Maids and Housekeeping Cleaners
37-2012.00Median wage $35,510/yr860,670 employed (US)Rank #682 of 923 scored · top 74% by substitution
Perform any combination of light cleaning duties to maintain private households or commercial establishments, such as hotels and hospitals, in a clean and orderly manner. Duties may include making beds, replenishing linens, cleaning rooms and halls, and vacuuming.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution20
Exposure8
Augmentation16
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
20 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
0%
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%11
panel mean rating 1.4/5 → substitution pressure 11/100
Technical feasibility todayw 20%4
panel mean rating 1.2/5 → substitution pressure 4/100
Cost vs. human wagew 15%4
panel mean rating 1.2/5 → substitution pressure 4/100
Adoption barriersw 20%inverted — strong barriers lower the score71
panel mean rating 2.2/5 (barrier strength) → substitution pressure 71/100
Sector adoption velocityw 10%2
panel mean rating 1.1/5 → substitution pressure 2/100
Task breakdown (20 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.
Request repair services and wait for repair workers to arrive.
37CI 30–44 · exposure 25 · augmentation 50 · importance 3.6/5 · click for rater detail
Request repair services and wait for repair workers to arrive.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Housekeeping services remain concentrated in small firms and in-house teams with low digitization; while hospitality chains may use scheduling tools, broader adoption of AI-driven repair coordination is still emerging and limited. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Hospitality and facilities services are lower-digitization sectors with slow, uneven adoption of AI tools for maintenance coordination. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered scheduling assistants and request forms can reduce administrative overhead and speed up submission, making the task somewhat more efficient for the human housekeeper even though they still wait for the repair worker. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based ticketing and scheduling apps can streamline the request submission process, though they don't affect the waiting/physical coordination portion of the task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Submitting a repair request and scheduling can be partially automated via chatbots or web forms, but waiting for arrival is purely passive; the task also requires real-time communication and responsive rescheduling if workers are late or unavailable, which AI cannot handle autonomously today. |
| Task automatability | claude-sonnet-5 | 2/5 | Requesting repair services (e.g., calling or filing a ticket) could be automated via scheduling software, but waiting for and coordinating with repair workers on-site requires physical presence that AI cannot replicate.the task is largely physical/logistical, not purely informational. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Repair service requests typically require human decision-making about what is broken and priority, and the property/safety liability usually falls on a human supervisor; these create moderate friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 1/5 | No licensing or legal requirement mandates a human specifically request repairs or wait for workers; this is a low-stakes administrative/physical task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI handling the request submission is very cheap, but the human must still wait (occupying their time regardless), and any coordination complexity quickly requires human intervention, limiting cost savings. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated ticketing systems are cheap relative to a human's time spent submitting requests, but the physical waiting component still requires paid human presence, keeping overall cost comparable to status quo. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Simple repair request submission can be automated through existing scheduling systems, but end-to-end task execution (request, coordinate arrival, handle unexpected delays) remains largely manual; few deployed systems handle the full workflow reliably without human oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Facilities management software and chatbots can log repair requests today, but the 'waiting for workers to arrive' portion is inherently physical and not something deployed AI products handle. |
Sweep, scrub, wax, or polish floors, using brooms, mops, or powered scrubbing and waxing machines.
24CI 15–33 · exposure 13 · augmentation 13 · importance 4.2/5 · click for rater detail
Sweep, scrub, wax, or polish floors, using brooms, mops, or powered scrubbing and waxing machines.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Cleaning services remain dominated by small firms, informal labor, and low digitization. Adoption of robotic floor cleaners is negligible outside a few large commercial/institutional facilities; sector-wide uptake is in pilot stage at best, not production deployment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Residential and commercial cleaning is a low-digitization, physically dispersed sector with minimal AI/robotic adoption in production at scale. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current floor-cleaning equipment (powered mops, buffers) provides modest physical assistance to human workers, but AI-driven augmentation (smart route planning, surface detection) is minimal. The task remains largely manual labor with little AI enhancement today. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Powered scrubbing/waxing machines already assist humans mechanically, but AI-specific augmentation (e.g., smart routing, sensing) is minimal and not widely deployed for this task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While floor cleaning machinery exists, current AI-equipped robots struggle with the variability of real residential/commercial floors (obstacles, fragility, surfaces) and require substantial manual intervention. End-to-end automation with 50% time savings at equal quality is not yet reliably demonstrated. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring mobility, dexterity, and adaptation to varied surfaces and obstacles; no off-the-shelf AI or robotic system performs this end-to-end with 50% time savings today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are no legal licensing or regulatory barriers to deploying floor-cleaning robots, but organizational friction is moderate: customers often prefer human cleaners for thoroughness and adaptability, and integration into existing cleaning contracts is slow. Physical liability for equipment damage provides some friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement, but physical environment variability, liability for property damage, and need for judgment on materials create moderate practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic floor cleaners are capital-intensive (tens of thousands of dollars), require maintenance and oversight, and operate slowly compared to human cleaners. The all-in cost per cleaned square foot far exceeds the loaded wage of a cleaner, especially in high-turnover, low-wage markets. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized floor robots have high upfront costs and limited capability, so for full task coverage across varied floor types and settings, human labor remains cheaper or comparable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some prototype robotic floor cleaners exist in research and limited deployment, but they have narrow scope (flat, obstacle-free spaces), high error rates (missing spots, uneven waxing), and remain expensive. No mature production systems handle the full range of floor types and conditions at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | Consumer robot vacuums/mops exist but perform only basic floor cleaning on simple surfaces, not scrubbing, waxing, or polishing in varied real-world housekeeping settings; no mature product handles the full task. |
Empty wastebaskets, empty and clean ashtrays, and transport other trash and waste to disposal areas.
22CI 15–29 · exposure 8 · augmentation 13 · importance 4.2/5 · click for rater detail
Empty wastebaskets, empty and clean ashtrays, and transport other trash and waste to disposal areas.
22| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Housekeeping remains a low-digitization, physically-grounded sector with minimal AI/robotic adoption in production; workforce is predominantly small operators and facility services with limited capital investment in automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Cleaning and janitorial services are a low-digitization, physically dexterous sector with minimal AI/robotic adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with task scheduling, route optimization, or waste stream classification, but the core task of physically emptying and transporting waste offers limited augmentation potential since the human must be physically present anyway. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers essentially no assistance to a human performing this specific physical trash-collection task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical manipulation of objects in unstructured home/office environments remains challenging for current robots. While some specialized waste-handling robots exist, they cannot reliably navigate variable room layouts, identify trash types, and transport waste with the consistency and speed of a human at 50% time savings across diverse settings. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring mobility, grasping, and navigation through varied environments; no off-the-shelf AI/robotic system performs this end-to-end today with meaningful time savings. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Limited regulatory barriers exist for this task, but customer preference for human cleaners, need for careful handling of certain waste types, and integration complexity into existing facilities create moderate friction. |
| Adoption barriers | claude-sonnet-5 | 1/5 | There is no licensing, liability, or regulatory requirement tying this task to a human specifically; barriers are purely physical/technical, not institutional. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic waste-handling systems are capital-intensive (tens of thousands of dollars) with high maintenance costs, while a minimum-wage housekeeper costs $15–20/hour; the per-task cost of automation far exceeds human labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Human labor for this task is cheap and requires no specialized capital; any robotic solution would require expensive hardware, maintenance, and infrastructure exceeding the cost of a low-wage worker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs this full task autonomously in production settings. Robotic prototypes exist but require heavily controlled environments, and their error rates and operational scope remain far below practical replacement standards. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed consumer or commercial product reliably empties wastebaskets and transports trash across varied real-world settings; robotics in this space remains research/pilot stage. |
Clean rugs, carpets, upholstered furniture, and draperies, using vacuum cleaners and shampooers.
22CI 15–29 · exposure 13 · augmentation 25 · importance 4.1/5 · click for rater detail
Clean rugs, carpets, upholstered furniture, and draperies, using vacuum cleaners and shampooers.
22| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Residential and commercial cleaning remain low-digitization, fragmented sectors with many small operators; adoption of advanced automation is minimal and concentrated in large facilities, typical of laggard sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Cleaning services is a low-digitization, physical-labor sector with minimal robotic/AI adoption in production beyond niche robot vacuums for floors only. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Robotic vacuums can assist by handling routine floor cleaning, but the human cleaner must still manage shampooing, upholstered items, and draperies, resulting in modest productivity gains rather than transformative assistance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Basic robot vacuums can assist with floor vacuuming but offer no meaningful help with upholstery, draperies, or shampooing, limiting overall augmentation of this composite task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While robotic vacuum cleaners exist, they cannot reliably handle the full scope of this task—shampooing upholstered furniture, cleaning draperies, and managing varied carpet types—without human intervention and adjustment. Current robots cannot match the speed and quality a trained human cleaner achieves across these diverse subtasks. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity, mobility, and adaptation to varied environments; no current AI system can perform the physical cleaning itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or legal requirement mandates human performance, but customer preference for human oversight, liability concerns over damage to high-value furnishings, and need for judgment in handling delicate items create meaningful friction to automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirements, but physical access to homes/offices, liability for damage to furniture/property, and trust/customer preference create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic cleaning equipment and integration costs remain high relative to the hourly wage of a housekeeping cleaner, and full-task automation would require expensive multi-capability systems that do not yet exist at competitive cost. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic cleaning hardware capable of this range of tasks is expensive relative to low-wage human labor, and lacks the versatility to handle upholstery and draperies, making AI/robotics costlier per task-equivalent today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed robotic vacuums exist but handle only basic floor cleaning; shampooing and upholstered furniture cleaning remain beyond reliable production automation. Products in real use are limited to flat floors and lack the dexterity and judgment needed for the full task scope. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed robotic products that reliably vacuum carpets, shampoo upholstery, and clean draperies in real-world home/commercial settings at production scale; consumer robot vacuums only partially address floor vacuuming. |
Clean rooms, hallways, lobbies, lounges, restrooms, corridors, elevators, stairways, locker rooms, and other work areas so that health standards are met.
21CI 10–33 · exposure 13 · augmentation 25 · importance 4.3/5 · click for rater detail
Clean rooms, hallways, lobbies, lounges, restrooms, corridors, elevators, stairways, locker rooms, and other work areas so that health standards are met.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hospitality and facility management are traditionally low-digitization sectors with high reliance on on-site labor; adoption of autonomous cleaning is minimal and limited to early pilots in large hotels or airports, not sector-wide production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hospitality and cleaning services are a low-digitization, physically intensive sector with minimal AI/robotic adoption in production for full-room cleaning tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools can assist with scheduling, quality inspection via computer vision, or inventory management, but offer minimal augmentation to the core physical cleaning work itself while a human remains in the loop. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some tools like scheduling software, checklists, or narrow robotic floor cleaners can support housekeepers, but there is little AI assistance for the core physical cleaning task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical cleaning of diverse spaces requires mobile manipulation, navigation, and adaptation to variable layouts. While robotic cleaning exists for limited domains (floor polishing), end-to-end coverage of hallways, restrooms, elevators, and complex furniture arrangements at equal quality and >50% time savings is not reliably demonstrated by current off-the-shelf systems. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical cleaning of rooms and facilities requires manual dexterity, mobility, and physical interaction with real-world environments that current AI systems cannot perform; this is a robotics/embodiment problem, not a cognitive one. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Health and safety standards apply to the task itself rather than licensing the performer; however, customer preference for human staff, liability for missed contamination, and organizational resistance to visible automation introduce moderate friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirements exist, but practical barriers include the need for physical dexterity, judgment about cleanliness standards, and customer/health inspection expectations that favor human execution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic cleaning equipment has high capital and maintenance costs; deployment remains expensive relative to low-wage human labor in most markets, particularly for irregular spaces and areas requiring dexterity. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for this physical labor at scale, so any hypothetical robotic solution would involve far higher capital and maintenance costs than employing a housekeeper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Autonomous cleaning robots operate in narrow, controlled settings (e.g., flat floors, simple geometries) but lack dexterity for restroom fixtures, stairways, locker rooms, and health-standard verification. No mature deployed product reliably handles the full scope at production scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs comprehensive physical cleaning of varied spaces like hotel rooms, restrooms, and stairways; robotic vacuums exist for narrow floor-cleaning but not the full task scope described. |
Disinfect equipment and supplies, using germicides or steam-operated sterilizers.
20CI 5–35 · exposure 13 · augmentation 25 · importance 4.3/5 · click for rater detail
Disinfect equipment and supplies, using germicides or steam-operated sterilizers.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption remains slow in typical housekeeping sectors (hotels, offices, residential). Specialized robotic disinfection is confined to hospitals and a few high-risk environments; mainstream commercial and residential cleaning services have not yet shifted to autonomous sterilization systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Housekeeping and janitorial sectors are low-digitization, physical-labor industries with minimal AI/robotic adoption for disinfection tasks to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI and robotics offer limited augmentation in this task; they may assist with scheduling or documentation, but the core manual and safety-critical work of applying germicides and operating sterilizers remains best performed hands-on by a trained human, limiting AI's assistive role. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could provide minor assistance such as scheduling reminders or protocol checklists, but offers little direct enhancement to the physical act of disinfecting equipment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While germicide application and steam sterilization are physically routine, the task requires judgment about which method suits each item, equipment setup, safety compliance, and verification of effective sterilization—most of which remain difficult for current robotics and AI without extensive domain-specific setup. Autonomous systems exist in highly controlled lab/medical settings but do not generalize to typical housekeeping environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring handling of chemicals, equipment, and sterilizers in varied physical environments; no current AI system can perform the physical disinfection itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: germicide and sterilizer use is often regulated; liability for incomplete or improper sterilization is asymmetric (errors cause illness/harm); many organizations and households prefer human oversight of disinfection for safety and accountability; and some settings legally require trained personnel. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this specific task, but physical presence, liability for chemical handling, and lack of appropriate low-cost robotic tools create practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current robotic disinfection systems (e.g., UV robots, electrostatic sprayers) carry high capital and maintenance costs that are not yet cheaper than paying a maids' hourly wage for the task, especially when amortized across typical housekeeping workloads. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Specialized robotic disinfection hardware, where it exists, is capital-intensive and narrow in scope, making it far more expensive per task-equivalent than a human housekeeper for general equipment/supply disinfection. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Limited commercial deployment: robotic disinfection uses UV or electrostatic spray in narrow use cases (hospitals, aircraft), but general-purpose household equipment sterilization remains largely manual. Existing systems either operate in constrained, pre-configured spaces or require significant human oversight. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical disinfection of equipment and supplies; robotic disinfection systems (e.g., UV robots) exist only in narrow, research or pilot deployments, not as general-purpose maid replacements. |
Keep storage areas and carts well-stocked, clean, and tidy.
19CI 15–24 · exposure 8 · augmentation 25 · importance 4.4/5 · click for rater detail
Keep storage areas and carts well-stocked, clean, and tidy.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Housekeeping is a low-digitization, labor-intensive sector with predominantly small operators; adoption of robotic or AI-driven automation remains minimal and confined to large hotel/hospital chains experimenting with limited-scope robots. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | The hospitality and cleaning services sector has very low AI/robotics adoption for physical tidying tasks, with automation efforts focused mostly on scheduling and dispatch rather than the physical task itself. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI inventory-tracking systems (RFID, computer vision) can assist in monitoring stock levels and flagging what needs replenishment, but the core physical work of stocking and tidying gains little productivity benefit from current AI tools. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with inventory tracking or restocking alerts via simple apps, but it offers minimal direct assistance to the physical act of stocking and tidying carts and storage areas. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical inventory management and restocking require mobile manipulation (moving heavy items, organizing shelves) that current robots cannot reliably perform in diverse, unstructured environments. While monitoring stock levels via computer vision is feasible, the actual restocking and tidying work remains largely manual. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation—organizing supplies, restocking carts, wiping surfaces—that current AI systems cannot perform without embodied robotics, which are not deployed for this task.4 |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few hard regulatory barriers exist for automation of cleaning and restocking itself, though in institutional settings (hospitals, hotels) customer expectations favor human staff. Physical access and safety constraints provide modest adoption friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically for this task, but physical dexterity and spatial judgment in unstructured supply closets create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic manipulation systems capable of this task cost tens of thousands to hundreds of thousands of dollars plus integration, whereas housekeeping workers earn modest hourly wages; total cost of ownership far exceeds human labor for this specific task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so a human housekeeper remains far cheaper than any hypothetical robotic solution, which would require expensive hardware and integration. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs this task end-to-end. Robotic cleaning and restocking exist only in narrow, controlled settings (e.g., warehouse automation); general housekeeping restocking in variable spaces lacks production systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No commercially deployed product performs physical inventory tidying and cart restocking in housekeeping settings; this remains outside the scope of current AI products. |
Replenish supplies, such as drinking glasses, linens, writing supplies, and bathroom items.
19CI 15–24 · exposure 8 · augmentation 13 · importance 4.2/5 · click for rater detail
Replenish supplies, such as drinking glasses, linens, writing supplies, and bathroom items.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Housekeeping is a low-digitization, labor-intensive sector with limited capital for automation; adoption of robotic supply replenishment remains negligible in real production environments. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hospitality and cleaning services are low-digitization, physically-oriented sectors with minimal AI/robotics adoption for such tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-powered inventory tracking or route optimization could modestly assist staff in planning efficient supply runs, but current systems offer only marginal assistance to the core physical task. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance to a human physically restocking supplies in a room; inventory tracking software might help logistics but not the task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While picking and placing items is mechanically straightforward, the task requires navigating varied room layouts, identifying correct quantities and types of supplies, and locating appropriate placement locations—challenges current robots struggle with at scale and speed sufficient for 50% time savings on par with human housekeeping staff. |
| Task automatability | claude-sonnet-5 | 1/5 | Restocking physical items in rooms requires physical manipulation and mobility that current AI systems cannot perform without robotic embodiment, which is not deployed for this task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing or legal requirements, the physical access to private spaces and expectation of human-provided service create organizational and customer preference barriers, though these are weaker than in regulated professions. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barriers exist, but the physical nature of the task and lack of robotic infrastructure in hospitality settings create practical adoption friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic systems capable of this task cost tens of thousands to hundreds of thousands of dollars, with high integration and maintenance overhead, far exceeding the loaded wage of minimum-wage housekeeping labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for physically restocking items, so human labor remains the only cost-effective option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs autonomous supply replenishment in diverse hotel or institutional environments at production scale; robotic solutions remain research-stage or extremely narrow in scope. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously replenishes hotel room supplies at scale; this remains a physical labor task performed by humans. |
Sort, count, and mark clean linens and store them in linen closets.
19CI 15–24 · exposure 8 · augmentation 25 · importance 4.1/5 · click for rater detail
Sort, count, and mark clean linens and store them in linen closets.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Housekeeping and hospitality sectors remain low-digitization, labor-intensive, and price-sensitive. Adoption of automation in these environments is laggard compared to information or financial sectors, with minimal reported deployment of robotic linen-handling systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | The hospitality/janitorial sector has very low AI and robotics adoption for physical tasks like linen handling, with automation efforts still experimental. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide modest assistance through computer vision for counting verification or inventory management systems, but the core physical sorting and storage task offers limited scope for augmentation without full robotics. Practical gains are marginal compared to the human workflow. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Simple inventory tracking software or barcode/RFID systems can assist in counting and marking linens, offering modest assistance, but the physical sorting and storing remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While sorting and counting linens could theoretically be partially automated using computer vision and robotic handling, the task involves physical manipulation in variable environments (different linen types, closet layouts) and requires reliable defect detection. Current systems cannot achieve the ≥50% time saving at equal quality threshold across realistic household or institutional settings. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of linens (sorting, folding, carrying, placing in closets) which current AI systems cannot perform without embodied robotics that are not generally available or reliable for this task.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | The task involves minimal regulatory requirements or licensing barriers specific to automation. However, organizational friction is moderate—facility operators may prefer human flexibility in adapting to varying linen quality, closet configurations, and inventory needs. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically, but the physical nature of handling and storing items in variable environments creates a practical barrier to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of a robotic system capable of safely handling diverse linens, combined with integration and maintenance, far exceeds the loaded hourly wage of a housekeeper for this relatively low-complexity manual task, even in institutional settings. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute in production, so the effective AI cost for this physical task is far higher than a human housekeeper's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform this task end-to-end in production. While individual components (vision-based counting, robotic bin picking) exist in research, the integrated solution—sorting varied textiles, quality-checking, and organizing storage—lacks mature commercial deployment in real housekeeping operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product autonomously sorts, counts, marks, and stores linens in real housekeeping settings; this remains a purely physical, human-performed task. |
Sort clothing and other articles, load washing machines, and iron and fold dried items.
19CI 15–24 · exposure 8 · augmentation 13 · importance 4.1/5 · click for rater detail
Sort clothing and other articles, load washing machines, and iron and fold dried items.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption in this sector remains negligible; most households and small cleaning services lack the capital and integration infrastructure for laundry automation, and the task remains heavily manual even in digitized commercial settings. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Domestic/commercial cleaning is a low-digitization, physical-labor sector with essentially no production AI/robotic adoption for this task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-powered fabric-sorting tools or stain-detection systems could assist workers in triaging loads, but current vision models are unreliable enough to require frequent human correction, limiting meaningful productivity gain. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance to a human performing sorting, loading, ironing, or folding of laundry. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While washing machines are automated, sorting delicate vs. heavy fabrics, loading without damage, and especially ironing and folding require dexterous manipulation and visual judgment of fabric type and condition that current robotics struggle with consistently. No current off-the-shelf system achieves 50% time savings on the full task end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity, mobility, and fine motor control that current robots cannot perform reliably or affordably outside narrow lab demos. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Modest friction exists: customers may prefer human touch for delicate garments, and household adoption faces space/installation barriers, but there are no legal licensing requirements or regulatory prohibition on automation of laundry work. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but physical/mechanical limitations and cost act as strong practical barriers rather than regulatory ones. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of any part of this task (folding, ironing) cost tens of thousands of dollars with significant maintenance and integration overhead, far exceeding the cost of a single human laborer completing the task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Robotic laundry systems capable of this are far more expensive than human labor, with no commercially viable automation option at comparable cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs clothing sorting, washing-machine loading, ironing, and folding at production scale. Prototype robotic systems exist in research, but none demonstrate consistent, cost-effective operation in real household or commercial settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed consumer or commercial product autonomously sorts, loads, irons, and folds laundry end-to-end; existing folding robots remain research/prototype stage. |
Wash windows, walls, ceilings, and woodwork, waxing and polishing as necessary.
19CI 15–24 · exposure 8 · augmentation 25 · importance 4.0/5 · click for rater detail
Wash windows, walls, ceilings, and woodwork, waxing and polishing as necessary.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Maid and housekeeping sectors are fragmented, employ low-wage workers, and operate in distributed residential settings with low digitization; adoption of cleaning robots has been minimal outside niche commercial applications. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Residential and commercial cleaning is a low-digitization, physical-labor sector with minimal AI/robotics adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI/robotics could assist with task planning or surface-condition detection, but currently offers minimal practical augmentation; the task remains fundamentally physical and human-dependent. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some tools like robotic window cleaners or scheduling/inventory apps offer minor assistance, but there is little AI augmentation for the core manual cleaning and polishing work. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Window and wall washing involves complex spatial navigation, dexterity, and judgment about surface damage/type that current robots struggle with at scale. While some robotic window cleaners exist in narrow settings (flat building exteriors), generalized cleaning of varied residential/commercial interiors with waxing and polishing remains largely manual. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity, mobility, and adaptation to varied surfaces and dirt levels; no off-the-shelf AI system can perform this end-to-end today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Physical access to homes/buildings and customer preference for human trust remain moderate friction points, though no hard legal licensing requirement exists for basic cleaning tasks. Property damage liability is a concern but not a regulatory blocker. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier prevents automation, but physical access to private/commercial spaces, liability for damage to surfaces, and customer trust create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic cleaning systems capable of this task remain capital-intensive and require significant infrastructure setup, making per-task cost far higher than a minimum-wage housekeeper for the foreseeable future. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Human labor with basic tools remains far cheaper than any robotic system capable of this varied physical cleaning, which would require expensive hardware and manipulation capability. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI/robotic systems reliably perform this full task (washing, waxing, polishing varied surfaces) in general indoor environments at production scale. Prototype robots exist but lack the versatility, reliability, and cost-effectiveness needed for real-world adoption. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic products reliably wash windows, walls, ceilings, and woodwork in real homes or facilities at scale; this remains research-stage robotics territory. |
Dust and polish furniture and equipment.
19CI 15–24 · exposure 8 · augmentation 0 · importance 4.0/5 · click for rater detail
Dust and polish furniture and equipment.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Residential cleaning remains dominated by small firms and individual workers with low digitization; homeowners retain strong preference for human labor in their private spaces, limiting any automation momentum. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Residential and commercial cleaning is a low-digitization, physically-intensive sector with minimal AI/robotic adoption for surface cleaning tasks like dusting and polishing. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI offers no meaningful assistance to a human dusting and polishing furniture; the task requires physical presence and tactile judgment that current AI tools cannot enhance. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers essentially no assistance to a human physically dusting and polishing furniture, as the task is manual and tactile with no digital or cognitive component to augment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Dusting and polishing involve physical manipulation of fragile items with complex shapes and varying surface conditions. Current robots struggle with dexterous handling, surface texture detection, and avoiding damage; while some industrial cleaning robots exist, they cannot reliably replicate the careful, adaptive touch required for household furniture without significant setup per environment. |
| Task automatability | claude-sonnet-5 | 1/5 | Dusting and polishing furniture requires physical manipulation in varied, unstructured environments; no off-the-shelf AI system can perform this manual task end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | The task occurs in private homes with high customer preference for human trustworthiness around personal possessions, and liability concerns around damage to furniture create friction, though no hard legal barriers exist. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but physical access to private/commercial spaces and customer trust around handling possessions create some friction, though not a hard regulatory barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robots capable of furniture care would require custom engineering, precise environmental mapping, and maintenance—costing tens of thousands of dollars—versus a maid earning $25–35k annually for this and other tasks combined. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this specific physical task, so any hypothetical solution would be far costlier than a human cleaner given current robotics costs and limitations. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed consumer or commercial product reliably performs end-to-end furniture dusting and polishing in real homes. Existing cleaning robots are limited to floors; furniture cleaning remains outside production robotics capability at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs general furniture dusting/polishing in real homes or offices; robotic cleaning remains limited to floor vacuuming, not surface dusting/polishing of varied furniture. |
Move and arrange furniture and turn mattresses.
19CI 15–24 · exposure 8 · augmentation 0 · importance 3.9/5 · click for rater detail
Move and arrange furniture and turn mattresses.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Housekeeping sectors remain largely traditional and low-digitization; adoption of robotic furniture handling is negligible. The sector relies heavily on human labor with minimal AI integration. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Housekeeping and hospitality cleaning is a low-digitization, physically intensive sector with minimal AI/robotics deployment for this kind of task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI and robotics offer negligible assistance to human housekeeping workers performing this physical task; no augmentation tools are in practical use. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for the physical acts of moving furniture or turning mattresses. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Moving and arranging furniture requires physical dexterity, spatial reasoning, and adaptability to varied room layouts. While AI robotics research exists, current deployed systems cannot reliably handle the full task end-to-end with sufficient speed and safety to meet the 50% time-saving threshold. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of heavy objects in unstructured environments, which no current AI/robotics system can perform reliably or at scale for general housekeeping. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Task requires physical presence in customer spaces and direct contact; there is customer preference for human presence. However, no legal licensing or strict liability barriers prevent automation if the technology existed. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barriers exist, but practical barriers around safety, liability for damage, and the need for dexterous physical presence in private/guest spaces limit substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of this task would cost far more than the labor of a housekeeper to perform it directly, making the cost ratio heavily unfavorable for automation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any robotic system capable of this physical task would require expensive hardware, far exceeding the low wage cost of a human housekeeper performing this manual labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No commercial product reliably performs this task today. Residential robots that can move furniture and turn mattresses do not exist in production; this remains primarily a research domain rather than a deployed capability. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs furniture moving or mattress turning in home/hospitality settings; general-purpose mobile manipulation robotics remains research-stage for such variable tasks. |
Replace light bulbs.
19CI 15–24 · exposure 8 · augmentation 0 · importance 3.7/5 · click for rater detail
Replace light bulbs.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hospitality and facility-management sectors show minimal production adoption of robotic light-bulb replacement. Adoption remains at pilot stage; most organizations continue manual replacement due to low task cost and reliability concerns with autonomous systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Housekeeping and janitorial sectors show minimal AI/robotics adoption for physical manipulation tasks like this, remaining a low-digitization, hands-on occupation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI systems offer no meaningful assistance to a human performing light-bulb replacement. The task is already simple and fast; AI augmentation (e.g., identifying burned-out bulbs) adds little value compared to visual inspection. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for the physical act of replacing a light bulb, though it might help order supplies or schedule maintenance separately. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While robotic systems can theoretically replace light bulbs, no current off-the-shelf AI or deployed robotic agent performs this task end-to-end with consistent 50% time savings at equal quality. The task requires physical manipulation in variable spatial contexts (heights, fixtures, types) that current general-purpose robotics struggles with reliably. |
| Task automatability | claude-sonnet-5 | 1/5 | Replacing a light bulb requires physical manipulation in a real-world environment, which current AI systems cannot perform without embodied robotics that don't exist for this purpose commercially.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Light-bulb replacement is routine housekeeping work with no licensing requirement or legal prohibition on automation. However, safety concerns (electrical, fall risk in occupied spaces) and customer comfort with robot presence in rooms create moderate organizational friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but the physical nature of the task (ladder use, fixture access, judgment about bulb type) creates practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic systems capable of light-bulb replacement cost orders of magnitude more per task than the labor cost of a housekeeper ($15–25/hour wage-loaded). Integration, maintenance, and oversight overhead further increase the cost per bulb replaced. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this physical task, so the human remains the only cost-effective option by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product in hospitality or facilities management reliably performs autonomous light-bulb replacement at scale today. Research-stage robotics exist but lack the safety, precision, and integration needed for production use in occupied spaces. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical bulb replacement in homes or facilities; this remains a purely physical task requiring human dexterity and mobility. |
Hang draperies and dust window blinds.
19CI 15–24 · exposure 8 · augmentation 13 · importance 3.6/5 · click for rater detail
Hang draperies and dust window blinds.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Housekeeping and maid services remain low-digitization, small-firm dominated sectors with limited robotics deployment; current adoption of autonomous systems for these specific tasks is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Domestic and commercial cleaning is a low-digitization, physical-labor sector with negligible AI/robotic adoption for this specific task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-powered scheduling and route optimization can modestly assist workers, but no AI currently augments the physical execution of hanging draperies or dusting blinds in meaningful ways. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI provides no meaningful assistance for the physical acts of hanging draperies or dusting blinds; there's no cognitive/planning bottleneck AI could ease here. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Hanging draperies requires precise spatial reasoning, threading hardware, and adjusting to variable window dimensions—tasks that current robots struggle with at consumer speed. Dusting blinds involves dexterous manipulation of fragile slats and is only partially automatable; end-to-end automation with 50% time savings is not demonstrated by off-the-shelf systems. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of fabric and blinds, ladder work, and dexterity that current AI systems (software or robotics) cannot perform end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No formal licensing is required for this task, and minimal liability barriers exist; adoption friction is low, though customer preference for human presence and organizational inertia in household/commercial cleaning provide modest resistance. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but physical environment variability (curtain types, window configurations, fragile blinds) creates practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic arms with vision and force control needed for drapery installation and blind dusting cost tens of thousands of dollars, while a housekeeper performs these tasks for $15–25/hour loaded cost; the total cost per task far exceeds human labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | No viable AI/robotic solution exists for this task, so any hypothetical automation would require expensive custom robotics far exceeding human labor cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably hangs draperies or dusts blinds autonomously today. Robotic vacuum cleaners exist, but hanging hardware and delicate blind manipulation remain research-stage or proof-of-concept only. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product performs draperies hanging or blind dusting; robotic vacuum/cleaning products don't address this task at all. |
Prepare rooms for meetings and arrange decorations, media equipment, and furniture for social or business functions.
19CI 15–24 · exposure 8 · augmentation 25 · importance 3.6/5 · click for rater detail
Prepare rooms for meetings and arrange decorations, media equipment, and furniture for social or business functions.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hospitality and events sectors show minimal production-level AI adoption for physical room setup; most remain labor-intensive and geographically dispersed with low digitization, typical of laggard sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hospitality and facilities services are a low-digitization, physical-labor sector with minimal AI/robotics adoption for tasks like room setup. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with checklist generation, scheduling coordination, or media equipment pre-programming, but augmentation is limited by the task's heavy reliance on physical presence, spatial judgment, and real-time problem-solving that human workers perform directly. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with scheduling, room-layout planning software, or checklists, but offers little assistance with the core physical arrangement work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could theoretically coordinate scheduling and planning, the physical manipulation of furniture, media equipment setup, and contextual decoration arrangement requires embodied robotics currently not deployed at scale. Simple ordering and checklist tasks could be partially automated, but the core work—moving items, arranging layouts, testing equipment—remains predominantly manual. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of furniture, decorations, and equipment in real-world space, which current AI systems cannot perform without embodied robotics that don't exist at deployable scale for this task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While not strictly licensed, there are moderate organizational and liability friction points: clients often prefer direct human coordination for bespoke arrangements, on-site assessment is crucial, and damage/setup errors carry reputational cost that slows automation adoption. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barriers exist, but the physical nature of arranging furniture and equipment in unstructured environments is a structural barrier to automation rather than a regulatory one. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current labor costs for a housekeeper performing this task are far lower than the capital, maintenance, and operational overhead of robots or AI systems capable of physical space reconfiguration and equipment setup. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that can substitute for the physical labor involved, so any hypothetical robotic solution would be far costlier than paying a housekeeping worker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs full room preparation, furniture arrangement, and decoration setup end-to-end. Robotic systems exist in research but lack the dexterity, adaptability, and common-sense reasoning needed for varied social/business function contexts in production settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical room setup and decoration arrangement; this remains purely a research-stage robotics problem, not a commercial offering. |
Polish silver accessories and metalwork, such as fixtures and fittings.
19CI 15–24 · exposure 8 · augmentation 0 · importance 3.4/5 · click for rater detail
Polish silver accessories and metalwork, such as fixtures and fittings.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Housekeeping is a low-digitization, physical-location-dependent sector with primarily small employers and limited capital deployment; AI adoption remains negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Household and commercial cleaning services are a low-digitization, low-tech-adoption sector with virtually no AI/robotic penetration for detailed physical polishing tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI systems offer no meaningful augmentation to human polishers—AI cannot assist with the manual physical work, item identification, or quality assessment in ways that would materially improve productivity. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers no meaningful assistance for the physical act of polishing silver; there's no software or planning component that would materially aid a housekeeper in this manual task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Polishing silver is physically demanding and requires dexterous manipulation of varied, irregularly-shaped objects. Current robotic systems lack the fine tactile control and adaptability to consistently polish delicate fixtures without damage, making end-to-end automation with 50% time savings unachievable today. |
| Task automatability | claude-sonnet-5 | 1/5 | Polishing silver requires physical dexterity, tactile assessment of tarnish, and manual application of polish and elbow grease—no AI system can perform this physical manipulation task today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Physical task requiring in-situ work on client premises with minimal regulatory barriers, though customer preference for human touch and the need for human judgment on which items need polishing creates modest friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or regulatory barriers exist, but the physical nature of the task and need for careful handling of household valuables create some practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A robotic polishing system would require significant capital investment, specialized tooling, and integration costs that far exceed the wage of a maid or housekeeping cleaner, making the cost-per-task uncompetitive. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute, so any AI-based approach (e.g., robotics) would be far more expensive than a human doing this simple manual task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs silver polishing autonomously. While experimental robotic arms exist in research settings, production systems that can handle the variability of real-world fixtures and fittings remain absent from the market. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed consumer or commercial product performs autonomous silver polishing; this remains purely a manual physical task with no robotic solution in production. |
Carry linens, towels, toilet items, and cleaning supplies, using wheeled carts.
15CI 15–15 · exposure 0 · augmentation 13 · importance 4.4/5 · click for rater detail
Carry linens, towels, toilet items, and cleaning supplies, using wheeled carts.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Housekeeping and cleaning are low-digitization sectors dominated by small firms and independent contractors; adoption of physical robots remains minimal despite decades of robotics research. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hospitality and cleaning services are a low-digitization, physical-labor sector with minimal AI/robotics adoption for manual material transport tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Wheeled carts themselves are a form of existing augmentation; AI-powered route optimization or inventory management could assist planning, but the core pushing and carrying task does not gain meaningful productivity lift from current AI. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers essentially no assistance for the physical act of carrying and transporting items via cart. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task is fundamentally physical and requires mobile manipulation in unstructured indoor environments. Current AI systems cannot reliably push wheeled carts, navigate hallways, or handle fragile linens at human speed and quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation and locomotion task requiring a robot capable of navigating varied hotel/residential environments and handling carts; no current off-the-shelf AI system performs this end-to-end.ggregate |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Physical automation faces material barriers (safety liability in occupied spaces, customer comfort with robots), but no strict licensing or legal requirement protects human housekeepers from future automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barriers exist, but practical barriers include environments unstructured for robots (stairs, tight halls, guest interactions) and capital cost of deployment. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A mobile robot capable of hauling linens and navigating hotel/institutional corridors costs tens of thousands of dollars upfront plus ongoing maintenance, far exceeding the loaded wage of housekeeping labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Physical robots capable of this task would require expensive hardware, navigation systems, and maintenance, far exceeding the cost of a human housekeeper for this simple task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs this physical task end-to-end. Robotic cart systems exist in research but have not achieved production deployment in housekeeping at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform general-purpose linen/supply transport via wheeled carts in housekeeping settings at scale; this remains research-stage robotics territory. |
Deliver television sets, ironing boards, baby cribs, and rollaway beds to guests' rooms.
13CI 10–15 · exposure 0 · augmentation 0 · importance 3.7/5 · click for rater detail
Deliver television sets, ironing boards, baby cribs, and rollaway beds to guests' rooms.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hospitality automation remains concentrated in back-of-house (laundry, dishwashing) rather than guest-facing delivery; physical delivery of furniture has seen minimal AI/robotic pilot deployment in real hotel operations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hospitality housekeeping is a low-digitization, physical-labor sector with minimal AI/robotics adoption for delivery tasks in production environments. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI does not meaningfully assist housekeeping staff in moving and positioning furniture; the task is purely physical execution with no practical assistive technology deployed in this setting. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical act of retrieving and delivering these items to guest rooms. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation and delivery of large items to specific guest rooms in real time, demanding mobility, navigation, and spatial reasoning in dynamic environments. Current AI systems cannot reliably handle the full workflow of moving, positioning, and placing furniture in varied hotel spaces. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical delivery and handling task requiring locomotion, lifting, and navigation of a real environment; no current AI system can perform this end-to-end without robotic embodiment that doesn't exist at scale. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Hotels may face customer expectations and service-quality norms favoring human workers, and liability concerns arise if furniture is damaged or placed incorrectly; however, no legal requirement mandates human delivery, creating some friction but not a hard barrier. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but the physical nature of navigating hallways, handling awkward items, and interacting with guests creates practical friction against non-human substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robots capable of autonomous furniture delivery remain expensive (significant capital outlay, maintenance, infrastructure), while a housekeeper can perform this task for standard hourly wages; the all-in cost per delivery heavily favors human labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this physical delivery task, so any hypothetical automation would require expensive custom robotics far costlier than a human worker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs end-to-end furniture delivery to guest rooms autonomously. While robotic systems exist for narrow warehouse tasks, none handle the variability of hotel environments, door negotiations, and placement finesse required here. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product delivers physical items like TVs, ironing boards, or cribs to hotel rooms; this remains purely human labor in production settings today. |
Observe precautions required to protect hotel and guest property and report damage, theft, and found articles to supervisors.
9CI 5–14 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Observe precautions required to protect hotel and guest property and report damage, theft, and found articles to supervisors.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Hospitality remains a labor-intensive, low-digitization sector. Few hotels have piloted autonomous inspection systems; adoption of any such technology would be slow and concentrated in high-end properties, not mainstream. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Hospitality housekeeping is a low-digitization, physical-labor sector with minimal AI agent deployment for this specific observational task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While a digital checklist or mobile app could assist housekeeping staff in documenting findings, current AI offers limited assistance with the core task of detecting and assessing damage in real-world, variable room conditions. Augmentation exists only at the administrative reporting layer, not the inspection judgment itself. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Digital reporting tools or apps can help staff log and report incidents faster, but AI does not meaningfully enhance the core observational task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Observing precautions and reporting damage/theft requires visual inspection of varied spaces, judgment about what constitutes damage or theft, and situational awareness. While computer vision could detect some physical damage, the contextual judgment (is this pre-existing? is this guest property or hotel property?) and the need to identify and report found articles in a natural, integrated way remain firmly in human judgment domain. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence, situational observation, and judgment while cleaning rooms—no current AI system can perform the physical inspection and reporting embedded in real-world housekeeping work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Hotels have strong liability and legal obligations around guest property protection; any automated system would require human sign-off and accountability. Guest privacy expectations and the need for human judgment on property disputes create organizational and regulatory friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement, but liability concerns around theft/damage claims and guest privacy in private rooms create meaningful organizational and legal friction against camera-based automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | A robotic or vision-based system capable of reliable room inspection would require significant hardware, deployment, and integration costs, plus ongoing maintenance and oversight. It would exceed the hourly wage of housekeeping staff in most markets, making it economically unviable today. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for this physical task, so any AI-based approach (e.g., cameras plus analysis) would add cost on top of, not instead of, human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs autonomous property inspection, damage assessment, and theft detection in hotel rooms at production scale. Security systems exist but require human interpretation; computer vision systems are research-stage for this specific nuanced task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs in-room observation of guest property damage/theft and reports it; this remains a purely human physical and judgment task. |
Related occupations — Building & Grounds Cleaning & Maintenance
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.