Tile and Stone Setters

47-2044.00
Median wage $55,690/yr35,850 employed (US)Rank #791 of 923 scored · top 86% by substitution

Apply hard tile, stone, and comparable materials to walls, floors, ceilings, countertops, and roof decks.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution15
Exposure6
Augmentation24

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

25 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%6

panel mean rating 1.3/5 → substitution pressure 6/100

Technical feasibility todayw 20%5

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

Cost vs. human wagew 15%5

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

Adoption barriersw 20%inverted — strong barriers lower the score54

panel mean rating 2.9/5 (barrier strength) → substitution pressure 54/100

Sector adoption velocityw 10%3

panel mean rating 1.1/5 → substitution pressure 3/100

Task breakdown (25 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.

Prepare cost and labor estimates, based on calculations of time and materials needed for project.

34

CI 25–44 · exposure 33 · augmentation 63 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Construction trades, especially smaller tile and stone setting operations, have laggard digitization relative to white-collar sectors. Adoption of AI estimating tools remains limited to mid-to-large contractors; many small firms still rely on manual or spreadsheet-based estimates.
Sector adoption velocityclaude-sonnet-52/5Construction and trades sectors are historically slow adopters of AI tools compared to information/finance sectors, with most estimating still done manually or with basic spreadsheets.
Augmentation potentialclaude-haiku-4-5-202510014/5AI can substantially assist by auto-populating material quantities from takeoff photos, flagging missing specs, pulling historical labor rates, and generating first-pass estimates that a human refines. This can meaningfully accelerate the estimating process while the setter maintains control.
Augmentation potentialclaude-sonnet-53/5AI-powered estimating tools and spreadsheets can speed up calculations and material lookups, offering real productivity gains while the human still verifies measurements and finalizes bids.
Task automatabilityclaude-haiku-4-5-202510013/5AI can automate material calculations and basic time estimates using historical data and project specifications, but the task requires judgment about site conditions, complexity variations, and labor productivity that still needs human oversight. A system could handle ~50% of the work (specs-to-materials lookup, standard labor tables), though custom adjustments remain needed.
Task automatabilityclaude-sonnet-52/5Estimation requires site-specific measurements, material knowledge, and judgment about labor conditions that AI cannot independently gather; AI can assist calculations but not fully replace the process end-to-end.dim
Adoption barriersclaude-haiku-4-5-202510014/5Liability and error-cost asymmetries are high—underestimating labor or materials directly impacts profitability and contractual risk. Industry practice and customer trust favor human estimator sign-off, and some jurisdictions may require licensed tradespeople to validate estimates.
Adoption barriersclaude-sonnet-52/5No licensing requirement for estimating itself, but customer trust, contract liability for inaccurate quotes, and site-specific idiosyncrasies create moderate friction against pure automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI systems capable of doing this task remain specialized and require integration into existing workflows; labor overhead and customization costs often exceed savings for smaller jobs typical in tile/stone setting contracting.
Cost vs. human wageclaude-sonnet-52/5Estimating software has licensing and setup costs that may not be justified for small tile-setting jobs, and human estimators often remain cheaper for one-off custom projects given the up-front data entry needed.
Technical feasibility todayclaude-haiku-4-5-202510012/5While takeoff software and basic estimating tools exist, few deployed products reliably handle the full scope without human review. Most systems require significant manual input and expert validation, falling short of reliable end-to-end automation in construction practice.
Technical feasibility todayclaude-sonnet-52/5Some construction estimating software with AI features exists, but for small-scale trade work like tile setting, adoption of automated estimating tools is narrow and often still manual or spreadsheet-based.

Select and order tile and other items to be installed, such as bathroom accessories, walls, panels, and cabinets, according to specifications.

31

CI 28–35 · exposure 25 · augmentation 50 · importance 3.5/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Construction and skilled trades lag in digitization. While some larger contractors use procurement software, the sector shows slow AI adoption overall. Most tile and stone setters source materials through traditional vendor relationships and manual specification review.
Sector adoption velocityclaude-sonnet-51/5Construction and trades sectors show low digitization and slow AI adoption for procurement and specification tasks.
Augmentation potentialclaude-haiku-4-5-202510013/5AI could assist by extracting specifications from blueprints, cross-referencing compatible materials, aggregating supplier prices, and generating ordering summaries. These augmentations would save time on catalog search and data entry while the installer remains the decision-maker.
Augmentation potentialclaude-sonnet-53/5AI-powered catalogs, visualization tools, and spec-matching software can meaningfully speed up material selection and ordering when used by the human worker.
Task automatabilityclaude-haiku-4-5-202510012/5While AI could help parse specifications and search supplier catalogs, the task requires understanding client needs, material compatibility, aesthetic judgment, and real-world constraints that typically need human oversight. Current systems cannot reliably handle the full procurement workflow end-to-end.
Task automatabilityclaude-sonnet-52/5AI can help generate specification lists or suggest materials from a design brief, but the physical selection, sourcing, and ordering process involves supplier coordination and material judgment that current systems cannot fully execute end-to-end.
Adoption barriersclaude-haiku-4-5-202510013/5Some organizational friction exists—procurement departments have established supplier relationships and approval workflows—but no licensing or legal barrier prevents AI from assisting with ordering. Customer relationships and preference for human consultation provide moderate friction.
Adoption barriersclaude-sonnet-52/5No licensing requirement for procurement decisions, though contractor liability for correct materials creates some incentive for human verification.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI-assisted tools (search, catalog matching) are inexpensive, but the task still requires significant human time for verification, supplier communication, and adjustment. Total cost savings would be modest compared to a skilled installer's hourly rate.
Cost vs. human wageclaude-sonnet-52/5Software tools for ordering exist cheaply, but human oversight, supplier relationships, and physical verification still require significant human time, keeping cost savings modest.
Technical feasibility todayclaude-haiku-4-5-202510012/5No mature production system demonstrates reliable end-to-end task performance for construction material selection and ordering. Chatbots and e-commerce platforms exist but lack the domain knowledge and human judgment required to match specifications to actual project requirements.
Technical feasibility todayclaude-sonnet-52/5Some e-commerce and design-configurator tools exist for material selection, but no deployed product reliably handles the full order-per-specification workflow for tile setters in production.

Study blueprints and examine surface to be covered to determine amount of material needed.

29

CI 23–35 · exposure 25 · augmentation 50 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Construction and trades sectors lag in digital adoption; most tile setters still use manual measurement and estimation, and site variability makes standardized AI solutions difficult to deploy at scale across diverse projects.
Sector adoption velocityclaude-sonnet-51/5Construction trades, especially small tile/stone setting businesses, show low digitization and slow AI adoption in practice compared to information-sector occupations.
Augmentation potentialclaude-haiku-4-5-202510013/5AI-assisted measurement tools (e.g., mobile apps that help calculate area from photos or blueprint overlays) can assist with documentation and calculation, speeding up the estimation process while the worker retains responsibility for final verification and judgment.
Augmentation potentialclaude-sonnet-53/5AI-powered takeoff and estimation tools can speed up quantity calculations from blueprints, providing useful assistance while the human still verifies conditions on-site.
Task automatabilityclaude-haiku-4-5-202510012/5Current AI can assist with blueprint interpretation and basic surface measurements through computer vision, but determining material quantity involves complex spatial reasoning, accounting for waste, cuts, patterns, and irregular surfaces that require integration with physical inspection. Most of the critical judgment and measurement work remains manual.
Task automatabilityclaude-sonnet-52/5AI can assist with reading blueprints and estimating material quantities from digital plans, but physical surface examination and on-site measurement require human presence and judgment, limiting full end-to-end automation.
Adoption barriersclaude-haiku-4-5-202510014/5Safety, liability, and accuracy requirements create meaningful barriers; mistakes in material estimation cause costly project delays and financial losses, and on-site work involves physical hazards and insurance considerations that favor human responsibility and sign-off on measurements.
Adoption barriersclaude-sonnet-52/5No licensing requirement specifically for this estimation step, but practical barriers exist since accurate takeoff requires physical inspection of irregular surfaces, corners, and existing conditions not captured in blueprints alone.
Cost vs. human wageclaude-haiku-4-5-202510012/5The cost of deploying vision systems, integrating with blueprint databases, and providing necessary oversight approximates or exceeds the cost of a skilled worker quickly surveying a site and calculating material needs on-site.
Cost vs. human wageclaude-sonnet-52/5Estimation software has licensing and setup costs plus still requires human site verification, so total cost savings versus a tradesperson doing a quick on-site assessment are modest, not order-of-magnitude.
Technical feasibility todayclaude-haiku-4-5-202510012/5While blueprint reading tools and measurement software exist, no deployed product reliably combines blueprint analysis with physical surface inspection autonomously to produce accurate material takeoffs at production scale. Most systems require significant human verification and manual measurement.
Technical feasibility todayclaude-sonnet-52/5Some construction estimation software with AI features exists (takeoff tools), but they are narrow, require clean digital inputs, and are not widely deployed as reliable substitutes for on-site assessment by tile setters.

Assist customers in selection of tile and grout.

29

CI 23–35 · exposure 25 · augmentation 50 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Tile and stone setting is a traditional, physical-trade sector with limited digital maturity. Adoption of AI-driven customer assistance is still nascent; most firms rely on in-person consultation and showroom experience.
Sector adoption velocityclaude-sonnet-51/5Construction and trades sectors are slow adopters of AI tools in customer-facing selection processes, with most work remaining manual and showroom-based.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can assist setters by instantly surfacing product specs, availability, and standard pairing suggestions, reducing lookup time and improving consistency. However, the human setter's eye and client rapport remain central to the final decision.
Augmentation potentialclaude-sonnet-53/5AI visualization tools (e.g., room design apps, color/pattern matching) can meaningfully help customers and installers narrow choices, complementing but not replacing human guidance.
Task automatabilityclaude-haiku-4-5-202510012/5Customer preference elicitation requires subtle interaction about aesthetics, durability, budget, and space-specific fit. While AI can provide product information and basic recommendations, closing a sale or confidently matching a customer's taste and constraints to specific products remains partly human-dependent and would not meet the 50% time-saving bar for end-to-end automation today.
Task automatabilityclaude-sonnet-52/5AI can provide design suggestions or visualize combinations via image tools, but the interactive, in-person, tactile aspect of showing physical samples and reading customer preferences resists full automation.
Adoption barriersclaude-haiku-4-5-202510014/5Customer trust and preference for human judgment in aesthetic and high-value purchase decisions create strong adoption friction. Sales interaction and liability for incorrect material choices (e.g., durability, finish mismatch) often require human accountability.
Adoption barriersclaude-sonnet-52/5No licensing requirement, but customer preference for tactile sample handling and personalized advice creates moderate friction against full automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5Implementing a reliable AI recommendation system (infrastructure, integration with inventory, ongoing tuning) and human oversight is costlier than a setter's hourly labor for routine selection tasks, especially in small/medium operations where volume is low.
Cost vs. human wageclaude-sonnet-52/5While generating visual suggestions is cheap, the human sales/consultation role still requires physical handling of samples and trust-building, so cost savings are limited without replacing the whole interaction.
Technical feasibility todayclaude-haiku-4-5-202510012/5Chatbots and recommendation engines exist for product selection, but deployed systems show high error rates in aesthetic judgment and fail to handle complex, multi-constraint customer needs. No production system reliably replaces a skilled setter's consultation at scale.
Technical feasibility todayclaude-sonnet-52/5Some design-visualization apps and chatbots exist for tile/grout selection, but they are narrow tools not widely deployed as reliable replacements for in-person consultation.

Determine and implement the best layout to achieve a desired pattern.

20

CI 10–30 · exposure 13 · augmentation 50 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Tile and stone work remains concentrated in small and mid-sized contractors with low digitization rates; CAD adoption for layout exists but is slower and patchier than in architecture or engineering.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical tasks, with minimal production deployment.
Augmentation potentialclaude-haiku-4-5-202510013/5AI can meaningfully assist by generating pattern options, visualizing layouts, and calculating cutting lists, improving a setter's efficiency in the planning phase while the human retains control over final aesthetic and practical decisions.
Augmentation potentialclaude-sonnet-53/5AI-based design tools and visualization software can help plan patterns and layouts digitally, offering moderate assistance before physical work begins.
Task automatabilityclaude-haiku-4-5-202510012/5Layout planning involves spatial reasoning and aesthetic judgment that AI can assist with (generating template options), but implementation requires real-time adaptation to irregular surfaces, cuts, and material constraints that currently demand human problem-solving in the field.
Task automatabilityclaude-sonnet-51/5This requires physical measurement of real-world irregular spaces, cutting materials, and hands-on manipulation of tile/stone that current AI cannot perform end-to-end.. Layout planning software can assist but the implementation step is entirely physical.
Adoption barriersclaude-haiku-4-5-202510013/5Layout decisions ultimately require human judgment about aesthetic outcomes and site-specific constraints, and the task is embedded in a craft where customer satisfaction and visual correctness demand human accountability and sign-off.
Adoption barriersclaude-sonnet-53/5No licensing strictly required in most jurisdictions, but physical dexterity, on-site variability, and craftsmanship expectations create strong practical barriers to automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI design tools carry licensing and integration costs, but the spatial reasoning and visualization they provide still require significant human expertise; total cost savings remain marginal compared to an experienced setter's wages.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute for physical installation, so any AI cost comparison is moot; human labor remains the only option for execution.
Technical feasibility todayclaude-haiku-4-5-202510012/5CAD-based layout design tools exist and can suggest patterns, but no deployed systems reliably handle the dynamic constraints of actual installation sites, irregular walls, and material variations without substantial human oversight and rework.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs the physical layout and installation of tile/stone; this remains a manual skilled trade task.

Cut, surface, polish, and install marble and granite or install pre-cast terrazzo, granite or marble units.

20

CI 10–30 · exposure 13 · augmentation 38 · importance 3.8/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510012/5Adoption remains slow; most tile and stone setting is performed by small firms and independent contractors in fragmented, site-specific contexts. While CNC cutting has some adoption, on-site installation automation has seen minimal real-world deployment in this traditionally low-digitization sector.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades are among the least digitized sectors with minimal AI/robotic adoption for physical fabrication and installation tasks like this.
Augmentation potentialclaude-haiku-4-5-202510013/5CNC cutting and polishing tools, often with AI-assisted design or pattern optimization, meaningfully assist workers in planning and material preparation. However, augmentation during the critical on-site installation phase remains limited, as the task relies heavily on human spatial reasoning and craft judgment.
Augmentation potentialclaude-sonnet-52/5AI can assist with design visualization, measurement calculations, or material ordering, but offers minimal direct assistance to the physical cutting, polishing, and installation process itself.
Task automatabilityclaude-haiku-4-5-202510012/5While cutting and polishing marble/granite can be partially automated with CNC machines and polishing equipment, the installation task requires on-site spatial judgment, structural assessment, and precision alignment that current AI and robots cannot reliably perform end-to-end. Installation demands real-world physical adaptation that far exceeds the 50% time-saving threshold.
Task automatabilityclaude-sonnet-51/5This is a highly physical task involving precise cutting, surfacing, polishing, and installation of heavy stone materials requiring manual dexterity, spatial judgment, and adaptation to irregular surfaces—no current AI system can perform this end-to-end.
Adoption barriersclaude-haiku-4-5-202510013/5No strict licensing barrier exists for automation itself, but liability for structural integrity and aesthetic quality (especially in high-end marble/granite work) creates organizational and legal friction. Customer preference for human craftspeople and building code compliance oversight present moderate adoption friction.
Adoption barriersclaude-sonnet-53/5While not licensed in the way medical or legal work is, this task requires specialized craft skill, safety training around heavy machinery and materials, and physical presence, creating substantial practical (though not regulatory) barriers to automation.
Cost vs. human wageclaude-haiku-4-5-202510012/5CNC cutting equipment is capital-intensive and requires skilled oversight; integrated installation automation would require expensive custom robotics. The total cost of automating both cutting and on-site installation remains higher than employing skilled setters for most job contexts.
Cost vs. human wageclaude-sonnet-51/5There is no AI system offering this capability, so the cost comparison favors the human tradesperson entirely; any robotic alternative would require enormous capital investment exceeding labor costs.
Technical feasibility todayclaude-haiku-4-5-202510012/5CNC cutting and polishing systems exist in production for controlled environments, but end-to-end autonomous installation of marble and granite in varied on-site conditions has no mature deployed product. Current robotic systems cannot reliably handle the variability of surfaces, substrates, and spatial constraints found in real installations.
Technical feasibility todayclaude-sonnet-51/5No deployed AI product performs marble/granite cutting, polishing, or installation; this remains purely a manual skilled-trade task with no commercial robotic or AI substitute in production.

Measure and mark surfaces to be tiled, following blueprints.

16

CI 5–28 · exposure 13 · augmentation 25 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5The construction trades, particularly small tiling firms, have low digital adoption rates and minimal AI tool integration in production workflows. The physical, on-site nature of the work and fragmented industry structure limits rapid AI adoption.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades are among the slowest sectors to adopt AI/robotics for hands-on physical tasks, with minimal digitization of on-site work.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could assist by pre-analyzing blueprints and suggesting optimal layout patterns, but the core task—actual on-site measurement and marking—remains primarily manual. The assistive potential is limited because the bottleneck is physical execution, not planning.
Augmentation potentialclaude-sonnet-52/5Digital layout tools, AR measurement apps, and blueprint-reading software can help visualize and calculate measurements, offering modest assistance but not transforming the core physical task.
Task automatabilityclaude-haiku-4-5-202510012/5While AI vision systems can analyze blueprints and theoretical layouts, the task requires physical measurement, marking, and spatial adaptation to real-world surface conditions (irregularities, existing structures) that demand human presence on-site. Current AI lacks the embodied capability to perform end-to-end measurement and marking with the precision and reliability needed in tiling work.
Task automatabilityclaude-sonnet-51/5This requires physical presence at a job site, physical measurement of real surfaces, and manual marking—no off-the-shelf AI system can perform the physical act end-to-end today.
Adoption barriersclaude-haiku-4-5-202510014/5This task requires a licensed tradesperson on-site to inspect physical surfaces, ensure safety compliance, and legally sign off on measurements before installation. Building codes and liability frameworks typically require a certified worker to take direct responsibility for layout accuracy.
Adoption barriersclaude-sonnet-53/5No licensing barrier specifically blocks automation, but the physical nature of the task (uneven surfaces, tools, precision) creates strong practical friction against substitution.
Cost vs. human wageclaude-haiku-4-5-202510012/5AI vision analysis tools exist but are not yet cost-competitive when factoring in integration, human oversight, and the need for human workers to still perform the actual physical measurement and marking on-site. The labor cost of a tile setter doing this task remains lower than the combined cost of AI infrastructure plus human labor.
Cost vs. human wageclaude-sonnet-51/5There is no AI system that substitutes for the physical labor, so the human remains the only cost-effective option for this physical task.
Technical feasibility todayclaude-haiku-4-5-202510012/5Some computer vision products can analyze blueprint images and generate layout recommendations, but no deployed system reliably performs live on-site measurement and marking independently. Existing tools require heavy human interpretation and physical execution by workers.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs physical on-site measuring and marking of tile surfaces; this remains purely a manual trade skill with no robotic substitute in production.

Finish and dress the joints and wipe excess grout from between tiles, using damp sponge.

15

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

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Tile and stone setting remains a small-firm, hands-on trade with low digitization and minimal AI/robotics adoption today; the sector lacks the infrastructure and capital intensity to drive rapid automation of manual finishing work.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades are among the least digitized sectors, with minimal AI/robotics adoption for hands-on finishing work like this.
Augmentation potentialclaude-haiku-4-5-202510011/5AI and current automation offer no meaningful assistance for the core task of grouting with a damp sponge; this is fundamentally manual, sensory-driven work where human judgment and tactile control remain irreplaceable.
Augmentation potentialclaude-sonnet-51/5AI tools offer essentially no assistance for this tactile, physical finishing step; no relevant software or hardware aids the sponge-and-wipe process.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires dexterous manipulation of a damp sponge in tight spaces between tiles, constant tactile feedback to avoid damaging grout lines, and judgment about when joints are sufficiently finished. Current robotic and AI systems lack the fine motor control and real-time sensory adaptation needed for reliable execution.
Task automatabilityclaude-sonnet-51/5This is a fine-motor physical task requiring tactile feedback and dexterity to finish tile joints and wipe grout; no current AI or robotic system performs this end-to-end in real work settings.
Adoption barriersclaude-haiku-4-5-202510012/5While not legally licensed, this work is embedded in a skilled trade with customer expectations for human craftsmanship, and liability for poor grouting (water damage, structural issues) creates modest friction against full automation adoption.
Adoption barriersclaude-sonnet-52/5No licensing specifically covers this micro-task, but the physical dexterity, quality judgment on joint finish, and workspace variability create strong practical barriers to substitution.
Cost vs. human wageclaude-haiku-4-5-202510011/5Specialized robotic systems capable of this grouting task would cost tens of thousands of dollars with significant setup and maintenance, whereas a skilled tile setter completes this work for standard wages with minimal overhead, making human labor substantially cheaper.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI/robotic substitute, so any hypothetical automation would require expensive custom robotics far exceeding the cost of a human tile setter for this task.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product exists that can autonomously finish and dress grout joints with a damp sponge at production quality. The task demands precise force control and spatial reasoning in unstructured tile layouts that current manipulation systems cannot handle reliably.
Technical feasibility todayclaude-sonnet-51/5No deployed products exist for automated grout finishing and joint dressing; this remains purely manual craftwork with no robotic solution in production.

Apply a sealer to make grout stain- and water-resistant.

15

CI 15–15 · exposure 0 · augmentation 13 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5The construction trades remain among the slowest sectors to adopt automation, with minimal evidence of AI or robotic adoption for precision finishing tasks like sealer application in production settings.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical hands-on tasks, with minimal digitization or automation penetration in tile setting.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could marginally assist with scheduling sealer application timing or quality monitoring via computer vision, but offers limited productivity enhancement to the core manual application task itself.
Augmentation potentialclaude-sonnet-51/5AI offers negligible assistance for the physical act of applying sealer; there is no meaningful software or planning component to this manual finishing step.
Task automatabilityclaude-haiku-4-5-202510011/5Applying sealer to grout requires precise physical manipulation in varied spatial configurations, real-time sensory feedback, and autonomous navigation across uneven surfaces—capabilities that current robotic systems lack at scale. No general-purpose AI system today can perform this manual dexterity task end-to-end with equal quality.
Task automatabilityclaude-sonnet-51/5Applying sealer requires physical dexterity, spatial reasoning around irregular tile surfaces, and manual application with tools like brushes or applicators—current AI systems have no embodied capability to perform this physical task.
Adoption barriersclaude-haiku-4-5-202510012/5While there are no strict licensing barriers for the task itself, the physical nature of the work, customer preference for human craftsmanship, and site-specific variability create moderate friction to substitution.
Adoption barriersclaude-sonnet-52/5No licensing specifically governs sealer application, but the physical nature of the work in variable indoor/outdoor environments creates practical friction for any automated approach.
Cost vs. human wageclaude-haiku-4-5-202510011/5Robotic systems capable of such precision application would require significant capital investment and integration costs far exceeding the loaded wage of a skilled tile setter for comparable output.
Cost vs. human wageclaude-sonnet-51/5There is no AI-driven alternative to compare costs against; a human tradesperson with basic tools remains the only viable and far cheaper option than any hypothetical robotic system.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed commercial products reliably perform autonomous grout sealing. This remains a specialized manual craft task without production-ready robotic solutions in real-world tile-setting environments.
Technical feasibility todayclaude-sonnet-51/5No deployed robotic or AI product performs sealer application on grout in real job sites; this remains purely a manual trade task with no automation products in production.

Measure and cut metal lath to size for walls and ceilings, using tin snips.

15

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

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Construction remains a low-automation sector with minimal adoption of AI-driven robotics for this class of manual work. Most tile and stone setting work continues using traditional hand tools and methods.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical fabrication tasks, with minimal production deployment of automation for lath cutting.
Augmentation potentialclaude-haiku-4-5-202510011/5AI systems offer no meaningful assistance for measuring and cutting metal lath with hand tools; the task is too tactile and spatially embedded in physical construction work for current AI augmentation.
Augmentation potentialclaude-sonnet-51/5AI offers essentially no assistance for this hands-on measuring and cutting task; there's no meaningful digital augmentation applicable to the physical act of cutting lath.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires physical manipulation of materials in three-dimensional space, precise measuring with hand tools, and the use of tin snips to cut metal lath. Current AI systems lack the embodied robotics and dexterous manipulation capabilities to perform this end-to-end in real job sites.
Task automatabilityclaude-sonnet-51/5This is a physical fabrication and installation task requiring manual measurement, hand tool manipulation, and adaptation to irregular surfaces; no current AI system can perform this physical work.
Adoption barriersclaude-haiku-4-5-202510012/5While there are no strict licensing barriers for this manual task, the inherent physical and spatial requirements create practical barriers to automation. Physical job-site constraints and the need for on-site adaptation provide some friction.
Adoption barriersclaude-sonnet-52/5No licensing specifically restricts this sub-task, but it requires physical dexterity, on-site presence, and craft skill that create practical (not regulatory) barriers to substitution.
Cost vs. human wageclaude-haiku-4-5-202510011/5The capital cost of industrial robotics with the precision and flexibility to measure and cut metal lath, plus integration and maintenance, far exceeds the loaded wage of a skilled worker performing this task.
Cost vs. human wageclaude-sonnet-51/5There is no AI substitute performing this physical cutting task, so the human worker remains the only cost-effective option; AI cost is effectively infinite/inapplicable.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed commercial product reliably performs the physical measurement and cutting of metal lath on construction sites today. This remains a task requiring human hands-on execution.
Technical feasibility todayclaude-sonnet-51/5No deployed product measures and cuts metal lath with tin snips; this remains a purely manual trade skill with no robotic or AI product addressing it in production.

Remove any old tile, grout and adhesive using chisels and scrapers and clean the surface carefully.

15

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

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Tile and stone setting is a craft trade with low tech adoption historically; most work remains on-site, manual, and performed by small crews. The sector has not moved toward robotics or AI integration, and construction automation lags far behind information-sector adoption.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical tasks, with minimal automation penetration in tile removal specifically.
Augmentation potentialclaude-haiku-4-5-202510011/5There is minimal opportunity for AI to assist a human performing this task; the work is inherently manual and site-specific, with no meaningful role for digital guidance, planning, or co-performance that would materially raise the worker's productivity.
Augmentation potentialclaude-sonnet-51/5AI offers essentially no assistance for this tactile, physical demolition and cleaning task; there's no meaningful software or AI tool that aids in chiseling or scraping old tile.
Task automatabilityclaude-haiku-4-5-202510011/5Removing old tile, grout, and adhesive requires precise manual dexterity, real-time tactile feedback, and adaptability to highly variable material conditions (different substrate types, grout hardness, damage patterns). Current AI systems cannot operate physical tools in unstructured environments at this level of manipulation skill.
Task automatabilityclaude-sonnet-51/5This is manual demolition work requiring physical dexterity, force modulation, and tactile feedback to avoid damaging substrates; no current AI/robotic system performs this end-to-end in real job settings.
Adoption barriersclaude-haiku-4-5-202510012/5While there are no strict licensing barriers to automating demolition work itself, the task requires operating in occupied or semi-occupied spaces, handling hazardous dust and materials, and ensuring structural safety—factors that create organizational and liability friction but not absolute legal prohibitions on substitution.
Adoption barriersclaude-sonnet-52/5No licensing requirement specifically bars automation, but physical unstructured work environments, variable surface conditions, and liability for damaging substrates create practical friction against automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of a mobile robot capable of this task, plus its integration, maintenance, and operator oversight, would far exceed the hourly cost of a skilled tradesperson performing manual removal work, especially given the low utilization and high per-job customization needed.
Cost vs. human wageclaude-sonnet-51/5No viable automated alternative exists, so a human tradesperson remains the only cost-effective option; any experimental robotic demolition equipment would be far more expensive than manual labor.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed robotic system reliably performs this demolition task in production. While some specialized robots exist for tile removal in controlled lab settings, none demonstrate reliable, generalizable performance across the variety of real-world surface conditions and material variations encountered on job sites.
Technical feasibility todayclaude-sonnet-51/5There are no deployed commercial products that autonomously remove old tile, grout, and adhesive from surfaces; this remains outside current robotics deployment for construction trades.

Lay and set mosaic tiles to create decorative wall, mural, and floor designs.

14

CI 10–19 · exposure 8 · augmentation 25 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Tile and stone setting is a physically localized, craft-oriented trade with low digital infrastructure and slow technology adoption. Most firms are small and operate on-site in environments poorly suited to robotic deployment, showing minimal meaningful AI or automation adoption.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades are among the slowest sectors to adopt AI/robotics due to physical site variability and low digitization.
Augmentation potentialclaude-haiku-4-5-202510012/5AI tools can assist with design visualization, pattern generation, and material estimation before installation begins, offering modest productivity gains in planning phases. However, the core task of physically laying tiles offers limited opportunity for AI assistance during execution, where human skill and judgment dominate.
Augmentation potentialclaude-sonnet-52/5AI can assist with design visualization, pattern planning, or material estimation software, but offers little help with the actual physical laying and setting of tiles.
Task automatabilityclaude-haiku-4-5-202510012/5Current AI systems cannot perform the full end-to-end task of laying and setting mosaic tiles. While AI can assist with design generation and planning, the physical manipulation, precision placement, grouting, and quality assessment of individual tiles in complex patterns remain beyond robotic capability in unstructured environments today.
Task automatabilityclaude-sonnet-51/5This is a physical, precision manual craft involving cutting, spacing, adhesive application, and aesthetic placement of tiles; no current AI/robotic system can perform this end-to-end.5",
Adoption barriersclaude-haiku-4-5-202510013/5Some organizational friction exists around adopting unfamiliar robotic systems, and customer preference for artisanal quality in decorative tilework creates mild market resistance. However, no licensing requirement mandates human labor, and liability risk is manageable, making these barriers moderate rather than hard.
Adoption barriersclaude-sonnet-53/5No licensing mandates a human specifically, but physical dexterity, on-site variability, and customer expectations for craftsmanship create strong practical barriers to automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of robotic systems capable of even basic tile placement, combined with integration and programming overhead, far exceeds the loaded wage of a skilled tile setter. For intricate mosaic work, the gap is even wider; human labor remains far cheaper.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI/robotic substitute, so cost comparison favors the human tradesperson entirely; any experimental robotics would be far more expensive per job.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed commercial product reliably performs this task in production. Robotic tile-laying exists only in narrow, highly controlled laboratory settings or simple grid patterns; artistic mosaic work with varied tile sizes, orientations, and grouting demands human judgment and manual dexterity that current robots cannot execute at acceptable quality.
Technical feasibility todayclaude-sonnet-51/5No deployed products install mosaic tile in real construction settings; robotic tiling remains experimental research/demo stage.

Cut and shape tile to fit around obstacles and into odd spaces and corners, using hand and power cutting tools.

13

CI 10–15 · exposure 0 · augmentation 25 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Tile and stone setting is a traditional trades occupation with low digital infrastructure and minimal AI adoption; pilot automation projects are extremely rare and confined to factory pre-fabrication.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical fieldwork, with minimal deployment of automation for this specific task.
Augmentation potentialclaude-haiku-4-5-202510012/5Vision aids and automated cutting recommendations could assist planning, but current AI offers minimal support for the core task of physically cutting and fitting tile in situ. Some CAD/design tools help pre-planning but don't augment the hands-on work.
Augmentation potentialclaude-sonnet-52/5AI could assist with measurement calculations or cut-layout planning via apps, but it offers minimal direct assistance to the physical cutting and fitting process itself.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires physical dexterity, spatial reasoning in real-time, and adaptive cutting of brittle materials around variable obstacles. Current AI has no robotic systems in widespread deployment that can reliably handle the precision cutting, fitting, and adjustment needed in unstructured job-site conditions.
Task automatabilityclaude-sonnet-51/5This requires physical dexterity, spatial reasoning, and precise manual manipulation of hand/power tools in variable real-world conditions—no current AI system can perform this physical cutting and fitting task.
Adoption barriersclaude-haiku-4-5-202510012/5While not a licensed profession everywhere, tile setting requires in-person physical work and site-specific judgment that creates natural friction. Liability for poor cuts damaging expensive materials and the need for human oversight add modest barriers.
Adoption barriersclaude-sonnet-53/5No licensing strictly requires a human for tile cutting, but physical dexterity, judgment for irregular spaces, and lack of any robotic solution create strong practical barriers to automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5The hardware required for a robotic tile-cutting system (precision grinder, vision, manipulation) costs far more than the loaded wage of a skilled tile setter, and integration overhead remains prohibitive.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI-driven substitute for this physical task, so any hypothetical automation (specialized robotics) would be far costlier than a skilled tradesperson today.
Technical feasibility todayclaude-haiku-4-5-202510011/5No commercial product today performs autonomous tile cutting and fitting around arbitrary obstacles and corners. While tile-cutting robots exist in controlled factory settings, none demonstrate reliable end-to-end performance on-site with hand/power tools in variable conditions.
Technical feasibility todayclaude-sonnet-51/5No deployed robotic or AI product performs on-site tile cutting and fitting around obstacles; this remains a manual craft skill with no commercial automation.

Apply mortar to tile back, position the tile, and press or tap with trowel handle to affix tile to base.

13

CI 10–15 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Construction remains a low-digitization, physically distributed sector with fragmented small firms and strong craft traditions. Adoption of automation in tile-setting is minimal; the task requires site-specific adaptation that favors human labor over capital-intensive robotics.
Sector adoption velocityclaude-sonnet-51/5Construction trades are among the least digitized and slowest to adopt AI/robotics in production, with physical dexterity tasks like tiling seeing negligible automation deployment.
Augmentation potentialclaude-haiku-4-5-202510012/5AI offers minimal augmentation for this task; there are no mature tools that meaningfully assist a human tile setter in mortar application, tile positioning, or affixing. Robotics research may eventually support heavy lifting, but does not yet enhance the core task.
Augmentation potentialclaude-sonnet-52/5AI could assist with tile layout planning, pattern visualization, or measurement calculations, but offers little direct assistance to the physical act of applying mortar and setting tile.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires precise physical manipulation in 3D space, handling fragile materials, real-time sensory feedback, and adaptation to uneven surfaces. Current AI and robotics lack the dexterous manipulation, spatial reasoning under variability, and real-time force control needed to reliably perform this end-to-end in construction environments.
Task automatabilityclaude-sonnet-51/5This is a precise physical manipulation task requiring hand-eye coordination, force sensing, and continuous adaptation to surface irregularities; no off-the-shelf AI or robotic system performs this today.
Adoption barriersclaude-haiku-4-5-202510013/5While no explicit legal licensing barrier prohibits automation, organizational and safety friction exists: contractors prefer proven human labor, quality standards rely on human judgment, and liability for failed tile installations creates reluctance to rely on unproven autonomous systems.
Adoption barriersclaude-sonnet-52/5No licensing specifically bars automation of this physical task, but practical barriers like variable job-site conditions, material handling, and quality assurance create meaningful friction beyond just regulation.
Cost vs. human wageclaude-haiku-4-5-202510011/5Tile-setting robots remain prohibitively expensive capital equipment with high integration and maintenance costs, while manual labor remains economical for this skilled trade. The all-in cost per tile set by current AI/robotics far exceeds the loaded wage of a skilled tile setter.
Cost vs. human wageclaude-sonnet-51/5Any robotic solution capable of this fine motor task would require expensive specialized hardware, setup, and supervision, making it far costlier than a skilled tradesperson today.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed, production-grade systems exist that can autonomously apply mortar, position tiles, and affix them reliably on job sites. Specialized robotics in research or narrow lab settings cannot yet match human performance in real-world conditions with variable substrates and tile types.
Technical feasibility todayclaude-sonnet-51/5There are no deployed products that apply mortar and set tile in real construction settings; robotic tile-setting remains at experimental/research prototype stages at best.

Brush glue onto manila paper on which design has been drawn and position tiles, finished side down, onto paper.

13

CI 10–15 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5The tile-setting industry remains largely manual with minimal adoption of advanced automation, reflecting the physical nature of the work, small job-site scale operations, and low sector digitization.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades are among the slowest sectors to adopt AI/robotics, with minimal automation deployed for physical craft tasks like manual tile-setting prep.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could potentially assist with design layout visualization or adhesive application guidance, but the task's manual, physically-grounded nature limits meaningful augmentation of the core tile-positioning work.
Augmentation potentialclaude-sonnet-52/5AI could assist with generating or scaling design patterns digitally beforehand, but it offers little direct assistance during the physical gluing and tile-positioning process itself.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires precise manual dexterity, spatial reasoning, and real-time adjustment of physical objects in 3D space. Current AI systems cannot reliably perform the physical manipulation of tiles and glue application with the precision needed for finished-side-down placement.
Task automatabilityclaude-sonnet-51/5This requires fine physical dexterity, spatial precision, and manipulation of physical materials (glue, paper, tiles) that current AI systems cannot perform without embodiment in a capable robot, which does not exist for this task today.'
Adoption barriersclaude-haiku-4-5-202510013/5While there are no formal licensing barriers, the task requires hands-on physical work that is difficult to substitute. Customer expectations for quality craftsmanship and the need for real-time problem-solving create moderate organizational adoption friction.
Adoption barriersclaude-sonnet-52/5No licensing specifically restricts this narrow assembly step, but physical workspace constraints, tactile feedback needs, and quality control by an experienced installer create practical friction.
Cost vs. human wageclaude-haiku-4-5-202510011/5Specialized robotic systems capable of this task would cost orders of magnitude more than a skilled tile setter's hourly wage, including integration, maintenance, and programming for each unique design.
Cost vs. human wageclaude-sonnet-51/5Without any functioning automated system, there is no viable AI cost basis to compare; a human tradesperson remains the only functional and cheaper option in practice.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs this end-to-end task in production. Robotic tile placement systems exist only in limited research contexts and cannot match the flexibility and accuracy required for variable tile designs and real-world conditions.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs this physical mosaic/tile assembly task; robotic manipulation for tile-setting remains research-stage at best.

Align and straighten tile using levels, squares, and straightedges.

10

CI 10–10 · exposure 0 · augmentation 25 · importance 4.7/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Construction and tile-setting remain low-digitization sectors with high fragmentation and on-site physical constraints; automation adoption in this domain is minimal and focused on material handling rather than precision alignment work.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades are among the least digitized, slowest-adopting sectors for AI/robotics automation, with tiling specifically seeing negligible deployment.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could potentially assist by analyzing camera feeds to detect misalignment or suggest corrective angles, but current computer vision and AR tools provide only marginal productivity gains compared to a skilled worker using traditional levels and straightedges.
Augmentation potentialclaude-sonnet-52/5Laser levels and digital layout tools (some AI-assisted) can help planning and measurement, but the core physical alignment work sees minimal AI-driven productivity enhancement.
Task automatabilityclaude-haiku-4-5-202510011/5Aligning and straightening physical tiles requires real-time 3D spatial perception, fine motor control, and adaptive adjustment to material surfaces—capabilities current AI systems cannot execute in the physical world without specialized hardware and human oversight for every step.
Task automatabilityclaude-sonnet-51/5This is a physical dexterity task requiring precise hand-eye coordination and manipulation of materials; no off-the-shelf AI system can perform tile alignment and setting today.
Adoption barriersclaude-haiku-4-5-202510013/5The task involves direct physical interaction with installed materials and structural alignment quality that affects building integrity; while no explicit licensing barrier exists, the risk of poor output and customer preference for human craftsmanship create moderate adoption friction.
Adoption barriersclaude-sonnet-53/5No licensing law mandates a human specifically align tile, but physical site variability, precision craftsmanship expectations, and lack of robotic infrastructure create strong practical barriers.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of specialized robotic systems or AI-controlled machinery capable of precise tile alignment far exceeds the wage cost of a skilled tile setter performing the work directly.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI/robotic substitute performing this task at scale, so any hypothetical system would require expensive custom robotics far costlier than a human tradesperson.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs autonomous tile alignment and straightening in production settings; this remains a hands-on manual craft task without viable robotic or AI automation in standard construction environments.
Technical feasibility todayclaude-sonnet-51/5No deployed products perform tile setting/alignment; construction robotics for tiling remain research-stage or highly experimental prototypes, not production tools.

Mix, apply, and spread plaster, concrete, mortar, cement, mastic, glue or other adhesives to form a bed for the tiles, using brush, trowel and screed.

10

CI 10–10 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Tile and stone setting is performed by small, often independent contractors in construction—a traditionally low-digitization sector with limited AI/robotics adoption infrastructure and slow capital investment cycles.
Sector adoption velocityclaude-sonnet-51/5Construction trades are a low-digitization, physical-labor sector with minimal AI/robotic adoption for hands-on installation tasks.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could potentially assist with some preparatory tasks (e.g., suggesting mortar mix ratios based on conditions or generating application patterns) but offers minimal productivity gain for the core skilled manual work of spreading and placement.
Augmentation potentialclaude-sonnet-52/5AI offers little direct assistance to the physical mixing and spreading process itself, though planning tools or material calculators could offer marginal support outside this specific task.
Task automatabilityclaude-haiku-4-5-202510011/5This task involves highly skilled manual dexterity, spatial reasoning in 3D environments, and real-time tactile feedback to achieve proper adhesive consistency and application. Current AI systems cannot physically manipulate tools, assess surface conditions by touch, or adapt application technique to environmental variables in real jobsites.
Task automatabilityclaude-sonnet-51/5This is a physical manual construction task requiring dexterity, material handling, and real-time tactile feedback on uneven surfaces; no current AI system can perform it end-to-end.
Adoption barriersclaude-haiku-4-5-202510013/5Physical labor tasks have moderate barriers: no strict licensing requirement, but worker safety regulations, jobsite variability, and customer preference for human craftsmanship create friction to automation adoption.
Adoption barriersclaude-sonnet-53/5No licensing strictly requires a human, but physical site variability, liability for structural/waterproofing failures, and customer expectations create real practical friction against automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5Specialized robotic systems capable of this work are extremely expensive to acquire, maintain, and integrate compared to a skilled tile setter's loaded hourly wage, with no clear cost-per-task advantage.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI/robotic substitute performing this task, so any hypothetical automation would require expensive custom robotics far costlier than a human tile setter for typical jobs.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed commercial products perform this end-to-end physical task. Robotic tile-setting remains largely experimental; systems lack the dexterity, environmental sensing, and real-world reliability needed for unsupervised adhesive preparation and application.
Technical feasibility todayclaude-sonnet-51/5No deployed product mixes and spreads mortar/adhesive beds for tiling; this remains firmly in the domain of human tradespeople and unaddressed by robotics products at scale.

Mix and apply mortar or cement to edges and ends of drain tiles to seal halves and joints.

10

CI 10–10 · exposure 0 · augmentation 13 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Construction remains a low-digitization, spatially variable sector with fragmented firms and strong craft traditions; robotic adoption for finishing work like mortar application is minimal.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical hands-on tasks, with minimal automation penetration in tile-setting and masonry work.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could potentially assist with mixing calculations or product recommendations, but the core physical task of applying mortar to edges and joints offers limited room for meaningful AI-assisted productivity gain.
Augmentation potentialclaude-sonnet-51/5Current AI offers essentially no meaningful assistance for the physical act of mixing and applying mortar to tile joints; this is a manual craft task outside AI's assistive capabilities.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires physical manipulation in three-dimensional space—spreading mortar/cement onto tile edges and joints with precision and dexterity. Current AI systems lack the embodied robotics capabilities to perform this consistently in varied field conditions.
Task automatabilityclaude-sonnet-51/5This requires physical manipulation of materials, precise hand-eye coordination, and mixing/applying viscous substances to physical objects—no off-the-shelf AI system can perform this manual construction task.
Adoption barriersclaude-haiku-4-5-202510013/5While there is no strict licensing barrier to automation itself, quality standards and site-specific conditions create practical friction; builders and property owners often prefer human oversight of sealing work due to its structural importance.
Adoption barriersclaude-sonnet-53/5While not licensed at a high regulatory level in most jurisdictions, this physical trade task requires hands-on skill, judgment about material consistency, and on-site adaptability that creates substantial practical barriers to automation, though not legal ones.
Cost vs. human wageclaude-haiku-4-5-202510011/5Specialized robotic systems capable of this task would cost tens of thousands of dollars plus integration, vastly exceeding the loaded hourly wage of a skilled tile setter for typical job volumes.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI/robotic system performing this task, so any hypothetical automation would require expensive specialized robotics far exceeding the cost of a human tradesperson.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed commercial product reliably performs autonomous mortar application and joint sealing at construction sites today. Robotics in this domain remain largely experimental or laboratory-bound.
Technical feasibility todayclaude-sonnet-51/5No deployed product exists that mixes mortar and applies it to seal drain tile joints; this remains firmly in the domain of skilled manual labor with no robotic or AI-based substitute in production.

Install and anchor fixtures in designated positions, using hand tools.

10

CI 10–10 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5The construction trades, especially tile and stone setting, have historically low digitization and AI adoption rates; most work remains on-site, manual, and resistant to automation due to site variability and craft skill requirements.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades are among the slowest sectors to adopt AI/robotics for hands-on physical tasks, with minimal production deployment of automation for fixture installation.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could potentially assist with positioning guidance (e.g., AR overlays for fixture placement) or measurement verification, but current systems offer minimal productivity enhancement for the core manual anchoring and installation work itself.
Augmentation potentialclaude-sonnet-52/5AI offers limited assistance here, perhaps in planning or measurement software, but does not materially enhance the physical act of installing and anchoring fixtures with hand tools.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires precise physical manipulation in three-dimensional space, real-time assessment of fixture alignment, and adaptation to variable surface conditions. Current AI systems lack the embodied dexterity and environmental sensing needed to perform this end-to-end at equal quality.
Task automatabilityclaude-sonnet-51/5Physical installation and anchoring of fixtures requires manual dexterity, spatial judgment, and precise tool manipulation in variable real-world conditions that current AI systems, including robotics, cannot perform end-to-end.
Adoption barriersclaude-haiku-4-5-202510013/5While there are no explicit licensing requirements for fixture installation, building codes and liability considerations create moderate friction; a human installer's work is typically inspected and guaranteed, creating organizational and legal accountability that automation would need to replicate.
Adoption barriersclaude-sonnet-53/5While not licensed in the same way as medical or legal work, this trade involves physical liability, building codes, and customer trust in a skilled human installer, creating moderate structural friction against automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5Specialized robotic systems capable of fixture installation and anchoring would cost tens of thousands to hundreds of thousands of dollars, vastly exceeding the loaded wage cost of a skilled setter performing the same work.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI/robotic substitute performing this at comparable quality, so any hypothetical automated solution would require expensive custom robotics far exceeding a tradesperson's wage.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed commercial products perform fixture installation and anchoring autonomously in real job sites. Robotic solutions exist in narrow, controlled settings but not in the variable, unstructured environments where tile and stone setters work.
Technical feasibility todayclaude-sonnet-51/5No deployed commercial product autonomously installs and anchors tile/stone fixtures using hand tools; robotic construction efforts remain research-stage or narrowly scoped to lab demonstrations.

Remove and replace cracked or damaged tile.

10

CI 5–15 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Tile setting is a small-firm, site-based trade with low digitization and no meaningful AI adoption in production. The sector remains heavily dependent on manual labor and lacks the infrastructure or incentive to deploy autonomous systems.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades are among the slowest sectors for AI/robotic adoption due to physical, unstructured environments and low digitization.
Augmentation potentialclaude-haiku-4-5-202510012/5AI could assist with defect detection (visual inspection via drones or handheld cameras) or material selection guidance, but the core removal and replacement work remains fundamentally manual; augmentation value is limited to narrow preparatory steps.
Augmentation potentialclaude-sonnet-52/5AI can help with tile ordering, color matching, or estimating job costs, but offers little assistance during the actual physical removal and installation process.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires physical manipulation in a spatially varied environment (identifying cracks, removing tiles without damaging surrounding areas, preparing surfaces, setting new tiles). Current AI lacks embodied robotics capable of the precision, dexterity, and real-time environmental adaptation needed for reliable execution.
Task automatabilityclaude-sonnet-51/5This is a physical manipulation task requiring demolition of damaged tile, surface prep, and precise placement of new material—no AI/robotic system today can perform this end-to-end reliably.
Adoption barriersclaude-haiku-4-5-202510014/5This is a licensed trade in many jurisdictions; work must meet building codes and often requires certification. Liability for structural or water-integrity failures creates high error costs, and customer expectations typically favor human craftsmanship and on-site problem-solving.
Adoption barriersclaude-sonnet-52/5No licensing strictly requires a human for tile repair, but physical site variability, homeowner trust, and liability for property damage create real practical friction.
Cost vs. human wageclaude-haiku-4-5-202510011/5The specialized hardware (robotic arms, vision systems, adhesive dispensing) and integration costs required would far exceed the loaded wage of a skilled tile setter for a single task execution.
Cost vs. human wageclaude-sonnet-51/5Any conceivable robotic solution would require expensive custom hardware and setup far exceeding the cost of a human tradesperson performing a routine repair.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed product reliably performs the full removal and replacement of damaged tile in production settings. While robotic applications exist in research, none demonstrate consistent performance across the variable geometries, materials, and adhesive conditions encountered in real installations.
Technical feasibility todayclaude-sonnet-51/5No deployed commercial product removes and replaces tile autonomously; robotic construction manipulation remains research-stage for such fine, variable physical work.

Cut tile backing to required size, using shears.

10

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

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Construction trades, especially tile setting, remain largely resistant to full automation due to site-specific variability, physical dexterity requirements, and high setup costs; meaningful AI adoption is minimal.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical fabrication tasks, with minimal production deployment.
Augmentation potentialclaude-haiku-4-5-202510011/5Current AI offers no meaningful assistance to a worker cutting tile backing; the task is straightforward manual labor that does not benefit from computational augmentation or algorithmic support.
Augmentation potentialclaude-sonnet-51/5AI offers no meaningful assistance for the physical act of cutting tile backing with shears; software-based measurement tools might help planning but not this specific cutting task.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires physical manipulation of materials with precision in a construction environment, which current AI systems cannot perform end-to-end. Robotic systems exist but are not off-the-shelf general solutions that provide 50% time savings at equal quality for typical tile-setting operations.
Task automatabilityclaude-sonnet-51/5This is a physical manipulation task requiring hand-eye coordination and tool use with shears on rigid materials; no off-the-shelf AI system can perform this physical cutting operation today.
Adoption barriersclaude-haiku-4-5-202510014/5Physical presence on-site and direct manipulation of construction materials creates substantial barriers; no fully autonomous system can legally or practically substitute for this work without significant human oversight and control.
Adoption barriersclaude-sonnet-52/5No licensing requirement specifically governs this cutting step, but physical dexterity, material handling, and on-site variability create practical friction against automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of deploying a robotic system capable of cutting tile backing would far exceed the loaded wage of a tile setter performing this work manually, making automation economically infeasible today.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI-driven alternative to compare costs against; a human with shears remains the only functional means, making AI substitution infeasible and thus more 'expensive' by default.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed commercial product reliably performs this specific physical task (measuring, positioning, and cutting tile backing) in real job sites today. This remains a task requiring human workers and specialized physical tools.
Technical feasibility todayclaude-sonnet-51/5No deployed product exists that autonomously cuts tile backing with shears in production settings; this remains firmly outside current robotic/AI product capability.

Level concrete and allow to dry.

7

CI 0–15 · exposure 0 · augmentation 13 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Tile and stone setting remains a traditional, physically-intensive construction trade in small firms and project-based work with minimal digitization and negligible AI/robotic adoption in production workflows.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades remain a laggard sector for AI/robotic adoption due to low digitization and the physical, on-site nature of the work.
Augmentation potentialclaude-haiku-4-5-202510011/5AI offers no meaningful assistance to a tile and stone setter performing manual concrete leveling and drying; the task is primarily physical execution with no information-processing component that AI tools could augment.
Augmentation potentialclaude-sonnet-52/5AI offers minimal direct assistance to the physical act of leveling and curing concrete, though tools like laser levels or planning software provide tangential support outside the core AI automation scope.
Task automatabilityclaude-haiku-4-5-202510011/5Leveling concrete and allowing it to dry requires on-site physical manipulation in variable environmental conditions (temperature, humidity, surface irregularities) that current AI and robotics cannot reliably automate end-to-end. This is a hands-on construction task with no demonstrated 50%-time-saving automation solution in production.
Task automatabilityclaude-sonnet-51/5This is a physical construction task requiring pouring, screeding, and finishing concrete by hand or with power tools, followed by curing time; no AI system can perform the physical labor involved.'
Adoption barriersclaude-haiku-4-5-202510015/5Concrete finishing is a licensed trade skill requiring on-site physical presence and human judgment to assess surface quality, drainage, and safety. Labor regulations and liability for structural integrity create hard barriers to full automation.
Adoption barriersclaude-sonnet-52/5No licensing specifically requires a human to level concrete, but physical site conditions, variable terrain, and lack of robotic infrastructure create practical friction against automation.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of deployed robotic systems capable of concrete leveling (purchase, setup, maintenance, operator oversight) far exceeds the loaded wage of a skilled tile and stone setter performing this task manually.
Cost vs. human wageclaude-sonnet-51/5Physical robotic systems capable of leveling concrete would require expensive specialized hardware and setup, far exceeding the cost of a human worker with basic tools for this task.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed commercial product reliably performs concrete leveling autonomously in real construction sites. Robotic concrete finishing exists in research/niche applications but is not a production standard in tile and stone setting work.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs physical concrete leveling; robotic concrete finishing exists only in narrow research/pilot contexts, not as reliable commercial products for this trade.

Prepare surfaces for tiling by attaching lath or waterproof paper, or by applying a cement mortar coat to a metal screen.

7

CI 5–10 · exposure 0 · augmentation 13 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Construction and trades remain low-digitization, fragmented sectors with limited capital per job and high variability; adoption of construction robotics remains nascent and concentrated in large commercial projects, not tile setting.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades are among the slowest sectors to adopt AI/automation for hands-on physical tasks, with minimal robotic deployment in residential or commercial tiling prep.
Augmentation potentialclaude-haiku-4-5-202510011/5Current AI tools offer no meaningful assistance for the hands-on work of attaching materials, spreading mortar, or preparing surfaces; this task is fundamentally manual and site-dependent.
Augmentation potentialclaude-sonnet-52/5AI could assist with measurement calculations, material estimation, or planning via apps, but offers little direct help with the physical act of attaching lath or applying mortar coats.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires physical manipulation of materials in variable, site-specific conditions (attaching lath, applying waterproof barriers, spreading mortar on metal screens). Current AI systems cannot perform end-to-end physical construction work with the precision and adaptability this preparation demands.
Task automatabilityclaude-sonnet-51/5This is a physical construction task requiring manual dexterity, spatial judgment, and material handling that current AI systems and robotics cannot perform outside narrow lab demos.no off-the-shelf system automates this end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5Surface preparation is typically part of licensed or apprenticed tile work with legal responsibility for quality and safety; building codes govern waterproofing and substrate requirements, creating regulatory and liability barriers to casual automation.
Adoption barriersclaude-sonnet-53/5No licensing specifically requires a human for this exact substep, but building codes, inspection requirements, and physical workmanship standards create practical barriers to non-human execution.
Cost vs. human wageclaude-haiku-4-5-202510011/5Autonomous systems capable of this task (if they existed) would require specialized robotics, vision systems, and extensive on-site integration—far exceeding the cost of a skilled tile setter's labor for this preparatory work.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI/robotic substitute for this task, so the effective AI cost is either infinite or requires expensive bespoke robotics far exceeding human labor costs.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed autonomous systems reliably perform surface preparation for tiling in production environments. Robotics research exists but does not translate to commercially available, deployable products that handle the variability of real construction sites.
Technical feasibility todayclaude-sonnet-51/5No deployed product performs surface preparation for tiling; this remains squarely in the domain of skilled tradespeople using hand tools.

Spread mastic or other adhesive base on roof deck to form base for promenade tile, using serrated spreader.

7

CI 5–10 · exposure 0 · augmentation 0 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5The construction and roofing trades remain among the slowest sectors to adopt automation; most work is site-dependent, physically varied, and handled by small crews with limited capital for robotics investment.
Sector adoption velocityclaude-sonnet-51/5Construction trades are among the slowest sectors to adopt AI/robotics for hands-on physical tasks, with minimal automation penetration in tile/roofing work.
Augmentation potentialclaude-haiku-4-5-202510011/5Current AI tools offer no meaningful assistance for spreading adhesive on roof decks; the task is fundamentally physical and requires no knowledge work, decision support, or data analysis that AI could augment.
Augmentation potentialclaude-sonnet-51/5AI offers essentially no assistance for the physical act of spreading adhesive with a serrated trowel; there's no meaningful digital or cognitive component to augment.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires physical manipulation of materials on a roof deck with precise application using hand tools. Current AI systems cannot operate in unstructured outdoor environments, handle physical objects, or use serrated spreaders to apply adhesive with the necessary consistency and coverage quality.
Task automatabilityclaude-sonnet-51/5This is a physical trowel-application task requiring dexterity, adhesive spread-rate judgment, and mobility on a roof deck; no current AI system can perform this manipulation end-to-end.
Adoption barriersclaude-haiku-4-5-202510014/5This task occurs on residential and commercial roofing projects, which typically involve licensed contractors and building code compliance. The work environment (rooftops, variable conditions) and safety regulations create substantial adoption friction beyond pure technical capability.
Adoption barriersclaude-sonnet-53/5No licensing specifically required for this sub-task, but it occurs within construction work often requiring supervision, quality standards, and physical site access that favor human workers.
Cost vs. human wageclaude-haiku-4-5-202510011/5Robotics capable of this task would require significant capital investment ($50k–$200k+), specialized maintenance, and site-specific setup, far exceeding the cost of a trained tile setter's labor for typical projects.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI/robotic substitute, so any automation attempt (custom robotics) would vastly exceed the cost of a human tradesperson performing this task.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed AI robotic systems reliably perform roofing adhesive application in production settings. Prototype robotic systems exist in controlled lab environments, but none demonstrate consistent, quality performance on actual job sites across varied roof conditions.
Technical feasibility todayclaude-sonnet-51/5No deployed robotic or AI product performs mastic spreading with serrated spreaders on roof decks; this remains purely manual skilled trade work.

Build underbeds and install anchor bolts, wires, and brackets.

7

CI 5–10 · exposure 0 · augmentation 25 · importance 3.2/5 · click for rater detail

Panel ratings per dimension for this task
DimensionModelRatingRationale
Sector adoption velocityclaude-haiku-4-5-202510011/5Construction and skilled trades remain among the slowest sectors to adopt automation. Physical, on-site, bespoke work with high variability in conditions and materials shows minimal AI/robotics adoption despite decades of interest; most tile and stone setting remains manual.
Sector adoption velocityclaude-sonnet-51/5Construction and skilled trades are among the slowest sectors to adopt AI/robotics for hands-on physical tasks, with minimal production deployment of automation for this specific work.
Augmentation potentialclaude-haiku-4-5-202510012/5Current AI offers limited assistance for this task. Augmented reality tools for layout visualization or digital measurement aids are emerging, but they support only planning stages; the core physical work of building underbeds and fastening components offers minimal scope for real-time AI augmentation.
Augmentation potentialclaude-sonnet-52/5AI could assist with planning, layout calculations, or material estimates, but offers negligible help with the physical execution of building underbeds and installing anchors.
Task automatabilityclaude-haiku-4-5-202510011/5This task requires precise physical manipulation in three-dimensional space, positioning of heavy materials, and securing components on vertical or horizontal surfaces—capabilities that current AI and robotics cannot reliably perform end-to-end. Underbeds involve laying foundations for tile/stone, while anchor bolts and brackets demand accuracy in positioning and torque application in varied, unstructured job-site conditions.
Task automatabilityclaude-sonnet-51/5This is a physical construction task requiring manual measurement, mixing/laying mortar beds, and precise placement of anchoring hardware—no off-the-shelf AI system can perform this physical labor today.
Adoption barriersclaude-haiku-4-5-202510014/5Building codes, safety regulations, and liability requirements around structural fastening (anchor bolts) create material regulatory barriers. Many jurisdictions require licensed or certified professionals to sign off on anchor installations, creating legal handoff requirements that hinder full automation.
Adoption barriersclaude-sonnet-53/5No licensing mandates a human specifically for this micro-task, but structural/safety implications of anchor installation create liability concerns and reliance on skilled labor and physical dexterity.
Cost vs. human wageclaude-haiku-4-5-202510011/5The cost of acquiring, maintaining, and deploying specialized robotics for this physical task—combined with integration, on-site setup, and continuous oversight—far exceeds the labor cost of skilled tile and stone setters, particularly for varied, site-specific work.
Cost vs. human wageclaude-sonnet-51/5There is no viable AI substitute performing this physical work, so any AI-based approach (e.g., robotics) would be far more costly than a human tradesperson given current lack of maturity.
Technical feasibility todayclaude-haiku-4-5-202510011/5No deployed commercial systems perform this integrated task reliably in production. While some robotic arms exist in controlled factory settings, deploying them on job sites to build underbeds and install fasteners with the required precision, adaptability, and safety oversight remains at the research or prototype stage.
Technical feasibility todayclaude-sonnet-51/5No deployed AI product installs anchor bolts or builds underbeds; this remains purely a manual trade skill performed by humans on job sites.

Related occupations — Construction & Extraction

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.