Locker Room, Coatroom, and Dressing Room Attendants

39-3093.00

Median wage $36,300/yr15,560 employed (US)Rank #641 of 923 scored · top 69% by substitution

Provide personal items to patrons or customers in locker rooms, dressing rooms, or coatrooms.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution21

Exposure13

Augmentation21

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

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

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

Tasks on the substitution scale

23 rated tasks, binned by substitution score.

Position among all scored occupations

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

Tasks with substitution ≥ 70

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

Why this score

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

Task automatabilityw 35%15

panel mean rating 1.6/5 → substitution pressure 15/100

Technical feasibility todayw 20%10

panel mean rating 1.4/5 → substitution pressure 10/100

Cost vs. human wagew 15%13

panel mean rating 1.5/5 → substitution pressure 13/100

Adoption barriersw 20%inverted — strong barriers lower the score57

panel mean rating 2.7/5 (barrier strength) → substitution pressure 57/100

Sector adoption velocityw 10%5

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

Task breakdown (23 tasks)

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

Stencil identifying information on equipment.

CI 24–88 · exposure 53 · augmentation 25 · importance 3.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Locker room and coatroom services remain largely in hospitality and athletic facilities with low automation penetration, and most businesses use simple manual stenciling rather than capital investment. Adoption is lagging compared to logistics and manufacturing sectors.
Sector adoption velocity	claude-sonnet-5	1/5	This occupation is in a low-digitization, physical-labor sector with minimal AI adoption or investment in automating small manual tasks like equipment stenciling.
Augmentation potential	claude-haiku-4-5-20251001	2/5	While AI-assisted positioning or template generation could modestly improve human stenciling accuracy or speed, the task is already straightforward and low-skill; augmentation offers limited productivity gains versus end-to-end automation.
Augmentation potential	claude-sonnet-5	2/5	AI could potentially help design or print stencil templates or track inventory labeling needs, but offers little direct assistance for the physical act of stenciling.
Task automatability	claude-haiku-4-5-20251001	5/5	Stenciling identifying information on equipment is a highly repetitive, rule-based task involving pattern application and text placement. Current computer vision and robotic systems can reliably perform this with optical character recognition, template matching, and automated spray/marking systems, achieving >50% time savings versus manual labor.
Task automatability	claude-sonnet-5	2/5	Physically stenciling equipment requires manual manipulation of objects and tools, which current AI systems cannot perform without robotic embodiment that is not generally available for this niche task.a
Adoption barriers	claude-haiku-4-5-20251001	1/5	Stenciling equipment has no regulatory requirement for human licensure, liability is low, and there is no customer preference for human contact in this back-of-house task. Adoption is purely economic and organizational, with minimal legal or institutional friction.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human specifically, but the physical nature of the task and lack of robotic infrastructure create practical barriers to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	Industrial marking robots and vision-guided automation systems cost thousands to tens of thousands upfront but operate continuously at negligible marginal cost per item, orders of magnitude cheaper than paying human attendant wages ($15–20/hr loaded) for the same throughput once amortized.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic substitute deployed for this specific manual labeling task, so the human remains the only cost-effective option.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed robotic marking and labeling systems exist in industrial and logistics contexts, though most implementations today are still semi-automated or domain-specific. Fully autonomous end-to-end stenciling requires integration but proven technologies are available in production warehouses and manufacturing.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs physical stenciling of identifying information onto locker room equipment; this remains a manual task requiring robotic hardware not in production for this use case.

Answer customer inquiries or explain cost, availability, policies, and procedures of facilities.

CI 47–56 · exposure 42 · augmentation 50 · 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 in locker rooms and coatroom services remains low; most facilities are small, non-digital operations without the infrastructure or motivation to deploy AI systems for customer inquiries. This is a laggard sector with limited digitization.
Sector adoption velocity	claude-sonnet-5	2/5	This occupation sits in low-digitization, physical-service sectors (recreation, fitness facilities) where AI adoption for customer service roles remains slow and inconsistent.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can usefully assist attendants by retrieving relevant policy information, drafting responses to common questions, or prompting them with standard answers, raising their efficiency. However, the human attendant typically remains essential for the final customer interaction and judgment.
Augmentation potential	claude-sonnet-5	3/5	AI-powered signage, kiosks, or apps can supplement attendants by handling routine questions, freeing them for tasks requiring physical presence.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI chatbots can provide scripted responses about basic policies and procedures, this task requires real-time interaction with varied customer inquiries, context-dependent problem-solving, and handling exceptions that fall outside standard policies. Current AI cannot reliably replicate the contextual judgment and empathy needed for a significant portion of inquiries.
Task automatability	claude-sonnet-5	3/5	Answering standardized inquiries about cost, availability, and policies is well within chatbot/voice-agent capability, but in-person, real-time interactions in a physical facility limit full end-to-end automation.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few formal licensing or regulatory barriers exist for this task; however, customer preference for human contact, organizational inertia, and liability concerns around policy misstatements create moderate friction against full automation. Most facilities prefer human attendants for the interpersonal trust aspect.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human answer these questions, though customers may prefer face-to-face interaction in a physical facility setting.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	The inference cost of an AI chatbot for handling inquiries is substantially lower than the loaded wage of a locker room attendant, especially when considering 24/7 availability without breaks. Even with integration and moderation overhead, the cost differential is significant.
Cost vs. human wage	claude-sonnet-5	4/5	A simple FAQ bot or kiosk answering routine policy/cost questions costs far less per interaction than paying an attendant's wage for that portion of the task.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Deployed chatbot products exist and can handle routine facility inquiries and policy explanations, but they struggle with nuanced customer concerns, out-of-policy edge cases, and maintaining the conversational flow expected in service interactions. Many organizations still rely on humans for this due to error costs and customer satisfaction concerns.
Technical feasibility today	claude-sonnet-5	3/5	Deployed chatbots and IVR systems handle similar FAQ-style customer service in many industries, but locker-room/facility-specific deployments are narrow and not widely documented in production.

Check supplies to ensure adequate availability, and order new supplies when necessary.

CI 33–67 · exposure 45 · 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	3/5	Hotels, gyms, and sports facilities are adopting inventory automation at a moderate pace; pilots are common but full deployment remains inconsistent. Small standalone facilities lag significantly behind large hospitality chains.
Sector adoption velocity	claude-sonnet-5	1/5	This occupation is in a low-digitization, physical service sector (recreation/personal care facilities) with minimal AI adoption reported.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI-driven inventory dashboards and automated alerts significantly assist attendants by highlighting shortages, suggesting order quantities, and reducing manual checking work, allowing them to focus on service delivery and facility quality.
Augmentation potential	claude-sonnet-5	2/5	Basic inventory apps or reminder systems could help attendants track supply levels, but the physical checking and ordering process sees limited AI-driven productivity gains today.
Task automatability	claude-haiku-4-5-20251001	4/5	AI systems can monitor inventory levels through sensors, computer vision, or database integration, automatically generate purchase orders when thresholds are breached, and trigger reorders with >50% time savings compared to manual checking and requisition. This requires minimal human judgment once thresholds are configured.
Task automatability	claude-sonnet-5	2/5	Inventory monitoring and reordering can be partially automated with sensors or software, but this task as typically performed involves physical checking of supplies in a locker room context, which requires physical presence and manipulation.4Off-the-shelf AI cannot fully replace this without hardware integration.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few regulatory or legal barriers exist for automating inventory checks. Adoption barriers are mainly organizational (integration with existing supply chains, staff familiarity) rather than hard licensing or liability constraints.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barriers, but the task is usually a minor part of a broader physical attendant role, creating organizational friction against standalone automation.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	Automated inventory systems (RFID, weight sensors, or camera-based stock monitoring with API-driven ordering) cost substantially less than hourly attendant labor for this task when amortized across multiple locker rooms or shifts.
Cost vs. human wage	claude-sonnet-5	2/5	Implementing sensors, RFID, or automated inventory systems for a low-wage, low-volume task like this would likely cost more than having an attendant glance at supplies as part of other duties.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Inventory management and automated reordering systems exist in production, but their reliability depends on proper sensor/database integration and exception handling. Current systems work well for standardized supplies but may struggle with edge cases or non-standard items common in locker room operations.
Technical feasibility today	claude-sonnet-5	2/5	Inventory management software exists and is deployed in retail/warehouse contexts, but purpose-built systems for locker room/coatroom supply checking are rare; most facilities still rely on manual visual checks.

Maintain inventories of clothing or uniforms, accessories, equipment, or linens.

CI 33–40 · exposure 25 · augmentation 50 · 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	Locker rooms and coatrooms are typically managed by small service operations, hospitality, and sports facilities with low digital maturity; adoption of inventory automation in these settings is slow and limited.
Sector adoption velocity	claude-sonnet-5	2/5	This occupation sits in low-digitization service sectors (recreation, hospitality) where AI and automation adoption for such tasks remains slow and pilot-stage at best.
Augmentation potential	claude-haiku-4-5-20251001	3/5	Basic inventory management software can assist attendants by providing real-time counts and alerts, improving their efficiency in tracking and locating items, though the task still relies heavily on human handling and responsiveness.
Augmentation potential	claude-sonnet-5	3/5	Simple inventory apps, spreadsheets, or barcode scanning can meaningfully speed up counting and tracking tasks, offering moderate productivity gains while the human still performs the physical work.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI could theoretically track inventory via computer vision or RFID systems, maintaining physical inventories requires handling items, checking storage, and managing real-world discrepancies—tasks that demand embodied robotic systems not yet deployed at scale in locker rooms. Current AI alone cannot perform the full end-to-end task of inventory maintenance with 50% time savings.
Task automatability	claude-sonnet-5	2/5	Inventory tracking can be assisted by software but the physical task of counting, tagging, storing, and reconciling items still requires human handling in most locker/coatroom settings.At best partial digitization occurs, not full automation of the task end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few hard legal barriers exist, but customer preference for human attendants, organizational inertia, and the need for responsive service (returning items, managing requests) create moderate friction against full automation.
Adoption barriers	claude-sonnet-5	1/5	There are no licensing, legal, or safety barriers preventing automation or software-assisted inventory tracking in this context.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	RFID systems, inventory software, and robotic handling carry significant capital and ongoing costs, while locker room attendants are relatively low-wage workers. Integration and maintenance costs would likely exceed the loaded hourly wage for this work.
Cost vs. human wage	claude-sonnet-5	2/5	Implementing RFID/software inventory systems requires upfront investment in tagging, scanners, and integration that often exceeds the low wage cost of an attendant manually tracking items, especially in small facilities.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Some inventory tracking software exists, but reliable end-to-end maintenance of physical clothing and linen inventories in locker rooms remains undemonstrated in production systems. The task involves tactile verification, handling, and environment-specific logistics that current deployed products do not reliably automate.
Technical feasibility today	claude-sonnet-5	2/5	Inventory management software and barcode/RFID systems exist and are used in some facilities, but they are typically deployed by staff rather than autonomously performing the physical inventory task; adoption in this specific low-tech occupational context is limited.

Operate controls that regulate temperatures or room environments.

CI 5–57 · exposure 25 · 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	Locker room and coatroom operations are low-digitization, typically small-venue settings with minimal automation adoption; facility management in these contexts remains largely manual and low-tech.
Sector adoption velocity	claude-sonnet-5	2/5	Locker rooms and recreational facilities are typically low-tech, small-scale environments with slow adoption of smart building automation compared to office/commercial sectors.
Augmentation potential	claude-haiku-4-5-20251001	2/5	Smart thermostats and monitoring dashboards can provide attendants with temperature data and alerts, but the core task of manual control operation and real-time adjustment offers limited augmentation potential since the attendant must already be present.
Augmentation potential	claude-sonnet-5	2/5	Automated thermostats can reduce the attendant's need to manually adjust controls, but this is a minor, already partially automated sub-task with limited overall productivity impact.
Task automatability	claude-haiku-4-5-20251001	1/5	Operating environmental controls requires real-time monitoring of physical conditions, adjustment of HVAC systems, and responsive decision-making based on occupant comfort—tasks that demand embodied presence and mechanical interaction with physical systems that current AI cannot perform end-to-end without human oversight.
Task automatability	claude-sonnet-5	3/5	Basic thermostat/environmental control operation could be automated via smart building systems, but this requires physical infrastructure setup rather than off-the-shelf AI applied to the current job.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.5%.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Building codes, HVAC licensing requirements, liability for occupant comfort/safety, and the need for physical hardware modifications create substantial regulatory and technical barriers to AI automation of this task.
Adoption barriers	claude-sonnet-5	1/5	No licensing or liability barriers prevent automated environmental controls; this is standard building management practice already common in many facilities.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The cost of installing, integrating, and maintaining AI-based environmental control systems would far exceed the wage of a part-time attendant who performs this task as part of broader facility duties.
Cost vs. human wage	claude-sonnet-5	3/5	Smart HVAC controllers cost money upfront but are cheaper per-action than paying attendant labor specifically for this sub-task, though installation costs offset savings for a minor task.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI product reliably operates building temperature controls autonomously in production locker room or coatroom settings; HVAC systems require licensed technicians and hardwired controls, not AI agents.
Technical feasibility today	claude-sonnet-5	3/5	Building automation systems (BAS) and smart thermostats already exist and are deployed in commercial facilities to regulate temperature and environment automatically.

Operate washing machines and dryers to clean soiled apparel and towels.

CI 24–29 · exposure 16 · augmentation 13 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Locker room and coatroom operations are typically small-scale, labor-intensive, and low-digitization environments in hospitality and recreation sectors with slow AI adoption overall.
Sector adoption velocity	claude-sonnet-5	1/5	This occupation sits in a low-digitization, physical-labor sector (recreational facilities, spas) with minimal AI or robotics adoption for laundry tasks reported industry-wide.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could provide scheduling suggestions or contamination detection alerts to assist an attendant, but the core mechanical operation requires minimal human cognition, limiting the value of augmentation.
Augmentation potential	claude-sonnet-5	1/5	Modern washing machines have basic automation (cycle settings, sensors) but this predates and is unrelated to AI assistance; there is no meaningful AI-driven productivity boost for the human operator.
Task automatability	claude-haiku-4-5-20251001	2/5	Operating washing machines and dryers is mechanically straightforward, but the task includes judgment about fabric type, water temperature, detergent dosing, and cycle selection that varies by soiled item type. Current AI systems cannot reliably sort, load, or diagnose contamination levels without significant human setup and oversight, limiting time savings to below 50% for end-to-end performance.
Task automatability	claude-sonnet-5	2/5	This is a physical task requiring loading/unloading machines, sorting items, and handling equipment, which off-the-shelf AI systems cannot perform without embodiment.It requires robotic manipulation, not just software automation, so it fails the 50% time-saving threshold for current AI.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Physical installation and equipment liability present modest friction, but there are no licensing requirements or legal mandates that a human must operate these machines, lowering the barrier rating.
Adoption barriers	claude-sonnet-5	1/5	There is no licensing, regulatory, or liability barrier specific to automating laundry operations; the barrier is purely physical/robotic capability, not legal or organizational.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The capital cost of robotic laundry systems, integration, and maintenance far exceeds the loaded wage of a part-time or full-time laundry attendant in locker rooms, where labor costs are already modest.
Cost vs. human wage	claude-sonnet-5	1/5	Robotic systems capable of sorting, loading, and operating laundry equipment are far more expensive to acquire and maintain than paying an attendant's wage for this simple manual task.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI product reliably operates washing and drying equipment end-to-end in production environments. Robotic laundry systems exist only in research or narrow industrial contexts, not in general coatroom/locker-room settings serving diverse garments.
Technical feasibility today	claude-sonnet-5	1/5	No deployed consumer or commercial product autonomously operates washing machines and dryers for attendants; laundry robotics remain research-stage or niche industrial pilots at best.

Clean facilities such as floors or locker rooms.

CI 19–33 · 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	Locker rooms and coatrooms are typically found in smaller, less digitized facilities (gyms, small venues, schools); capital equipment adoption in this sector is slow and bottom-up resistance is high.
Sector adoption velocity	claude-sonnet-5	1/5	Janitorial and facility attendant work is a low-digitization, physical-labor sector with minimal AI/robotic adoption in production settings today.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI-powered floor-mapping or scheduling tools could help attendants plan cleaning routes, but the core task of hands-on cleaning, judgment about items, and facility management offers limited augmentation value today.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer negligible assistance to a human physically cleaning floors or locker rooms; there's no meaningful software augmentation for this manual task.
Task automatability	claude-haiku-4-5-20251001	2/5	Robotic floor cleaners exist but struggle with the clutter, obstacles, and varied surfaces typical of locker rooms; human judgment on what to clean and how to handle personal items remains essential, preventing the 50% time-saving threshold at equal quality.
Task automatability	claude-sonnet-5	1/5	Physical cleaning of floors and locker rooms requires manipulation of real-world objects and spaces, which current AI systems (software-based) cannot perform; this is a robotics/physical labor task outside generative AI's scope.rating
Adoption barriers	claude-haiku-4-5-20251001	2/5	No licensing or legal requirement for human attendants exists; adoption is mainly economic and operational, with some customer preference for human attendants in semi-public facilities.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but physical facility access, liability for injury/property damage, and the need for judgment in varied real-world conditions create moderate friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Robotic cleaning systems (purchase, maintenance, integration) remain expensive relative to low-wage locker room attendant labor; per-task costs still favor human workers in this low-margin setting.
Cost vs. human wage	claude-sonnet-5	2/5	Even where robotic floor cleaners exist, capital costs, maintenance, and limited scope mean they rarely undercut low-wage human labor for this specific multi-task cleaning role.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Autonomous cleaning robots operate in some controlled environments, but commercial deployment in active locker rooms with lockers, benches, and personal belongings is limited and unreliable; most production systems serve open floors or warehouses, not the complex spaces here.
Technical feasibility today	claude-sonnet-5	1/5	While some commercial floor-cleaning robots exist, they are narrow-purpose (e.g., vacuuming) and not integrated with locker room attendant duties like sanitizing surfaces, restocking, or handling irregular spaces reliably in production.

Clean and polish footwear, using brushes, sponges, cleaning fluid, polishes, waxes, liquid or sole dressing, and daubers.

CI 15–28 · exposure 8 · 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	Locker room and coatroom services operate in small, physically decentralized settings with low capital budgets and high customer-interaction expectations—sectors lagging in automation adoption. No industry data suggests meaningful AI/robotic uptake in this task.
Sector adoption velocity	claude-sonnet-5	1/5	This occupation sits in a low-digitization, physical-labor service sector showing negligible AI or robotics adoption for this specific task.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could theoretically assist with material-type identification or recommendation of cleaning product selection, but the core task—manual brushing, polishing, and detailed finishing—offers limited augmentation upside without a human applying the work directly.
Augmentation potential	claude-sonnet-5	1/5	AI tools offer no meaningful assistance for the physical act of brushing, polishing, or waxing footwear.
Task automatability	claude-haiku-4-5-20251001	2/5	While brush-and-polish motions are theoretically scriptable, the task demands tactile feedback (detecting shoe material, moisture, dirt texture) and adaptive pressure—difficult for current robots. Automated shoe-cleaning machines exist but are highly specialized, stationary systems not deployable by general AI agents; end-to-end performance with quality parity remains infeasible.
Task automatability	claude-sonnet-5	1/5	This is a physical manual task requiring dexterous manipulation of footwear, brushes, and polish; no off-the-shelf AI system can perform this end-to-end today.wait
Adoption barriers	claude-haiku-4-5-20251001	2/5	Customer preference for human touch, inspection, and customization (choice of polish, handling of delicate materials) creates friction; there are no hard legal barriers, but organizational and customer-satisfaction concerns inhibit substitution.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barrier exists, but the physical dexterity and customer-facing service nature create practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	A skilled attendant earns modestly (≈$25–35k annually loaded); specialized robotic systems for footwear cleaning would require significant capital investment and integration, making per-task cost likely comparable to or exceeding human labor for most small-to-medium operations.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic solution for this physical task, so any hypothetical automation would require expensive custom robotics far costlier than a low-wage human attendant.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI system or commercial product reliably cleans and polishes diverse footwear at scale. Specialized industrial shoe-cleaning equipment exists but is not an AI/agent solution; research prototypes and one-off robotic arms have not reached production deployment in real locker rooms.
Technical feasibility today	claude-sonnet-5	1/5	No deployed robotic or AI product performs shoe cleaning/polishing in production; this remains a purely human manual service task.

Report and document safety hazards, potentially hazardous conditions, and unsafe practices and procedures.

CI 14–28 · exposure 20 · augmentation 38 · importance 3.5/5 · 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	Locker room and coatroom attendant roles are concentrated in small facilities, gyms, and entertainment venues with low digitization; AI adoption in these low-margin, physical-space operations remains minimal.
Sector adoption velocity	claude-sonnet-5	1/5	This occupation is in a low-digitization, physical-presence-dependent sector (recreational/facility services) where AI adoption for hazard detection and reporting is minimal to nonexistent.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI could assist attendants by flagging potential hazards in images or video for human verification and documentation, raising the thoroughness and consistency of inspections without removing human judgment from the final safety report.
Augmentation potential	claude-sonnet-5	2/5	AI could assist with drafting reports, organizing documentation, or suggesting hazard checklists, but it cannot replace the human observation and judgment central to identifying hazards.
Task automatability	claude-haiku-4-5-20251001	2/5	AI vision systems could identify certain physical hazards (clutter, spills, damaged equipment) from images or video, but the task requires contextual judgment about unsafe practices and procedures specific to locker room operations, and would need consistent integration with human oversight rather than autonomous end-to-end execution.
Task automatability	claude-sonnet-5	2/5	While AI could help draft or format hazard reports, the core task requires physical presence, visual inspection of locker rooms, and human judgment to identify unsafe conditions in real time, which current AI cannot perform.5b
Adoption barriers	claude-haiku-4-5-20251001	4/5	Safety reporting and documentation carries legal liability; in many jurisdictions, facility managers and designated safety officers must sign off on hazard reports, creating a human-oversight requirement that prevents full substitution regardless of technical capability.
Adoption barriers	claude-sonnet-5	3/5	While not licensed work, safety reporting often carries organizational liability and procedural requirements that favor human accountability and sign-off for hazard documentation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	The cost of installing and maintaining AI vision infrastructure, plus ongoing human review and verification of flagged hazards, approaches or exceeds the wage of a part-time locker room attendant performing routine inspections.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute for the physical monitoring and judgment involved, so the human labor cost remains the only practical option, making AI not cheaper for this specific task.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Computer vision tools exist for hazard detection in controlled settings, but deployed products for locker room safety documentation are limited and typically require human review to verify findings and document procedures correctly; production use remains narrow and inconsistent.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously patrols physical spaces like locker rooms to detect and report hazards; this remains a human observational task with no mature automation solution.

Assign dressing room facilities, locker space, or clothing containers to patrons of athletic or bathing establishments.

CI 5–35 · exposure 13 · 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	This occupation operates in small, locally managed facilities with minimal digital infrastructure or capital investment. Adoption velocity for AI in this domain is near zero; the sector remains highly resistant to automation due to labor cost and customer preference for human interaction.
Sector adoption velocity	claude-sonnet-5	2/5	Athletic and bathing establishments are a low-digitization, physically-oriented sector with slow adoption of automation for this specific task.
Augmentation potential	claude-haiku-4-5-20251001	1/5	There is minimal opportunity for AI to augment this task. The attendant's role is primarily responsive to patron requests and physical coordination; AI offers no meaningful productivity enhancement for direct patron service or facility assignment logistics.
Augmentation potential	claude-sonnet-5	2/5	Digital check-in or locker management software can assist attendants in tracking assignments, but offers limited transformation of the core task.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires real-time physical interaction with patrons, understanding contextual needs (party size, preference, accessibility), and making discretionary assignments in a dynamic environment. Current AI systems cannot navigate physical spaces, interact with patrons face-to-face, or manage the embodied logistics of assigning and tracking physical facilities at scale.
Task automatability	claude-sonnet-5	2/5	Simple assignment logic could be handled by a digital kiosk or app, but the physical handoff of keys, towels, and space allocation still requires a human presence in most facilities.rating limited by physical component.rating stays low.rating 2 fits.rating.rationale complete.rating.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.rationale.
Adoption barriers	claude-haiku-4-5-20251001	4/5	This task has strong adoption barriers: direct human-customer contact is essential (patrons expect personalized service, privacy, and responsive assistance), liability concerns around lost items and facility management, and no automated system has proven capable of handling the social and physical demands reliably.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but facilities often prefer human staff for security, customer service, and handling of valuables or disputes.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The cost of deploying AI (hardware for navigation/sensing, integration with facility management systems, continuous oversight) would far exceed the wage of a part-time locker room attendant, particularly given the low-margin service economy context.
Cost vs. human wage	claude-sonnet-5	2/5	Automated locker/kiosk systems have upfront hardware costs comparable to or exceeding low-wage attendant labor in many facilities, especially smaller ones.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably performs patron-facing facility assignment and coordination in athletic or bathing establishments. This task remains entirely human-performed in production environments and has not been automated by any commercial system.
Technical feasibility today	claude-sonnet-5	2/5	Self-service locker systems with electronic key assignment exist in some gyms and pools, but most facilities still use human attendants for physical distribution and oversight.

Issue gym clothes, uniforms, towels, athletic equipment, and special athletic apparel.

CI 15–24 · exposure 8 · augmentation 13 · importance 3.8/5 · 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	Gyms and locker room operations are labor-intensive, lower-digitization sectors with minimal documented automation adoption; automation in this niche is rare and not trending upward.
Sector adoption velocity	claude-sonnet-5	1/5	Locker rooms and athletic facilities are low-digitization, physical-service environments with minimal AI or robotics adoption for attendant-type tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI offers no meaningful assistance to a human performing item issuance; the task is fundamentally physical and does not benefit from digital intelligence, inventory system, or decision support in ways that would measurably raise productivity.
Augmentation potential	claude-sonnet-5	2/5	Inventory management software or simple digital tracking could assist attendants in knowing what to issue, but this offers only marginal productivity gains for the core physical handoff task.
Task automatability	claude-haiku-4-5-20251001	1/5	The task requires physically handling and distributing physical items (clothes, towels, equipment) in real-time interaction with customers, which current AI cannot perform. No current system can autonomously retrieve, verify fit/suitability, and hand over items.
Task automatability	claude-sonnet-5	2/5	This is a physical task of retrieving and handing over items to customers, requiring in-person presence and physical dexterity that current AI cannot perform end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While there are no strict legal barriers to automation, customer expectations for human attendants and the low labor cost of the role create moderate organizational friction to replacement.
Adoption barriers	claude-sonnet-5	2/5	No licensing or regulatory barriers exist, but the physical nature of the task and customer expectation of human service create some friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	An AI-powered system capable of physical item retrieval, verification, and customer interaction would require expensive robotics, integration, and maintenance far exceeding the wage of a part-time locker room attendant.
Cost vs. human wage	claude-sonnet-5	1/5	Robotic automation for item retrieval and handoff would require significant capital investment in physical infrastructure, far exceeding the cost of a human attendant for this simple task.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI product reliably performs end-to-end item issuance in locker rooms. Robotic systems exist in narrow domains but are not production-ready for the diverse, dynamic nature of gym attendant tasks.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product performs physical issuance of clothing, towels, or equipment to patrons; this remains a research-stage robotics problem at best, not commercially deployed for this use case.

Set up various apparatus or athletic equipment.

CI 15–24 · exposure 8 · augmentation 13 · importance 2.7/5 · 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	Locker room and athletic facility operations are traditionally low-tech, small-scale, labor-intensive environments with low capital budgets. Adoption of robotics or advanced automation in these settings is minimal and progresses slowly.
Sector adoption velocity	claude-sonnet-5	1/5	This occupation sits in a low-digitization, physical-labor sector with minimal AI or robotics adoption for equipment handling tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist by providing setup instructions, equipment inventory tracking, or maintenance reminders via digital interfaces, but the core physical assembly task offers limited augmentation value; the human still performs nearly all manual work.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer no meaningful assistance for the physical act of setting up athletic equipment or apparatus.
Task automatability	claude-haiku-4-5-20251001	2/5	Setting up athletic equipment requires spatial reasoning, physical manipulation, and understanding of equipment-specific assembly. While AI could theoretically guide setup via vision-based instructions, the physical manipulation and validation of correct assembly remain beyond current autonomous systems without specialized robotics.
Task automatability	claude-sonnet-5	1/5	Setting up physical apparatus and athletic equipment requires manual handling, positioning, and physical dexterity that current AI systems cannot perform without embodied robotics, which are not deployed for this task.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Minimal regulatory barriers exist for the task itself, but organizational preference for on-site human staff to manage the locker room (due to security, personalization, and emergency response needs) creates moderate friction against pure automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human specifically, but the physical, spatial variability of equipment setup creates practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	A locker room attendant performs this task as part of hourly labor (~$15–25/hour all-in). Current robotic systems with vision and manipulation capable of this work cost far more to deploy, operate, and maintain than the human wage for this task.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic solution for this task, so the human worker remains far cheaper than any hypothetical automated alternative given equipment and integration costs.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI systems reliably perform end-to-end physical setup of diverse athletic equipment. Vision systems can detect and classify equipment, but autonomous physical assembly and positioning are not productionized at scale.
Technical feasibility today	claude-sonnet-5	1/5	No commercial products exist that autonomously set up gym or athletic equipment in locker rooms or facilities; this remains an unaddressed physical labor task.

Maintain a lost-and-found collection.

CI 14–24 · exposure 16 · augmentation 25 · 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	Locker room and coatroom services are low-digitization, small-venue operations with minimal automation infrastructure. Adoption of AI solutions in this sector is negligible.
Sector adoption velocity	claude-sonnet-5	1/5	This occupation is in a low-digitization, physical-service sector with minimal AI adoption or investment in automating such tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI-assisted image cataloging or barcode scanning could reduce data-entry burden, but the task's dependence on physical custody, judgment calls, and interpersonal dispute resolution limits meaningful productivity enhancement.
Augmentation potential	claude-sonnet-5	2/5	Simple digital logging or inventory software could help track items, but this offers only marginal assistance to the core physical task.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI could potentially catalog items and manage a database, the core task requires physical handling, storage organization, and matching found items to claimants—tasks demanding human judgment about item identification and customer interaction. AI cannot meaningfully reduce time-to-completion by 50% end-to-end.
Task automatability	claude-sonnet-5	2/5	Physical handling, sorting, and storage of lost items requires physical manipulation and judgment that current AI cannot perform end-to-end; only ancillary logging/tracking could be automated.'
Adoption barriers	claude-haiku-4-5-20251001	4/5	Liability concerns around custody of lost valuables, customer trust requirements, and legal/insurance obligations create significant friction against full automation. A human attendant must retain accountability and customer-facing responsibility.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but physical custody of others' property, security, and liability for lost/damaged items create some organizational and trust-based friction.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	AI implementation (vision systems, database infrastructure, integration oversight) would exceed the cost of a person physically managing a small lost-and-found collection, which is typically part-time or merged with other attendant duties.
Cost vs. human wage	claude-sonnet-5	1/5	AI cannot substitute for the physical labor involved, so there is no meaningful cost comparison for full task replacement; a human must still be paid to do the physical work.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably performs the full lost-and-found management task autonomously. Image recognition could assist cataloging, but customer verification, secure storage decisions, and item-to-owner matching remain manual and context-dependent.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product manages physical lost-and-found collections; at most, simple inventory apps exist but don't perform the physical attendant work.

Provide towels and sheets to clients in public baths, steam rooms, and restrooms.

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

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	This task occurs in hospitality, gyms, and service sectors with slow digitization and limited capital investment in automation. No measurable AI or robotics adoption signal exists in this domain.
Sector adoption velocity	claude-sonnet-5	1/5	This occupation is in a low-digitization, physical-labor sector (personal services/recreation) with minimal AI or robotics adoption to date.
Augmentation potential	claude-haiku-4-5-20251001	1/5	AI offers negligible assistance for this fundamentally physical and interpersonal task. Software cannot help a human attendant dispense towels or navigate a locker room more efficiently.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no assistance for the physical act of distributing towels and sheets in these environments.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical movement in space, handling of textiles, and direct interaction with clients to assess their needs and deliver items. Current AI lacks the embodied robotics and environmental navigation capability to perform this reliably in a real locker room setting.
Task automatability	claude-sonnet-5	1/5	This is a physical task requiring in-person handling and distribution of items in wet, human-occupied spaces; no current AI system can perform this physically.
Adoption barriers	claude-haiku-4-5-20251001	2/5	No legal licensing requirement exists, but strong organizational preference for human contact and hygiene norms create meaningful friction to automation. Clients expect human attendants in intimate spaces like locker rooms and bathhouses.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but the task requires physical presence and human judgment about hygiene, safety, and customer service in intimate public settings.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The capital and operational costs of a robot system (hardware, maintenance, integration) far exceed the loaded wage of a part-time or full-time locker room attendant, which is typically entry-level labor.
Cost vs. human wage	claude-sonnet-5	1/5	AI has no viable mechanism to perform this physical distribution task, so any comparison to human wage cost favors the human by default.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial product reliably performs towel and sheet provisioning to clients in public facilities. Humanoid robots remain prototypes; deployed systems cannot navigate variable locker-room layouts or respond to dynamic client requests.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product substitutes for handing out towels/sheets in a locker room or bath setting; this is purely physical, manual labor.

Store personal possessions for patrons, issue claim checks for articles stored, and return articles on receipt of checks.

CI 5–24 · exposure 8 · 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	Locker room and coatroom services are typically found in lower-digitization venues (gyms, theaters, sports facilities, restaurants) with strong preferences for human staff and limited capital investment in automation infrastructure.
Sector adoption velocity	claude-sonnet-5	1/5	This occupation sits in low-digitization, physical service sectors (recreation, hospitality) with minimal reported AI/robotics adoption for this kind of task.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist with check generation, inventory logging, or retrieval reminders, but these represent minor support functions. The dominant tasks—physical handling and patron interaction—remain human-dependent, limiting meaningful augmentation.
Augmentation potential	claude-sonnet-5	2/5	Simple digital tools (e.g., barcode/RFID systems, digital claim-check apps) could modestly assist tracking and retrieval, but this offers limited productivity transformation for the core physical task.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical interaction with tangible objects (storing and retrieving personal possessions) in a service environment, which current AI systems cannot perform. While inventory tracking could be partially automated, the core act of accepting, storing, and physically returning items remains fundamentally dependent on human dexterity and presence.
Task automatability	claude-sonnet-5	2/5	This is a physical task requiring handling of tangible items, manual storage, and retrieval based on physical tokens, which current AI systems cannot perform end-to-end without robotic embodiment.the digital claim-check matching could be automated but the core physical handling cannot.
Adoption barriers	claude-haiku-4-5-20251001	4/5	There is an implicit human-contact and human-presence expectation in this service role; patrons expect to interact with a person to secure their belongings. Regulatory or insurance requirements around liability for lost/damaged items may also require human accountability and authorization.
Adoption barriers	claude-sonnet-5	2/5	No licensing or regulatory requirement mandates human performance, but physical presence, custody, and security/trust considerations around handling patrons' possessions create moderate organizational friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The hardware cost of a capable robotic system to perform physical item handling, storage, retrieval, and check processing would substantially exceed the loaded wage of a locker room attendant, with implementation and maintenance adding further expense.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI-only substitute for the physical handling involved, so any AI cost is essentially irrelevant next to the low-wage human labor already performing this task cheaply.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI product performs this task end-to-end in production. The physical handling and human-facing service components require embodied systems that do not exist in commercial deployment at meaningful scale.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product performs physical item storage, retrieval, and matching against claim checks in production; this remains firmly in the physical/manual domain outside current AI product capability.

Provide or arrange for services such as clothes pressing, cleaning, or repair.

CI 5–24 · exposure 8 · augmentation 25 · 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	This occupation is in hospitality and personal services—traditionally low-digitization, laggard sectors with strong preference for human interaction and manual craftsmanship. AI adoption in this domain remains minimal.
Sector adoption velocity	claude-sonnet-5	1/5	Locker room and attendant services are low-digitization, physical-labor sectors with minimal AI adoption or investment in automating this type of task.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist with scheduling, inventory tracking, or customer communication, but the core tasks of pressing, cleaning, and repair coordination depend heavily on human skill, judgment, and direct customer interaction where augmentation potential is limited.
Augmentation potential	claude-sonnet-5	2/5	AI could help schedule, track, or coordinate outsourced pressing/cleaning/repair services via apps or software, but offers no assistance with the physical work itself.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI could potentially schedule or coordinate pressing/cleaning services via systems, the core task requires physical manipulation of garments and direct service arrangement with customers. Current AI cannot reliably perform the hands-on work or navigate the variability of customer requests and garment types at scale.
Task automatability	claude-sonnet-5	1/5	This requires physically handling garments, operating pressing equipment, and coordinating with vendors or performing repairs in person—none of which current AI systems can execute end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Significant barriers exist: direct customer contact and service quality expectations are high, liability for damage to personal garments is substantial, and workers must respond to individual customer preferences and urgent requests that require human judgment and trust.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barrier exists, but the inherently physical, hands-on nature of the task creates a structural barrier to any digital automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The loaded cost of human labor for pressing, cleaning, and arranging repair services remains far cheaper than the combined cost of specialized robotics, computer vision, and integration infrastructure needed to automate these tasks.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI substitute for the physical labor involved, so the human remains the only viable and thus more cost-effective option currently.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI system can autonomously provide clothes pressing, cleaning, or repair at production scale. Robotic systems for garment handling exist only in research or highly controlled laboratory settings and do not meet real-world reliability standards.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product performs physical clothes pressing, cleaning, or repair; this remains firmly in the physical service domain.

Monitor patrons' facility use to ensure that rules and regulations are followed, and safety and order are maintained.

CI 5–16 · exposure 5 · 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	2/5	Adoption of AI for facility monitoring remains limited. Most locker rooms, gyms, and coatrooms continue to rely on human attendants; automation pilots exist but have not achieved mainstream production deployment due to safety and liability concerns.
Sector adoption velocity	claude-sonnet-5	1/5	This occupation sits in a low-digitization, physical-service sector with minimal AI adoption for this type of in-person supervisory task.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI-powered cameras and alert systems can assist human attendants by flagging potential safety issues or unusual activity patterns, allowing attendants to focus on high-risk areas and respond more effectively, but the human attendant remains essential.
Augmentation potential	claude-sonnet-5	2/5	Basic sensors or cameras could alert attendants to issues like overcrowding or unattended areas, but this offers only marginal assistance to the core human judgment and interpersonal task.
Task automatability	claude-haiku-4-5-20251001	1/5	Monitoring facility use to enforce rules, detect safety violations, and maintain order requires real-time situational awareness, judgment about patron behavior, and intervention that depends on complex social and physical context. Current AI systems cannot reliably perform this end-to-end supervision task.
Task automatability	claude-sonnet-5	1/5	Requires physical presence, real-time observation, and intervention in a locker/dressing room environment—AI systems cannot perform this end-to-end today.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Liability and duty-of-care asymmetry is high: facilities remain legally responsible for patron safety and rule enforcement, and liability exposure deters full automation. Most facilities retain human attendants as the primary safeguard and trusted authority figure.
Adoption barriers	claude-sonnet-5	4/5	Privacy concerns (locker/dressing rooms), safety liability, and the need for human judgment and physical intervention create strong practical and ethical barriers to camera-based or automated monitoring.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Camera systems with AI analytics and ongoing human oversight still require significant infrastructure, software licensing, and oversight labor to manage false positives and ensure safety compliance, making the combined cost comparable to or exceeding a locker room attendant's wage.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute performing this physical presence/enforcement role, so AI cost comparison is effectively moot; the human remains the only option.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While CCTV monitoring systems with AI can detect some anomalies (motion, crowds), they lack the contextual judgment to distinguish rule-breaking from legitimate behavior and cannot reliably trigger appropriate interventions. No deployed product performs this task reliably as a complete replacement for human attendants.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product substitutes for a human attendant physically monitoring patron behavior and enforcing rules in these facilities.

Procure beverages, food, and other items as requested.

CI 5–15 · 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	Locker room and dressing room attendant roles are in low-digitization, small-firm hospitality environments with minimal recorded AI adoption. Physical service roles in these settings lag far behind adoption trends in information and finance sectors.
Sector adoption velocity	claude-sonnet-5	1/5	This occupation is in a low-digitization, physically-oriented service sector with minimal AI or robotics adoption for these tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI offers no meaningful assistance in helping an attendant procure physical items on demand. The task is fundamentally about human presence, physical action, and immediate responsiveness in real-world settings where AI assistants do not yet operate.
Augmentation potential	claude-sonnet-5	1/5	AI offers negligible assistance for physically procuring and delivering items in this context; there's little digital component to augment.
Task automatability	claude-haiku-4-5-20251001	1/5	Procuring beverages, food, and other items on demand requires real-world navigation, inventory knowledge, payment handling, and responsiveness to variable requests in physical spaces. Current AI systems cannot reliably execute these physical procurement actions end-to-end without human intervention.
Task automatability	claude-sonnet-5	1/5	This requires physically locating, retrieving, and delivering physical items in a real-world facility, which current AI systems cannot perform end-to-end without robotic embodiment far beyond deployed capability.
Adoption barriers	claude-haiku-4-5-20251001	4/5	This task involves direct customer interaction, payment handling, and personal service in a hospitality context where human presence is strongly preferred. Customers expect human judgment, personalization, and trust in service delivery, creating substantial organizational and customer-preference friction against automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barrier exists, but physical presence, trust, and personal service expectations create moderate organizational friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	An AI system capable of autonomous procurement would require significant robotics, supply-chain integration, and physical infrastructure—far more expensive than the loaded wage of a locker room attendant performing this task today.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute for this physical fetch-and-deliver task, so any AI-based approach (e.g., robotics) would be far costlier than a human attendant today.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI product independently procures and delivers physical goods in response to customer requests in locker room or hospitality settings. This task requires autonomous movement through real environments and interaction with suppliers or inventory systems that are not yet automated at scale.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product physically procures and delivers beverages, food, or personal items in locker/dressing rooms; this remains purely a human physical service task.

Collect soiled linen or clothing for laundering.

CI 5–15 · 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	Locker room and coatroom operations are small, distributed, and low-margin. Few organizations have invested in automating linen collection, and no sector trend toward AI agents in these roles exists.
Sector adoption velocity	claude-sonnet-5	1/5	This occupation sits in a low-digitization, physical-labor sector where AI adoption is minimal and robotic solutions for such tasks are not in production deployment.
Augmentation potential	claude-haiku-4-5-20251001	1/5	AI offers negligible assistance for a task that is largely manual item recognition and transport. There is no material way current systems augment human productivity on collection itself.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no meaningful assistance for the physical act of collecting soiled linens or clothing.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical presence in a space, visual identification of soiled items, and manual collection—capabilities current AI systems lack. A mobile robot could theoretically perform collection, but widespread deployment in locker rooms raises privacy and practical barriers not yet solved.
Task automatability	claude-sonnet-5	1/5	This is a physical task requiring manipulation of soft, deformable materials in varied real-world settings; no current AI system can perform the physical collection of soiled linens.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Strong privacy concerns arise from robots operating in locker rooms and dressing areas; liability and workplace culture also resist automation. The human touchpoint is valued for discretion and context-aware handling of personal spaces.
Adoption barriers	claude-sonnet-5	2/5	No licensing or regulatory barriers exist, but the physical nature of the task and need for a mobile agent to navigate spaces creates practical friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The capital cost of a reliable autonomous collection robot, plus integration and maintenance, far exceeds the wage cost of a human attendant performing this task, especially in lower-volume facilities.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI-driven substitute for physical collection, so any hypothetical robotic solution would be far more costly than the low-wage human labor currently performing this task.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial AI system reliably performs end-to-end linen collection in locker rooms or coatrooms today. The task combines physical manipulation, space navigation, and item recognition in unstructured human environments where production reliability remains unproven.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs physical laundry collection; this remains squarely in the domain of human labor or at best basic robotics research, not commercial attendants replacement.

Provide assistance to patrons by performing duties such as opening doors or carrying bags.

CI 5–15 · exposure 0 · augmentation 13 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Adoption of robotic attendants in locker rooms and coatrooms is virtually non-existent; the hospitality sector has not meaningfully deployed autonomous systems for this function.
Sector adoption velocity	claude-sonnet-5	1/5	Locker room and attendant services are a low-digitization, physical-labor sector with essentially no AI/robotic adoption for these hands-on duties.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could potentially assist by tracking bag locations or alerting attendants, but the core task of physically opening doors and carrying bags offers minimal opportunity for AI-assisted productivity gains while a human remains in the loop.
Augmentation potential	claude-sonnet-5	1/5	Current AI offers no meaningful assistance for the physical acts of opening doors or carrying bags for patrons.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical manipulation in real-world environments (opening doors, carrying bags) and responsive human interaction with patrons. Current AI systems lack the embodied capabilities and dexterity needed to reliably perform these physical actions at scale.
Task automatability	claude-sonnet-5	1/5	This is a physical task requiring presence, mobility, and manipulation of objects in a real-world space, which current AI systems cannot perform without embodiment in advanced robotics that is not generally available.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Patrons expect human service and direct interaction in hospitality contexts; there are also liability and safety concerns around autonomous systems handling personal belongings and operating in occupied spaces.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human for this task, but customer expectations of personal service and physical safety considerations create some friction.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Humanoid or mobile robots capable of grasping and carrying tasks remain expensive to acquire and maintain, with infrastructure requirements far exceeding the loaded wage of a locker room attendant.
Cost vs. human wage	claude-sonnet-5	1/5	Physical robotic systems capable of this task are far more expensive to deploy and maintain than a human attendant at typical wages for this role.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed products today can autonomously open doors and carry bags for patrons in locker room or coatroom settings. Robotics in this domain remain research-stage or extremely narrow-purpose, without reliable real-world deployment.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product reliably opens doors or carries patrons' bags in a service setting; this remains at the research/prototype stage for general-purpose robotics.

Attend to needs of athletic teams in clubhouses.

CI 5–10 · exposure 0 · augmentation 0 · 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	Athletic and recreation sectors show minimal adoption of automation for attendant roles; the need for human judgment, responsiveness, and presence in team spaces makes this a laggard domain for such displacement.
Sector adoption velocity	claude-sonnet-5	1/5	This occupation is in a low-digitization, physically-oriented service sector with no evidence of AI or robotic adoption for these duties.
Augmentation potential	claude-haiku-4-5-20251001	1/5	AI offers no meaningful assistance to a locker room attendant; the task is fundamentally about physical presence, material logistics, and interpersonal service that AI tools do not enhance.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no meaningful assistance for the physical, hands-on nature of attending to athletes' needs in a clubhouse.
Task automatability	claude-haiku-4-5-20251001	1/5	Attending to athletic teams in clubhouses requires dynamic physical presence, real-time responsiveness to unpredictable human needs, and personal interaction that current AI systems cannot perform. The task involves movement, material handling, and situational judgment that falls entirely outside AI capabilities today.
Task automatability	claude-sonnet-5	1/5	This task requires physical presence, handling equipment/uniforms, and personal interaction with athletes in a locker room setting, none of which current AI systems can perform.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Strong adoption barriers exist due to the requirement for human presence and direct interaction with athletes, implicit trust and privacy expectations in team environments, and organizational norms around personal service that strongly favor human staff.
Adoption barriers	claude-sonnet-5	3/5	While not licensed work, the task requires physical presence, trust, and personal service that create strong practical (though not legal) barriers to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Physical robot systems capable of clubhouse attendance would be vastly more expensive than deploying a human attendant, requiring substantial infrastructure, maintenance, and customization for a single facility.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI substitute for this physical service task, so any AI cost comparison is moot—human labor remains the only viable option.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI product can physically attend to athletes, manage clubhouse operations, or respond to team needs in real time. This is a fundamentally embodied service task with no production automation solution.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product exists that physically attends to athletic teams' needs in clubhouses; this is purely a physical, in-person service role.

Refer guest problems or complaints to supervisors.

CI 0–10 · exposure 0 · 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	Locker room and coatroom work is in low-digitization, small-firm service settings with minimal automation adoption; the personal nature of guest complaints makes this a laggard-sector task unlikely to see AI deployment in the foreseeable future.
Sector adoption velocity	claude-sonnet-5	1/5	This occupation is in a low-digitization, physical-service, small-establishment sector (recreation/attendant services) with minimal AI adoption in daily operations.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could potentially help an attendant draft or summarize a complaint for a supervisor, but the core task—listening to a guest and deciding on escalation—inherently centers on human judgment and interpersonal skill, leaving little room for meaningful AI assistance.
Augmentation potential	claude-sonnet-5	2/5	AI could help log complaints or suggest response scripts for supervisors, but it offers little direct assistance to the attendant performing the immediate escalation.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires human judgment to assess complaint severity, empathy, and real-time decision-making about escalation—capabilities current AI cannot reliably perform in a service context where the attendant must understand nuanced guest concerns and decide appropriately whether to escalate.
Task automatability	claude-sonnet-5	1/5	This requires physically present human judgment to recognize a complaint, interact with an upset guest, and route it to the right supervisor in real time within a physical facility—AI cannot perform this end-to-end today.
Adoption barriers	claude-haiku-4-5-20251001	5/5	This task involves direct human interaction with guests and judgment calls about service recovery; customer expectations, liability for mishandled complaints, and the need for human accountability in service contexts create strong organizational and reputational barriers to full automation.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement exists, but strong customer-service norms and organizational hierarchy mean a human presence and human escalation path is expected and preferred.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	An AI system capable of reliably handling guest complaints and escalation would require significant infrastructure, training, and oversight—substantially more expensive than a low-wage attendant performing this occasional task as part of their role.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI system performing this physical, in-person escalation task, so any AI solution would cost more or simply not function as a substitute.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product can autonomously handle guest complaints and complaints escalation in a locker room setting; this requires genuine understanding of context, emotional intelligence, and organizational judgment that exceeds current AI capabilities in production environments.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product handles in-person guest complaint escalation in locker/coatroom settings; this remains a human interpersonal task with no AI product substituting for it.

Activate emergency action plans and administer first aid, as necessary.

CI 0–0 · exposure 0 · 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	This task occurs in low-digitization, physical-presence environments (locker rooms, coatrooms) where adoption of even basic automation is minimal, and emergency response remains firmly a human responsibility by law.
Sector adoption velocity	claude-sonnet-5	1/5	This occupation and task domain (physical facility attendants handling emergencies) shows minimal AI adoption given the physical, safety-critical nature of the work.
Augmentation potential	claude-haiku-4-5-20251001	1/5	While AI could theoretically assist by dispatching emergency services via a call system, this is a minor peripheral function. The core task of administering first aid and making real-time medical decisions cannot be meaningfully augmented by current AI.
Augmentation potential	claude-sonnet-5	2/5	AI could provide protocol reminders or emergency dispatch alerts, but offers little assistance during the actual hands-on emergency response and first aid administration.
Task automatability	claude-haiku-4-5-20251001	1/5	Emergency response and first aid require real-time decision-making, physical intervention, and human judgment in high-stakes, unpredictable situations that current AI cannot handle autonomously. No AI system can perform CPR, apply tourniquets, or assess trauma competently without human presence.
Task automatability	claude-sonnet-5	1/5	Emergency response requires physical presence, real-time judgment, and hands-on first aid delivery that current AI cannot perform end-to-end.'},
Adoption barriers	claude-haiku-4-5-20251001	5/5	Legal and regulatory requirements mandate that actual humans trained in first aid and certified in emergency response must perform these duties. Liability, duty-of-care, and occupational safety regulations create hard legal barriers to any substitution.
Adoption barriers	claude-sonnet-5	5/5	Emergency response and first aid often require certified training, legal duty of care, and immediate human physical intervention, creating hard barriers to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	AI cannot perform this task at any cost; the comparison is not meaningful. A human attendant trained in first aid is the only current option, making human cost the baseline.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute performing this physical emergency task, so cost comparison favors the human by default.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI product reliably activates emergency plans or administers first aid in real-world settings. This task fundamentally requires human presence and physical capability that current AI systems lack entirely.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product physically administers first aid or activates emergency plans autonomously in real-world facility settings today.

Related occupations — Personal Care & Service

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.