Tire Repairers and Changers

49-3093.00

Median wage $37,710/yr108,410 employed (US)Rank #757 of 923 scored · top 82% by substitution

Repair and replace tires.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution16

Exposure5

Augmentation14

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

24 rated tasks, binned by substitution score.

Position among all scored occupations

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

Tasks with substitution ≥ 70

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

Why this score

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

Task automatabilityw 35%5

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

Technical feasibility todayw 20%6

panel mean rating 1.2/5 → substitution pressure 6/100

Cost vs. human wagew 15%4

panel mean rating 1.2/5 → substitution pressure 4/100

Adoption barriersw 20%inverted — strong barriers lower the score64

panel mean rating 2.4/5 (barrier strength) → substitution pressure 64/100

Sector adoption velocityw 10%2

panel mean rating 1.1/5 → substitution pressure 2/100

Task breakdown (24 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.

Order replacements for tires or tubes.

CI 28–77 · exposure 50 · augmentation 63 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire repair is a small, traditionally-operated sector with low digitization; adoption of AI ordering systems remains minimal, with the industry preferring established manual or basic ERP workflows.
Sector adoption velocity	claude-sonnet-5	3/5	Auto repair and tire retail is a moderately digitized sector; larger chains have adopted automated inventory/ordering systems, but many small independent shops still order manually or by phone.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI could assist by flagging low inventory, suggesting reorder quantities based on historical sales, or auto-populating supplier forms; this would reduce busywork while a human makes the final ordering decision.
Augmentation potential	claude-sonnet-5	4/5	AI-assisted inventory systems can flag low stock, suggest optimal order quantities, and auto-generate purchase orders, meaningfully speeding up the task even where a human still reviews or approves it.
Task automatability	claude-haiku-4-5-20251001	2/5	Ordering tires involves data entry and supplier communication, which are partially automatable, but requires knowledge of inventory levels, customer specifications, and supplier relationships that typically need human oversight and decision-making.
Task automatability	claude-sonnet-5	4/5	Ordering replacement tires/tubes is largely a structured procurement task (matching SKU, checking inventory, placing purchase orders) that current AI/software systems handle well via inventory management integrations.》Human confirmation of physical inspection may still be needed, but the ordering itself is highly automatable.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Supplier relationships, account-specific pricing, and the need for human judgment on inventory levels and tire specifications create moderate friction; no strict licensing barrier exists, but organizational preference for familiar ordering methods is common.
Adoption barriers	claude-sonnet-5	1/5	There is no licensing or regulatory requirement for a human to place a parts order; it's a routine administrative/procurement task.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Current AI ordering systems require integration, training, and ongoing monitoring that can approach or exceed the cost of a shop employee spending time on routine ordering tasks, especially for small repair shops.
Cost vs. human wage	claude-sonnet-5	4/5	Automated ordering software costs a small fraction of the labor time a person would spend manually checking stock and placing orders, especially at scale across multiple locations.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While basic purchase-order systems exist, tire shops rarely use fully autonomous ordering; most rely on manual reordering, phone calls, or email to suppliers, with limited AI-based demand forecasting in the industry.
Technical feasibility today	claude-sonnet-5	4/5	Automated parts-ordering and inventory replenishment systems are already deployed widely in auto shops and retail tire chains, using barcode/VIN lookup and reorder triggers.

Place wheels on balancing machines to determine counterweights required to balance wheels.

CI 15–29 · exposure 13 · augmentation 25 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire repair is a laggard sector characterized by small independent shops with limited digitization and capital investment capacity. Adoption of robotic wheel-handling systems has been minimal, with most shops continuing manual labor practices.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair shops are a low-digitization, physical-labor sector with minimal AI/robotics adoption for this kind of task.
Augmentation potential	claude-haiku-4-5-20251001	2/5	Balancing machines already automate the measurement itself, but AI offers limited augmentation for the manual placement task. Digital readouts and feedback on counterweights provide some assistance, but the core physical handling remains unchanged.
Augmentation potential	claude-sonnet-5	2/5	Modern wheel balancers already have digital displays and software to calculate counterweights, offering some assistance, but the physical placement itself is not augmented by AI.
Task automatability	claude-haiku-4-5-20251001	2/5	While balancing machines themselves are automated and can measure imbalance, the task of physically placing wheels on the machine and interpreting results requires robotic manipulation in a physical shop environment. Current AI and robotics can perform individual steps but not the full end-to-end task with 50% time savings at equal quality without significant setup.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring lifting a wheel onto a machine, which current AI systems cannot perform without robotic embodiment far beyond off-the-shelf availability.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While there are no strict licensing requirements for wheel balancing itself, the task occurs in physical shop environments with liability concerns around wheel handling, and most shops lack the infrastructure or capital to invest in automation, creating organizational friction.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement specifically bars automation, but the physical, dexterous nature of handling heavy wheels safely creates practical organizational friction against non-human solutions.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Robotic systems capable of handling and positioning wheels on balancing machines are capital-intensive and expensive to integrate, making them significantly more costly than paying a tire technician to perform the task manually.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute for this physical action, so any hypothetical robotic solution would be far more expensive than a human tire technician performing the task.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Although wheel balancing machines exist and are automated, deploying a system to physically manipulate and position wheels on these machines reliably in real shop conditions remains largely in research/prototype stages. No mature product currently performs the full placement-and-reading task autonomously at scale in production tire shops.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously places wheels on balancing machines in shops today; this remains a manual technician task.

Locate punctures in tubeless tires by visual inspection or by immersing inflated tires in water baths and observing air bubbles.

CI 15–29 · exposure 13 · augmentation 25 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire repair is performed mainly by small and mid-sized independent shops and dealerships with low digitization, slow technology adoption, and strong reliance on manual processes. No evidence of systematic AI deployment for puncture detection in the industry.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair and tire service is a low-digitization, physically-intensive sector with minimal AI/robotics adoption for hands-on manual inspection tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	Computer vision could assist by highlighting suspected puncture locations on a display, but the water bath immersion process and final confirmation still require human observation of bubbles, limiting the scope of meaningful augmentation.
Augmentation potential	claude-sonnet-5	2/5	AI-powered visual inspection tools (e.g., camera-based defect detection) could theoretically assist in spotting punctures, but such tools are not commonly deployed in typical tire repair shops today.
Task automatability	claude-haiku-4-5-20251001	2/5	Visual inspection of punctures has some automatable elements via computer vision, but detecting air bubbles in water baths requires real-time 3D spatial reasoning and subtle visual cues that current systems struggle with reliably. The task also involves physical handling and positioning of tires, which limits end-to-end automation.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation and inspection task requiring handling tires, submerging them in water, and visually spotting bubbles—no current AI system can perform these physical actions end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	2/5	No strict licensing barrier exists for this specific task, but there is organizational friction: the task is embedded in a hands-on repair workflow where human judgment about severity and location inform downstream repair decisions, creating incentive to keep a human in the loop.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal barrier exists, but the physical, hands-on nature of tire handling and water immersion creates practical friction against automation without specialized robotics.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Automated vision systems for puncture detection require significant hardware (cameras, lighting, water bath integration) and ongoing maintenance, making the all-in cost likely higher than a human tire repairer performing the task quickly through practiced inspection.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute for the physical task, so any AI-based approach (e.g., robotic inspection) would be far more costly than a human technician performing this quickly with basic tools.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While computer vision systems can identify some surface defects, production-ready systems for reliably detecting punctures through water immersion and bubble observation are not widely deployed in tire repair operations. This task remains largely manual because of the need for consistent accuracy and the physical setup required.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs physical tire submersion and puncture detection in production; this remains a manual shop task.

Reassemble tires onto wheels.

CI 5–35 · exposure 13 · augmentation 25 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Tire service remains heavily manual and fragmented across small, independent shops with low digitization. Adoption of automation in this sector has been slow outside large tire manufacturers and fleet operations, with most shops still hand-mounting or using older machine designs.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair and tire service is a low-digitization, physically intensive sector with minimal AI or robotics adoption for hands-on manual tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	Torque-assist tools and pneumatic wrenches provide modest labor-saving, but AI systems offer limited augmentation for the core reassembly task, which is already labor-efficient and relies on tactile feedback and judgment that current AI cannot enhance meaningfully.
Augmentation potential	claude-sonnet-5	2/5	AI could assist with diagnostics, inventory, or scheduling around this task, but offers little direct assistance to the physical act of mounting a tire onto a wheel.
Task automatability	claude-haiku-4-5-20251001	2/5	While automated tire assembly equipment exists in manufacturing, reassembling tires onto wheels in a service context requires handling variable wheel conditions, precise torque specifications, and alignment checks that current general-purpose robots struggle with reliably. The task is partially automatable but falls short of the ≥50% time-saving threshold for end-to-end performance at equal quality in typical service shops.
Task automatability	claude-sonnet-5	1/5	This is a physical manual task requiring fine motor manipulation of tires, rims, and pneumatic tools; no current AI system can perform this end-to-end without a robotic platform, which is not commercially deployed for this purpose.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Safety liability is significant: improper reassembly can cause wheel failure and accidents, creating strong incentives for human accountability. Many service shops rely on warranty and customer trust tied to visible human workmanship, adding organizational and reputational friction against automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing requires a human specifically for this task, but the physical dexterity, safety requirements (tire pressure, wheel balancing) and equipment setup create practical barriers to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Dedicated automated tire-assembly systems cost $50k–$200k+ with installation and integration; labor for a single tire reassembly is low-cost ($15–30 equivalent loaded wage). The all-in per-task AI cost remains higher than human labor in most service contexts.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI-driven robotic solution for this task at any scale, so AI cost is effectively infinite or nonexistent compared to a human technician's wage.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Specialized tire-mounting machines exist in factories and some service centers, but they are expensive, require exact positioning, and handle only standard wheel/tire combinations. Field-ready, affordable systems that work across the variety of vehicle types and wheel conditions in general service shops are not deployed at scale.
Technical feasibility today	claude-sonnet-5	1/5	No deployed products perform automated tire reassembly onto wheels; tire shops rely entirely on human technicians using manual/pneumatic tire changing machines operated by hand.

Inspect tire casings for defects, such as holes or tears.

CI 10–30 · exposure 13 · augmentation 38 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Tire repair is a fragmented, small-shop dominated sector with limited digitization infrastructure. Most tire repair operations are not early adopters of automation technology, and AI vision adoption in this space remains pilot-stage rather than production-stage.
Sector adoption velocity	claude-sonnet-5	1/5	Tire repair is a low-digitization, physical trade sector with minimal AI adoption or investment in automating this specific inspection task.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI-assisted defect highlighting (e.g., flagging regions of concern for human review) can usefully augment a technician's inspection process, reducing inspection time and improving consistency, though the human must still make final defect judgments and severity assessments.
Augmentation potential	claude-sonnet-5	2/5	Computer vision could theoretically assist in flagging surface defects from images, but this is not commonly deployed in tire repair shops today, offering only marginal potential assistance.
Task automatability	claude-haiku-4-5-20251001	2/5	Visual inspection of tire casings could theoretically use computer vision, but real-world deployment faces challenges: tires are three-dimensional objects requiring multiple angles, lighting conditions vary in repair shops, and defect severity assessment requires nuanced judgment that current systems struggle with reliably. No end-to-end AI solution achieves the 50% time-saving threshold at equal quality in production today.
Task automatability	claude-sonnet-5	1/5	Physical inspection of tire casings requires tactile handling and visual assessment of a physical object; no off-the-shelf AI system performs this end-to-end in a shop setting today.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Liability concerns exist around missed defects leading to tire failure and accidents, creating organizational friction around full automation. However, no strict legal requirement mandates a licensed human perform the inspection, allowing for some AI-assisted workflows if implemented cautiously.
Adoption barriers	claude-sonnet-5	3/5	No formal licensing is required, but safety liability (undetected defects can cause blowouts/accidents) creates strong incentive for careful human judgment and physical handling, raising practical barriers to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Computer vision hardware (cameras, lighting) plus integration and continuous oversight costs are comparable to or exceed the hourly wage of a tire repair technician, especially when accounting for the need for human verification of borderline cases and retraining costs.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI-driven robotic or vision-only solution replacing the human labor cost for this task, so AI is not cheaper—it's effectively unavailable as a substitute.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While computer vision systems can detect some tire defects in controlled laboratory settings, production deployment in tire repair shops is limited and demonstrates material error rates, particularly with subtle or partially worn defects. Existing industrial solutions are narrow in scope and typically require human confirmation.
Technical feasibility today	claude-sonnet-5	1/5	No deployed consumer/industrial product autonomously inspects and handles tires for defects in production tire repair shops; this remains a manual task performed by technicians.

Clean and tidy up the shop.

CI 20–20 · exposure 0 · augmentation 0 · importance 4.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire repair shops are small, physical operations with low digitization and limited capital budgets; they represent a laggard sector for automation adoption. No evidence of AI or robotic cleaning deployment in this sector exists.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair shops are a low-digitization, physical-labor sector with minimal AI/robotics adoption for facility maintenance tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI offers no meaningful assistance to a human cleaning and tidying a shop; the task is fundamentally physical and does not benefit from language models, computer vision analytics, or other deployed AI modalities in any augmentative way.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer no meaningful assistance for physical cleaning and tidying of a workshop space.
Task automatability	claude-haiku-4-5-20251001	1/5	Cleaning and tidying a physical shop requires physical manipulation of objects, navigation of variable shop layouts, and handling of diverse materials (grease, tools, debris). Current AI systems lack mobile manipulation capabilities and embodied autonomy to perform this task end-to-end in real-world shop environments.
Task automatability	claude-sonnet-5	1/5	Physical cleaning and tidying of a shop requires manipulation of diverse objects, tools, and spaces that current AI/robotics cannot handle end-to-end without major hardware investment.'
Adoption barriers	claude-haiku-4-5-20251001	1/5	No legal, regulatory, or licensing barriers exist to automating shop cleaning. The only friction is organizational preference and the current lack of reliable technical solutions.
Adoption barriers	claude-sonnet-5	1/5	There are no licensing, regulatory, or liability barriers to automating shop cleaning; it's a purely physical task with no legal requirement for human performance.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Even basic mobile robots capable of shop cleaning cost tens of thousands of dollars with integration and ongoing maintenance, far exceeding the hourly wage cost of a shop worker assigned to cleaning duties.
Cost vs. human wage	claude-sonnet-5	1/5	Any robotic solution capable of general tidying would require expensive custom hardware and supervision, far exceeding the cost of a low-wage worker performing this task manually.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI product reliably cleans and tidies physical shop spaces autonomously in production today. Robotic cleaning exists for controlled environments (warehouses, floors) but not the complex, hazard-laden, variable spaces of tire repair shops.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs general shop cleaning/tidying in an automotive repair context; commercial cleaning robots are limited to flat-floor vacuuming, not organizing tools or debris.

Prepare rims and wheel drums for reassembly by scraping, grinding, or sandblasting.

CI 10–28 · exposure 8 · augmentation 13 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire repair remains a small-shop, local-service sector with low capital intensity and high fragmentation; digital/AI adoption is minimal. Most shops use manual or pneumatic tools rather than any automated prep systems.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair and tire service is a low-digitization, physical-labor sector with minimal AI/robotic adoption for manual surface prep tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist via computer-vision monitoring to flag when surfaces meet spec, but the core task—physically grinding or sandblasting—is largely mechanical and benefits only modestly from AI guidance once a technician is already performing it.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no assistance for the physical scraping, grinding, or sandblasting motions themselves; any AI role would be tangential (e.g., scheduling) rather than task-specific.
Task automatability	claude-haiku-4-5-20251001	2/5	While scraping, grinding, and sandblasting are mechanical processes that could theoretically be automated, the task requires visual inspection and judgment to determine when surfaces are adequately prepared—detecting rust depth, corrosion extent, and surface defects. Current AI-vision systems struggle with real-time quality assessment in dusty/wet shop conditions, and no end-to-end autonomous system reliably handles rim prep without human oversight.
Task automatability	claude-sonnet-5	1/5	This is a hands-on physical task requiring manipulation of tires, rims, and abrasive tools on irregular surfaces; no current AI system can perform this physical labor end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Safety regulations (dust exposure, tool operation) and worker compensation liability create moderate friction, but no hard legal requirement prevents automation. Customer expectations that skilled humans inspect wheels also provide soft barriers.
Adoption barriers	claude-sonnet-5	2/5	No licensing barrier specifically blocks automation, but the physical nature of the work and need for tactile inspection of wear/damage creates practical friction against remote or software-based substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Specialized grinding and sandblasting hardware (abrasive equipment, containment, dust collection) carries high capital and maintenance costs; labor cost for a tire technician is modest, making full automation economically unfavorable for small to mid-size shops that dominate the sector.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute for 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 production-grade automated systems exist that independently prepare rims via scraping, grinding, or sandblasting with acceptable quality control in tire shops. Industrial automation in this domain remains research or laboratory stage; deployed tire shop equipment requires human operators.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs rim/drum surface preparation; this remains purely manual work done with grinders, scrapers, and sandblasting equipment operated by humans.

Unbolt and remove wheels from vehicles, using lug wrenches or other hand or power tools.

CI 15–20 · exposure 5 · augmentation 25 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire repair shops are predominantly small, physical operations with limited digitization and capital. Few shops have invested in automation; adoption remains negligible in this sector.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair shops are a low-digitization, physically-oriented sector with minimal AI/robotics adoption for hands-on mechanical tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	Power tools and impact wrenches already augment human effort substantially. AI offers minimal additional productivity gains beyond existing pneumatic or electric tooling, though sensors might eventually warn of over-torque or stuck lugs.
Augmentation potential	claude-sonnet-5	2/5	AI offers limited assistance here beyond power tool torque sensors or diagnostic guidance; the core physical unbolting action itself isn't meaningfully augmented by AI.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires precise physical manipulation in varied vehicle configurations, safe torque application, and real-time tactile feedback. Current AI and robotics lack the dexterous, adaptable hardware and real-time perception needed to reliably perform wheel removal across vehicle types at scale.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring dexterity, force, and mobility around a vehicle; no current AI or robotic system can perform this reliably outside narrow lab settings.
Adoption barriers	claude-haiku-4-5-20251001	2/5	No strict legal licensing or regulatory requirement mandates human performance, but organizational friction is substantial: shops lack the capital and space for specialized robotics, and liability for damage to customer vehicles creates practical resistance to automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement exists, but physical workspace variability, safety concerns (lifted vehicles, torque specifications), and lack of mature robotics create practical barriers to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Robotic wheel-removal systems capable of handling variable real-world conditions would require significant capital investment, integration, and maintenance—substantially exceeding the cost of a tire technician performing the work.
Cost vs. human wage	claude-sonnet-5	1/5	Any robotic solution would require expensive specialized hardware, calibration, and maintenance far exceeding the low hourly cost of a human tire changer.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While specialized robotic systems exist in some controlled automotive manufacturing settings, they operate in highly standardized environments with pre-positioned vehicles. No deployed products reliably perform this task across the diverse vehicles, lug configurations, and corrosion states encountered in repair shops.
Technical feasibility today	claude-sonnet-5	1/5	No deployed commercial product autonomously unbolts and removes vehicle wheels; robotic tire-change concepts remain experimental or prototype-stage.

Hammer required counterweights onto rims of wheels.

CI 15–15 · exposure 0 · augmentation 0 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire repair remains a fragmented, labor-intensive sector with slow digital adoption; most shops are small independent operators without the scale or capital to deploy robotic solutions.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair and tire service is a low-digitization, physical-labor sector with minimal AI/robotic adoption for hands-on wheel servicing tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	AI offers no meaningful assistance to a human physically hammering counterweights onto rims; the task is primarily mechanical-manual and does not benefit from computational support.
Augmentation potential	claude-sonnet-5	1/5	Current AI offers no meaningful assistance for the physical act of hammering counterweights onto rims, as this is a purely manual mechanical task.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires precise physical manipulation of counterweights onto rotating rims using hand tools in a three-dimensional space. Current AI lacks the dexterous robotic embodiment and real-time sensorimotor feedback needed to reliably hammer weights onto wheels at the speed and accuracy of human workers.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring hand-eye coordination, force application, and handling of tools on irregular wheel surfaces; no off-the-shelf AI system performs this manual labor.'
Adoption barriers	claude-haiku-4-5-20251001	2/5	While not legally gated, the task occurs in distributed small shops with low capital budgets and high physical variability (different rim types, weights, positions), creating organizational friction against automation adoption.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement exists for this task, but it requires physical dexterity and presence at a vehicle, creating practical friction against remote or software-based automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	A specialized robotic arm with sensory feedback, end-effectors, and integration into a tire shop workflow would cost significantly more than the hourly wage of a tire technician, with high setup and maintenance overhead.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI-only solution for this manual task, so any automation would require expensive custom robotics far exceeding a human worker's wage cost.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial products perform this specific hammering task on wheel rims at production scale. While industrial robots exist for some tire work, none reliably automate the fine-tuned percussion-based attachment of counterweights.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs this specific physical hammering task; wheel balancing robots exist in some tire shops but automated hammering of counterweights is not a standard commercial product.

Seal punctures in tubeless tires by inserting adhesive material and expanding rubber plugs into punctures, using hand tools.

CI 15–15 · exposure 0 · augmentation 13 · importance 4.3/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire repair is performed in small, fragmented shops with limited digitization and capital investment; the sector shows minimal AI adoption and strong reliance on skilled manual labor.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair and tire service is a low-digitization, physical-labor sector with minimal AI or robotic adoption for hands-on repair tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI tools offer no meaningful assistance with the core task of physically sealing punctures; this is fundamentally a manual dexterity task where AI has no augmentative role.
Augmentation potential	claude-sonnet-5	2/5	AI could assist with diagnostics (e.g., detecting puncture location via sensors or computer vision) but offers little help with the physical plug insertion process itself.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires precise physical manipulation of small objects in varied orientations, insertion of adhesive materials, and tactile judgment about seal quality—capabilities far beyond current robotic systems in unstructured shop environments. End-to-end automation with 50% time savings is not demonstrated by any deployed system.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring dexterity to insert plugs into tire punctures using hand tools; no current AI system can perform this physical work.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While not strictly licensed, tire repair occurs in physical shop settings where liability for safety (tire failure risk) creates some friction, and customer preference for human verification of safety-critical work provides modest adoption resistance.
Adoption barriers	claude-sonnet-5	2/5	No licensing is typically required for tire repair, but the physical dexterity and judgment needed to assess and seal punctures safely creates a natural barrier to automation beyond simple robotics.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized robotic systems capable of this work would be vastly more expensive to purchase, maintain, and integrate than the loaded wage of a tire repairer, with high error recovery costs.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic substitute, so any hypothetical automation solution (specialized robotics) would be far more costly than a human tire changer's wage.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No commercial product reliably performs tire puncture sealing autonomously. While research robotics exists, no production system in tire shops performs this task without human intervention.
Technical feasibility today	claude-sonnet-5	1/5	No deployed robotic or AI product performs tire puncture repair in production; this remains purely manual mechanical work.

Buff defective areas of inner tubes, using scrapers.

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

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire repair is performed in small shops and service centers with limited digitization and capital for robotics; adoption of automation has historically been extremely slow in this sector.
Sector adoption velocity	claude-sonnet-5	1/5	Tire repair is a low-digitization, physical trade with minimal AI/robotics adoption in this specific task domain.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI offers no meaningful assistance to a worker manually buffing an inner tube, as the task is fundamentally hands-on sensorimotor work with no decision support or informational component.
Augmentation potential	claude-sonnet-5	1/5	AI offers no meaningful assistance for the physical act of buffing rubber defects with hand tools.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires precise sensorimotor control, tactile feedback, and real-time judgment about surface condition. Current robots struggle with unstructured manipulation of deformable materials like inner tubes, and no general-purpose AI system can reliably perform this buffing operation end-to-end.
Task automatability	claude-sonnet-5	1/5	This is a manual, physical task requiring hand-eye coordination and tactile feedback to buff rubber surfaces; no current AI system (software or robotic) performs this end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While there are no strict licensing requirements for this task, physical proximity to customers' vehicles, low-volume production, and the need for quality inspection create moderate organizational friction against automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human perform this, but the physical dexterity and variability of defects create practical barriers to automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized robotic systems capable of this task (if they existed) would cost far more than the low hourly wages of tire repair workers, making automation economically unfeasible.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic substitute for this task, so any hypothetical automation would require costly specialized robotics far exceeding the low cost of manual labor.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial products perform autonomous buffing of tire inner tubes. Specialized industrial automation exists for rigid surfaces but not for the delicate, variable work required on tubes.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product exists that autonomously buffs inner tube defects; this remains squarely a manual repair shop activity.

Apply rubber cement to buffed tire casings prior to vulcanization process.

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

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire manufacturing remains a traditional, capital-constrained sector with entrenched manual labor processes. Adoption of advanced automation for this specific sub-task has been slow; most tire plants still rely on human workers or legacy hard-coded robotic arms rather than AI-driven approaches.
Sector adoption velocity	claude-sonnet-5	1/5	Tire repair is a low-digitization, physical trade with minimal AI adoption; this granular manual task shows no evidence of AI-driven displacement or tooling adoption in the sector.
Augmentation potential	claude-haiku-4-5-20251001	1/5	This task does not lend itself to meaningful AI assistance while a human remains in the loop. Rubber cement application is either automated or manual; there is no natural augmentation scenario where an AI system productively assists a human performing the task.
Augmentation potential	claude-sonnet-5	1/5	AI offers no meaningful assistance for this tactile, physical application step; there is no software or cognitive component where AI tools could enhance the worker's performance.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires precise application of rubber cement to buffed tire casings in preparation for vulcanization—a physical manipulation task that demands fine motor control, spatial judgment, and real-time adjustment to surface irregularities. Current AI systems cannot reliably perform this hands-on work end-to-end.
Task automatability	claude-sonnet-5	1/5	This is a manual, physical task requiring hand-eye coordination to apply cement evenly to a tire casing, which no current AI system can perform end-to-end; it requires robotic actuation, not just cognitive processing.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While there are no strict licensing barriers for the task itself, tire manufacturing is heavily capital-intensive and union-represented in many jurisdictions, creating organizational friction. The task is part of a tightly integrated vulcanization process with limited modularity for substitution.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human perform this specific step, but the physical, tactile nature of proper cement application creates practical (not regulatory) barriers to substitution by non-robotic AI.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized robotic systems capable of this task are extremely expensive to purchase, integrate, and maintain, while the labor cost for a tire repairer is relatively modest. The all-in cost of automation exceeds the human wage.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI system that performs this physical application task, so there is no cost basis for comparison; a human worker with basic tools remains the only current means of performing it cheaply.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed robotic or AI system reliably performs rubber cement application to tire casings at production scale today. While industrial robots exist in tire manufacturing, they are narrowly specialized to specific casing geometries and require extensive task-specific programming; general-purpose AI cannot do this.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product performs this specific manual tire-repair sub-step in production; any automation here would be a specialized robotics application, not an off-the-shelf AI product, and none exist at scale.

Inflate inner tubes and immerse them in water to locate leaks.

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

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire shops are typically small, owner-operated, low-tech facilities with minimal digitization and capital investment in automation; adoption of robotics remains negligible.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair and tire service is a low-digitization, physical-labor sector with minimal AI/robotics adoption for hands-on diagnostic tasks like this.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI offers no meaningful assistance for physically inflating tubes or detecting leaks visually; the task offers minimal opportunity for AI-augmented human productivity.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer no meaningful assistance for the physical process of inflating tubes and visually detecting leaks in water.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical manipulation of tires and tubes in a shop environment, including inflation, submersion, and visual inspection for leaks—capabilities that current AI systems lack. No deployed robotic system performs this reliably end-to-end as a routine job function.
Task automatability	claude-sonnet-5	1/5	This requires physical manipulation of tires and tubes, submerging them in water, and visually inspecting for bubbles—a manual, physical task with no current AI system able to perform it end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Physical automation barriers are high (no reliable deployed hardware), though no legal licensing requirement protects the human role. Customer preference for on-site human technicians and shop infrastructure inertia provide some friction.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement exists for this specific task, but it requires physical dexterity, specialized equipment, and workshop presence, creating practical (not regulatory) barriers to remote automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The capital and maintenance costs of robotic systems capable of handling, inflating, and inspecting tire tubes far exceed the low hourly wage of tire repair technicians, making AI substitution economically infeasible.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI-driven alternative to perform this physical task, so the AI cost comparison is not applicable and effectively infinite relative to a human worker.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No mature production system performs this task autonomously. While robotic arms exist, integrating them into tire shop workflows for consistent leak detection remains research-stage.
Technical feasibility today	claude-sonnet-5	1/5	No deployed robotic or AI product performs tire inflation and leak-testing in water; this remains a manual shop task performed by humans.

Clean sides of whitewall tires.

CI 15–15 · exposure 0 · augmentation 0 · importance 3.1/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire repair is a low-digitization, physical-service industry with minimal AI adoption. Small shops dominate the sector and invest conservatively in automation.
Sector adoption velocity	claude-sonnet-5	1/5	Tire repair and automotive service is a low-digitization, physical-labor sector with minimal AI/robotic adoption for such menial physical tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	AI offers no meaningful assistance to a human cleaning whitewall tires; the task is entirely manual and sensory-dependent, with no informational or decision-support component where AI could add value.
Augmentation potential	claude-sonnet-5	1/5	AI offers no meaningful assistance for the simple manual act of wiping down a tire sidewall.
Task automatability	claude-haiku-4-5-20251001	1/5	Cleaning whitewall tire sides requires fine motor control, spatial awareness, and judgement about surface texture and cleanliness standards. Current AI systems have no deployed robotics capable of reliably manipulating and cleaning curved tire surfaces to human standards.
Task automatability	claude-sonnet-5	1/5	This is a physical manual task requiring handling of tires and cleaning materials; no off-the-shelf AI system can perform this physical action today.atable
Adoption barriers	claude-haiku-4-5-20251001	2/5	The task is performed in commercial tire shops with no strict licensing or regulatory requirement to use human labor, though customer expectations and shop workflows favor human technicians. Organizational friction is low.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human perform this task, but physical dexterity and low value of automating such a small task create practical friction against investment in automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	A tire repair technician performs this task as part of their loaded labor cost (~$25–40/hour). Purchasing, maintaining, and integrating a robotic system capable of this specialized physical task would far exceed the cost of human labor.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic substitute, so the comparison defaults to human labor being the only cost-effective option; any hypothetical robotic solution would be far more expensive than a worker with a rag and cleaner.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No commercial AI or robotic product reliably performs whitewall tire cleaning at production scale. This is a manual dexterity task requiring physical interaction with irregular surfaces that remains outside the scope of deployed automation.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI or robotic product exists for cleaning whitewall tires in production shops; this remains a manual task performed by hand or simple tools.

Identify tire size and ply and inflate tires accordingly.

CI 5–20 · exposure 5 · augmentation 25 · importance 4.5/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	The tire repair industry remains highly fragmented, dominated by small shops with limited digitization. Adoption of advanced AI or robotic solutions for physical repair tasks is minimal and lagging far behind information-intensive sectors.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair and tire service is a low-digitization, physical-labor sector with minimal AI agent adoption for hands-on mechanical tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist by reading tire specs from images or providing inflation-pressure guidance, but current augmentation tools are limited. A technician would still perform all physical work, and the gains in productivity are modest compared to the worker's existing experience and intuition.
Augmentation potential	claude-sonnet-5	2/5	AI could provide reference lookup for tire size/ply specifications or digital tire pressure monitoring guidance, offering minor informational assistance, but does not meaningfully change the physical labor productivity.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical manipulation (inflation) and hands-on inspection of physical tires. Current AI systems cannot perform the robotic/mechanical actions needed to identify tire specifications and operate inflation equipment end-to-end.
Task automatability	claude-sonnet-5	1/5	This is a physical inspection and manual inflation task requiring hands-on manipulation of tires and equipment; no current AI system can perform this end-to-end physical work.'
Adoption barriers	claude-haiku-4-5-20251001	4/5	This task is tied to physical proximity and hands-on work at a repair location; regulatory and safety standards typically require a qualified technician to sign off on tire inflation and repairs, creating legal barriers to full automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement specifically blocks automation, but the physical nature of the task and need for hands-on equipment handling create practical friction against non-human/non-robotic substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The cost of deploying robotic systems capable of tire identification and inflation significantly exceeds the loaded wage of a tire repair worker, making AI automation economically infeasible for this task today.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI substitute for the physical labor involved, so AI cost is not comparable; a human worker with basic tools remains the only viable option.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Computer vision can identify tire size from sidewall markings in controlled settings, and basic ply identification is feasible, but no deployed product reliably does this in the variable conditions of a repair shop or field setting. Actual inflation remains out of reach for current AI without specialized robotics.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product performs physical tire identification and inflation; this remains entirely a manual, physical task performed by humans.

Glue tire patches over ruptures in tire casings, using rubber cement.

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

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire repair shops are small, distributed, and low-digitization environments with limited capital for automation. Adoption of AI or robotics in this sector remains negligible.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair and tire service is a low-digitization, physical-labor sector with minimal AI/robotics adoption for manual repair tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Current AI systems offer no meaningful assistance in the core physical task of applying adhesive and patches. Computer vision inspection tools exist but do not substantively augment the technician's productivity on this specific repair task.
Augmentation potential	claude-sonnet-5	1/5	AI offers no meaningful assistance for the physical application of rubber cement and patches; this is a purely manual craft task.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires precise physical manipulation—positioning patches, applying adhesive uniformly, and ensuring proper cure contact—in a tactile, sensory-rich environment. Current robotic systems and AI cannot reliably perform the dexterous handling and quality assessment needed for a safety-critical component.
Task automatability	claude-sonnet-5	1/5	This is a physical manual task requiring dexterity to apply rubber cement and patches to a tire casing; no off-the-shelf AI system performs this physical manipulation.ithat is entirely mechanical.itiated
Adoption barriers	claude-haiku-4-5-20251001	3/5	While there are no strict licensing barriers to the task itself, tire safety carries liability risk, and shops prioritize human accountability for safety-critical repairs, creating moderate friction against full automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement specifically bars automation, but the physical dexterity and variable tire conditions create practical friction against automation rather than legal barriers.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Industrial robots capable of this task would require significant capital investment, custom tooling, and integration costs that far exceed the labor cost of a human technician performing the repair in a shop setting.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic substitute for this physical task, so the human worker remains the only cost-effective option today.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial product automates tire patch application today. Tire repair remains a manual craft requiring human judgment about rupture severity, patch placement, and material inspection that has not been solved by existing AI or robotic systems.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI or robotic product performs tire patch gluing in production; this remains a manual shop task performed by humans.

Assist mechanics and perform various mechanical duties, such as changing oil or checking and replacing batteries.

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

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	The automotive service sector, particularly independent tire shops and smaller repair facilities, has low digitization and minimal adoption of automation for mechanical tasks. Adoption remains negligible and limited to large dealerships in pilot phases.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair and physical trades are among the slowest sectors for AI/robotics adoption, with minimal deployment of autonomous systems in shop-floor tasks.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI offers minimal assistance to a human performing routine oil changes and battery replacement. Diagnostic tools and inventory management systems provide some support, but the core manual task itself receives little productivity boost from current AI technology.
Augmentation potential	claude-sonnet-5	2/5	AI can assist with diagnostics, scheduling, or parts lookup, but offers little direct help with the hands-on mechanical work of oil changes or battery swaps.
Task automatability	claude-haiku-4-5-20251001	1/5	This task involves physical manipulation of vehicle components (oil changes, battery replacement) and requires tactile judgment, spatial reasoning, and real-time problem-solving in a non-standard environment. Current AI and robotics cannot reliably perform these mechanical duties end-to-end in the diverse, unstructured conditions of an auto shop.
Task automatability	claude-sonnet-5	1/5	This requires physical manipulation of vehicles, tools, and fluids in unstructured shop environments—no current AI system can perform physical automotive maintenance tasks end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	3/5	While no explicit licensing requirement mandates human performance, there are practical barriers: liability concerns if automation fails, customer preference for human mechanics, and organizational friction in retrofitting existing shops with unfamiliar technology. However, these are not hard legal barriers.
Adoption barriers	claude-sonnet-5	2/5	No licensing strictly requires a human for basic maintenance tasks, but physical dexterity, liability for vehicle damage, and customer trust create moderate friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The capital cost of specialized robotic systems capable of performing these mechanical tasks (if they existed in production) would far exceed the loaded labor cost of a tire repairer performing routine maintenance. Integration and maintenance would add further cost burden.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI-driven substitute for this physical labor, so any hypothetical robotic solution would be far more expensive than a human tire repairer's wage today.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably performs oil changes or battery replacement in production automotive settings. Specialized robotic systems exist in research or highly controlled factory environments, but general-purpose automation for this work remains research-stage.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs oil changes or battery replacement autonomously in production; robotics for this remains research-stage at best.

Rotate tires to different positions on vehicles, using hand tools.

CI 10–15 · exposure 0 · augmentation 0 · importance 4.2/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire service shops are typically small, localized businesses with limited capital for automation; the sector shows very low adoption of AI or robotic solutions compared to information-intensive industries.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair and tire service is a low-digitization, physical-labor sector with minimal AI/robotics adoption for hands-on mechanical tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	Tire rotation is a mechanical task with limited scope for AI assistance; augmentation would be minimal since the core work is physical manipulation rather than decision-making or information processing.
Augmentation potential	claude-sonnet-5	1/5	AI offers essentially no assistance to the physical act of rotating tires with hand tools; diagnostic software may help elsewhere but not this specific manual task.
Task automatability	claude-haiku-4-5-20251001	1/5	Tire rotation requires physical manipulation of heavy objects in varied spatial configurations and repositioning them on a vehicle—tasks that current mobile robotics and AI systems cannot reliably perform end-to-end without bespoke mechanical infrastructure.
Task automatability	claude-sonnet-5	1/5	This is a manual physical task requiring lifting, jacking, and manipulating heavy tires with hand tools; no current AI system can perform this physical labor end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	3/5	While there is no strict licensing requirement, liability concerns around vehicle safety and customer preference for human oversight of maintenance work provide meaningful friction to full automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement to rotate tires, but physical workspace, liability for improper installation, and equipment costs create some friction against non-human automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Current robotic systems capable of tire handling remain expensive and require specialized facility setup, making them significantly more costly than paying a human tire technician to perform the task.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic substitute for this manual task at comparable cost; human labor remains far cheaper than any hypothetical automation solution.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial product performs tire rotation autonomously in production tire shops; existing tire automation is limited to high-volume manufacturing, not service scenarios.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product performs tire rotation autonomously; robotic tire-changing exists only in narrow industrial contexts, not general vehicle service settings.

Separate tubed tires from wheels, using rubber mallets and metal bars or mechanical tire changers.

CI 10–15 · exposure 0 · augmentation 0 · importance 4.0/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire repair shops are predominantly small, physical operations with low digital infrastructure and minimal automation adoption history. No production evidence of AI or robotic displacement in this sector exists.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair and tire service is a low-digitization, physically dominated sector with minimal AI agent adoption for hands-on mechanical tasks.
Augmentation potential	claude-haiku-4-5-20251001	1/5	AI tools offer no meaningful assistance to a human performing this physical, tool-dependent task; the work is inherently hands-on and already mechanized through simple tools like mallets and tire machines.
Augmentation potential	claude-sonnet-5	1/5	AI offers no meaningful assistance to the physical act of separating a tire from a wheel using mallets or bars.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires physical manipulation in 3D space—separating a tire from a wheel using tools like rubber mallets and metal bars. Current general-purpose robotics cannot reliably perform this task end-to-end with 50% time savings; it demands dexterous handling of varied tire conditions, sizes, and wheel types in unstructured shop environments.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring force application and dexterity with tools on physical objects; no AI system can perform this manual labor end-to-end today.'
Adoption barriers	claude-haiku-4-5-20251001	3/5	While not legally restricted to licensed humans, adoption barriers include the need for physical robustness in dirty, variable shop conditions, liability concerns over tire damage, and organizational friction—small tire shops lack IT infrastructure and capital for robotics.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement specifically for this task, but it requires physical presence, tools, and dexterity that create a natural barrier to any automated substitution beyond dedicated hardware.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The capital cost of a robot capable of this task (if it existed in production form) plus integration, maintenance, and oversight would far exceed the hourly loaded wage of a tire changer, especially given the low margins in tire repair.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI-based substitute performing this physical task, so AI cost is not comparable; human labor with mechanical tools remains the only viable option.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial products reliably perform tire-to-wheel separation autonomously in production garages. Specialized industrial automation exists only in controlled factory settings for new tires; repair shops do not use autonomous systems for this task.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI product performs physical tire separation; this remains purely a human/robotic mechanical task outside current AI product scope.

Patch tubes with adhesive rubber patches or seal rubber patches to tubes, using hot vulcanizing plates.

CI 10–15 · exposure 0 · augmentation 13 · importance 3.9/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire repair remains a small-scale, physical, labor-dependent sector with low digitization and minimal automation adoption. Most shops are independent or small chains with limited capital for robotics investment.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair and tire service is a low-digitization, physical-labor sector with minimal AI or robotics adoption for hands-on repair tasks like this.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could potentially assist with diagnostic imaging (detecting tube defects) or quality inspection post-patching, but current systems offer minimal augmentation for the core manual patching and vulcanizing steps themselves.
Augmentation potential	claude-sonnet-5	1/5	AI offers no meaningful assistance for this specific physical patching and vulcanizing task, which involves manual dexterity and tactile judgment rather than information processing.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires precise manual manipulation of rubber tubes, application of adhesive patches, and operation of specialized hot vulcanizing plates. Current robotics and AI lack the dexterity, sensory feedback, and real-time adaptation needed for reliable, quality-equivalent output at scale.
Task automatability	claude-sonnet-5	1/5	This is a physical manual task requiring dexterity to position patches, operate vulcanizing equipment, and handle tires/tubes; current AI systems cannot perform physical manipulation tasks like this.
Adoption barriers	claude-haiku-4-5-20251001	3/5	While no explicit licensing requirement exists for the task itself, quality and safety standards in tire repair create organizational friction; most shops rely on experienced human judgment to assess tube condition and patch quality, and liability for failure falls on the shop.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement exists for this task, but the physical nature of manipulating rubber, adhesive, and heated vulcanizing equipment creates practical barriers to any non-human automation approach.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Automation would require custom industrial robotics, specialized end-effectors, and vision systems—all substantially more expensive than the loaded hourly wage of a tire repairer, with no clear cost recovery on typical service volumes.
Cost vs. human wage	claude-sonnet-5	1/5	There is no AI system that performs this physical repair task, so no meaningful cost comparison exists; a human worker with basic tools remains the only viable option.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial products perform end-to-end tire tube patching with vulcanizing automation. The task combines physical manipulation, heat control, and quality inspection in ways that remain research-stage rather than production-ready.
Technical feasibility today	claude-sonnet-5	1/5	No deployed robotic or AI product performs tube patching with vulcanizing plates in production; this remains firmly in the domain of human manual labor.

Raise vehicles, using hydraulic jacks.

CI 5–15 · exposure 0 · augmentation 0 · importance 4.6/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire shops are predominantly small to mid-sized operations with low digitization levels, conservative adoption patterns, and little investment in automation. The physical, location-bound nature of the work and fragmented market structure slow adoption velocity.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair and tire service is a low-digitization, physical trade sector where AI/robotic adoption for manual lifting tasks is minimal to nonexistent.
Augmentation potential	claude-haiku-4-5-20251001	1/5	AI offers no meaningful assistance to a human performing the basic mechanical task of raising a vehicle with a hydraulic jack; the task is straightforward physical operation requiring no decision support or information augmentation.
Augmentation potential	claude-sonnet-5	1/5	Current AI offers no meaningful assistance for the physical act of positioning and operating a hydraulic jack to raise a vehicle.
Task automatability	claude-haiku-4-5-20251001	1/5	Raising vehicles with hydraulic jacks requires precise physical manipulation in variable environments, positioning equipment safely under different vehicle geometries, and real-time judgment about load distribution and contact points. Current AI systems cannot perform this end-to-end physical task reliably.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring positioning of jacks under vehicles and operating hydraulic mechanisms; no off-the-shelf AI system performs this today, though basic robotics exist in narrow contexts like automated lifts.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Safety regulations and liability create strong barriers: worker safety standards strictly govern vehicle lifting equipment operation, and any automated system would face certification requirements and liability exposure if equipment failure caused injury. Human oversight and sign-off would likely be mandated.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement specifically bars automation, but physical safety liability (vehicle falling, crushing risk) creates strong practical caution against under-tested automated lifting solutions.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized hydraulic lifting equipment and robotic systems that could theoretically perform this task cost tens of thousands of dollars and require continuous maintenance, while a tire technician's labor cost per operation remains low.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI/robotic substitute deployed at scale, so any hypothetical automation (custom robotics) would be far more capital-intensive than paying a technician to use a jack.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed AI product reliably performs this task independently. Robotics for vehicle lifting exist in controlled industrial settings but are task-specific, expensive, and not general-purpose systems for tire shop environments.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously raises arbitrary vehicles with hydraulic jacks in general tire shop settings; automated lift systems require fixed infrastructure, not the handheld jack use described here.

Remount wheels onto vehicles.

CI 5–15 · exposure 0 · augmentation 13 · importance 4.6/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire repair and wheel service remains a highly physical, low-digitization sector dominated by small shops and traditional service facilities with minimal automation adoption to date.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair and tire service is a low-digitization, physically manual sector with minimal AI/robotic adoption for hands-on tasks like this.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could marginally assist with work-order management or diagnostic routing, but the core task of physically remounting wheels offers little opportunity for meaningful human-AI collaboration in real-time.
Augmentation potential	claude-sonnet-5	1/5	Current AI offers no meaningful assistance to a human performing the physical act of remounting a wheel; torque tools and lifts are mechanical, not AI-driven.
Task automatability	claude-haiku-4-5-20251001	1/5	Remounting wheels requires physical manipulation of heavy components, precise alignment with vehicle hubs, and torque specifications that demand dexterity and spatial reasoning. Current AI systems lack the embodied robotics capabilities to reliably perform this end-to-end task in diverse vehicle types and conditions.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring lifting, aligning, and bolting a heavy wheel onto a hub, which current AI systems (software or general robotics) cannot perform end-to-end without specialized hardware not in general deployment.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Safety-critical automotive service work carries high liability exposure if automation fails, and customer expectations strongly favor human technicians for vehicle servicing. Regulatory oversight of vehicle maintenance and OEM specifications create friction against full substitution.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement exists for this task, but physical safety concerns (improperly mounted wheels causing accidents) create liability and quality-control friction against automation.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Specialized robotic systems capable of wheel work would require significant capital investment, integration, and maintenance, making them substantially more expensive than a skilled tire technician's loaded hourly wage for this task.
Cost vs. human wage	claude-sonnet-5	1/5	Any robotic solution capable of this physical task would require expensive specialized hardware far exceeding the cost of a human tire changer's wage for this simple task.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed products perform wheel remounting reliably in production environments today. While robotic arms exist in research and specialized manufacturing, they are not used for dynamic vehicle service work at scale.
Technical feasibility today	claude-sonnet-5	1/5	No deployed AI or robotic product remounts wheels onto vehicles in commercial tire shops today; automated wheel-mounting exists only in some manufacturing lines, not for general repair contexts.

Replace valve stems and remove puncturing objects.

CI 5–15 · exposure 0 · augmentation 13 · importance 4.4/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	The tire repair and service industry remains heavily manual and relatively low-digitization. Adoption of automation in this sector has been minimal, with work still predominantly performed by humans in physical service locations.
Sector adoption velocity	claude-sonnet-5	1/5	Automotive repair and tire service is a low-digitization, physical-labor sector with minimal AI/robotic adoption for manual repair tasks like this.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI could assist with diagnostics (e.g., identifying puncture locations via image analysis) or inventory management, but offers limited productivity gain for the core physical manipulation tasks of valve replacement and object removal.
Augmentation potential	claude-sonnet-5	1/5	Current AI tools offer negligible assistance for the physical act of locating punctures and swapping valve stems; diagnostic AI does not meaningfully aid this specific manual task.
Task automatability	claude-haiku-4-5-20251001	1/5	This task requires precise physical manipulation in a constrained environment (tire removal, valve stem replacement) and object detection/removal under variable conditions. Current AI systems lack the dexterity, real-time sensorimotor feedback, and spatial reasoning necessary to reliably perform this work end-to-end.
Task automatability	claude-sonnet-5	1/5	This is a physical manipulation task requiring hand tools, tire removal, and dexterity to locate and extract objects from rubber; no AI system today can perform this physical action.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Physical work on vehicles carries liability and safety concerns; customers typically prefer human technicians for vehicle maintenance. Workshop-based tasks also have organizational friction and require on-site manual work that cannot be remotely executed.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement generally exists for tire changers, but the physical dexterity and variability of punctures create practical friction against automation, though not regulatory barriers.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	Even if prototype robotic systems existed at scale, they would require significant capital investment, maintenance, and physical infrastructure that would substantially exceed the hourly wage of a tire technician.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute; deploying a robotic system to replace stems and remove objects would be far more expensive than a human technician's tools and labor.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed commercial products reliably perform tire valve stem replacement or puncture-object removal autonomously. Robotic systems for tire servicing exist only in research prototypes and are not in production use at scale.
Technical feasibility today	claude-sonnet-5	1/5	There are no deployed robotic products performing valve stem replacement or puncture removal in real shops; this remains firmly outside current automation products.

Drive automobile or service trucks to industrial sites to provide services or respond to emergency calls.

CI 0–14 · exposure 5 · augmentation 25 · importance 3.4/5 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	1/5	Tire repair and service fleet operations remain highly traditional sectors with slow digitization; adoption of autonomous service trucks is minimal and largely pilot-stage, with the vast majority of service calls still driven by licensed human operators.
Sector adoption velocity	claude-sonnet-5	1/5	Tire repair and mobile service trucking is a low-digitization, physical-labor sector with minimal AI/autonomous vehicle adoption for this kind of task.
Augmentation potential	claude-haiku-4-5-20251001	2/5	AI can assist with route optimization and dispatch logistics, but the core driving task itself offers limited augmentation—the driver must remain in control and alert throughout, reducing AI's productivity multiplier on the actual driving component.
Augmentation potential	claude-sonnet-5	2/5	AI can assist with route optimization, dispatch scheduling, and traffic navigation, but offers only marginal assistance to the core driving and physical service task.
Task automatability	claude-haiku-4-5-20251001	1/5	Driving to industrial sites and responding to emergency calls requires real-world navigation, safety judgment, and reactive decision-making in unpredictable conditions. Current AI systems cannot reliably operate vehicles autonomously in all weather, traffic, and emergency conditions at production scale.
Task automatability	claude-sonnet-5	1/5	Driving to industrial sites and responding to emergency calls requires physical presence, vehicle operation, and situational judgment that current AI cannot perform end-to-end; autonomous driving is not deployed for this use case.
Adoption barriers	claude-haiku-4-5-20251001	5/5	Regulatory, insurance, and liability barriers are substantial: autonomous vehicles must meet safety certifications, liability frameworks are unclear for emergency response, and professional driver licensing is legally required. Many jurisdictions still prohibit full autonomy for commercial service trucks.
Adoption barriers	claude-sonnet-5	4/5	Driving on public roads requires a licensed driver, liability for accidents falls on humans, and emergency response demands physical presence and accountability, creating strong regulatory and safety barriers.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Autonomous vehicle systems (hardware, insurance, fleet management, oversight) remain expensive compared to paying a truck driver's loaded wage. The infrastructure and liability costs of deployed autonomous fleets are still high relative to human drivers.
Cost vs. human wage	claude-sonnet-5	1/5	There is no viable AI substitute performing this driving/dispatch task, so AI cost is effectively infinite relative to a human driver-technician.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While autonomous vehicle research is advanced, no deployed product reliably drives service trucks to arbitrary industrial sites and handles emergency response logistics at scale in production. Geofenced or limited-route autonomous systems exist but do not cover the generality of this task.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously drives service vehicles to job sites and performs emergency response duties; this remains research-stage for general road autonomy in commercial fleets.

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.