Glaziers
47-2121.00Median wage $57,080/yr58,480 employed (US)Rank #711 of 923 scored · top 77% by substitution
Install glass in windows, skylights, store fronts, and display cases, or on surfaces, such as building fronts, interior walls, ceilings, and tabletops.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution19
Exposure12
Augmentation27
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
27 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%13
panel mean rating 1.5/5 → substitution pressure 13/100
Technical feasibility todayw 20%10
panel mean rating 1.4/5 → substitution pressure 10/100
Cost vs. human wagew 15%11
panel mean rating 1.4/5 → substitution pressure 11/100
Adoption barriersw 20%inverted — strong barriers lower the score49
panel mean rating 3.0/5 (barrier strength) → substitution pressure 49/100
Sector adoption velocityw 10%6
panel mean rating 1.2/5 → substitution pressure 6/100
Task breakdown (27 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.
Assemble and cement sections of stained glass together.
49CI 10–87 · exposure 45 · augmentation 38 · importance 3.2/5 · click for rater detail
Assemble and cement sections of stained glass together.
49| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Large-scale glass fabrication and window manufacturing have rapidly adopted CNC and robotic assembly systems over the past decade. Mass production segments show high automation penetration, though small artisanal shops lag significantly. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Glazing and craft trades are physical, low-digitization occupations with minimal AI/robotics adoption for hands-on fabrication tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI and automation can assist human glaziers by automating precise cutting and initial positioning, freeing them to focus on quality inspection and artistic adjustments. However, the degree of assistance varies with the complexity and customization of the glass project. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with design pattern generation or layout planning for stained glass, but offers little help with the physical assembly and cementing itself. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Automated systems can now handle glass cutting, positioning, and adhesive application with high precision and speed. Robotic arms and CNC machinery can assemble and cement glass sections faster and more consistently than human glaziers, meeting the 50% time-saving threshold at equal or superior quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a fine manual craft task requiring physical dexterity, spatial judgment, and hands-on manipulation of fragile materials, none of which current AI systems can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or legal barriers prevent automation of glass assembly itself. However, some bespoke architectural and artistic stained glass projects remain customized and low-volume, creating organizational friction for full automation adoption in specialized studios. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandates a human specifically, but the physical dexterity, craftsmanship, and customer expectation of handmade quality create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated glass assembly (CNC cutting, robotic positioning, and adhesive dispensing) operates at a fraction of the labor cost for high-volume production, easily achieving an order-of-magnitude cost advantage per unit over manual labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI or robotic substitute for this physical assembly task, so AI cost comparison is not applicable and effectively far more expensive than a skilled human. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Industrial glass-cutting and assembly automation is deployed in production at scale in large fabrication facilities. While some bespoke stained glass work remains manual, standardized panel assembly and cementing processes are reliably automated in modern glass manufacturing plants. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product or robotic system performs stained glass assembly and cementing in production; this remains purely a manual artisan skill. |
Set glass doors into frames and bolt metal hinges, handles, locks, or other hardware to attach doors to frames and walls.
46CI 10–81 · exposure 45 · augmentation 38 · importance 4.2/5 · click for rater detail
Set glass doors into frames and bolt metal hinges, handles, locks, or other hardware to attach doors to frames and walls.
46| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Large door and window manufacturers have adopted robotic assembly; however, most small and mid-size glazier shops remain labor-intensive. Adoption is occurring but piecemeal and slower than in fully digitized sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical installation tasks, with minimal deployment of automation in this niche. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Power tools and measurement aids assist glaziers in alignment and fastening speed, and AR guides could help with positioning. However, the task is already fairly straightforward, so augmentation offers modest productivity lift compared to full automation potential. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI offers limited assistance here, perhaps in measurement calculations or scheduling, but not in the core physical task of setting and bolting hardware. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | This task involves routine mechanical operations (setting glass into frames, bolting hinges and hardware) that are highly standardized and repeatable. Robotic systems can perform glass placement, alignment, and fastening with precision, achieving well over 50% time savings compared to manual labor while maintaining quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This is precise physical manipulation of heavy, fragile materials requiring fine motor control, strength, and spatial judgment that no current robotic or AI system can perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing or legal requirement mandates human performance of this mechanical assembly task. Primary barriers are organizational (retrofitting existing shops, worker displacement concerns) and logistical (on-site vs. factory conditions), not regulatory. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing law mandates a human specifically for this step, but safety liability (glass breakage, injury risk) and building code compliance create practical friction against any automated substitute. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Industrial robotic systems for assembly (including integration and oversight) cost thousands to tens of thousands annually, while a glazier's loaded wage is $60–80k+. Per-unit task cost via automation is substantially lower, especially at volume. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so the human glazier remains the only cost-effective option for this physical task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Robotic arms with gripper and fastening attachments are deployed in manufacturing and some fabrication shops for glass and door assembly. While not universal across all glazier work, mature systems exist and perform these operations reliably in controlled environments, though field installation remains less automated. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No commercial product installs glass doors or bolts hardware autonomously; this remains purely manual skilled trade work today. |
Measure mirrors and dimensions of areas to be covered to determine work procedures.
44CI 24–65 · exposure 41 · augmentation 63 · importance 4.0/5 · click for rater detail
Measure mirrors and dimensions of areas to be covered to determine work procedures.
44| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Glaziers and small construction trades are laggards in AI adoption; most firms remain manually-driven with low digitization. Pilot adoption of measurement tech is growing but production-scale displacement remains rare in this sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and glazing trades are a low-digitization, physical-labor sector with minimal AI agent adoption for field measurement tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted measurement tools (e.g., image analysis to propose dimensions, AR overlays to check coverage) substantially boost a glazier's speed and reduce errors in the field. The human glazier retains judgment on work procedures while the tool handles data capture and calculation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-powered measurement apps (e.g., LiDAR-based smartphone tools) can assist glaziers in capturing and calculating dimensions faster, though a human still performs the physical measuring. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI-powered vision systems and measurement tools can reliably extract dimensions from images or site specifications with high accuracy, potentially saving 70%+ of manual measurement time. However, some complex spatial reasoning or field verification may still require human oversight to ensure work-procedure decisions are sound. |
| Task automatability | claude-sonnet-5 | 2/5 | Measuring physical spaces and mirrors requires on-site physical presence and manual tool use, which current AI cannot perform end-to-end; only planning/documentation portions could be assisted.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing requirement exists for measurement; the task is routine and has low liability if an AI tool aids or partially automates it. Customer preference for in-person verification and integration into existing workflows provide moderate friction, but no hard legal barrier. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for measuring, but physical presence and precision needs for safety/fit create practical barriers to remote automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI measurement tools (software + inference) cost pennies per use, while a glazier's fully-loaded hourly rate for measurement and planning is $25–50+. Even accounting for oversight, AI is likely 10–20× cheaper per task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the physical measurement, so any deployment would add cost on top of the human labor still required, making it more expensive overall. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Computer vision and mobile measurement apps exist in production (e.g., AR measuring tools, automated dimension extraction from photos), but glazing-specific deployment at scale is limited and integration with work-procedure planning varies. Error rates on complex geometries or occlusions remain material. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously performs physical measurement of installation sites for glazing work; this remains a manual field task. |
Confer with customers to determine project requirements or to provide cost estimates.
33CI 30–35 · exposure 25 · augmentation 63 · importance 3.9/5 · click for rater detail
Confer with customers to determine project requirements or to provide cost estimates.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Glazing and general construction trades remain relatively low-digitization sectors where small firms dominate; while some larger glazing firms may use CRM and estimate software, actual AI agent deployment for customer conferencing is rare and adoption is slow. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Construction and trade services are historically slow adopters of AI-driven customer interaction tools compared to office-based professional services. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by surfacing relevant products, calculating standard estimates, and organizing customer input into a structured brief, materially speeding up the back-office work of quoting, even if the primary conversation remains human-led. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully help glaziers draft estimate templates, respond to initial customer inquiries, and calculate material costs, improving efficiency even though a human closes the loop. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft templated cost estimates and answer routine questions, conferring with customers to understand nuanced project requirements—which often involve site-specific conditions, aesthetic preferences, and trade-off discussions—requires human judgment and presence that current AI cannot reliably handle end-to-end at scale. |
| Task automatability | claude-sonnet-5 | 2/5 | AI chatbots can gather basic requirements or provide rough cost ranges, but real glazing estimates require in-person or photo-based site assessment, measurement, and nuanced negotiation that current systems cannot fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Customers often prefer speaking to a human to discuss their project, and there is organizational friction in changing established sales workflows; however, there are no hard legal barriers to using AI tools to support or partially automate initial estimate conversations. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for sales conversations, but customer trust and the need for physical inspection to give accurate quotes creates practical friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | An AI-assisted estimate system costs money to build, integrate, and oversee; for a small glazier firm doing relatively few custom projects, the integration and maintenance overhead may exceed the wages saved by automating a single conversation per project. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI intake tools are cheap to run, but since they can't finalize actual estimates, a human still must do the substantive work, so total cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Chatbots and CRM tools can handle initial inquiries and generate basic estimates, but deployed systems still struggle with complex, multi-faceted customer conversations that involve clarifying ambiguous requirements and building trust, leading to material error rates in real-world deployment. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some contractor-facing estimating and chatbot intake tools exist, but they are narrow and typically require human follow-up for accurate quotes on custom glazing jobs. |
Read and interpret blueprints or specifications to determine size, shape, color, type, or thickness of glass, location of framing, installation procedures, or staging or scaffolding materials required.
28CI 25–30 · exposure 25 · augmentation 50 · importance 4.4/5 · click for rater detail
Read and interpret blueprints or specifications to determine size, shape, color, type, or thickness of glass, location of framing, installation procedures, or staging or scaffolding materials required.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Glazing is a traditional trades sector with low digital maturity and fragmented small-firm operations. Adoption of automated blueprint interpretation remains minimal; most firms still rely on manual review by experienced workers on-site. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Construction and trades sectors show slow, uneven AI adoption due to fragmented small businesses and physical, site-dependent work, with pilots more common than production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted blueprint parsing (auto-highlighting dimensions, materials lists, or flagging specification inconsistencies) could improve glazier productivity during the interpretation phase. However, augmentation is limited to faster information extraction rather than transformative decision-support. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools (e.g., document summarization, image-based blueprint analysis) can help glaziers quickly extract relevant specs or highlight required materials, improving efficiency while the human still verifies and applies judgment on-site. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Current AI can extract and interpret some blueprint elements (dimensions, specifications) from digital documents, but the spatial reasoning required to determine optimal glass sizing, framing locations, and staging materials for complex installations remains largely manual. End-to-end automation with 50% time savings is not demonstrable. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help parse and summarize blueprint text/specs, but interpreting construction drawings for glass specification and translating them into physical installation plans requires spatial reasoning and on-site judgment that current systems cannot fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety and liability create strong barriers: installation errors from misinterpreted specs can result in structural failure, injury, or code violations. Industry standards and site-specific safety requirements typically mandate human review and sign-off by licensed professionals before glazing work proceeds. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement to read blueprints, but liability for structural/safety errors and reliance on experienced tradespeople creates meaningful organizational friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI document processing and vision systems require significant integration costs and skilled oversight to validate interpretations. The loaded labor cost of a skilled glazier reading blueprints is relatively low per task, making the all-in AI cost comparable or higher. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Using AI to assist with document review could save some time, but human verification, site-specific judgment, and liability concerns keep overall costs comparable to skilled labor rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While OCR and document parsing products exist, reliable interpretation of architectural blueprints—especially non-standard or hand-drawn specifications—with consistent accuracy in production remains limited. Most deployed solutions handle only structured data extraction, not the full spatial and procedural reasoning this task demands. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some CAD/BIM-integrated tools and vision models can extract dimensions or annotations from drawings, but no widely deployed product reliably performs full blueprint interpretation for glazing-specific installation planning. |
Select the type or color of glass or mirror according to specifications.
28CI 23–33 · exposure 25 · augmentation 50 · importance 3.8/5 · click for rater detail
Select the type or color of glass or mirror according to specifications.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glazing is a traditional, physically-constrained trade with low digital infrastructure. Selection typically happens on-site or via phone/email with suppliers. Sector digitization and AI adoption are minimal; no evidence of production AI deployments in this role. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Glazing is a physical, low-digitization trade with minimal AI/robotic adoption for material selection tasks in practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by organizing and searching specification documents, cross-referencing material databases, and flagging similar past projects. However, the human glazier must still verify color and type against the job context, so assistance is useful but limited to information retrieval and matching support. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based spec lookup, inventory databases, or color-matching apps can help glaziers quickly identify correct glass/mirror type from specifications, improving accuracy and speed. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Interpreting specifications and matching them to glass types/colors involves some visual and textual understanding, but requires domain knowledge of material properties and access to physical samples for verification. Most selection logic could be rule-based, but current AI lacks reliable integration with actual inventory systems and cannot physically verify color matches without human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Selecting glass or mirror type/color from specifications requires physical inspection, matching, and knowledge of stock/inventory, which is not a pure information task despite being simple; AI could assist but not fully replace this without physical integration."}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Glaziers must interpret client/architect specifications accurately; selection errors result in costly rework and customer dissatisfaction. Professional liability, verification requirements, and the need for the glazier to sign off on material choice create strong friction against full automation. Human judgment and responsibility are deeply embedded in practice. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for glass selection, though it's embedded in a broader trade task with some quality/safety accountability. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The task requires minimal setup and labor (typically 5–15 minutes), making the loaded human wage very low. AI systems (material databases, image recognition APIs) would cost more or require substantial custom integration overhead relative to a glazier quickly selecting from standard options or consulting a supplier. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human labor cost for this quick sub-task is already low, and any AI system would require sensors/vision plus integration with physical inventory, making AI not clearly cheaper for this narrow step. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No production system reliably performs end-to-end glass/mirror selection autonomously. Image-based color matching tools and basic material databases exist, but they lack the integration with specification documents, inventory systems, and physical verification needed for reliable task performance in a glazing context. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed products autonomously select physical glazing materials on job sites; software can help compare specs digitally but the physical selection step remains manual. |
Grind or polish glass, smoothing edges when necessary.
25CI 15–35 · exposure 13 · augmentation 25 · importance 3.8/5 · click for rater detail
Grind or polish glass, smoothing edges when necessary.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption is confined to large-scale industrial glass manufacturing; small glazing shops and custom work dominate the occupation and remain largely manual, indicating slow overall sectoral adoption despite pockets of automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Glazier trades are a physical, low-digitization sector with minimal AI adoption; mechanization here is via traditional CNC glass equipment, not AI-driven agents. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-guided or robotic-assisted grinding stations can help a glazier monitor edge quality and adjust parameters, raising throughput and consistency on high-volume work, though the human typically remains hands-on for quality control and complex geometries. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no direct assistance to the physical act of grinding or polishing glass edges, though it might help with scheduling or design specs unrelated to this specific task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While grinding and polishing are physically repetitive operations, glass work requires adaptive sensing of surface finish, edge quality, and material properties that current robots cannot reliably assess without significant custom setup. End-to-end automation meeting the 50% time-saving threshold is not demonstrated in general production. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterous handling of fragile, heavy glass with hand or power tools; no current AI system can perform this physical process end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No licensing requirement exists for this task itself, but occupational and safety regulations around equipment handling and workplace standards provide minor friction. Customer expectation for human craftsmanship and quality assurance also moderately protects the role. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for grinding/polishing glass, but physical equipment costs, safety requirements, and craftsmanship standards create moderate practical barriers to any automation approach. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized robotic glass finishing systems are capital-intensive and require integration; the all-in cost per unit likely exceeds the labor cost for small to medium-batch glazier work, though large industrial glass plants may achieve better ratios. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based alternative performing this physical task, so AI inference cost is irrelevant; any automation would rely on specialized robotics/CNC equipment, not AI software, making cost comparison inapplicable or unfavorable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some robotic systems exist for specialized glass grinding in controlled industrial settings, but they require custom programming per glass type and geometry. No off-the-shelf product reliably handles the variety of edge profiles, thicknesses, and finish standards that glaziers encounter in practice. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product grinds or polishes glass; this remains a manual/mechanical trade task, at most performed by non-AI CNC glass-edging machines operated by humans. |
Measure and mark outlines or patterns on glass to indicate cutting lines.
23CI 14–33 · exposure 20 · augmentation 25 · importance 3.8/5 · click for rater detail
Measure and mark outlines or patterns on glass to indicate cutting lines.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glazing remains a primarily small-firm, on-site, physically manual trade with limited digitization and low automation adoption rates. Most operations are local, custom-focused, and lack the centralized manufacturing infrastructure where robotic marking would be cost-justified. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and glazing trades are physical, low-digitization sectors with minimal AI adoption for hands-on fabrication tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Digital layout visualization and measurement assist tools exist, but the task itself—physically marking glass—offers limited augmentation potential since a human must still perform the core marking action. AI could assist with design templates or measurement planning, but meaningful productivity gain on the marking step itself is modest. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Digital measurement tools, laser levels, and CAD-based cut-list software can assist in planning cuts, but the actual measuring and marking on physical glass is still manually performed with limited AI-specific augmentation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can recognize and process patterns digitally, physically measuring and marking glass with precision requires calibrated robotic equipment and real-time adaptation to glass surface variations. Current general-purpose systems cannot reliably handle the end-to-end task of measuring, marking precise cutting lines, and adapting to material imperfections without significant human oversight and manual intervention. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical manipulation of materials in real-world space with precise tactile measurement, which current AI systems cannot perform end-to-end without robotic hardware that doesn't exist at scale for this task.ed manipulation of materials in real-world space with precise tactile measurement, which current AI systems cannot perform without specialized robotics.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.edicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware.dedicated hardware. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Glaziers typically hold apprenticeships and trade certifications, and the quality and accuracy of cutting directly impact customer satisfaction and safety. The liability asymmetry (a cutting error ruins expensive glass) and the preference for human accountability in custom installation work create substantial organizational and regulatory friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically restricts this sub-task, but it requires hands-on physical presence and precision on-site, limiting remote AI substitution though not through regulatory barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Custom robotic marking systems are capital-intensive and require integration, training, and maintenance costs that exceed the wage of skilled glaziers in most small-to-medium operations where this task is performed. For a one-off or low-volume job, the setup and operational costs remain substantially higher than manual marking. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven system replacing this physical measuring/marking task, so AI cost comparison is not applicable and effectively more expensive due to lack of a functioning substitute. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some specialized robotic systems exist in high-precision glass manufacturing, but they are narrow-purpose machines requiring significant integration and are not deployed as general off-the-shelf products in typical glazing shops. Most practical glazing operations still rely on human measurement and marking due to the variety of glass types, frames, and custom specifications. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product measures and marks physical glass sheets for cutting; this remains a manual skilled-trade task performed with hand tools and templates. |
Load and arrange glass or mirrors onto delivery trucks, using suction cups or cranes to lift glass.
21CI 10–33 · exposure 13 · augmentation 13 · importance 4.1/5 · click for rater detail
Load and arrange glass or mirrors onto delivery trucks, using suction cups or cranes to lift glass.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glazing is a traditional trade-based sector with limited digitization; most firms are small to medium and lack the capital or technical infrastructure for robotic automation. Adoption remains minimal despite decades of robotics availability. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and glazing trades are low-digitization, physical-labor sectors with minimal AI/robotics adoption for material handling tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-guided robotic arms or augmented-reality assistance for positioning glass could support workers, but current systems offer limited real-time visual feedback and safety augmentation for this task. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance for the physical act of lifting and arranging glass onto trucks; this remains purely manual and equipment-based work. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Loading and arranging fragile glass onto trucks requires spatial reasoning, delicate handling, and adaptation to variable truck configurations and glass sizes. While suction-cup robots and cranes exist, orchestrating the full task—positioning glass safely, arranging for weight distribution, and responding to breakage risk—remains beyond reliable autonomous capability today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical loading/rigging task requiring manual handling of fragile, heavy materials with careful placement; no off-the-shelf AI system can perform this manipulation end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Safety and liability concerns around breakage and damage are moderate friction points; there is no strict licensing requirement for the loading task itself, but customer preference for careful human handling and organizational inertia create some adoption resistance. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for loading glass, but safety regulations, liability for breakage/injury, and physical workplace constraints create moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Custom robotic glass-handling systems are capital-intensive and require ongoing maintenance and programming for different truck configurations. The cost per task remains higher than paying glazier labor, especially for small to medium operations. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute deployed for this task, so any hypothetical automation (custom robotics) would be far more costly than a human worker doing it today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some specialized robotic systems for glass handling exist in controlled factory settings, but production-scale reliable automation for general delivery-truck loading with varied glass types and truck layouts is not yet deployed at standard glazier operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs autonomous loading of glass/mirrors using suction cups or cranes in production; this remains firmly a manual/robotics research problem, not a fielded solution for this niche task. |
Score glass with cutters' wheels, breaking off excess glass by hand or with notched tools.
20CI 5–35 · exposure 13 · augmentation 25 · importance 3.8/5 · click for rater detail
Score glass with cutters' wheels, breaking off excess glass by hand or with notched tools.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Glazing is a traditional, craft-oriented trade with low digitization and predominantly small, locally-based firms. Automation has been slow and limited to large industrial glass manufacturers; most on-site glazing work remains manual, with few examples of AI agent deployment in production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and glazing trades show minimal AI/robotic adoption for hands-on fabrication tasks, remaining a low-digitization physical trade sector. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | Computer vision could assist by measuring glass dimensions and predicting optimal cutting lines, and robotic guidance could help a glazier align the cutter wheel, moderately raising productivity. However, the core manual skill of applying the right pressure remains human-dependent, limiting transformative augmentation potential. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful real-time assistance for the physical act of scoring and breaking glass; guidance or measurement apps provide only tangential support. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Scoring and breaking glass requires precise force calibration and real-time visual feedback sensitive to glass thickness and type. While robotic arms could theoretically perform the cutting motion, adaptive response to glass properties and the manual breaking step (detecting stress lines, applying variable force) remain challenging for current systems at equal quality and time-saving threshold. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual dexterity task requiring precise hand-eye coordination, tactile feedback, and force control on a physical object; no off-the-shelf AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Glass-cutting and fitting is part of licensed glazing work that often requires proof of skill, apprenticeship completion, and in some jurisdictions licensure. Liability for improper cutting (safety hazards, breakage, rework costs) creates organizational friction and regulatory oversight, protecting human glaziers from direct substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically restricts this cutting action to a certified person, but liability for glass breakage/injury and the need for physical dexterity create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic glass-scoring systems are expensive to acquire, set up, and maintain relative to the hourly wage of a glazier. Integration costs and the need for human oversight of quality further add expense, making the all-in cost comparable to or higher than human labor for typical jobs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-only solution for this physical task, so any comparison would require specialized robotics far more costly than a glazier's wage for equivalent flexible output. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Industrial robots exist for some glass-cutting tasks in highly controlled environments (flat sheets, standard dimensions), but production systems struggle with variability in glass thickness, edge condition, and the tactile feedback required for hand-breaking. No deployed general-purpose system reliably handles this end-to-end in typical glazing work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products cut and break glass sheets in production; robotic glass-cutting exists only in narrow industrial CNC contexts, not as a general AI-driven glazier substitute. |
Create patterns on glass by etching, sandblasting, or painting designs.
20CI 10–30 · exposure 13 · augmentation 50 · importance 3.2/5 · click for rater detail
Create patterns on glass by etching, sandblasting, or painting designs.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Glazier shops tend to be small, locally-rooted businesses with low digital infrastructure; adoption of CNC pattern automation remains sparse and slow outside large industrial glass operations. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Glazing and glass artisan trades are a low-digitization, physical craft sector with minimal AI/robotics adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI design tools and pattern visualization software can assist with mockups and design iteration, and CNC-guided sandblasting can augment skilled workers' productivity on repetitive patterns while they focus on finishing and artistic refinement. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI design tools can help generate or refine pattern designs and templates for etching/sandblasting stencils, aiding the planning phase even though execution remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI-guided automation could assist with design transfer and routing for sandblasting machines, the full task requires real-time physical control, artistic judgment of depth/finish, and handling of fragile materials—current systems cannot reliably achieve 50% time saving end-to-end at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical craft task requiring manual dexterity, tool handling (sandblasters, etching acid, brushes), and artistic judgment applied directly to physical glass, which current AI systems cannot perform. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No hard legal licensing barriers exist, but customer preference for handcrafted or bespoke quality, physical workspace constraints, and the need for human artistic judgment create moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandates a human specifically for design etching, but the physical craftsmanship, customization, and quality-control expectations create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized equipment (sandblasters, etching tools) and significant setup overhead for pattern automation offset labor savings, while skilled glaziers command wages that remain competitive with the all-in cost of AI + machinery + oversight. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical fabrication, so human labor remains the only cost-effective option; any hypothetical robotic system would be far more expensive than a glazier for this bespoke work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some CAD-to-CNC routing exists for simple designs, but deployed products struggle with the aesthetic nuance, surface variability, and adaptive physical control this task demands; most applications remain semi-manual or research prototypes. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product physically etches, sandblasts, or paints glass; this remains purely a manual craft process with no robotic or AI production system in real-world use. |
Drive trucks to installation sites and unload mirrors, glass equipment, or tools.
16CI 5–28 · exposure 13 · augmentation 13 · importance 4.1/5 · click for rater detail
Drive trucks to installation sites and unload mirrors, glass equipment, or tools.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and trades sectors show slow adoption of automation; truck driving and site logistics remain heavily human-operated, with autonomous vehicles largely confined to research and limited pilot programs rather than broad deployment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades sectors show minimal AI/robotics adoption for physical logistics tasks like driving and unloading materials, remaining a laggard sector for automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist with route planning and load optimization, but the physical driving and unloading tasks offer limited scope for meaningful AI assistance while humans remain in the loop on fragile material handling. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance to the physical acts of driving a truck and unloading glass or tools at a site. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While autonomous vehicles are progressing, end-to-end task automation (driving to dynamic installation sites, unloading fragile materials) requires level 4-5 autonomy not yet reliably deployed at scale. Human drivers remain essential for safety and navigation complexity. |
| Task automatability | claude-sonnet-5 | 1/5 | Driving trucks and physically unloading heavy, fragile materials at job sites requires real-world mobility and manipulation that current AI systems cannot perform end-to-end.WELL beyond software automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers include regulatory licensing requirements for commercial driving, liability concerns with autonomous vehicles near job sites, insurance implications, and organizational friction in adopting unproven autonomous solutions for safety-critical tasks. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Commercial driving requires licensing (CDL in many cases) and safe handling of fragile, heavy goods creates liability concerns, though no special professional certification uniquely protects this task beyond standard trucking regulations. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current autonomous vehicle and robotics systems for unloading remain expensive relative to human labor for this task; the all-in cost of autonomous solutions does not yet undercut loaded wages for truck driving and unloading. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven alternative for this physical task, so the human remains the only cost-effective option; any hypothetical robotic solution would be far more expensive than a human driver today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Autonomous delivery systems exist but primarily for controlled environments and standard routes; reliable production systems for dynamic construction sites with unloading of specialized equipment and materials are not yet demonstrated at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product today autonomously drives commercial trucks to job sites and unloads fragile glass materials; autonomous trucking remains limited to highway pilots, not last-mile delivery with unloading. |
Measure, cut, fit, and press anti-glare adhesive film to glass or spray glass with tinting solution to prevent light glare.
15CI 15–15 · exposure 0 · augmentation 25 · importance 3.3/5 · click for rater detail
Measure, cut, fit, and press anti-glare adhesive film to glass or spray glass with tinting solution to prevent light glare.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glaziers work primarily in construction and small trades with low capital availability and high site-specificity; adoption of automation in this manual trade remains minimal and is not a pattern in laggard, physical, low-digitization sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and glazing trades show minimal AI or robotics adoption for physical installation tasks, reflecting the broader lag in physically-intensive, low-digitization trades. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by using computer vision to measure glass dimensions or recommend tinting coverage, but the core task—physically cutting, fitting, and applying materials—remains dependent on human execution. Augmentation potential is limited to planning and measurement phases. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with measurement calculations or generating cut templates from digital measurements, but it offers little help with the physical application and pressing of film or spraying tint. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise manual dexterity, spatial judgment, and physical manipulation of materials in three dimensions—measuring glass surfaces, cutting film to fit irregular shapes, and applying adhesive or spray without defects. Current AI systems cannot perform these sensorimotor operations end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical craft task requiring hand-eye coordination to measure, cut film precisely, and apply it bubble-free to glass, or spray tinting solution evenly - no AI system today can perform this physical manipulation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are moderate barriers: customer preference for human craftsmanship, liability concerns around coating application quality affecting visibility and safety, and the need for on-site adaptation to irregular glass shapes and conditions. However, no legal licensing requirement mandates human performance. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing strictly requires a human glazier for tinting/film application in most jurisdictions, though quality and safety expectations (avoiding bubbles, correct fitting) create some practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of a robotic system capable of measuring, cutting, fitting, and applying film or spray, plus integration and ongoing maintenance, vastly exceeds the loaded wage of a glazier performing this task manually. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so the human remains the only cost-effective option; any robotic solution would require far more capital than the labor cost it replaces. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can reliably measure, cut, fit, and apply adhesive film or tinting spray to glass surfaces autonomously. This requires robotic hardware integration, real-time visual feedback, and material handling capabilities not yet in production at scale in glazing contexts. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs film cutting/fitting or spray tinting on glass in production; this remains entirely a manual skilled trade task. |
Move furniture to clear work sites and cover floors or furnishings with drop cloths.
15CI 15–15 · exposure 0 · augmentation 0 · importance 3.2/5 · click for rater detail
Move furniture to clear work sites and cover floors or furnishings with drop cloths.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glazing firms are small, trade-based, and physically rooted; they lag in digital adoption and have minimal incentive to invest in costly robotic solutions for low-skill prep work given labor cost. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical site tasks, with minimal automation of manual prep work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | This task offers little opportunity for AI assistance; it is fundamentally manual, physical work with no information component, design phase, or decision-making that AI could augment. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance for physically moving furniture or covering surfaces with drop cloths. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of furniture and materials in variable environments with no standardized layout, which current AI robotics cannot reliably perform at scale. The task involves spatial reasoning, obstacle navigation, and object handling that exceeds the capability of deployed systems. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring mobility, strength, and spatial navigation in unstructured environments; no off-the-shelf AI system can perform this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict legal barriers to automation, the high costs and immature technology create practical friction. Jobsite safety liability and the need for on-site presence also present moderate barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier prevents automation of this specific prep task, but the practical/physical barrier of manipulating diverse furniture and job sites is very high. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Deploying autonomous robots for this task would cost orders of magnitude more than paying a worker to spend 15–30 minutes clearing and protecting a site, making economic substitution infeasible today. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so the human laborer remains the only cost-effective option; any hypothetical robotic solution would be far more expensive than manual labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No production AI systems today can autonomously move furniture and deploy drop cloths on jobsites. While research robots exist, they are not deployed in commercial glazing work and lack the dexterity and environmental adaptability required. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product moves furniture and lays drop cloths on job sites; this remains far outside current robotics deployment in trades work. |
Prepare glass for cutting by resting it on rack edges or against cutting tables and brushing thin layer of oil along cutting lines or dipping cutting tools in oil.
14CI 5–24 · exposure 8 · augmentation 13 · importance 3.6/5 · click for rater detail
Prepare glass for cutting by resting it on rack edges or against cutting tables and brushing thin layer of oil along cutting lines or dipping cutting tools in oil.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glazing remains a traditional, small-shop craft with low digital infrastructure and limited venture investment in automation; firms are geographically dispersed and rely on skilled labor, typical of laggard sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and glazing trades are a low-digitization, physically-oriented sector with minimal AI/robotics adoption for manual prep tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI vision systems could potentially guide placement or detect cutting lines, but the tactile feedback, intuitive material handling, and judgment required for safe oil application offer limited scope for meaningful augmentation of human workers. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance for the physical act of positioning glass and applying oil along cutting lines. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical manipulation of fragile glass material on racks and precise oiling requires dexterous robotic handling and visual alignment that current general-purpose AI systems struggle with reliably. While a specialized robotic arm might perform repetitive resting and oiling, the cost and integration complexity exceed the value saved for a task achievable by a skilled worker in minutes. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual, tactile physical task requiring positioning heavy fragile glass and applying oil precisely along cutting lines; no AI system today performs this physical manipulation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Glaziers operate under craft traditions and apprenticeship requirements; many jurisdictions regulate glazing work through licensing and liability rules tied to human responsibility for material integrity and safety, creating organizational and legal friction against automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for this sub-task, but physical workspace constraints, variable glass sizes, and safety concerns around handling glass and cutting tools create practical friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Custom robotic systems capable of handling glass and applying lubricant with precision would require significant capital investment and integration costs that far exceed the hourly wage of a glazier performing this brief preparatory task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Any automation would require expensive custom robotics/machine vision integration, far exceeding the cost of a glazier's manual prep work for typical jobs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No commercial product reliably performs the full task of positioning glass on racks, assessing alignment, and applying oil along precise cutting lines with the speed and care required in production settings. This remains largely a manual craft task without deployed automation solutions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs this specific manual glass-prep task; robotic glass handling exists only in narrow industrial contexts, not as general glazier tooling. |
Operate cranes or hoists with suction cups to lift large, heavy pieces of glass.
14CI 0–28 · exposure 13 · augmentation 13 · importance 4.2/5 · click for rater detail
Operate cranes or hoists with suction cups to lift large, heavy pieces of glass.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and glazing remain low-adoption sectors for advanced automation; equipment operation is site-specific, mobile, and embedded in safety-critical work. Adoption of AI-controlled heavy lifting is negligible in the glazing industry today. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and glazing trades are a low-digitization, physical-labor sector with minimal AI/robotics adoption for material handling tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI offers minimal assistance; some cranes include load-monitoring sensors and camera assist for positioning, but these are narrow tools rather than transformative augmentation. The human operator remains fully responsible and in control of core decisions. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers no meaningful real-time assistance to a glazier physically operating suction lifting equipment; this is a purely manual mechanical task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Operating cranes/hoists for glass installation requires precise spatial reasoning, real-time adjustment to load dynamics, and immediate response to equipment feedback—tasks where current AI systems lack reliable end-to-end autonomy in unstructured job sites. While individual components (positioning, load monitoring) could be partially automated, the integrated safety-critical operation and unpredictable physical variables prevent 50% time savings with equal quality today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring on-site equipment operation, spatial judgment, and precise coordination with unpredictable glass loads and worksite conditions; no off-the-shelf AI system performs this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Heavy equipment operation is heavily regulated by OSHA and state licensing requirements; operators must be certified and legally responsible for equipment and safety. Liability for damage or injury creates hard barriers—no insurance or legal framework yet exists for full autonomous operation of cranes in this context. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no license is specifically required for crane/suction operation, safety liability for handling heavy glass near people and property, plus OSHA-type safety oversight, creates meaningful friction against unproven automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized crane operators command significant wages, and autonomous or AI-assisted systems capable of reliable, safe glass-lifting operations would require custom integration, extensive validation, and liability coverage that currently exceeds the cost of human labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system substituting for this physical task, so any hypothetical robotic solution would require expensive specialized hardware far exceeding the cost of a trained glazier operating standard equipment. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No mature, production-deployed AI system reliably operates heavy lifting equipment end-to-end on construction sites; remote or autonomous crane operation exists only in narrow, controlled industrial settings. Current systems lack the robust perception and real-time hazard response required for this task in typical glazing environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product operates cranes or hoists to lift glass in commercial glazing settings; this remains a manual, human-operated trade skill. |
Cut and attach mounting strips, metal or wood moldings, rubber gaskets, or metal clips to surfaces in preparation for mirror installation.
12CI 0–24 · exposure 8 · augmentation 25 · importance 4.0/5 · click for rater detail
Cut and attach mounting strips, metal or wood moldings, rubber gaskets, or metal clips to surfaces in preparation for mirror installation.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glaziers and construction trades are among the slowest-adopting sectors for AI and robotics; the work is site-specific, requires high dexterity, and is performed by small firms and independent contractors with limited capital for automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and glazing trades are among the least digitized sectors with minimal AI/robotic adoption for hands-on installation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-powered measurement tools or material-cutting guides could modestly assist glaziers in planning and layout, but the core task of cutting, positioning, and attaching hardware remains primarily manual and offers limited augmentation opportunities. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with measurement calculations, cut-list generation, or material ordering, but offers little direct help with the physical cutting and attachment process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While cutting materials can be partially automated with CNC machinery, the task requires physical handling, precise surface preparation, and attachment of diverse mounting hardware in varied real-world contexts. Current AI systems cannot perform the full end-to-end physical manipulation and spatial judgment needed to meet the 50% time-saving bar. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical fabrication and installation task requiring cutting materials to precise dimensions and physically attaching them to surfaces, which current AI cannot perform without a capable robotic system. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task is embedded in skilled trades work requiring licensing and hands-on craft judgment; mirror installation quality and safety depend on correct mounting, creating high liability for errors. The requirement for site-specific adaptation and physical presence creates hard organizational and legal barriers to full substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for this sub-task, but practical barriers include the need for physical dexterity, precise measurement, and on-site variability that keep this human-performed. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automation would require custom robotic systems, material handling equipment, and integration costs that far exceed the loaded wage of a glazier performing this preparatory work by hand. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute performing this physical task, so any hypothetical automation (custom robotics) would be far more costly than a glazier's labor for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs this multi-step physical task—cutting, measuring, preparing surfaces, and affixing different material types—reliably in real construction or glazing settings. This remains a hands-on trade task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product performs mounting strip cutting and attachment for mirror installation in production settings; this remains firmly manual skilled trade work. |
Cut and remove broken glass prior to installing replacement glass.
12CI 5–19 · exposure 8 · augmentation 13 · importance 3.9/5 · click for rater detail
Cut and remove broken glass prior to installing replacement glass.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glazing remains a traditional, labor-intensive, on-site trade with low digitization and fragmented small firms. Adoption of automation in this sector has been minimal, with most work still performed manually by trained craftspeople. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors for AI/robotic adoption, especially for hands-on physical tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist glaziers with visual inspection or damage assessment tools, but the core physical task of cutting and safely removing broken glass remains primarily manual. Augmentation opportunities are limited given the hands-on, real-time nature of the work. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools offer no meaningful assistance for the physical act of cutting and removing broken glass on-site. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can identify broken glass and robots could theoretically perform cutting with precise measurement, the physical dexterity, safety hazards (sharp edges, fragmentation), and need to assess structural integrity and surrounding frame conditions make end-to-end automation without significant human oversight infeasible today. Current systems cannot reliably handle the variability of real-world broken glass scenarios. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of hazardous, irregular broken glass in varied on-site conditions—well beyond current robotics or AI capability for end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations governing glass handling, worker protection standards, and liability for improper glass removal create meaningful barriers. Additionally, the physical hazard profile and need for on-site judgment about structural safety mean organizations retain human oversight requirements, limiting full automation deployment. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing strictly requires a human, but safety liability (handling broken glass), physical dexterity needs, and site variability create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic solutions for glass handling are expensive (equipment, integration, maintenance), while skilled glaziers perform this task for moderate wages. The all-in cost of automation is substantially higher than human labor, making substitution uneconomical. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that can substitute for this physical labor, so the human remains the only cost-effective (indeed only) option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products reliably perform glass cutting and removal as a standalone autonomous task in production settings. Robotic systems exist in controlled lab environments but are not operationally deployed by glazing contractors at scale, and the task remains almost entirely manual in practice. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform physical glass removal and cutting; this remains a manual skilled-trade task with no robotic analog in production. |
Determine plumb of walls or ceilings, using plumb lines and levels.
10CI 5–15 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Determine plumb of walls or ceilings, using plumb lines and levels.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and glazing trades remain largely low-digitization, small-firm dominated sectors with strong on-site craft traditions. Adoption of AI-driven automation for field-level quality checks is minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and glazing trades show very low AI/robotics adoption for physical measurement tasks, consistent with low-digitization physical trades. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Digital levels and laser levels already augment the manual plumb-line method, but these are instruments rather than AI systems. AI-based computer vision for post-hoc wall analysis offers marginal assistance and is not yet reliable enough for mainstream field use. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Digital levels and laser tools (some computer-assisted) can aid the task, but general AI systems offer minimal enhancement to this specific physical measurement activity. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical positioning of plumb lines and levels, in-situ measurement of walls and ceilings, and real-time spatial judgment in uncontrolled construction environments. Current AI cannot deploy these instruments or make the tactile adjustments needed for accurate plumb determination. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence, tool handling, and on-site measurement of physical structures, which current AI systems cannot perform end-to-end without robotic embodiment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Building codes and safety standards typically require a licensed glazier or contractor to certify plumb and level before installation, and error consequences (structural failure, water ingress, misalignment) create high liability. Regulatory and professional licensing requirements strongly protect this task. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically requires a human for this micro-task, but it inherently requires physical on-site presence and tool manipulation, creating a natural barrier to remote automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of robotic systems capable of deploying plumb lines and levels, plus integration and operation on a construction site, would far exceed the labor cost of a skilled glazier performing this quick, on-site check. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for physically checking plumb, so any AI-based approach (e.g., robotics) would be far more costly than a human worker with a level today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs physical plumb measurement and assessment in field conditions. While computer vision exists for structural inspection, it is not production-grade for the precise, real-time plumb-checking that glaziers rely on. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously determines plumb of walls/ceilings using physical levels; this remains a manual physical trade skill. |
Install pre-assembled metal or wood frameworks for windows or doors to be fitted with glass panels, using hand tools.
10CI 5–15 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Install pre-assembled metal or wood frameworks for windows or doors to be fitted with glass panels, using hand tools.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and skilled trades have lagged in automation adoption; site-specific variability, regulatory friction, and capital constraints mean AI-based automation of frame installation remains rare or non-existent in production. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction trades show very low AI/robotic adoption for physical installation tasks, remaining a laggard sector with minimal automation penetration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with design-to-installation workflows (measurement capture, layout visualization) but offers minimal real-time support during the hands-on installation work itself. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with measurement calculations, ordering specifications, or scheduling, but offers little direct help with the hands-on physical installation process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of heavy materials, precise spatial alignment, and hand-tool operation in variable on-site conditions. Current AI systems cannot perform end-to-end physical installation work at any meaningful scale. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical installation task requiring manual manipulation of heavy frameworks, precise fitting, leveling, and fastening in varied site conditions—no current AI system can perform physical manipulation tasks. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Building codes and safety regulations typically require licensed or certified tradespeople to install structural frames; liability for structural integrity and water-sealing is high, creating legal and contractual barriers to full substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing strictly requires a human for this specific installation step, though building codes and safety standards for structural fitting create some procedural expectations; the main barrier is technical infeasibility rather than regulation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotics capable of performing construction-grade frame installation would require significant capital investment and bespoke integration far exceeding the loaded cost of a glazier's labor for equivalent output. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this physical labor, so any hypothetical automation would require expensive custom robotics far exceeding a glazier's wage costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products perform framework installation for windows or doors. This remains firmly in the domain of human skilled trades; no production systems exist. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product installs window or door frameworks; robotics for construction installation remains research-stage and cannot handle the variability of job sites and materials. |
Pack spaces between moldings and glass with glazing compounds and trim excess material with glazing knives.
10CI 10–10 · exposure 0 · augmentation 13 · importance 4.0/5 · click for rater detail
Pack spaces between moldings and glass with glazing compounds and trim excess material with glazing knives.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The glazing trade is composed largely of small firms and independent contractors in the construction sector, which shows laggard digital and automation adoption compared to information-intensive industries. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and glazing trades are a low-digitization, physical-labor sector with minimal AI/robotics adoption for hands-on installation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal productivity assistance for this task; computer vision could theoretically assist with defect detection post-application, but provides little value during the core manual application and trimming process itself. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for the physical act of packing and trimming glazing compound; this is a purely manual skill with no digital augmentation pathway currently in use. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise manual dexterity, three-dimensional spatial reasoning, and tactile feedback to apply compounds uniformly and trim excess material cleanly. Current AI robotics lack the fine motor control and real-time sensory adaptation needed to perform this consistently on varied molding geometries and glass surfaces. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a fine-motor, physical task requiring hands-on manipulation of glazing compound and precise trimming with a knife, which current AI systems cannot perform end-to-end.dispatch No robotics system is deployed for this specific task.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While no strict licensing requirement mandates human glaziers, there is moderate friction from quality standards, customer expectations for human craftsmanship, and the need for real-time judgment calls on material consistency and aesthetics that organizations would hesitate to fully automate. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing specifically bars automation, but physical dexterity requirements, variable site conditions, and quality/safety implications of improperly sealed glass create strong practical barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A specialized robotic system capable of performing this task would require significant custom engineering, integration, and maintenance costs that far exceed the hourly wage of a skilled glazier, making it economically unviable today. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute at any cost; the human glazier remains the only practical option, making AI comparatively far more expensive or simply unavailable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products reliably perform glazing compound application and trimming in production settings. While robotic arms exist, they lack the specialized end-effectors, force feedback, and environmental adaptation required for this nuanced material-handling task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No commercial product performs glazing compound application and trimming; this remains firmly in the domain of skilled manual trades work. |
Secure mirrors in position, using mastic cement, putty, bolts, or screws.
10CI 5–15 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Secure mirrors in position, using mastic cement, putty, bolts, or screws.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glazing remains a traditional craft sector with low digitization, small firms, and physical on-site work—all markers of laggard adoption of automation technologies. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics for physical installation work, with minimal automation penetration in mirror/glass installation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with layout planning or fastener specification via recommendation systems, the core task of physically securing mirrors offers minimal opportunity for AI assistance to meaningfully boost human productivity. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with measurement calculations, material estimation, or job scheduling, but offers little direct assistance to the physical act of securing mirrors. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Securing mirrors requires physical manipulation in 3D space (positioning, fastening with varied materials), real-time tactile feedback, and judgment about proper alignment—capabilities far beyond current AI systems without sophisticated robotics integration. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical installation task requiring manipulation of heavy, fragile materials with precise alignment and application of adhesives/fasteners; no current AI or robotic system can perform this end-to-end reliably. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task requires on-site, safety-critical work (working at height, with large fragile materials) and involves direct physical interaction with building elements, creating both regulatory and practical barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing strictly requires a human glazier for this specific act, but physical dexterity, on-site variability, and liability for broken glass/mirrors create practical barriers to any automated substitute. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic systems capable of precision placement and fastening remain prohibitively expensive compared to the hourly wage of a skilled glazier performing this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system that performs this physical task, so any comparison of AI cost versus human wage is inapplicable; the human remains the only viable option and thus far cheaper in practice. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product autonomously secures mirrors today; this remains a manual trade task requiring human dexterity, site-specific judgment, and adaptation to variable conditions. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs mirror installation with mastic cement or fasteners; this remains entirely a manual skilled-trade task with no robotic automation in production. |
Fabricate or install metal sashes or moldings for glass installation, using aluminum or steel framing.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.2/5 · click for rater detail
Fabricate or install metal sashes or moldings for glass installation, using aluminum or steel framing.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glazing is a small, traditional, physically-grounded trade with limited digitization. Adoption of automation in this sector remains minimal, and most firms remain small, local operations without infrastructure for robotic or AI integration. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics, with physical, on-site fabrication work seeing minimal automation penetration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide modest assistance in design layout, material cost estimation, or work scheduling, but offers little real-time support for the core fabrication and installation tasks that demand skilled hand-work and spatial judgment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with measurement calculations, design specifications, or CAD-based templates for framing, but offers little direct help with the physical fabrication and installation itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task involves physical fabrication and installation of metal components that require precise cutting, bending, and on-site assembly with glass. Current AI systems cannot operate power tools, handle materials, or perform spatially-adaptive assembly work on physical objects. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical fabrication and installation task requiring manual cutting, fitting, and mounting of metal framing on-site, which current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Glazing work typically requires licensed tradespeople in most jurisdictions, and safety liability is high (working at heights, handling glass and metal). Building codes and customer contracts often mandate human craftsperson accountability and sign-off. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed everywhere, safety codes, building inspections, and quality/liability concerns around glass and structural framing create moderate friction against unproven automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The material handling, specialized equipment operation, and physical dexterity required make human labor far more cost-effective than any current robotic or AI alternative for this specialized trade work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so the AI cost is effectively infinite relative to a human glazier's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products can autonomously fabricate or install metal sashes and moldings. This remains purely a human-performed task; no production systems exist for this work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product fabricates or installs metal sashes or moldings; this remains purely a manual skilled-trade activity with robotics research not yet applied here. |
Cut, fit, install, repair, or replace glass or glass substitutes, such as plastic or aluminum, in building interiors or exteriors or in furniture or other products.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Cut, fit, install, repair, or replace glass or glass substitutes, such as plastic or aluminum, in building interiors or exteriors or in furniture or other products.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and glazing trades are among the lowest AI-adoption sectors, characterized by small firms, site-specific work, and low digital infrastructure. No meaningful production AI adoption is visible in this domain. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics due to physical, on-site, variable-condition work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might assist with cutting-plan optimization or measurement visualization, but the embodied nature of fitting and installation leaves limited room for digital augmentation. Assistance would be marginal and on planning phases only. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with measurement calculations, ordering materials, or design visualization, but offers minimal help with the core physical cutting and installation work. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires physical manipulation of glass and its installation in varied spatial contexts—cutting to precise measurements, physically fitting into frames, securing with adhesives or hardware, and adapting to irregularities in the field. Current AI systems lack embodied robotics and dexterity to perform this work autonomously at production quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical trade task requiring precise manual cutting, handling heavy/fragile glass, and installation in varied physical environments; no AI system can perform the physical manipulation involved. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Glazing involves significant safety liability (handling breakage, fall hazards on exterior work), building code compliance, and customer safety expectations that typically mandate trained, licensed, or bonded human workers. Legal and liability frameworks protect this task from substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not strictly licensed in all jurisdictions, safety codes, liability for broken glass/injury, and building code compliance create meaningful barriers to non-human execution, though not a hard licensing wall. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of specialized robotics for glass handling, along with high integration and site-specific setup overhead, vastly exceeds the hourly cost of a skilled glazier. The economics do not favor automation at current technology costs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute for the physical labor, so the human remains the only cost-effective option; any robotic attempt would be far more expensive than a glazier's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs end-to-end glass cutting, fitting, and installation. While some robotic research exists, there are no production systems handling the variability of on-site installation (weather, substrate irregularities, custom dimensions). |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs glass cutting, fitting, or installation; robotics for this specific fragile-material trade work remains research-stage at best. |
Fasten glass panes into wood sashes or frames with clips, points, or moldings, adding weather seals or putty around pane edges to seal joints.
7CI 5–10 · exposure 0 · augmentation 0 · importance 3.8/5 · click for rater detail
Fasten glass panes into wood sashes or frames with clips, points, or moldings, adding weather seals or putty around pane edges to seal joints.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glazing is a small, craft-oriented, predominantly non-digital sector with low mechanization historically; adoption of AI-driven automation has been negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI/robotics due to physical, variable, on-site work environments and low digitization. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI systems offer no meaningful assistance to a glazier performing fitting, fastening, and sealing work in situ; this is purely manual craft labor. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers minimal assistance to the physical act of fitting glass and sealing joints, though it might help with measurement calculations or scheduling outside the core task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Fastening glass panes into frames requires precise physical manipulation, spatial alignment, and dexterity in a 3D environment—tasks far beyond current AI robotic capabilities in real-world conditions with variable materials and frame geometries. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual, physical fabrication task requiring precise handling of fragile glass, hand tools, and materials like putty and sealant; no current AI system can perform this physical manipulation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Building codes, liability for structural integrity and weatherproofing, customer expectations for craftsmanship, and the requirement for on-site judgment and real-time adjustment create significant organizational and regulatory friction against automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed nationwide, building codes, safety standards, and customer expectations for quality craftsmanship in construction create real friction against automation, though no strict professional licensure gates the task itself. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of specialized robotics, integration, and maintenance to handle variable frame types and glass sizes far exceeds the loaded wage of a glazier for this hands-on task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this physical task, so any hypothetical automation would require expensive custom robotics far exceeding the cost of a human glazier. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial system reliably performs the full end-to-end task of fitting, fastening, and sealing glass panes in diverse frames at production scale; this remains a skilled manual craft. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product installs glass panes and applies weather seals/putty in real-world glazing work; this remains firmly in the domain of skilled manual labor. |
Cut, assemble, fit, or attach metal-framed glass enclosures for showers, bathtubs, display cases, skylights, solariums, or other structures.
7CI 5–10 · exposure 0 · augmentation 25 · importance 3.7/5 · click for rater detail
Cut, assemble, fit, or attach metal-framed glass enclosures for showers, bathtubs, display cases, skylights, solariums, or other structures.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Glazing is a skilled trade concentrated in small to mid-sized firms and characterized by on-site, custom work in variable conditions. Sector digitization and automation adoption remain low compared to information or finance sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and skilled trades are among the slowest sectors to adopt AI or robotics for physical fabrication and installation work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with design visualization or material measurement estimation, but current systems offer minimal practical benefit to the core tasks of cutting, assembly, and fitting, which remain primarily manual and craft-based. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with design specifications, measurements, or CAD-based cutting plans, but offers minimal help with the physical cutting, fitting, and attaching process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation in variable on-site conditions, including cutting glass to custom dimensions, handling fragile materials, and performing spatial assembly with metal frames. Current AI systems lack the embodied robotics and real-time adaptive control to perform this end-to-end in unstructured environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical fabrication and installation task requiring precise cutting, fitting, and mounting of glass and metal frames on-site, which current AI systems cannot perform without embodied robotic capability far beyond deployed tech. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Building codes and safety regulations typically require licensed or certified glaziers to install glass enclosures, especially for structural and safety-critical applications like shower enclosures and skylights. Liability for defective installation creates high error-cost asymmetry. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing mandate specifically requires a human glazier by law in most jurisdictions, but liability for glass breakage/injury, precision fitting needs, and customer expectations create meaningful friction against any automation attempt. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of specialized robotics for glass cutting and assembly, combined with integration and on-site deployment, far exceeds the loaded wage of skilled glaziers who already possess the necessary tools and training. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-driven alternative to perform this physical work, so AI cost per task-equivalent is effectively infinite compared to a human glazier's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs the full sequence of cutting, assembling, fitting, and attaching framed glass structures. Specialized glass-cutting robots exist in controlled manufacturing settings but cannot handle the custom, field-based installation work described. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No commercial product installs metal-framed glass enclosures autonomously; this remains purely a manual skilled-trade task performed by human glaziers. |
Assemble, erect, or dismantle scaffolds, rigging, or hoisting equipment.
3CI 0–5 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Assemble, erect, or dismantle scaffolds, rigging, or hoisting equipment.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Construction and glazing work remain in laggard sectors for AI adoption, with limited digitization and strong physical/manual labor characteristics that resist automation today. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and trades sectors show slow, shallow AI adoption, especially for physical site tasks like scaffold erection. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | While AI could assist with planning layout designs or safety compliance checking before assembly, current systems offer minimal practical augmentation for the on-site assembly work itself, which relies on tacit spatial and mechanical knowledge. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning, load calculations, or safety checklists, but offers minimal direct assistance to the physical assembly process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Scaffolding and rigging assembly requires physical manipulation in 3D space, site-specific spatial reasoning, and real-time safety verification that current AI cannot perform autonomously. While AI could theoretically plan layouts, it cannot execute the mechanical work or adapt to dynamic on-site conditions without human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, hands-on construction task requiring manipulation of heavy equipment in unstructured environments, far beyond current robotics or AI capabilities. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task has hard regulatory and safety barriers: OSHA certification and licensing requirements mandate that only trained and certified workers perform scaffold assembly due to fall and collapse risks. Liability and worker safety regulations create legal requirements for human professional judgment and sign-off. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Scaffolding and rigging work is subject to strict OSHA safety regulations, often requiring certified/competent persons to erect and inspect equipment, creating strong regulatory and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of specialized robotic systems capable of performing this task (if available at all) would far exceed the wages of trained glaziers and scaffold workers for the foreseeable future. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI system to perform this physical task, so any AI substitute would be far more costly (or impossible) than a human worker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI systems can autonomously assemble physical scaffolding or rigging equipment in real-world construction environments today. This remains firmly in the domain of specialized human workers and potentially future robotics. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs scaffold or rigging assembly autonomously; this remains entirely manual skilled labor. |
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.