Automotive Glass Installers and Repairers
49-3022.00Median wage $47,630/yr20,310 employed (US)Rank #859 of 923 scored · top 93% by substitution
Replace or repair broken windshields and window glass in motor vehicles.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution12
Exposure2
Augmentation23
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
18 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%3
panel mean rating 1.1/5 → substitution pressure 3/100
Technical feasibility todayw 20%1
panel mean rating 1.1/5 → substitution pressure 1/100
Cost vs. human wagew 15%3
panel mean rating 1.1/5 → substitution pressure 3/100
Adoption barriersw 20%inverted — strong barriers lower the score50
panel mean rating 3.0/5 (barrier strength) → substitution pressure 50/100
Sector adoption velocityw 10%1
panel mean rating 1.1/5 → substitution pressure 1/100
Task breakdown (18 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.
Cut flat safety glass according to specified patterns or perform precision pattern making and glass cutting to custom fit replacement windows.
23CI 10–35 · exposure 13 · augmentation 38 · importance 3.5/5 · click for rater detail
Cut flat safety glass according to specified patterns or perform precision pattern making and glass cutting to custom fit replacement windows.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Automotive glass shops are typically small, independent, or franchised operations with limited digitization and capital budgets. Adoption of advanced automation remains sparse and pilot-stage; most shops still rely on manual measurement and cutting by skilled workers. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive glass repair is a small-shop, physically intensive trade with minimal digitization or AI/robotics adoption reported industry-wide. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted pattern recognition (e.g., automated frame imaging and preliminary cut guidance) and CNC integration could reduce measurement time and rework, helping technicians work faster and with fewer errors. However, the physical variability and final fitting still require human expertise and adjustment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Computer-aided pattern design or measurement software can assist in planning cuts, but the actual cutting and fitting remains manual with limited AI augmentation. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While glass cutting machinery can be numerically controlled, the task requires custom pattern-making, measurement from vehicle-specific frames, and adaptation to damaged frames—inputs that vary significantly and currently demand human judgment and physical sensing. No end-to-end AI system achieves 50% time savings on the full task today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical, hands-on cutting and fitting task requiring dexterity, tactile judgment, and manipulation of glass and tools that current AI systems cannot perform end-to-end.atal |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While not legally licensed like medical or legal work, automotive glass installation often requires insurance qualification and customer safety liability, creating moderate organizational and reputational friction against full automation. Many shops retain humans for final verification and warranty purposes. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing law mandates a human specifically cut glass, but physical manipulation, liability for cracked/improperly cut glass, and lack of robotic infrastructure create practical barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Precision glass-cutting equipment and supporting measurement systems are capital-intensive and require frequent recalibration. When amortized across varied, low-volume custom cuts, the per-unit cost often exceeds the labor cost of a skilled technician, especially accounting for integration overhead. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute performing this physical task, so any hypothetical AI-robotic solution would require expensive custom robotics far costlier than a human installer. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Automated glass cutting machines exist but are constrained to pre-programmed designs and flat stock; they do not reliably handle the measurement, pattern adaptation, and quality control needed for custom-fit automotive replacement. Deployed systems lack the flexible perception and in-context adjustment this task requires. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product cuts or fits custom automotive glass; this remains a manual skilled trade task performed by technicians with specialized tools. |
Obtain windshields or windows for specific automobile makes and models from stock and examine them for defects prior to installation.
19CI 5–33 · exposure 13 · augmentation 25 · importance 4.5/5 · click for rater detail
Obtain windshields or windows for specific automobile makes and models from stock and examine them for defects prior to installation.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Automotive glass repair shops are small, distributed, often independent operations with low digitization. Adoption of AI for inventory and inspection is lagging; most shops still rely on manual stock management and technician inspection without sensor integration. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive repair and glass installation is a low-digitization, physical-labor sector with minimal AI/robotics adoption for such logistics and inspection tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist by flagging potential defects to draw technician attention, but the task itself is relatively narrow and visual judgment-focused; augmentation gains are modest compared to tasks requiring data synthesis or research. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with inventory lookup or defect-detection via computer vision cameras integrated into scanning tools, but this is not yet standard practice and offers only partial support to the human task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Obtaining stock items requires inventory system navigation and retrieval that AI could theoretically assist with, but visual inspection for defects on glass (checking for cracks, scratches, inclusions) involves nuanced quality judgment in a physical environment. Current AI vision can detect obvious defects but struggles with the full range of subtle automotive glass flaws and their pass/fail determination. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physically retrieving glass stock and visually/manually inspecting it for defects, a physical manipulation and perceptual task that current AI systems cannot perform end-to-end without robotic embodiment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Liability is a substantial barrier: installing a defective windshield that fails creates safety and warranty risks, and shops would face pressure to retain human sign-off on final acceptance. Insurance and customer expectations strongly favor human accountability for quality gates. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically bars a machine from doing this, but practical barriers of warehouse logistics, physical dexterity, and quality control create moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of deploying and maintaining a reliable automated vision system, plus inventory integration, would exceed the wage cost of a technician performing manual inspection, especially given the low volume per shop and need for high accuracy to avoid liability from missed defects. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute performing the physical retrieval and inspection, so any hypothetical robotic system would be far more costly than a technician performing this routine task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Vision systems exist for defect detection in manufacturing, but their deployment in automotive repair shops for real-time windshield inspection is limited; systems that do exist have material false-positive and false-negative rates on edge cases. Physical retrieval from stock remains manual and unautomated in most settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously retrieves warehouse glass stock and performs physical defect inspection in an automotive repair setting; this remains outside current commercial AI/robotics deployment. |
Select appropriate tools, safety equipment, and parts, according to job requirements.
16CI 5–28 · exposure 8 · augmentation 38 · importance 4.7/5 · click for rater detail
Select appropriate tools, safety equipment, and parts, according to job requirements.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Automotive glass installation remains a hands-on, field-based trade with limited digitization of selection workflows. Adoption of AI for this specific task is minimal; most shops rely on technician experience and paper/basic digital job specs rather than automated selection systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive glass repair is a low-digitization, physical trade sector with minimal AI agent deployment for hands-on tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by providing real-time checklists, part specifications from job orders, or flagging common mistakes (e.g., incompatible parts), improving technician efficiency. However, the core selection still depends on human expertise and physical inspection, limiting the augmentation uplift. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could provide reference guidance (e.g., checklists or manuals for tool/parts selection based on vehicle model) but offers limited real-time assistance for physical tool handling. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI could assist in suggesting tools and parts based on job specifications, but the physical selection, inspection for quality/fit, and verification against real-world job site constraints requires human judgment and sensory assessment. This falls well short of 50% time-saving at equal quality for end-to-end automation. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical selection and handling of tools, safety gear, and parts in a shop environment based on tactile and visual assessment of a specific vehicle, which current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | OSHA regulations and liability considerations create material barriers: workers must personally ensure safety equipment is appropriate and compliant; manufacturers often require trained personnel to certify tool suitability; and liability for improper part selection falls on the organization and certified technician, not an AI system. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically governs tool selection, but physical presence and hands-on judgment about safety equipment create practical barriers to any automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | An AI system capable of this task would require significant infrastructure (vision systems, inventory management integration, safety compliance databases), making it comparable to or more expensive than a skilled technician's wage for this subtask, especially accounting for oversight and error correction. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so AI cost is effectively infinite relative to human labor for this specific action. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs autonomous tool and equipment selection for automotive glass work in production environments. While computer vision for part identification exists, actual on-site selection requiring safety compliance, fit verification, and equipment condition assessment is not yet deployed at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical tool/parts selection for automotive glass work; this remains a manual, on-site decision made by a technician. |
Replace or adjust motorized or manual window-raising mechanisms.
14CI 5–24 · exposure 8 · augmentation 25 · importance 3.8/5 · click for rater detail
Replace or adjust motorized or manual window-raising mechanisms.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Automotive service sectors, particularly independent repair shops and dealerships performing this task, remain heavily human-dependent with minimal AI adoption. The long tail of vehicle models and custom configurations makes centralized automation economics unattractive. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive repair and glass installation is a physical, low-digitization trade with minimal AI/robotics adoption for hands-on mechanical repairs. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide diagnostic support (image recognition for mechanism type, repair procedures) and parts identification, but the physical work of replacement and adjustment remains human-dependent. Augmentation value is limited to knowledge assistance rather than productivity multiplication. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with diagnostics, repair manuals, or parts lookup, but offers little direct assistance in the physical act of replacing or adjusting the mechanism. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task involves physical manipulation in constrained spaces (vehicle doors/windows) with precise mechanical assembly. While vision-based systems could guide diagnostics, the actual replacement and adjustment requires dexterous robotic handling of small parts and fixtures that current off-the-shelf robotics cannot reliably perform in the field without extensive custom integration. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical mechanical repair task requiring manipulation of hardware inside a vehicle door, well beyond current AI or robotic capability in unstructured settings. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This work typically requires automotive technician certification or manufacturer training, and liability concerns around vehicle safety systems create friction. Customer expectation for qualified human technicians and potential regulatory oversight of vehicle safety repairs add organizational and legal barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for this task, though it requires physical dexterity, tools, and vehicle access that create practical (not regulatory) barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current robotic systems capable of this task would require significant capital investment, specialized tooling, and integration costs far exceeding the loaded wage of a technician performing this service. The task is too variable across vehicle makes/models to amortize costs effectively. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based alternative performing this physical task, so AI cost is effectively infinite relative to a human technician's wage. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic product reliably performs automotive window mechanism replacement in production service environments. Prototype robotic arms exist for structured manufacturing, but field repair in varied vehicle models remains research-stage only. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical replacement or adjustment of window regulator mechanisms; this remains purely manual work done by technicians. |
Replace all moldings, clips, windshield wipers, or other parts that were removed prior to glass replacement or repair.
13CI 10–15 · exposure 0 · augmentation 13 · importance 4.3/5 · click for rater detail
Replace all moldings, clips, windshield wipers, or other parts that were removed prior to glass replacement or repair.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Automotive service remains a physical, location-bound sector with low digitization of core repair tasks. Adoption of automation in glass installation shops is minimal; most work is performed by technicians with hand tools, and economic incentives do not yet favor robotics. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive glass repair is a low-digitization, physically-oriented trade with minimal AI/robotics adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with computer vision guidance (e.g., identifying correct clip positions or wiper blade orientation) or documenting which parts were removed, but such assistance would offer only marginal productivity gain given the task's inherent physical nature and reliance on human manual dexterity. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI tools offer negligible assistance for the physical act of reattaching moldings, clips, and wipers, as this is a manual, tactile task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation, spatial reasoning, and dexterity to reattach diverse parts (moldings, clips, wipers) to a vehicle in their correct positions. Current AI systems lack the embodied manipulation capabilities and real-world sensorimotor control necessary to perform this end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity to reattach clips, moldings, and wipers to a vehicle; no current AI system can perform physical reassembly work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing barriers to automating this mechanical task, quality assurance requirements, customer expectations for human craftsmanship on vehicles, and the diversity of vehicle models create moderate organizational and market friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically requires a human for reinstalling parts, but physical dexterity and quality-control expectations create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Deploying robotic systems capable of precise glass-frame component reassembly would require significant capital investment, integration, and maintenance, making the all-in cost far higher than paying a skilled technician for this labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven substitute for this physical task, so any hypothetical robotic solution would be far more expensive than a technician's wage given current hardware costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed autonomous systems reliably perform automotive reassembly tasks of this type in production environments. Robotic solutions for this work remain research-stage or highly specialized, and require extensive task-specific engineering rather than general-purpose AI. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical automotive reassembly tasks like reattaching moldings and wipers; this remains firmly in the domain of human technicians and robotics research at best. |
Install rubber channeling strips around edges of glass or frames to weatherproof windows or to prevent rattling.
13CI 10–15 · exposure 0 · augmentation 25 · importance 3.4/5 · click for rater detail
Install rubber channeling strips around edges of glass or frames to weatherproof windows or to prevent rattling.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Automotive glass installation is performed by small shops and service centers with low digitization; the sector has shown minimal adoption of robotic automation for this type of manual dexterity task. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive glass repair is a physically hands-on trade with minimal AI/robotics adoption; the sector shows low digitization for manual installation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with task planning or quality inspection, but offers minimal productivity boost to a technician actively performing the physical installation itself. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostics, part ordering, or instructional guidance, but offers little direct help with the physical act of fitting and installing rubber channeling. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation in three dimensions, tactile feedback for proper seal fitting, and adaptation to variable frame geometries—capabilities far beyond current robotic systems in real-world deployment. The weatherproofing requirement demands quality judgment that AI cannot currently perform autonomously. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual, physical dexterity task requiring precise fitting and pressing of rubber strips around glass edges; no current AI system or robot can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While the task has no strict licensing requirement, customer preference for skilled manual work, quality liability for improper weatherproofing (water leaks, rattles), and the need for human judgment on fit and finish create moderate friction to automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for channeling installation, but physical manipulation, variable glass/frame fits, and need for a controlled workshop environment create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of a robotic system capable of handling the precision, flexibility, and quality control needed for this task would vastly exceed the loaded wage of a skilled automotive glass installer. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so the all-in AI cost is effectively infinite relative to a technician's wage for this specific manual step. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No production-grade AI or robotic system today reliably installs rubber weatherstripping on automotive glass frames at scale. This remains a manual craft skill performed by human technicians. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product installs weatherstripping on automotive glass; this remains a manual trade skill performed by human technicians. |
Install replacement glass in vehicles.
12CI 10–14 · exposure 0 · augmentation 25 · importance 4.6/5 · click for rater detail
Install replacement glass in vehicles.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Automotive glass installation remains a small, geographically distributed, skill-dependent trade with minimal automation adoption even in large dealerships. Most shops are independent or small operations with limited capital for specialized equipment, resulting in laggard sector characteristics. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive repair is a physically-oriented, low-digitization trade sector with minimal AI or robotic adoption for hands-on installation work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools could assist with vehicle model identification, material specification lookup, or documentation, but the core physical task of glass installation offers limited productivity enhancement via augmentation alone. The task is fundamentally hands-on and spatially constrained. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostics, parts ordering, or scheduling, but offers minimal direct assistance to the physical act of removing and installing glass. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Installing replacement glass requires precise physical manipulation in confined spaces, alignment with seals, and real-time adaptation to vehicle-specific geometries. Current AI systems lack the dexterous manipulation, environmental perception, and force control needed to perform this end-to-end today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring removal of old glass, adhesive application, precise fitting, and sealing on vehicle bodies—current AI systems have no capability to perform this manual, dexterous work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Safety regulations and liability concerns create moderate friction—improper glass sealing can affect airbag deployment and vehicle structural integrity—but no explicit legal barrier prevents automation. Quality standards and customer preference for skilled human installation add organizational friction. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed in most jurisdictions, this task requires physical dexterity, specialized tools, and quality assurance (leak-proof seals, safety glass standards) that create practical barriers to automation without dedicated robotics. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized robotic systems capable of glass installation are capital-intensive and require significant setup per vehicle model. Total cost of ownership, including integration and maintenance, remains substantially higher than a skilled technician's loaded wage for most automotive glass shops. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-driven alternative to compare costs against; a human installer with tools remains the only viable option, making AI cost per task-equivalent effectively infinite or nonexistent. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product performs autonomous vehicle glass installation reliably in production settings. While robotic arms exist in controlled factory environments, the variability of field installation (different vehicles, damaged frames, environmental conditions) remains beyond current deployed systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product installs automotive glass in production settings; this remains a manual trade performed entirely by human technicians. |
Prime all scratches on pinchwelds with primer and allow to dry.
10CI 5–15 · exposure 0 · augmentation 13 · importance 4.9/5 · click for rater detail
Prime all scratches on pinchwelds with primer and allow to dry.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Automotive glass installation is a physical, on-site service task concentrated in small to mid-size shops with limited digitization and minimal AI adoption; the sector remains largely manual and low-tech. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive glass repair is a physical, low-digitization trade with minimal AI or robotics adoption for hands-on surface prep tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI vision could theoretically assist in identifying scratches needing primer, but the physical execution and quality control remain entirely human; practical augmentation potential is minimal for this concrete, manual subtask. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical act of applying primer to scratches and waiting for it to dry; this is a manual craft step outside AI's current capabilities. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Priming scratches on pinchwelds requires precise manual control, spatial awareness, and judgment about scratch severity and primer application consistency. Current AI/robotic systems cannot reliably identify, access, and prime pinchweld scratches to automotive standards without human oversight. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring hands-on application of primer to a vehicle surface and inspection of scratches; no current AI system can perform this physical manipulation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Automotive glass repair involves customer vehicles and safety-critical components; regulatory standards, manufacturer specifications, and liability concerns create significant friction against full automation without certified human sign-off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing law mandates a human specifically prime pinchwelds, but physical dexterity, material handling, and quality-critical adhesion work create practical barriers to automation beyond simple worker preference. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of specialized robotic arms, vision systems, and safety/liability infrastructure to automate this niche task would far exceed the loaded hourly wage of automotive glass technicians performing it manually. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-driven alternative to compare costs against; a human technician performing this task is the only viable option, making AI cost effectively infinite/inapplicable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI or robotic product currently performs this task end-to-end in automotive glass repair/installation workflows. Pinchweld priming remains a human-performed task in production settings with no mature automation solution. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical primer application on automotive pinchwelds; this remains entirely a manual trade task with no robotic or AI substitute in production. |
Remove all dirt, foreign matter, and loose glass from damaged areas, apply primer along windshield or window edges, and allow primer to dry.
10CI 5–15 · exposure 0 · augmentation 13 · importance 4.8/5 · click for rater detail
Remove all dirt, foreign matter, and loose glass from damaged areas, apply primer along windshield or window edges, and allow primer to dry.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Automotive glass repair remains a highly localized, physical service sector with low automation penetration. Most shops are small, independently operated, and lack the capital or technical infrastructure to deploy robotic systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive glass repair is a small-shop, physical trade sector with minimal AI or robotics adoption; this is a laggard sector for automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist via computer vision to identify debris or problem areas for the technician, but the core task of manual surface preparation offers limited augmentation since it is primarily dexterous and inspection-based rather than cognitively demanding. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no meaningful assistance for the physical cleaning and primer application steps of this task. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation in real-world environments—removing debris, applying primer precisely along glass edges, and monitoring drying times. Current AI systems cannot execute the fine motor control, environmental sensing, and adaptability needed to handle variable damage patterns and surfaces. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring cleaning debris and applying primer to vehicle glass edges, which requires dexterity, tactile judgment, and precision that current AI systems cannot perform. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety and quality standards for windshield repair are regulated; improper primer application or incomplete debris removal can compromise structural integrity and customer safety, creating liability concerns that favor human oversight and accountability. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing strictly requires a human, but physical dexterity, variable damage conditions, and quality/safety consequences of poor windshield adhesion create practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized equipment (robotic arms, computer vision, environmental sensors) needed for this task would cost significantly more than the loaded wage of a skilled glass technician, without yet achieving reliability. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this physical task, so any hypothetical automation solution (specialized robotics) would be far more expensive than a human technician performing it manually. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed robotic or AI systems reliably perform automotive glass surface prep and primer application at production scale. Specialized industrial robots exist in controlled factories but cannot generalize to the variability of field repair work on damaged windshields. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs automotive glass surface prep and primer application in production; this remains firmly in the domain of manual technician work. |
Check for and remove moisture or contamination in damaged areas and keep areas dry until repairs are complete.
10CI 5–15 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Check for and remove moisture or contamination in damaged areas and keep areas dry until repairs are complete.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Automotive repair remains a low-digitization, hands-on trade with strong physical presence requirements. No measurable adoption of autonomous systems for pre-repair environmental preparation has occurred in the sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive glass repair is a low-digitization, physical trade sector with minimal AI adoption for hands-on repair steps. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist by providing moisture/humidity monitoring alerts via IoT sensors, but the core work—manual contamination removal and drying—offers limited augmentation potential given its tactile and judgment-intensive nature. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could provide minor assistance such as flagging humidity/weather conditions or referencing repair protocols, but offers little direct help with the physical moisture-removal task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires hands-on inspection and manipulation of physical materials in varied damage scenarios, with real-time judgment about moisture levels and contamination types. Current AI systems have no capability to physically access vehicles, assess moisture conditions, or execute removal actions. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical inspection and moisture-control task requiring hands-on manipulation of glass, tools, and drying equipment; no AI system can perform the physical actions involved. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | While not strictly licensed, the task is embedded in vehicle repair workflows where customer safety and warranty liability create strong friction. Defective moisture removal directly affects repair longevity and customer satisfaction, creating error-cost asymmetry that favors human accountability. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for this micro-task, but it's embedded in a physical repair workflow requiring in-person presence and tactile judgment, creating practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Any hypothetical robotic system capable of this work (sensing, moisture removal, contamination handling) would cost orders of magnitude more than the loaded hourly wage of a skilled technician, with no cost advantage whatsoever. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for this physical task, so AI cost is irrelevant/infinite relative to a human performing the manual work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs this task reliably; it requires integrated robotics, environmental sensing, and physical intervention in a dynamic, unstructured automotive environment—well beyond current autonomous systems in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical moisture detection/removal on automotive glass; this remains purely a manual, tactile task performed by technicians. |
Remove broken or damaged glass windshields or window glass from motor vehicles, using hand tools to remove screws from frames holding glass.
10CI 5–15 · exposure 0 · augmentation 13 · importance 4.3/5 · click for rater detail
Remove broken or damaged glass windshields or window glass from motor vehicles, using hand tools to remove screws from frames holding glass.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The automotive glass repair sector remains labor-intensive with low digitization; adoption of AI or robotics for this specific manual task is minimal, with small independent shops dominating the market. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive glass repair is a low-digitization, physical trade sector showing no meaningful AI/robotic adoption for manual glass removal tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist with task planning or documentation (e.g., identifying screw locations, scheduling), but offers limited augmentation for the core physical removal work that requires human judgment and dexterity. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance for the physical act of unscrewing frames and removing broken glass; this is a manual dexterity task outside current AI's scope. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation of fragile glass, removal of fasteners in confined spaces, and real-time tactile feedback to avoid further damage. Current AI systems lack the embodied dexterity, force control, and environmental adaptation needed to reliably perform end-to-end glass removal. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity to remove screws and extract fragile/broken glass from vehicle frames; no AI system today can perform this manual labor. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety-critical nature of windshield installation (affects structural integrity and passenger safety) creates both regulatory oversight and liability concerns; technicians typically require certification or licensing, and insurance/legal liability for improper removal creates strong friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically bars automation, but safety concerns around broken glass and vehicle damage create moderate liability and quality-control friction against unproven automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of glass handling and fastener removal would require substantial capital investment and customization per vehicle model, making per-unit costs far exceed the loaded wage of a skilled technician performing the task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic solution for this task, so any hypothetical automation would require expensive custom robotics far exceeding the cost of a human technician. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product can autonomously remove windshields or window glass from vehicles. The task demands manipulation of delicate materials in variable physical conditions, which remains beyond the scope of production robotics for this industry. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical glass removal from vehicles; this remains purely a research-stage robotics challenge given the variability of vehicle models and broken glass handling. |
Remove moldings, clips, windshield wipers, screws, bolts, and inside A-pillar moldings and lower headliners in preparation for installation or repair work.
10CI 5–15 · exposure 0 · augmentation 25 · importance 4.3/5 · click for rater detail
Remove moldings, clips, windshield wipers, screws, bolts, and inside A-pillar moldings and lower headliners in preparation for installation or repair work.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Automotive repair remains a low-digitization, small-shop dominated sector with limited AI/robotics adoption. The physical, site-specific nature of the work and lack of scalable automation investments place this in laggard adoption categories. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive repair and glass installation is a low-digitization, physical trade sector with minimal AI/robotics adoption for manual disassembly tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI offers minimal assistance; computer vision might guide technicians on fastener identification or documentation, but the core manual task of removal requires direct human physical labor with little opportunity for meaningful AI partnership. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could provide reference guides, repair manuals, or diagrams to assist technicians in identifying fastener locations, but offers minimal direct assistance to the physical removal process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical dexterity, spatial reasoning, and precise manipulation of small parts in constrained vehicle spaces. Current AI/robotics systems cannot reliably perform end-to-end removal of multiple fastener types and moldings with the consistency required for automotive work. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical disassembly task requiring manual dexterity, tool use, and adaptation to vehicle-specific fasteners and trim; no AI system can perform this manipulation today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant barriers exist: improper removal can damage vehicle interiors and affect structural integrity, creating liability concerns; the task requires direct physical access inside customer vehicles; and labor is typically in-person with limited remote oversight. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically governs this disassembly step, but physical workspace constraints, vehicle variability, and lack of robotic infrastructure create practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Developing and deploying a robotic system capable of this task would cost substantially more than the loaded wage of an automotive glass technician performing the work, with ongoing maintenance and integration overhead. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-driven robotic solution available for this task, so any hypothetical automation would require expensive custom robotics far exceeding human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed production system performs this task autonomously in automotive repair settings. While industrial robotics exists, automotive glass removal and molding disassembly requires adaptive, context-aware manipulation that current systems cannot deliver reliably. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical removal of automotive trim and fasteners; this remains squarely in the domain of human technicians with robotics research not addressing this niche task. |
Cool or warm glass in the event of temperature extremes.
10CI 5–15 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Cool or warm glass in the event of temperature extremes.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Automotive glass repair remains a traditional, shop-based service with low digitization and limited AI adoption; most businesses in this sector are small independents without infrastructure for autonomous thermal systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive glass repair is a low-digitization, physical trade with minimal AI adoption for hands-on tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist by monitoring temperature sensors and alerting technicians to optimal timing, but the core task—physically controlling thermal equipment—remains manual, limiting meaningful augmentation value. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could provide guidance on optimal temperature ranges or scheduling but offers little direct assistance to the physical act of warming or cooling glass. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Cooling or warming glass in response to temperature extremes is a real-time thermal management task requiring physical manipulation of equipment and immediate environmental sensing. Current AI systems cannot physically control HVAC systems or thermal tools in the field, making end-to-end automation infeasible. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical thermal-conditioning step requiring manual handling of glass and equipment in a shop setting; no AI system can perform this physical manipulation.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | There are moderate-to-strong barriers: the task requires hands-on physical work in a shop environment, immediate judgment about glass condition, and potential safety concerns around thermal stress that favor human oversight and liability assignment. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier specifically for this sub-task, but it requires physical presence and tactile judgment about glass condition, which is a practical (not regulatory) barrier to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Since automation is not feasible with current technology, AI provides no cost advantage; human technicians remain the only viable option, making the cost ratio heavily favor the human baseline. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI-based alternative to compare costs against; a human technician remains the only means of performing this physical task, making AI substitution infeasible and thus not cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs this thermal regulation task autonomously. The task requires physical intervention in real-world conditions where AI systems lack embodied capability and real-time thermal sensing integration. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical glass temperature conditioning; this remains a manual craft task with no robotic or AI product addressing it. |
Install new foam dams on pinchwelds, if required.
10CI 5–15 · exposure 0 · augmentation 13 · importance 3.6/5 · click for rater detail
Install new foam dams on pinchwelds, if required.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Automotive glass installation remains largely manual and performed by small shops and service centers with limited digitization or automation adoption patterns. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive glass repair is a manual trade with minimal AI/robotic adoption; this specific installation step sees no meaningful automation deployment in the field. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide minor assistance through diagnostic imaging or foam-dam placement guides, but meaningful augmentation is limited given the hands-on, spatial nature of the work. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers no meaningful assistance to a technician physically placing foam dams on a pinchweld; this is a tactile, dexterity-driven step outside AI's current assistive scope. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Installing foam dams on pinchwelds requires precise manual placement, adhesive application, and alignment judgment in tight vehicle spaces. Current AI systems have no demonstrated capability to perform this hands-on, spatially-aware assembly task end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a precise manual manipulation task requiring physical dexterity to fit foam dams onto pinchwelds, which current AI systems cannot perform without robotic embodiment far beyond available off-the-shelf capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | There are moderate-to-strong barriers: automotive glass installation often falls under vehicle manufacturer specifications and warranty requirements, and customer safety perception strongly favors human oversight for structural vehicle components. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for this micro-task, but it's embedded in a physical trade requiring hands-on skill and correct fitment to prevent leaks, creating quality-control friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Robotic systems capable of this task are expensive to develop, integrate, and maintain; human installers remain significantly cheaper for this low-volume, high-variability job. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this at any cost, so the human remains the only cost-effective option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product performs automotive glass installation tasks autonomously. The task demands fine motor control and tactile feedback that are not yet reliably automated in production environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs this specific physical automotive glass installation subtask; it remains purely manual work performed by technicians. |
Hold cut or uneven edges of glass against automated abrasive belts to shape or smooth edges.
10CI 5–15 · exposure 0 · augmentation 13 · importance 3.6/5 · click for rater detail
Hold cut or uneven edges of glass against automated abrasive belts to shape or smooth edges.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Automotive glass installation and repair is a traditional, localized trade with many small independent shops and regional operations—sectors that historically show slow AI/automation adoption and limited capital investment in robotics. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive glass repair is a small-business-dominated, physically hands-on trade with minimal AI or robotics adoption reported industry-wide. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist via visual inspection of edge quality or tool maintenance alerts, but the core manual holding and positioning task leaves little room for meaningful AI augmentation while the worker remains in the loop. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI tools (LLMs, vision models) offer no meaningful real-time assistance to a worker physically grinding glass edges against an abrasive belt. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise 3D manipulation of glass edges against moving abrasive equipment, real-time tactile feedback to detect edge smoothness, and adaptive hand positioning—all in a physical manipulation context where current robots lack the dexterity and sensing to reliably handle fragile glass without breaking it or causing unsafe outcomes. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterous handling of fragile glass against machinery, well outside current AI capabilities which lack embodied robotic dexterity for this precise, variable manual work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations around high-speed abrasive equipment, worker proximity, and glass handling create strong workplace safety and liability barriers. OSHA standards and equipment guarding requirements make automated substitution legally and operationally complex. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically governs this micro-task, but physical workspace constraints, cost of specialized robotic tooling, and low volume per shop create practical friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A specialized robotic cell with vision, force feedback, and safety interlocks would cost hundreds of thousands of dollars in capital plus maintenance, far exceeding the loaded wage of a technician performing this repetitive task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI or robotic system currently deployed for this task, so no viable cost comparison exists; a human worker remains the only option, making AI substitution costlier or infeasible. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial systems reliably perform this specific task. While industrial robots exist for grinding, none are proven in production for holding and shaping automotive glass edges against abrasive belts with the required precision and safety margins. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs this specific glass-shaping task; it remains a manual skilled-trade operation with no robotic automation in production for automotive glass repair shops. |
Allow all glass parts installed with urethane ample time to cure, taking temperature and humidity into account.
9CI 0–18 · exposure 0 · augmentation 38 · importance 4.8/5 · click for rater detail
Allow all glass parts installed with urethane ample time to cure, taking temperature and humidity into account.
9| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Automotive repair remains a largely traditional, human-dependent sector; while some shops use timers and environmental monitors, AI-driven automation of cure-time decisions is minimal even in advanced facilities. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive glass repair is a low-digitization, physical trade with minimal AI adoption for hands-on procedural steps like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could assist by logging environmental conditions in real time and alerting technicians when standard cure windows are likely met, raising confidence in compliance without replacing technician judgment. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Simple digital tools or apps could reference temperature/humidity-adjusted cure charts, offering minor decision support, but this is basic lookup rather than AI-driven productivity transformation. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires continuous environmental monitoring and judgment calls based on real-time temperature/humidity conditions to determine cure completion—a nuanced, context-dependent decision that current AI cannot execute autonomously without human oversight of physical installation outcomes. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a passive waiting/monitoring step tied to physical curing chemistry, not a cognitive or manipulable digital task; there is nothing an AI system can do to perform the waiting itself. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Safety and liability are substantial barriers: the technician's judgment and sign-off on cure completion is legally and practically required before returning a vehicle; errors risk safety hazards and warranty claims. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing barrier specifically for curing time, but safety-critical outcome (windshield adhesion) creates liability pressure to follow manufacturer-specified cure times precisely, favoring conservative human adherence. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automated monitoring sensors exist but integrating them with decision-making agents and providing the required human oversight would likely exceed the cost of a technician simply observing standard cure protocols. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task, so cost comparison favors the human/process default entirely; a sensor timer is not an 'AI' solution and adds cost without labor replacement. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can independently monitor cure processes, interpret environmental data, and make real-time decisions about when glass installations are safe for use without human technician verification. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs curing time management; it is inherently a physical-world timing decision requiring only reference charts, not automation. |
Apply a bead of urethane around the perimeter of each pinchweld and dress the remaining urethane on the pinchwelds so that it is of uniform level and thickness.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.8/5 · click for rater detail
Apply a bead of urethane around the perimeter of each pinchweld and dress the remaining urethane on the pinchwelds so that it is of uniform level and thickness.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Automotive glass repair and installation remain largely craft-based, performed by independent shops and dealerships with low digitization. Adoption of even semi-automated systems is slow; most shops use manual application methods and resist capital-heavy automation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive glass repair is a physical, hands-on trade with minimal digitization or robotic automation adoption in this specific application step. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI vision systems could help detect pinchweld boundaries or flag uneven application, but the core motor task of applying and dressing urethane offers limited augmentation value—technicians already have visual feedback and tactile judgment that is difficult for AI to enhance without replacing the human. |
| Augmentation potential | claude-sonnet-5 | 2/5 | Some tools (guided bead applicators, thickness sensors) could assist consistency, but current general AI offers little direct assistance to this tactile, manual dressing process. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation of urethane adhesive around complex automotive body contours, with real-time tactile feedback and spatial judgment. Current AI/robotic systems cannot reliably perform the fine motor control, edge detection, and uniform material application needed on varied vehicle geometries without human intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires precise physical manipulation of adhesive material with tactile feedback and visual inspection on varying vehicle geometries; no current AI/robotic system performs this dexterous manual task end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety and quality liability are substantial: defective urethane application creates water leaks, structural failure, and safety hazards in a regulated industry (automotive OEM standards, ISO certifications). Manufacturers require human sign-off and have strong quality-control traditions preventing full automation handoff. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement specifically for this step, but safety-critical nature (improper seal risks windshield detachment) creates strong liability and quality-control incentives to keep skilled humans in control. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automotive glass installation robots are capital-intensive ($500k–$1M+ per system) with high integration costs; technician labor remains cheaper per unit for small-to-medium volume operations, especially when accounting for setup, maintenance, and downtime. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI or robotic substitute available at any cost for this specific field task, so human labor remains the only cost-effective option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs urethane bead application and dressing on automotive pinchwelds. While robotic arms exist for structured tasks, the variability of pinchweld geometry, adhesive behavior, and surface preparation across vehicle models prevents production-scale deployment. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product performs automotive urethane bead application and dressing in the field; this remains a manual skilled-trade task. |
Install, repair, or replace safety glass and related materials, such as back glass heating elements, on vehicles or equipment.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.1/5 · click for rater detail
Install, repair, or replace safety glass and related materials, such as back glass heating elements, on vehicles or equipment.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Automotive glass repair is a distributed, small-shop trade with low digitization and minimal automation adoption to date. The physical, location-dependent nature of the work and fragmented market structure (independent shops, dealerships) limit rapid AI/robotic adoption. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Automotive repair is a physical, low-digitization trade sector with minimal AI/robotics adoption for hands-on installation work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist in damage assessment and parts ordering via image analysis, but the core task of physically handling and installing glass offers minimal augmentation opportunity since the human technician must remain fully present and in control throughout the delicate process. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with diagnostics, parts lookup, or calibration software guidance for ADAS-equipped glass, but offers little help with the core physical installation and repair work. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation in three dimensions, handling fragile materials, and real-time adaptation to vehicle geometry and damage assessment. Current AI systems lack the dexterous robotics, spatial reasoning, and tactile feedback necessary to perform glass installation/replacement end-to-end at human speed and quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring removal of old glass, adhesive application, precise fitting, and calibration of embedded electronics; no AI system can perform this manual work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety glass installation on vehicles is subject to automotive safety standards (DOT, FMVSS) and insurance requirements that typically mandate certified technician installation and sign-off. Liability exposure for defective glass installation is high, creating strong regulatory and legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing is typically required, but liability for safety glass failure (structural integrity, ADAS calibration) and physical dexterity requirements create moderate barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Automotive glass installation requires specialized equipment, trained labor, and liability insurance. The current cost of robotic arms and vision systems capable of handling fragile glass safely would far exceed the loaded wage of a skilled glass technician. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so AI cost is irrelevant; the human labor remains the only viable option, making AI comparatively more expensive or nonexistent as an alternative. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed production systems can reliably install, repair, or replace automotive safety glass. This remains a manual craft requiring human technicians; no commercial automation exists at scale for this task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI or robotic product installs or repairs automotive glass in production; this remains entirely a hands-on trade skill. |
Related occupations — Installation, Maintenance & Repair
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.