restaurant/catering kitchens with variable ingredients and ad-hoc demands, deployed systems are narrow in scope and require frequent manual intervention or setup changes.
Technical feasibility today	claude-sonnet-5	1/5	Robotic food prep exists only in narrow research or highly controlled pilot settings (e.g., single-item cutting demos), not deployed at scale for varied cleaning/peeling/slicing/trimming tasks.

Clean and sanitize work areas, equipment, utensils, dishes, or silverware.

CI 23–35 · exposure 25 · augmentation 25 · importance 4.7/5 · 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	Food service is fragmented, small-margin, and low-tech digitally. While large institutional kitchens (hospitals, universities) have adopted commercial dishwashers, widespread robotic or autonomous cleaning automation in restaurants and catering remains negligible despite decades of possible development.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physically intensive sector with minimal robotic cleaning adoption; automation here lags far behind information-sector AI adoption.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist with scheduling or reminding workers when to clean surfaces, or detecting soiling via computer vision, but the core physical and tactile work of cleaning—scrubbing, rinsing, handling fragile items—remains solidly human-dependent and offers limited augmentation upside.
Augmentation potential	claude-sonnet-5	2/5	AI offers minimal direct assistance to a human physically cleaning and sanitizing; at most, smart scheduling or monitoring tools provide marginal support to this manual task.
Task automatability	claude-haiku-4-5-20251001	2/5	While some industrial dishwashing and surface-cleaning equipment is automated, the task requires dexterity, spatial reasoning, and judgment about what surfaces need what treatment. Current AI systems cannot perform the full end-to-end workflow—identifying soiled items, choosing appropriate sanitizers, handling fragile dishes, and verifying cleanliness—at time-parity with human workers.
Task automatability	claude-sonnet-5	2/5	Physical cleaning and sanitizing of kitchen surfaces and utensils requires manipulation of diverse objects in unstructured environments, which current general-purpose robotics cannot reliably perform end-to-end today.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Health and safety regulations (food code, sanitation standards) typically mandate human verification of cleanliness and proper sanitizer concentration; liability for contamination is high and falls on the establishment. Customers also strongly prefer human accountability in food prep environments.
Adoption barriers	claude-sonnet-5	2/5	Food safety regulations mandate proper sanitation but do not require a specific licensed human to perform cleaning; some automation (dishwashers) is already normalized, but full robotic replacement faces physical and hygiene-verification friction.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Industrial cleaning robots and specialized equipment are capital-intensive and require significant infrastructure investment, oversight, and maintenance. For most food-service operations, the all-in cost of automation exceeds the wage cost of a single or few cleaning workers.
Cost vs. human wage	claude-sonnet-5	2/5	Industrial dishwashers reduce labor for dishware, but full sanitizing of equipment and workspaces still requires human labor or expensive specialized robotics with high integration and maintenance costs, often exceeding low-wage human labor costs.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Robotic arms for dishwashing exist in research and limited industrial settings, but production systems are narrow (e.g., rack-based dishwashers only), require setup specific to kitchen layout, and cannot handle varied utensil types, broken items, or complex soiling reliably at the scale restaurants need.
Technical feasibility today	claude-sonnet-5	2/5	Some commercial dishwashing machines and limited robotic cleaning arms exist, but no deployed product autonomously cleans and sanitizes varied kitchen work areas, equipment, and utensils at production scale.

Vacuum dining area and sweep and mop kitchen floor.

CI 14–35 · exposure 13 · 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	2/5	Adoption of cleaning robots in food service is nascent and concentrated in large chains or high-tech venues; most small and mid-size restaurants and kitchens continue to use manual labor, reflecting slow real-world displacement.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor-heavy sector with minimal robotic adoption for general cleaning tasks like this.
Augmentation potential	claude-haiku-4-5-20251001	1/5	AI and robotics offer minimal assistance to a human actively vacuuming, sweeping, or mopping; this is primarily a physical task where current tools do not enhance worker productivity in meaningful ways.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no meaningful assistance to a human performing manual vacuuming, sweeping, and mopping.
Task automatability	claude-haiku-4-5-20251001	2/5	While robotic floor-cleaning systems exist, they require significant setup, mapping, and human oversight in unstructured dining and kitchen environments with furniture, obstacles, and variable debris. Current systems cannot reliably handle the full task end-to-end at equal quality without substantial human intervention.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring mobility, dexterity, and navigation of unstructured environments; current AI (including robotics) cannot reliably perform this end-to-end with time savings at equal quality in typical restaurant settings.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Health and safety regulations govern kitchen cleanliness, and many establishments prefer human oversight for food-safety compliance; however, there is no strict legal requirement that a human must perform the cleaning, creating moderate friction rather than hard barriers.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barriers, but practical friction exists: kitchens are cluttered, hygiene-sensitive environments requiring flexible physical adaptation that robots struggle with.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Robotic cleaning systems require high capital investment ($20k–$100k+) and ongoing maintenance, while the loaded wage for a food prep worker is typically $15–$25/hour; payback periods are long for this low-wage task in small to medium establishments.
Cost vs. human wage	claude-sonnet-5	2/5	Robotic cleaning equipment exists but has high upfront capital cost, maintenance, and limited capability compared to a low-wage human worker who can flexibly handle furniture, spills, and edge cases.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Autonomous floor-cleaning robots operate in controlled environments but have limited real-world deployment in busy kitchens and dining areas due to safety concerns, interaction with staff, and inability to handle complex obstacles or sudden layout changes reliably.
Technical feasibility today	claude-sonnet-5	1/5	While some commercial floor-cleaning robots exist, they are not deployed at scale for restaurant kitchen/dining cleaning combining vacuuming, sweeping, and mopping around furniture, staff, and food debris.

Portion and wrap food, or place it directly on plates for service to patrons.

CI 15–33 · 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	1/5	Food-service automation adoption is slow and shallow, concentrated mainly in large chains and central kitchens; most restaurants remain low-digitization, labor-intensive operations with limited capital for automation, making this a laggard sector for AI/robotic adoption.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor-heavy sector with minimal AI/robotic adoption for hands-on food preparation tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI vision systems could assist by suggesting portions or flagging plating errors, but current food-prep augmentation tools are minimal; the task is largely hands-on execution rather than decision-making, limiting meaningful AI-assisted productivity gains while a human remains in control.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no assistance to a human physically portioning and plating food; this is a manual task outside typical AI augmentation use cases.
Task automatability	claude-haiku-4-5-20251001	2/5	Robotic systems can handle repetitive, standardized portions in controlled settings (e.g., fast-food patties), but plating requires fine motor control, visual judgment of portion aesthetics, and adaptation to varied dish types and presentations—areas where current robots struggle significantly and still require human oversight to meet service quality standards.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring dexterous handling of variable food items and plating; no off-the-shelf AI system can perform this end-to-end today.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few hard regulatory barriers exist for automating food portioning and plating; however, customer preference for human food handling, food-safety liability concerns, and organizational inertia in restaurant operations create moderate friction against rapid substitution.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but food safety handling standards, kitchen space constraints, and need for physical dexterity in variable environments create moderate practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Robotic food-prep systems are capital-intensive (six figures+) with integration and maintenance costs; the payback period is long for typical food-service labor rates, making AI/robotics more expensive than hiring workers for this low-wage task in most real-world scenarios.
Cost vs. human wage	claude-sonnet-5	1/5	Specialized food-handling robots and vision systems require expensive capital investment, integration, and maintenance that exceed the cost of low-wage kitchen labor for this task.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Limited deployed automation exists; some industrial food-prep robots handle basic tasks like slicing or portioning uniform items, but end-to-end plating and wrapping with consistent quality remains largely research-stage or confined to highly controlled, repetitive environments, not reliable production systems across diverse restaurant settings.
Technical feasibility today	claude-sonnet-5	1/5	There are no deployed AI/robotic products reliably portioning and plating diverse foods in commercial kitchens at scale; robotic food assembly remains niche and narrow (e.g., pizza or burger lines) rather than general practice.

Make special dressings and sauces as condiments for sandwiches.

CI 24–24 · exposure 16 · augmentation 25 · 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	The food service sector, especially small and mid-sized sandwich shops and restaurants, has minimal adoption of robotic dressing/sauce production. Labor remains cheap and flexible; kitchen automation is limited to large industrial food manufacturers, not front-line food prep.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor-heavy sector with minimal AI/robotic adoption for hands-on food preparation tasks like this.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist by suggesting recipe variations or timing alerts, but current systems offer minimal practical aid to actual condiment production. The task is largely manual and tactile, so digital augmentation provides limited value to the worker actually making sauces.
Augmentation potential	claude-sonnet-5	2/5	AI can suggest recipes, flavor pairings, or scaling calculations for sauces, offering minor assistance, but does not meaningfully change the hands-on preparation process.
Task automatability	claude-haiku-4-5-20251001	2/5	Creating dressings and sauces requires precise ingredient measurement, mixing, and quality control that current AI cannot physically perform. Recipes are somewhat standardizable, but the task fundamentally demands hands-on kitchen work that robotic systems cannot yet do reliably at scale, especially variations for taste adjustment.
Task automatability	claude-sonnet-5	2/5	Physical preparation of dressings and sauces requires manual measuring, mixing, tasting, and adjusting in a kitchen environment, which current AI cannot perform end-to-end; AI could only assist with recipe formulation, not execution.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Food safety regulations and health codes require documented food handling, but these do not legally mandate human labor per se. However, liability for contamination and strong customer preference for fresh human-made condiments create moderate friction to full automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement exists, but food safety handling practices and kitchen workflow integration create some practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Food preparation robots capable of making sauces with consistent quality remain prohibitively expensive (hundreds of thousands of dollars) compared to paying a food worker minimum to low wages. Equipment maintenance and integration costs further worsen the ratio.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI system replacing this physical task, so any hypothetical robotic solution would require expensive specialized hardware far costlier than a food prep worker's wage.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI system reliably makes dressings and sauces from raw ingredients end-to-end. Food preparation robotics exist only in narrow research contexts and cannot handle the variability, texture assessment, and quality checks required for condiment production.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product physically prepares condiments in commercial kitchens; robotic food prep for customized sauces remains research-stage or niche automation, not mainstream.

Add cutlery, napkins, food, and other items to trays on assembly lines in hospitals, cafeterias, airline kitchens, and similar establishments.

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 remains slow; most hospital and airline kitchens still rely on manual assembly lines. Pilots occur in high-volume, high-cost-pressure settings, but the capital expense and customization needs limit production-scale deployment.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and institutional catering are low-digitization, physical-labor sectors with minimal AI/robotics adoption for this specific task.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI-assisted picking or vision-guided arrangement could help workers organize items or flag errors, but the physically dexterous, real-time nature of assembly-line packing offers limited scope for augmentation without full automation.
Augmentation potential	claude-sonnet-5	1/5	Current AI offers essentially no assistance to a human physically assembling trays; there's no software layer that meaningfully speeds this manual task.
Task automatability	claude-haiku-4-5-20251001	2/5	While robotic arms and vision systems could theoretically pick and place items, the task requires handling fragile items (dishes, glasses), adapting to variable tray layouts, and ensuring proper spacing—challenges that current general-purpose systems struggle with at scale. Specialized robotic solutions exist but are not yet at the 50% time-saving threshold for general adoption.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring picking, placing, and assembling varied items on a moving line, which current AI systems (software-based) cannot perform; robotics for this remains experimental, not deployed at scale.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Food safety regulations and hygiene standards require oversight, and some facilities may have union agreements or customer expectations favoring human preparation. However, no explicit licensing requirement prevents automation, creating moderate but not insurmountable friction.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but food safety/hygiene regulations, physical workspace constraints, and need for adaptable dexterity create moderate practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Custom robotic solutions for tray assembly are capital-intensive ($200k–$500k+) with ongoing maintenance, making them comparable to or more expensive than hiring assembly-line workers at typical food-service wages over a multi-year horizon.
Cost vs. human wage	claude-sonnet-5	1/5	Robotic solutions for flexible tray-line assembly require expensive custom automation, sensors, and maintenance far exceeding the low wage cost of human food prep workers.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Narrow robotic applications exist in some high-volume settings, but no mainstream deployed product reliably handles the full complexity of cutlery placement, napkin folding, food item arrangement, and quality assurance. Most solutions require heavy customization and have material error rates in real kitchens.
Technical feasibility today	claude-sonnet-5	1/5	No mature commercial product performs general tray assembly with diverse items in hospitals or airline kitchens; existing food-line robotics are narrow, costly pilots, not widespread deployments.

Load dishes, glasses, and tableware into dishwashing machines.

CI 15–29 · exposure 13 · 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 largely manual and low-digitization; robotic dishware loading is a laggard technology with minimal real-world deployment in standard kitchens. Adoption is confined to large institutional or specialized facilities, not the mainstream food prep sector.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a physically demanding, low-digitization sector with minimal robotic automation deployed for this specific task.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI or robotic systems offer no meaningful on-the-job assistance to a human loading dishes; the task is fundamentally manual and spatially grounded, with no practical assistive software or co-robot deployment at scale.
Augmentation potential	claude-sonnet-5	1/5	Current AI offers essentially no assistance for the physical act of loading dishes into a washer.
Task automatability	claude-haiku-4-5-20251001	2/5	Manual loading of varied dishware into dishwashing machines requires physical manipulation, spatial reasoning about bin arrangement, and hand-eye coordination that current robotic systems struggle with at scale. While some industrial automation exists, it requires extensive setup per facility and cannot match the flexibility a human worker has with diverse or unusual shapes.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring picking, sorting, and placing varied fragile items into a machine; no off-the-shelf AI or robotic system does this reliably at commercial kitchen speed and cost.'
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few regulatory or licensing barriers exist for the automation itself, but tight kitchen space, hygiene concerns, integration complexity, and low per-location task volume create practical friction. Most establishments lack the capital or operational stability to justify robotic investment.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human do this, but practical barriers like kitchen space constraints, breakage liability, and workflow integration add friction.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Current robotic dishware-loading systems are capital-intensive ($100k–$500k+ installation) and require ongoing maintenance, compared to a food prep worker wage. Per-task cost strongly favors human labor in typical restaurant and institutional settings.
Cost vs. human wage	claude-sonnet-5	1/5	Robotic manipulation systems capable of handling diverse glassware and dishes are far more expensive to acquire, program, and maintain than the low wage cost of a human food prep worker.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Prototype robotic systems exist in research and limited industrial deployment, but they remain unreliable with variable dishware, have high error rates (dropping, misplacement), and depend on custom integration per kitchen. No general-purpose off-the-shelf product reliably performs this task in standard food service.
Technical feasibility today	claude-sonnet-5	1/5	No deployed commercial product performs dish-loading in restaurant settings; robotic dishwashing loaders remain research/pilot stage at best.

Receive and store food supplies, equipment, and utensils in refrigerators, cupboards, and other storage areas.

CI 15–29 · exposure 13 · augmentation 25 · 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 preparation is predominantly small-firm, physical, and low-digitization work; automation adoption remains limited to large institutional kitchens and chain restaurants, and even there, receiving and storage remain largely manual.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physically intensive sector with minimal robotic automation deployed for inventory receiving and storage tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist through inventory tracking software and shelf-management alerts, but the core task of physically receiving, handling, and placing items offers limited productivity uplift from current AI tools without substantial robotic hardware.
Augmentation potential	claude-sonnet-5	2/5	Inventory management software or barcode/scanning apps can help track and organize stock, offering modest assistance, but the core physical task remains manual.
Task automatability	claude-haiku-4-5-20251001	2/5	While inventory receiving (scanning, logging) can be partially automated, the physical handling, placement, and verification of diverse food items across multiple storage locations requires significant manipulation and spatial reasoning that current robots handle inconsistently at scale.
Task automatability	claude-sonnet-5	1/5	This is a physical task requiring receiving, lifting, sorting, and placing food items and equipment into storage; no off-the-shelf AI system can perform the physical handling and placement.'
Adoption barriers	claude-haiku-4-5-20251001	2/5	While there are no strict licensing requirements, health and safety regulations (food handling, temperature control) and the need for staff to verify quality and placement create operational friction; organizational inertia in small-to-medium foodservice also slows adoption.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but food safety handling protocols and manual dexterity/judgment needs create some practical friction to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Robotic receiving and storage systems carry high capital and maintenance costs that far exceed the loaded wage of a food preparation worker performing these tasks manually in most restaurant and foodservice settings.
Cost vs. human wage	claude-sonnet-5	1/5	Robotic manipulation and mobile handling systems capable of this task are far more expensive than a low-wage food prep worker performing the same function.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Automated storage systems exist in large commercial kitchens but are specialized, expensive, and limited to specific inventory types; most food operations still rely on human workers for this task, and no generalist deployed system reliably handles the variety and fragility of foodstuffs across typical kitchen environments.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously receives and physically stores kitchen supplies; robotic solutions for this remain research-stage or absent in typical food service settings.

Scrape leftovers from dishes into garbage containers.

CI 15–29 · exposure 8 · augmentation 0 · importance 3.7/5 · 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 preparation is a laggard sector for automation adoption, with most establishments remaining small, under-capitalized, and reliant on manual labor; digitization of the kitchen remains limited and adoption of specialized robots is minimal.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and dishwashing are low-digitization, physically intensive sectors with minimal AI/robotics adoption for manual kitchen tasks like scraping plates.
Augmentation potential	claude-haiku-4-5-20251001	1/5	AI and current automation offer no meaningful assistance to a human scraping dishes—this is a purely manual, low-skill task where machine vision or decision support adds no value to worker productivity.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no assistance for this manual, physical task; there is no software or cognitive component that could meaningfully augment a worker scraping dishes.
Task automatability	claude-haiku-4-5-20251001	2/5	While conveyor-based dishwashing systems can scrape bulk waste, manually scraping varied leftovers from diverse dish shapes, sizes, and materials with safe handling of breakable items falls short of 50% time savings end-to-end; the task requires visual inspection, judgment about what is waste, and delicate handling that current robots struggle with reliably.
Task automatability	claude-sonnet-5	1/5	This requires physical manipulation of dishes and food waste in varied, unstructured kitchen environments, which current AI systems (software-based) cannot perform; robotics for this remains research-stage.'
Adoption barriers	claude-haiku-4-5-20251001	2/5	Food safety regulations and health codes require oversight of food handling and waste disposal, creating modest friction; however, no strict licensing requirement prevents machine substitution, and the task carries low liability risk relative to other food-service tasks.
Adoption barriers	claude-sonnet-5	1/5	There are no licensing, regulatory, or liability barriers preventing automation of this simple manual chore; the only barrier is technical feasibility, not legal or professional protection.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Current robotic systems capable of any dish-scraping would cost tens of thousands of dollars in capital plus maintenance, far exceeding the wage cost of a food-prep worker performing this task for multiple years.
Cost vs. human wage	claude-sonnet-5	1/5	Human labor for this simple manual task is cheap, while any robotic system capable of scraping dishes would require expensive hardware, sensors, and maintenance far exceeding minimum-wage labor costs.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial product reliably performs this task autonomously in production kitchens today; robotic scraping systems exist only in research or highly controlled laboratory settings, not in real restaurant or food-service operations at scale.
Technical feasibility today	claude-sonnet-5	1/5	No deployed commercial product performs dish-scraping in real food service settings; any robotic dish-clearing systems are experimental prototypes, not production tools.

Assemble meal trays with foods in accordance with patients' diets.

CI 14–28 · exposure 20 · augmentation 38 · 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	Healthcare food service remains labor-intensive and distributed across small production units (hospital kitchens) with low digitization and high fragmentation, showing minimal AI agent adoption or measured automation displacement.
Sector adoption velocity	claude-sonnet-5	1/5	Institutional food service (hospitals, care facilities) is a low-digitization, physically intensive sector with minimal AI/robotic adoption for tray assembly tasks today.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI could assist by suggesting menu items matching diet restrictions, flagging portion errors, or providing dietary decision support, raising worker productivity on compliance checks, though the core physical assembly task remains human-centered.
Augmentation potential	claude-sonnet-5	2/5	Software can assist by generating diet-compliant tray manifests or checklists, but it offers little help with the physical assembly and verification process itself.
Task automatability	claude-haiku-4-5-20251001	2/5	While picking and placing items on a tray could theoretically be automated, the task requires interpreting dietary restrictions, identifying correct portions, and verifying diet compliance—tasks that demand reliable vision, dexterous manipulation in varied settings, and error-checking logic that current autonomous systems struggle with reliably at scale.
Task automatability	claude-sonnet-5	2/5	This requires physical manipulation of food items and trays based on dietary rules, which is a physical assembly task not addressable by current AI without robotics that can reliably handle diverse food items and containers.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Healthcare food service is regulated for safety and traceability; dietary errors carry patient harm and liability risk, creating regulatory and quality-control friction that favors human oversight and accountability over unsupervised automation.
Adoption barriers	claude-sonnet-5	3/5	Food safety regulations, dietary/medical accuracy requirements for patient safety, and hygiene standards create meaningful oversight and liability friction, though not a strict licensing requirement for this specific task.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Robotic systems capable of food handling, vision-based diet verification, and tray assembly remain capital-intensive and labor-intensive to maintain, making them more expensive per meal tray than the loaded cost of a food prep worker in most healthcare settings.
Cost vs. human wage	claude-sonnet-5	1/5	Robotic systems capable of this physical dexterity and food-safety compliance would be far more expensive to develop, deploy, and maintain than paying a food prep worker.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Experimental robotic systems exist in controlled lab and pilot settings, but no mature deployed product reliably assembles mixed trays with correct diet adherence, varied food types, and portion control in real hospital kitchens at production scale.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs physical tray assembly matching diets in production settings; robotic food handling remains research-stage for this level of variability and precision.

Store food in designated containers and storage areas to prevent spoilage.

CI 15–24 · exposure 8 · 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	Food service remains largely low-tech with minimal automation adoption; kitchens are small-scale, distributed operations with high physical variability, limiting the economic and logistical incentive to automate storage tasks.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor-heavy sector with minimal robotic automation of basic kitchen tasks like this in production today.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could provide limited assistance through inventory tracking systems or expiration alerts, but the core physical placement task offers minimal augmentation potential since workers already follow simple, procedural rules.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no meaningful assistance for the physical act of sorting and storing food items in designated containers and areas.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI could theoretically guide or monitor storage decisions, the physical task of moving food items into containers and storage areas requires embodied manipulation that current robotic systems struggle with in kitchen environments. The variability of food types, containers, and spatial configurations makes end-to-end automation below the 50% time-saving threshold today.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring picking up food items, judging appropriate containers, and placing them in specific storage areas (coolers, freezers, dry storage) - current AI has no embodied capability to perform this end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Food safety regulations (e.g., FDA, HACCP) and health codes require documented proper storage, but they typically specify procedures rather than mandating human performance; liability concerns exist but are not insurmountable with proper system validation.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement exists, but food safety regulations (HACCP, health codes) require accountable human judgment on spoilage risk and proper storage practices, creating some liability-driven friction.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The capital cost and integration overhead for robotic storage systems far exceeds the loaded wage of a food preparation worker, especially given the low margins in food service and the task's relatively low cognitive demands.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic system to compare costs against for this physical task; a human worker remains far cheaper than any hypothetical robotic solution given current hardware costs.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably performs autonomous food storage in real kitchens; this remains a research and robotics development area. General-purpose manipulation robots lack the dexterity and environmental adaptation needed for production use in food service.
Technical feasibility today	claude-sonnet-5	1/5	No deployed commercial product performs physical food storage tasks in commercial kitchens; robotic manipulation in unstructured kitchen environments remains research-stage.

Wash, peel, and cut various foods, such as fruits and vegetables, to prepare for cooking or serving.

CI 15–24 · exposure 8 · augmentation 13 · 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 largely low-digitization, labor-intensive sector with high staff turnover and fragmented, small-operator base; automation adoption in meal prep is lagging significantly compared to information and finance sectors.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor-heavy sector with minimal AI/robotics adoption for prep tasks; kitchens overwhelmingly rely on manual labor.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI-powered vision systems could assist workers by identifying optimal cutting patterns or sorting produce by ripeness, but current tools offer only marginal assistance; the core manual dexterity and judgment remain largely human-dependent.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer essentially no assistance to a worker physically washing, peeling, or cutting food; there is no software layer that meaningfully speeds this manual task.
Task automatability	claude-haiku-4-5-20251001	2/5	While industrial slicing and peeling machines can automate parts of this task, current AI systems cannot reliably handle the full end-to-end workflow of washing, peeling, and cutting diverse, variable produce with the speed and quality required to meet the 50% time-saving threshold for deployment in typical food service environments.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring dexterity, vision-guided cutting, and handling of irregular objects, which current AI systems (software-based) cannot perform; robotics for this remains experimental and not deployed at scale.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Food safety regulations and health codes create some friction, and customer expectations about freshness and human-prepared food exist, but neither constitutes a strict legal prohibition on mechanization, allowing gradual adoption where economically justified.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but food safety handling standards, equipment costs, and the need for adaptability to varied produce create moderate organizational and physical barriers to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Current robotic systems capable of any part of this task (specialized peelers, slicers) remain capital-intensive and require significant setup and maintenance, making total cost per task substantially higher than paying a food preparation worker.
Cost vs. human wage	claude-sonnet-5	1/5	Human labor for this task is cheap and flexible, while robotic systems capable of general produce prep require expensive specialized hardware, integration, and maintenance, making AI/robotics costlier per unit output today.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed production systems exist that reliably perform the combined washing, peeling, and cutting of various foods autonomously. Robotics research prototypes exist but are narrow in scope, slow, and not yet in reliable commercial use at food service scale.
Technical feasibility today	claude-sonnet-5	1/5	No commercially deployed robotic system reliably washes, peels, and cuts diverse produce in real kitchen settings; existing food-prep robots are narrow, expensive pilots limited to specific items.

Prepare a variety of foods, such as meats, vegetables, or desserts, according to customers' orders or supervisors' instructions, following approved procedures.

CI 15–24 · exposure 8 · 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 preparation occurs predominantly in small-to-medium foodservice operations with low digitization and limited capital investment in automation. Adoption of AI/robotics in this sector remains negligible, confined to a handful of pilot projects.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor sector with minimal AI/robotics penetration in actual food preparation tasks; automation here remains experimental and rare in production.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist with recipe lookup, inventory checks, or timer management, providing marginal productivity gains. However, the core manual and sensory components of food preparation—chopping, seasoning, plating to standard—see limited augmentation from current systems.
Augmentation potential	claude-sonnet-5	2/5	AI can assist with order intake, recipe standardization, or scheduling, but offers minimal direct assistance to the physical act of preparing food itself.
Task automatability	claude-haiku-4-5-20251001	2/5	Food preparation requires significant dexterity, real-time sensory feedback, and physical manipulation of diverse ingredients. Current AI systems cannot reliably handle the full range of cutting, cooking, plating, and quality-assurance steps at speed; robotic arms exist but with narrow scope and high error rates in unstructured kitchen environments.
Task automatability	claude-sonnet-5	1/5	Physical food preparation requires manual dexterity, sensory judgment, and real-world manipulation that current AI systems cannot perform; this is a robotics/embodiment problem, not a cognitive one solvable by today's AI.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Health and safety regulations require human oversight of food handling, and customer expectations strongly favor human preparation. However, these are not absolute legal prohibitions—regulatory barriers exist but are not absolute blocks to automation in all contexts.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement for food prep workers specifically, though health/safety codes and equipment certification create some friction for automated systems in commercial kitchens.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized robotic systems capable of any meaningful portion of food prep remain extremely expensive (six figures+) compared to the modest loaded wage of food preparation workers, making AI uneconomical even for partial automation.
Cost vs. human wage	claude-sonnet-5	1/5	Specialized food-prep robotics remain expensive to purchase, install, and maintain relative to low-wage food prep labor, making AI/robotics costlier per unit output today.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial system reliably performs food preparation end-to-end across a variety of dishes. Limited robotic pilots exist in controlled settings, but they do not meet production-scale deployment standards for restaurants or catering operations.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product autonomously prepares varied foods to order in commercial kitchens; existing food-prep robots are narrow, single-task pilots (e.g., burger flipping) rather than general food preparation systems.

Stock cupboards and refrigerators, and tend salad bars and buffet meals.

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	Food service remains a labor-intensive, fragmented sector with limited automation infrastructure. Adoption of robotics for stocking tasks is negligible in current production environments.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor sector with minimal AI/robotic adoption for these hands-on restocking and food-tending tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI-driven inventory management or food freshness detection could assist workers marginally, but current tools offer limited practical augmentation for the core physical stocking and arrangement task.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer essentially no meaningful assistance for the physical act of stocking cupboards or tending buffet lines.
Task automatability	claude-haiku-4-5-20251001	2/5	While robotic systems could physically move items into storage, the task involves judgment about placement, rotation, spoilage detection, and arrangement that requires adaptation to varied environments and food types. Current deployed systems cannot reliably handle the full task end-to-end.
Task automatability	claude-sonnet-5	1/5	This requires physical manipulation of food items, restocking shelves, and monitoring buffet freshness—tasks requiring mobile physical dexterity that current AI systems cannot perform end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Food safety regulations and health codes create some compliance friction, but no legal requirement mandates a licensed human perform stocking. The main barriers are technical and economic rather than regulatory.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barrier exists, but the physical nature of stocking and food safety handling creates practical friction against automation despite no formal prohibition.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized robotic systems capable of food handling remain expensive to purchase, integrate, and maintain, far exceeding the loaded wage of a food prep worker for the output delivered.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute performing this physical task, so any hypothetical robotic solution would be far more expensive than a human worker today.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No production-grade AI or robotic systems today reliably stock refrigerators or manage salad bars autonomously. Some experimental prototypes exist, but nothing demonstrably works at scale in commercial kitchens.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously stocks kitchen storage or tends buffet lines; this remains firmly in the physical/robotic domain outside current AI product scope.

Butcher and clean fowl, fish, poultry, and shellfish to prepare for cooking or serving.

CI 15–24 · exposure 8 · 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	1/5	Adoption of automated butchering remains confined to large-scale industrial processors and is not widespread in restaurants, grocery stores, or small food service operations. The sector is dominated by manual labor with slow digital/automation penetration.
Sector adoption velocity	claude-sonnet-5	1/5	Food preparation is a low-digitization, physical-labor sector where AI/robotic adoption for tasks like butchering is minimal and largely confined to large-scale industrial meat processing, not typical food prep workers.
Augmentation potential	claude-haiku-4-5-20251001	2/5	Current AI offers minimal assistance to a human butcher—perhaps computer vision for quality inspection or weight/yield calculations, but the core motor and craft skill of cutting remains unaugmented by deployed AI tools today.
Augmentation potential	claude-sonnet-5	1/5	Current AI offers essentially no meaningful real-time assistance to a worker physically butchering or cleaning fowl, fish, or shellfish.
Task automatability	claude-haiku-4-5-20251001	2/5	While some repetitive cuts can be semi-automated (industrial filleting machines exist), the task requires nuanced handling of varied product geometry, identifying quality/defects, and precise anatomical cuts that current off-the-shelf AI systems cannot perform reliably end-to-end. Significant human oversight and skill remain necessary.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring dexterity, force control, and visual/tactile judgment on irregular biological materials; no off-the-shelf AI system can perform this end-to-end today.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Food safety regulations and hygiene standards apply to the process, but no specific licensing requirement mandates a human must perform these cuts (though inspection/certification of the facility is required). Some organizational preference for human labor exists, but the barrier is primarily operational friction rather than legal prohibition.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement blocks automation, but food safety regulations, physical workspace constraints, and the need for careful handling of varied raw ingredients create moderate practical friction.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Industrial butchering equipment is expensive to purchase, integrate, and maintain, and still requires skilled human oversight. The all-in cost per task remains higher than paying a food preparation worker, especially for the variety and quality demanded in most food service contexts.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI-driven substitute performing this task, so any specialized robotic equipment capable of it would be far more expensive per unit output than a human worker in most food service settings.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial AI system reliably performs butchering and cleaning of diverse fowl, fish, poultry, and shellfish at production quality. Robotic applications exist only in narrow, controlled industrial settings with heavy setup; they are not general-purpose solutions available today.
Technical feasibility today	claude-sonnet-5	1/5	No deployed consumer or commercial AI product butchers or cleans fowl, fish, or shellfish; robotic meat-processing systems exist only in narrow industrial contexts, not general kitchen prep.

Mix ingredients for green salads, molded fruit salads, vegetable salads, and pasta salads.

CI 15–24 · 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 a low-digitization sector with small firms and high-touch labor. Automation of salad mixing is rare in production; the sector adopts AI slowly and fragmented kitchens lack the standardization to support such systems.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor-heavy sector with minimal robotic automation of manual food prep tasks in production today.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist by suggesting recipe proportions or flagging ingredient spoilage, but current systems offer minimal real-time support for the core manual mixing and plating tasks that define the work.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools (e.g., recipe suggestion apps) offer negligible direct assistance to a worker physically mixing salad ingredients.
Task automatability	claude-haiku-4-5-20251001	2/5	While precise ingredient measurement and physical mixing could theoretically be automated, the task involves visual judgment of ingredient quality, texture assessment, and proportional balancing that current robotics struggle with consistently. Real-world kitchen salad preparation requires handling fragile ingredients (leafy greens, soft fruits) without damage—a dexterity and sensory challenge that goes beyond simple mixing.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring hands to cut, combine, and toss ingredients; no off-the-shelf AI system performs this end-to-end today.'
Adoption barriers	claude-haiku-4-5-20251001	2/5	Health and food-safety regulations, customer expectations for human food handling, and the need for visual inspection and quality control create modest friction, but no explicit legal barrier requires human labor for salad mixing specifically.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement for this task itself, though food safety/handling regulations and kitchen equipment certification create some friction for automated deployment.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Current robotic food-prep systems (where they exist) carry high capital and maintenance costs that vastly exceed the loaded wage of a food preparation worker; no cost advantage exists in general commercial kitchens.
Cost vs. human wage	claude-sonnet-5	1/5	Physical robotic systems capable of varied ingredient handling and mixing are far costlier to acquire, integrate, and maintain than paying a food prep worker's wage.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed, production-scale AI system reliably prepares mixed salads end-to-end today. Robotic systems for food preparation remain largely in research or specialized industrial settings (not general food service), and lack the flexibility to adapt to varying ingredient quality and customer preferences.
Technical feasibility today	claude-sonnet-5	1/5	No deployed commercial product prepares mixed food salads autonomously in real kitchens; robotic food prep remains experimental and narrow (e.g., single-ingredient pizza or salad-assembly robots in pilot settings).

Stir and strain soups and sauces.

CI 15–24 · 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 fragmented, labor-intensive, and low-digitization; adoption of automation for basic food prep tasks is minimal in production kitchens, with only large chains experimenting with limited robotics.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physically intensive sector with minimal AI/robotic adoption for hands-on cooking tasks; automation here lags far behind information-work sectors.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI/robotic systems offer minimal augmentation to a human stirring and straining; process monitoring or timer automation might assist marginally, but the core manual task itself is not meaningfully enhanced by current AI tools.
Augmentation potential	claude-sonnet-5	1/5	Current AI offers no meaningful real-time assistance to a worker physically stirring and straining food; this is a manual task outside typical AI augmentation use cases like recipe suggestions or scheduling.
Task automatability	claude-haiku-4-5-20251001	2/5	Manual stirring and straining involve fine motor control, real-time feedback, and variable task parameters (viscosity, temperature, consistency). While robotic arms exist in research settings, they lack the sensory integration and adaptive dexterity to reliably perform these coupled tasks at production speed without significant engineering per recipe/context.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring dexterity and real-time judgment of texture/consistency in a kitchen setting; no off-the-shelf AI system can perform this physical action end-to-end today.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Food safety regulations and health codes typically require human oversight of cooking processes; however, these are oversight rather than absolute legal prohibitions on mechanical assistance, so barriers are moderate rather than absolute.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barrier exists, but physical/organizational friction is high: kitchens need flexible, low-cost labor and lack infrastructure for robotic manipulation of hot liquids and cookware.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The capital and integration cost of a robotic system capable of stirring, temperature sensing, and straining far exceeds the hourly wage of a food preparation worker, with ongoing maintenance and programming overhead making per-task cost prohibitively high.
Cost vs. human wage	claude-sonnet-5	1/5	Any robotic solution capable of this task would require expensive specialized hardware, integration, and maintenance far exceeding the low wage cost of a human food prep worker performing this simple manual action.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial products reliably perform stirring and straining of soups and sauces end-to-end in food service environments. Robotic food prep remains largely experimental; production systems do not yet exist at scale in restaurants or commercial kitchens.
Technical feasibility today	claude-sonnet-5	1/5	No deployed consumer or commercial product performs manual stirring and straining of food; kitchen robotics for this specific task remain research-stage or extremely narrow pilot deployments.

Assist cooks and kitchen staff with various tasks as needed, and provide cooks with needed items.

CI 15–15 · exposure 0 · augmentation 13 · importance 4.6/5 · 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 remains a low-digitization, highly physical sector with slow automation adoption. Kitchen robotics are niche and experimental, not deployed at scale.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a physically-oriented, low-digitization sector with minimal AI/robotics adoption for hands-on kitchen support tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could marginally assist with ingredient inventory management or recipe lookups, but the core task—physical assistance and item fetching—offers minimal augmentation opportunity with current technology.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer essentially no meaningful assistance for the physical fetching and ad hoc support this task requires.
Task automatability	claude-haiku-4-5-20251001	1/5	This task involves dynamic physical assistance, fetching items, and real-time responsiveness to cook needs in a kitchen environment. Current AI systems cannot navigate physical kitchens, retrieve items, or adapt to unpredictable requests at the speed and reliability required.
Task automatability	claude-sonnet-5	1/5	This task requires physical manipulation of objects, navigating a kitchen, and fetching items on demand—capabilities far beyond current AI systems, which lack embodied physical presence in typical commercial kitchens.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few hard legal barriers exist, but physical safety requirements, kitchen environment hazards, and the need for real-time human coordination create moderate friction to automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barriers exist, but physical environment constraints, safety requirements around hot equipment, and organizational reliance on flexible human labor create moderate friction.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Humanoid robots or specialized kitchen assistants capable of this task remain prohibitively expensive—orders of magnitude more costly than the minimum wage workers who currently fill this role.
Cost vs. human wage	claude-sonnet-5	1/5	Physical robotic systems capable of this flexible assistance are far more expensive to build, deploy, and maintain than paying a low-wage kitchen worker.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably performs physical kitchen assistance at scale. While robotic arms exist in labs, they are not in production kitchens performing this ad-hoc support role.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs general-purpose physical kitchen assistance and fetching tasks reliably; robotic kitchen assistants remain research/pilot stage and are not in widespread commercial use.

Distribute food to waiters and waitresses to serve to customers.

CI 15–15 · exposure 0 · augmentation 13 · importance 4.6/5 · 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 remains a low-digitization, physically demanding sector with fragmented ownership and tight margins. Adoption of automation for food distribution is extremely limited and restricted to niche high-volume or controlled environments.
Sector adoption velocity	claude-sonnet-5	1/5	placeholder
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist with order scheduling or route optimization if integrated into kitchen management systems, but such systems see limited adoption in typical food service. The primary work—physical transport of plates—offers minimal augmentation opportunity.
Augmentation potential	claude-sonnet-5	1/5	placeholder
Task automatability	claude-haiku-4-5-20251001	1/5	Food distribution to waiters requires physical coordination, real-time prioritization of orders, and adaptability to changing service conditions. Current AI and robots cannot reliably handle the unpredictable spatial arrangements, fragile items, and timing demands of restaurant service environments at scale.
Task automatability	claude-sonnet-5	1/5	Physically handing plated food to waitstaff in a kitchen environment requires manipulation and coordination that current AI/robotics cannot reliably perform end-to-end at equal quality or speed."},"feasibility":{"rating":1,"rationale":"No deployed product performs this physical hand-off task in commercial kitchens at scale; conveyor/pass systems exist but are not AI-driven autonomous solutions."},"cost_ratio":{"rating":1,"rationale":"Robotic systems capable of this physical task would require expensive hardware and integration, far exceeding the cost of a low-wage human worker."},"barriers":{"rating":2,"rationale":"No licensing barrier, but kitchen workflow, food safety handling norms, and physical space constraints create moderate organizational friction against automation."},"adoption_velocity":{"rating":1,"rationale":"Food service is a low-digitization, physically intensive sector with minimal AI/robotics adoption for such hands-on tasks."},"augmentation":{"rating":1,"rationale":"AI offers essentially no meaningful assistance to a human physically distributing plates to waitstaff."}},
Adoption barriers	claude-haiku-4-5-20251001	2/5	While there are few direct legal licensing requirements for the task itself, organizational friction, customer preference for human service, and the tight operational margins of most food service establishments create modest adoption friction rather than hard barriers.
Adoption barriers	claude-sonnet-5	2/5	placeholder
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The capital cost of automation (robotic systems, integration, maintenance) far exceeds the loaded wage of a food preparation worker, particularly given the low per-task cost of human labor in this sector and the short payback horizon in many food service businesses.
Cost vs. human wage	claude-sonnet-5	1/5	placeholder
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably performs end-to-end food distribution in live restaurant settings. Robotic solutions exist in controlled lab or specialty contexts but lack production deployment at meaningful scale in typical food service operations.
Technical feasibility today	claude-sonnet-5	1/5	placeholder

Carry food supplies, equipment, and utensils to and from storage and work areas.

CI 15–15 · exposure 0 · augmentation 13 · 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 a low-digitization, physical-labor-dependent sector with limited current adoption of automation, especially for supply handling in active kitchen environments.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor-heavy sector with minimal robotic automation deployed at scale for material handling tasks like this.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could potentially assist with inventory tracking or logistics optimization for supply chains feeding kitchens, but offers minimal assistance in the physical carrying task itself as executed by workers.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer essentially no assistance for the physical act of carrying supplies and equipment between storage and work areas.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical manipulation and transport of items in kitchens, which demands mobile manipulation in unstructured, cluttered environments. Current AI systems lack the embodied capability to reliably grasp, lift, and carry diverse food items and equipment in real kitchen settings.
Task automatability	claude-sonnet-5	1/5	This is a physical manual labor task requiring mobility, dexterity, and navigation of kitchen environments; no off-the-shelf AI system performs this end-to-end today.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While not strictly licensed, there are occupational safety concerns (food handling, equipment safety) and kitchen layout variability that create moderate friction. No hard legal requirement exists, but operational and safety oversight would be needed.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human specifically, but kitchen layouts, safety, and physical unpredictability create practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Robotics capable of this task (manipulation, navigation, safety) remain significantly more expensive than paying a food prep worker, when accounting for hardware, integration, and facility modification.
Cost vs. human wage	claude-sonnet-5	1/5	Any robotic solution capable of this task would require expensive hardware, integration, and maintenance far exceeding the low wage cost of a human food prep worker.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product system can autonomously perform full-cycle food supply transport in working kitchens. Experimental robots exist but are not in production use for this task at food service scale.
Technical feasibility today	claude-sonnet-5	1/5	No deployed products perform general-purpose carrying of food supplies and equipment in commercial kitchens; robotics for this remain research or narrow pilot stage.

Remove trash and clean kitchen garbage containers.

CI 15–15 · exposure 0 · augmentation 0 · 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	Food service remains a laggard sector in automation adoption, with high labor cost sensitivity but low capital investment in robotics. Trash removal is not a target for current AI/robot deployment in kitchens.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor-heavy sector with minimal AI/robotics adoption for menial cleaning tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI systems offer no meaningful assistance for trash removal and container cleaning; this is purely a physical execution task with no cognitive or decision-support component where AI could augment performance.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no assistance for this physical cleaning and waste-removal task.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical manipulation in unstructured environments (moving trash, handling containers of varying shapes/contents, accessing confined spaces) where current robotics cannot reliably operate at cost-parity with human labor. No off-the-shelf AI system can end-to-end automate this with 50% time savings.
Task automatability	claude-sonnet-5	1/5	This is a physical manual task requiring mobility, dexterity, and handling of waste materials, which current AI systems cannot perform without embodied robotics that are not generally available.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Minimal regulatory barriers exist for automating trash removal; however, physical kitchen constraints, hygiene standards, and the need for reliable performance in messy, variable conditions create moderate practical friction to automation adoption.
Adoption barriers	claude-sonnet-5	2/5	No licensing or regulatory requirement mandates a human for this task, but practical barriers like need for physical robots capable of navigating kitchens and handling waste remain significant.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized garbage-handling robots (if available) cost tens of thousands of dollars with high integration and maintenance overhead, far exceeding the loaded wage of a food preparation worker performing this task.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic solution for this task, so any hypothetical system would be far more expensive than simply having a low-wage worker perform it.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	While some industrial robotics exist for specialized waste handling, no deployed product reliably performs this general kitchen trash and container-cleaning task at scale in typical food service environments. Research prototypes exist but production deployment is negligible.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs trash removal and container cleaning in commercial kitchens; robotic waste handling remains research-stage and far from kitchen deployment.

Package take-out foods or serve food to customers.

CI 10–19 · exposure 8 · augmentation 25 · 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 primarily human-labor sector with slow automation adoption. Most small-to-medium restaurants lack the capital and technical infrastructure to deploy robotic systems, and the sector skews toward labor-intensive, low-digitization operations.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physically demanding sector where AI and robotic adoption for hands-on food handling and serving remains minimal and experimental.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI and robotics offer minimal meaningful assistance to food workers performing packaging and customer service. Temperature alerts or inventory tracking can help marginally, but the core task of handling food and interacting with customers remains predominantly human-driven with little opportunity for productivity enhancement through AI.
Augmentation potential	claude-sonnet-5	2/5	AI can assist with order-taking, ticket management, or kitchen display coordination, but offers little direct enhancement to the physical act of packaging or serving food.
Task automatability	claude-haiku-4-5-20251001	2/5	Packaging pre-portioned foods could be partially automated by robotic systems in controlled settings, but serving food to customers involves physical manipulation, navigation in variable environments, and real-time interaction that current AI and robotics cannot reliably handle at scale. Even partial automation requires significant setup and is not yet at 50% time savings at equal quality across typical food service contexts.
Task automatability	claude-sonnet-5	1/5	Physically packaging food and serving customers requires manipulation of varied objects and physical presence; current AI systems cannot perform this end-to-end without robotics that are not generally available.
Adoption barriers	claude-haiku-4-5-20251001	3/5	While there are no strict licensing requirements for food packaging, health and safety regulations, food handling standards, and customer preference for human service create meaningful friction. Liability concerns around contamination and worker displacement also slow adoption.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement exists, but food safety handling standards, customer interaction expectations, and physical environment constraints create moderate friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Robotic food packaging and serving systems require substantial capital investment, integration, and maintenance, making them significantly more expensive than paying a food preparation worker at typical wages for the foreseeable future. Economies of scale have not yet tipped.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute for the physical labor involved, so any hypothetical robotic solution would currently cost far more than a human worker performing this task.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	While food packaging robots exist in research and limited commercial settings, deployed products do not reliably handle the full range of take-out packaging tasks or customer service interactions in typical food service environments. Current systems are too narrow and error-prone for production use in most establishments.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product reliably packages or serves take-out food in real restaurant settings; this remains a manual, physical task performed by humans.

Place food trays over food warmers for immediate service, or store them in refrigerated storage cabinets.

CI 10–15 · 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	Food service remains a low-digitization, labor-intensive sector with limited automation adoption for direct food handling tasks. Most kitchens continue using human labor for tray placement and storage.
Sector adoption velocity	claude-sonnet-5	1/5	Food service is a low-digitization, physical-labor sector with minimal robotic automation of individual plating/storage actions in production kitchens today.
Augmentation potential	claude-haiku-4-5-20251001	1/5	AI offers no meaningful assistance for this physical manipulation task; augmentation would require deployed robotic systems, which remain outside the norm for food prep work.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no assistance for this discrete physical placement action; it is not a cognitive or planning task amenable to current AI tools.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical manipulation of food trays in a dynamic kitchen environment with spatial reasoning about placement and storage. Current AI systems lack the general robotic manipulation and environmental understanding needed to reliably handle food handling safety and storage placement.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring picking up trays and placing them in specific locations, which current AI systems cannot perform without embodied robotics far beyond typical deployment today.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Food safety regulations and health codes apply, but the barrier is primarily practical (robotics immaturity) rather than a legal prohibition on automation. Some organizations may prefer human oversight for food handling due to liability concerns.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human specifically for this action, though food safety handling norms and kitchen workflow integration create some practical friction.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized food-service robotics capable of this task would cost substantially more than the loaded hourly wage of a food prep worker, with significant integration and maintenance overhead.
Cost vs. human wage	claude-sonnet-5	1/5	Robotic systems capable of this simple physical task would require expensive specialized hardware and integration, far exceeding the low wage cost of a human food prep worker performing it in seconds.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed production system today reliably performs this physical task end-to-end in real kitchen settings. Robotic arms exist in research contexts but are not deployed at scale for food service operations.
Technical feasibility today	claude-sonnet-5	1/5	No deployed commercial products place food trays into warmers or refrigerators in real food service settings; this remains outside current robotic deployment in restaurants and kitchens.

Distribute menus to hospital patients, collect diet sheets, and deliver food trays and snacks to nursing units or directly to patients.

CI 5–10 · 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	Healthcare is a highly regulated, conservative sector with strong labor unions and patient-safety protocols. Adoption of automation in food service is minimal; hospitals continue to rely on human workers due to regulatory friction, liability concerns, and operational complexity in clinical environments.
Sector adoption velocity	claude-sonnet-5	1/5	Food service and hospital support roles are low-digitization, physical-labor sectors with minimal AI/robotic adoption for these specific delivery tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	Current AI offers minimal assistance to food service workers on this task. While simple scheduling or inventory software exists, it does not meaningfully augment the core activities of menu distribution, diet sheet management, or food delivery in real time within patient care units.
Augmentation potential	claude-sonnet-5	2/5	AI could help manage diet sheet data entry or menu tracking systems, but offers little assistance to the physical act of distributing menus and delivering trays.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical presence in hospital settings, navigation of complex layouts, handling fragile food items, and direct patient interaction—capabilities far beyond current AI systems. No part of the task (menu distribution, diet collection, food delivery, patient engagement) can be meaningfully automated end-to-end today.
Task automatability	claude-sonnet-5	1/5	This task requires physical navigation of a hospital, manual handling of trays and menus, and direct interaction with patients—none of which current AI systems can perform without robotics far beyond deployed capability.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Strong barriers exist: healthcare facility regulations often require human staff for infection control and food safety handling, hospital liability concerns with autonomous systems near patients, union agreements in many hospitals, and patient preference for human interaction during meal service. Legal accountability for food safety and patient contact requirements provide substantial protection.
Adoption barriers	claude-sonnet-5	3/5	While not licensed work, hospital settings impose infection control, safety, and patient-interaction protocols that favor human staff, plus practical barriers to robotic navigation in clinical environments.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The capital and operational costs of autonomous robots capable of navigating hospital environments, handling food safely, and interacting with patients would far exceed the loaded wage of a food service worker in any realistic deployment scenario.
Cost vs. human wage	claude-sonnet-5	1/5	Human food service workers are far cheaper than any robotic or AI-driven physical delivery system capable of navigating hospital units and interacting with patients.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product performs this task reliably. While robots exist in research or limited pilot contexts, none operate at production scale in hospitals handling the full workflow of menu distribution, diet sheet collection, and individualized food delivery to patients.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product distributes menus or delivers food trays to patients; this remains a physical logistics/service task performed by humans, with only isolated robotic delivery pilots in narrow hospital corridors.

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.