Helpers--Installation, Maintenance, and Repair Workers
49-9098.00Median wage $39,630/yr95,580 employed (US)Rank #659 of 923 scored · top 71% by substitution
Help installation, maintenance, and repair workers in maintenance, parts replacement, and repair of vehicles, industrial machinery, and electrical and electronic equipment. Perform duties such as furnishing tools, materials, and supplies to other workers; cleaning work area, machines, and tools; and holding materials or tools for other workers.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution20
Exposure12
Augmentation32
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
16 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%12
panel mean rating 1.5/5 → substitution pressure 12/100
Technical feasibility todayw 20%10
panel mean rating 1.4/5 → substitution pressure 10/100
Cost vs. human wagew 15%10
panel mean rating 1.4/5 → substitution pressure 10/100
Adoption barriersw 20%inverted — strong barriers lower the score59
panel mean rating 2.7/5 (barrier strength) → substitution pressure 59/100
Sector adoption velocityw 10%9
panel mean rating 1.3/5 → substitution pressure 9/100
Task breakdown (16 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 new parts to maintain inventory.
62CI 52–72 · exposure 62 · augmentation 75 · importance 3.6/5 · click for rater detail
Order new parts to maintain inventory.
62| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Inventory automation is moderately adopted in maintenance organizations, with many larger firms using ERP systems, but widespread deep adoption remains limited in small shops and field operations. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Installation, maintenance, and repair trades are physical, less digitized sectors where inventory automation adoption lags behind information/finance sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI systems can significantly augment human helpers by flagging low-stock alerts, recommending parts based on maintenance schedules, and generating draft orders, allowing workers to focus on verification and supplier coordination. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-based inventory forecasting and reorder alerts significantly help workers avoid stockouts and reduce manual tracking effort while a human still oversees final decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can partially automate inventory monitoring and part ordering through predictive analytics and automated purchase order generation, but the task typically requires human judgment about supplier selection, quantity thresholds, and equipment-specific requirements that vary by context. |
| Task automatability | claude-sonnet-5 | 4/5 | Reordering parts based on inventory thresholds is a structured, rules-based data task that AI-driven inventory management systems can largely automate, though physical verification and edge-case judgment remain. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few legal or regulatory barriers exist to automating parts ordering; the main friction is organizational (supplier relationships, approval workflows) and technical (system integration), which are readily surmountable. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates human ordering, though some organizational approval workflows and vendor relationship preferences create mild friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | The cost of AI-driven inventory systems (software licensing, integration, maintenance) is roughly comparable to the labor cost of a helper managing routine parts ordering, especially when accounting for setup and ongoing tuning. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated ordering systems run at very low marginal cost compared to a human manually tracking stock and placing orders, though integration and setup costs exist. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Enterprise resource planning and inventory management systems exist and can automate routine reordering, but they require significant setup and human oversight to handle exceptions, supplier relationships, and parts verification in real installations. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Automated inventory replenishment and procurement software (e.g., ERP systems with reorder-point automation) are widely deployed in production across maintenance and repair operations today. |
Examine and test machinery, equipment, components, and parts for defects to ensure proper functioning.
33CI 30–35 · exposure 25 · augmentation 50 · importance 4.0/5 · click for rater detail
Examine and test machinery, equipment, components, and parts for defects to ensure proper functioning.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufacturing and maintenance sectors show pockets of sensor and condition-monitoring adoption, but widespread deployment of autonomous or semi-autonomous inspection is still in pilot phase. Most field helpers continue hands-on testing, indicating slow velocity outside digitized, high-volume industrial settings. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Installation, maintenance, and repair trades are physical, low-digitization sectors where AI adoption for hands-on inspection tasks remains largely pilot-stage rather than widespread production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered diagnostic tools (image analysis, sensor dashboards) can assist helpers by highlighting suspicious areas or anomalies, reducing search time and improving coverage. The human remains essential for judgment, safe disassembly, and contextual decision-making, making this a genuine productivity multiplier for assistive use. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-powered diagnostic tools, vision-based defect detection, and predictive analytics can meaningfully assist workers in identifying issues faster, even though the physical examination itself remains human-performed. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While visual inspection and some sensor-based defect detection are feasible with AI, this task requires interpreting context-dependent failure modes, assessing complex mechanical systems, and making judgment calls about marginal defects. Current AI can flag obvious anomalies but cannot reliably replace the full diagnostic reasoning a human helper applies, falling short of 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical inspection, handling, and testing of physical machinery and parts, which current AI systems cannot perform end-to-end without robotic embodiment; only narrow sensor-based diagnostics can be automated.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Equipment testing and sign-off often involve warranty, safety, and liability concerns that create preference for human accountability. However, no strict legal mandate requires a licensed human to perform this task in most contexts, so adoption barriers are moderate rather than hard. |
| Adoption barriers | claude-sonnet-5 | 2/5 | There are few licensing requirements for this entry-level helper role, though safety protocols and employer liability for equipment failures create some friction against fully unsupervised automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI vision and sensor systems, plus integration and human oversight, remain comparable to or exceed the loaded cost of a helper wage when accounting for false positives, rework, and the need for human verification on ambiguous cases. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Deploying sensors, cameras, and robotic testing infrastructure for diverse equipment is costly relative to a helper's wage, making AI often comparable or more expensive once integration and hardware are factored in. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision systems can detect surface defects and some mechanical wear, and condition-monitoring sensors exist for limited domains (e.g., vibration analysis). However, no mature deployed product reliably performs end-to-end diagnostic testing across the variety of machinery and components this role covers, and error rates remain material for safety-critical assessments. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some sensor/vision-based predictive maintenance products exist in production for specific equipment types, but general physical examination and hands-on testing of diverse machinery is not reliably automated by deployed products today. |
Tend and observe equipment and machinery to verify efficient and safe operation.
33CI 30–35 · exposure 30 · augmentation 63 · importance 3.9/5 · click for rater detail
Tend and observe equipment and machinery to verify efficient and safe operation.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of automated monitoring is slow in many sectors (small repair shops, on-site maintenance crews). While large manufacturing and data centers deploy sensors, the fragmented nature of helper roles and the diversity of equipment types limit broad velocity. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Installation, maintenance, and repair trades are physical, lower-digitization sectors where AI/robotic adoption for direct equipment tending remains slow and pilot-stage rather than widespread. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered dashboards, real-time alerts, and predictive maintenance platforms significantly assist helpers and technicians by flagging issues early, prioritizing tasks, and reducing manual inspection time. Augmentation is strong even where full automation is not possible. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based sensor dashboards, anomaly detection alerts, and predictive maintenance tools can meaningfully assist workers in noticing issues sooner, even though the physical tending task itself remains human-performed. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI vision systems and sensors can monitor some equipment parameters and flag anomalies, but verifying 'safe operation' across diverse machinery types requires contextual judgment, physical inspection, and intervention that current systems cannot fully automate. Observation and reporting could be partially automated, but human judgment remains essential for complex safety decisions. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires physical presence, sensory monitoring, and situational judgment in a physical environment, which current AI cannot perform end-to-end without robotic embodiment and sensor integration far beyond typical deployment.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Workplace safety regulations and liability create some friction—organizations typically require human sign-off on safety determinations. However, no license is legally mandated for helper-level equipment observation, so barriers are moderate rather than strict. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Safety-critical monitoring often has regulatory and liability considerations, and many workplaces require a human present for safety compliance, though not always formally licensed. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Sensor networks, vision systems, and continuous monitoring platforms have significant upfront capital costs and integration overhead. For routine observation tasks across heterogeneous equipment, the total cost of deployment and false-alarm management often exceeds the loaded wage of a helper performing spot checks. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Sensor networks, monitoring software, and integration costs are substantial relative to a helper's wage, and human oversight is still typically required, so cost savings are modest at best. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Deployed IoT monitoring systems and computer vision tools can track equipment status and basic anomalies in controlled environments, but reliability remains material—false positives/negatives are common, and systems struggle with unexpected failure modes or novel equipment configurations in real-world maintenance settings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Industrial IoT sensors and predictive maintenance software exist and are deployed in some plants, but full replacement of human tending/observation with AI-only systems is narrow and not general-purpose across this occupation. |
Clean or lubricate vehicles, machinery, equipment, instruments, tools, work areas, and other objects, using hand tools, power tools, and cleaning equipment.
23CI 10–35 · exposure 13 · augmentation 13 · importance 3.9/5 · click for rater detail
Clean or lubricate vehicles, machinery, equipment, instruments, tools, work areas, and other objects, using hand tools, power tools, and cleaning equipment.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption remains slow and spotty; automated car washes and some industrial washers are deployed in capital-intensive sectors, but general adoption of multipurpose cleaning/lubrication robots in repair and maintenance shops is minimal, reflecting immaturity of the technology. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Installation, maintenance, and repair trades are physical, low-digitization sectors with minimal AI/robotic adoption for hands-on cleaning and lubrication tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI and automation offer limited augmentation for this manual task—power tools and equipment are already optimized, and machine vision or scheduling assistance is marginal compared to the core requirement of skilled hand application of lubricants and cleaners. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers little direct assistance for physical cleaning and lubrication work itself, though scheduling or diagnostic tools may indirectly support the broader job role. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical cleaning and lubrication require dexterous manipulation in unstructured environments with variable geometries and material properties. While narrow repetitive cleaning on fixed equipment could be partially automated, current robots lack the general-purpose dexterity and environmental adaptation needed for the full range of objects and surfaces mentioned. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring mobility, dexterity, and handling of tools and equipment in varied environments; current AI systems cannot perform physical cleaning/lubrication end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No legal licensing requirement exists to perform cleaning and lubrication, but adoption is constrained by organizational risk aversion (equipment damage liability), worker safety concerns around machinery access, and the organizational friction of retrofitting shops for robotic systems. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human perform cleaning/lubrication, but physical presence, mobility, and manipulation in unstructured environments create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current robotic cleaning systems with the necessary sensing, gripper technology, and safety infrastructure are expensive to deploy, program, and maintain—substantially exceeding the cost of low-wage helper labor in most sectors. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this broad physical task, so a helper's wage remains far cheaper than any hypothetical automation solution requiring custom robotics and sensors. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Specialized industrial robots exist for specific cleaning tasks (e.g., car wash bays), but these are narrowly scoped and not generalist solutions. No deployed product reliably handles the diverse mix of hand/power tool use, lubrication precision, and object variety described in production settings at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed general-purpose product performs physical cleaning and lubrication of diverse vehicles, machinery, and tools; robotic solutions remain narrow, task-specific, and research/pilot stage. |
Adjust, maintain, and repair tools, equipment, and machines, and assist more skilled workers with similar tasks.
20CI 5–35 · exposure 13 · augmentation 38 · importance 3.6/5 · click for rater detail
Adjust, maintain, and repair tools, equipment, and machines, and assist more skilled workers with similar tasks.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of autonomous repair automation is slow; most deployment is in digitization of diagnostic and planning workflows, not embodied execution. Small and medium repair shops dominate the sector, lack digital integration, and continue to rely on traditional human apprenticeship models rather than AI-driven substitution. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Installation, maintenance, and repair trades are physical, low-digitization sectors with minimal AI/robotic adoption for hands-on repair tasks today. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered diagnostics, fault prediction, and step-by-step guidance can usefully assist maintenance workers in identifying problems and planning repairs, raising their efficiency on diagnosis and documentation. However, the augmentation is partial—the core physical work and judgment remain human-dependent. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with diagnostics, manuals, or troubleshooting guidance via mobile apps, but offers limited help with the core physical adjustment and repair work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI-guided diagnostics and remote monitoring can support troubleshooting, the physical manipulation of tools, equipment, and machines—adjusting, maintaining, and repairing—requires embodied robotics and sensorimotor skills that current AI systems cannot reliably execute end-to-end. The assistive role to skilled workers also requires contextual judgment that remains predominantly human. |
| Task automatability | claude-sonnet-5 | 1/5 | This is physical manual labor requiring hands-on manipulation of tools and equipment in varied environments; current AI cannot perform physical repair tasks. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory frameworks often require licensed, human certification for safety-critical repairs in automotive, HVAC, and industrial equipment. Liability and error asymmetry is high—a failed repair can cause injury or equipment loss—and many organizations mandate human sign-off and accountability, creating substantial legal and organizational friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No formal licensing typically required for helper-level tasks, but physical presence and safety/liability concerns around equipment repair create some friction against remote automation, though not legal barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tooling (diagnostics, planning, monitoring) has significant infrastructure and integration costs, and still requires human technicians to execute physical work. The all-in cost per repair remains well above the loaded wage of a helper, especially when factoring in oversight and liability. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so the AI-vs-human cost comparison does not favor AI at all; a human must be employed. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs full maintenance and repair tasks autonomously. Computer vision can identify faults and LLMs can suggest repair steps, but integration into functioning repair workflows at production scale is absent; most deployment remains in pilots or narrow, controlled settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical adjustment, maintenance, or repair of equipment; robotics for unstructured repair work remains research-stage. |
Apply protective materials to equipment, components, and parts to prevent defects and corrosion.
20CI 10–30 · exposure 8 · augmentation 25 · importance 3.6/5 · click for rater detail
Apply protective materials to equipment, components, and parts to prevent defects and corrosion.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption remains slow outside large-scale manufacturing. Most field maintenance and repair work is performed by small to medium service firms with limited digitization and capital investment in automation infrastructure. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This occupation sits in low-digitization, physical trades where AI/robotic adoption for manual protective-material application is minimal and not accelerating. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI-powered inspection systems (computer vision for defect detection) offer marginal assistance in identifying where protection is needed, but the actual application task itself benefits minimally from current AI augmentation tools. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could offer minor assistance via instructions, checklists, or defect-detection guidance, but it does not meaningfully enhance the physical application process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | The task requires precise spatial judgment, dexterity, and decision-making about which protective materials to apply to specific equipment in varied configurations. While material application itself is routine, the assessment and adaptation to different component geometries and material types remains largely manual and context-dependent. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual task requiring hands to apply coatings, sealants, or protective materials to physical equipment; no current AI system can perform this physical manipulation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Some organizational and safety friction exists: material handling requires training, workplace safety protocols govern chemical exposure, and quality control typically requires human inspection. However, no legal licensing or human sign-off requirement prevents automation in principle. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically, but the physical nature of the task and workplace safety/quality control norms create some structural friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized robotic coating systems and custom setup are expensive relative to a helper's wage. Integration, calibration, and material waste from autonomous systems often exceed the labor cost for manual application in non-mass-production contexts. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI has no mechanism to physically apply materials, so there is no viable AI cost comparison; robotics for this niche task would be more costly than human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform end-to-end protective material application to varied equipment autonomously. Robotic systems exist for highly standardized manufacturing (e.g., coating assembly lines), but general-purpose application to diverse components requires human oversight and is not yet production-ready at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical application of protective coatings; this remains purely a human/robotic manual labor task outside AI's scope. |
Prepare work stations for use by mechanics and repairers.
19CI 15–24 · exposure 8 · augmentation 25 · importance 3.3/5 · click for rater detail
Prepare work stations for use by mechanics and repairers.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Repair and maintenance shops, especially small and mid-sized operations where helpers work, remain low-digitization sectors with limited automation investment. Adoption of autonomous systems for this task is negligible in current market data. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This occupation is physical, low-digitization, and in a sector (maintenance/repair) with minimal AI/robotic adoption for such manual prep tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with work-station management via digital inventory tracking or scheduling tools, but such augmentation is indirect and marginal compared to the core physical preparation task that dominates the helper role. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could offer minor assistance such as checklists, scheduling, or inventory tracking for what tools/parts are needed, but it does not meaningfully transform the physical setup task itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Preparing work stations involves physical setup (arranging tools, cleaning surfaces, organizing parts) and environmental assessment that requires mobility and dexterity in varied, unstructured settings. Current AI systems lack embodied robotics reliable enough for this multi-step physical coordination task at scale, though discrete elements like inventory tracking could be partially automated. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical task involving moving tools, equipment, and materials into position for a technician; current AI systems have no general-purpose robotic capability to perform this reliably in unstructured shop environments. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Physical work-station preparation has modest barriers: no legal licensing requirement, but organizational friction around robot reliability, worker safety concerns, and the need for human oversight in dynamic repair environments provide some friction against adoption. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically for this prep work, but practical/organizational barriers (physical dexterity, environment variability) make substitution impractical rather than legally barred. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Autonomous robots capable of this task remain extremely expensive to deploy and integrate, with high infrastructure costs, while the human labor being replaced is relatively low-wage helper work. The all-in cost of AI (hardware, integration, maintenance) far exceeds the loaded wage of a helper. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this at scale, so the effective AI cost is prohibitively high compared to a low-wage human helper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed autonomous systems reliably perform full work station preparation in real repair shops today. While industrial robots exist for narrowly controlled environments, the ad-hoc nature of repair work—varying layouts, tool locations, and setup requirements—places this beyond current production AI capability. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that autonomously preps physical workstations for repair work; this remains beyond research-stage robotics for varied real-world shop settings. |
Design, weld, and fabricate parts, using blueprints or other mechanical plans.
18CI 5–30 · exposure 13 · augmentation 38 · importance 3.0/5 · click for rater detail
Design, weld, and fabricate parts, using blueprints or other mechanical plans.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption is concentrated in large-scale manufacturing (automotive, aerospace) with standardized, high-volume parts; small fabrication shops and custom work (where helpers are common) see minimal AI penetration. General adoption remains slow and narrow. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction, manufacturing, and repair trades are among the slowest sectors to adopt AI/robotics for hands-on physical tasks, with pilots mostly confined to large-scale fixed automotive welding lines. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted design tools (CAD co-pilots, parameter suggestion from blueprints) and welding simulation can help helpers learn and plan faster, but the physical skill and judgment required to execute quality fabrication remain primarily human-driven with modest AI support. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can help interpret blueprints, generate design suggestions, or assist with CAD/CAM planning, but offers little direct assistance to the physical welding and fabrication execution itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can interpret blueprints and generate welding parameters, the physical execution—design iteration based on material properties, precision welding, and real-time quality judgment—requires embodied skill and adaptive decision-making that current AI cannot perform autonomously. Design and planning phases might see some automation, but fabrication quality control and execution remain fundamentally manual. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical welding and fabrication require hands-on manipulation of tools and materials in variable physical environments, which current AI systems cannot perform end-to-end; robotic welding exists only in fixed, pre-programmed industrial settings, not general fabrication from blueprints. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Liability and safety certification create substantial barriers: welded parts often enter safety-critical applications (pressure vessels, structural steelwork), so human inspection and sign-off are legally and contractually required in most jurisdictions. Regulatory compliance and product liability make full automation legally risky. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing strictly requires a human for basic fabrication helper work, but liability for structural/safety-critical welds and physical workspace realities create real friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Robotic welding systems and AI design software are capital-intensive upfront, and the integration costs for blueprint interpretation, tool setup, and quality oversight remain high relative to a skilled helper's wage, especially for small-batch or custom work. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Achieving flexible design-to-fabrication capability would require expensive robotics, sensors, and integration far exceeding the cost of a helper's wage for this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI design tools and welding simulation software exist in research and niche industrial settings, but deployed products do not reliably handle the full design-to-fabrication workflow autonomously. Welding robots are task-specific and require extensive setup; they do not meet the general end-to-end requirement without human intervention. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously reads arbitrary blueprints, designs fabrication steps, and welds/fabricates parts as a helper would; industrial robotic welders require extensive pre-programming for narrow repetitive tasks, not this open-ended task. |
Install or replace machinery, equipment, and new or replacement parts and instruments, using hand or power tools.
17CI 10–24 · exposure 8 · augmentation 25 · importance 4.0/5 · click for rater detail
Install or replace machinery, equipment, and new or replacement parts and instruments, using hand or power tools.
17| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Installation, maintenance, and repair work remains labor-intensive and manual-skill dependent; adoption of automation in this sector is slow relative to information-based work, with most organizations still relying on human technicians and minimal AI/robotic integration. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Installation, maintenance, and repair trades are physical, low-digitization occupations where AI/robotic adoption for hands-on tasks remains minimal and slow. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could augment workers via computer vision diagnostics, procedure guidance, or documentation, but current systems offer limited real-time assistance during the hands-on mechanical work itself, making practical augmentation modest compared to tasks with stronger AI tool support. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with instructions, diagnostics, or documentation lookup via mobile apps, but offers little direct enhancement to the physical act of installing or replacing parts. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical installation and replacement of machinery requires dexterous manipulation, spatial reasoning, and real-time adaptation to environmental conditions. While AI can guide or document procedures, current robotics cannot reliably handle the full range of mechanical assembly tasks with quality parity to human workers without extensive custom engineering. |
| Task automatability | claude-sonnet-5 | 1/5 | This is physical hands-on installation and replacement work requiring manipulation of tools, machinery, and parts in varied real-world environments, which current AI systems cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict licensing requirements for helpers, workplace safety regulations, warranty requirements, and equipment-manufacturer specifications often mandate that installations be performed or verified by qualified personnel, creating moderate friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | There are no licensing requirements specific to helpers, but physical presence, dexterity, and situational adaptability create strong practical barriers to automation even though not regulatory ones. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized installation robots with the precision and adaptability required for machinery assembly remain expensive to acquire and deploy, typically costing more than employing skilled human helpers, especially when integration and maintenance are factored in. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any hypothetical robotic solution would require far more capital investment than the loaded wage of a helper, making AI comparatively far more expensive today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products perform end-to-end machinery installation and part replacement autonomously. Robotic systems exist for narrow, highly structured tasks but lack the generalization and dexterity required for the varied equipment and contexts implied by this task statement. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs physical installation or replacement of machinery and instruments; robotics for such generalized, unstructured physical tasks remains research-stage or narrowly confined to fixed industrial settings. |
Assemble and maintain physical structures, using hand or power tools.
15CI 15–15 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Assemble and maintain physical structures, using hand or power tools.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of automation in construction, maintenance, and repair work remains low; most sites remain labor-intensive and rely on human workers due to task variability and unstructured environments. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and physical trades are among the lowest-digitization, slowest-adopting sectors for AI and robotics automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools can assist with task planning, maintenance scheduling, or diagnostic support via computer vision, but current systems offer limited real-time assistance to a worker actively assembling or repairing with tools. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with tool diagnostics, instructions, or scheduling but offers minimal direct enhancement to the physical assembly and maintenance work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Assembling and maintaining physical structures requires dexterous manipulation, real-time spatial reasoning, and adaptation to variable physical conditions. Current AI systems lack embodied capability to operate hand and power tools reliably in unstructured environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring dexterity, mobility, and real-world tool use that current AI systems cannot perform end-to-end; no software-only AI can assemble or maintain physical structures. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Physical job sites often require on-site presence and safety compliance, but there are no hard legal or licensing requirements preventing automation attempts. The main barriers are technical and economic rather than regulatory. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing typically required for helper-level work, but physical environments, safety requirements, and liability for equipment/structural damage create moderate friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotic systems capable of any meaningful subset of this work (welding, assembly in factories) are extremely expensive to procure, maintain, and reprogram compared to the wage of a general-purpose helper. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Physical robotic systems capable of general assembly/maintenance work are far more expensive than a helper's wage once hardware, deployment, and supervision costs are included. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs general assembly and maintenance of physical structures with tools. While robotic arms exist for narrow, controlled tasks, they cannot match the flexibility and problem-solving required across diverse repair and assembly scenarios. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products perform general physical assembly/maintenance work; robotics for such unstructured tasks remains research-stage or narrowly deployed in controlled factory settings, not general helper tasks. |
Hold or supply tools, parts, equipment, and supplies for other workers.
15CI 15–15 · exposure 0 · augmentation 0 · importance 3.8/5 · click for rater detail
Hold or supply tools, parts, equipment, and supplies for other workers.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Tool-holding and supply assistance is performed by low-skill helpers in construction, maintenance, and repair sectors that are traditionally slow to adopt AI and robotics. No measurable production-scale displacement or agent adoption is evident in these sectors today. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Installation, maintenance, and repair trades are physical, low-digitization sectors with minimal AI/robotic adoption for basic physical assistance tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI systems cannot augment a worker's ability to hold or supply physical tools and parts, since the task is fundamentally about physical positioning and responsiveness. No AI software or lightweight tool can meaningfully improve this purely physical support role. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers essentially no assistance for the physical act of holding or supplying tools and parts on a job site. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time physical presence, spatial awareness, and responsive interaction with human workers in dynamic work environments. Current AI systems cannot physically manipulate, hold, or position tools and parts, nor can they reliably anticipate which items a worker will need next without human guidance. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical presence, manual dexterity, and real-time coordination with a human worker in a physical workspace, which current AI systems cannot perform without embodied robotics far beyond today's capabilities. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | There are minimal legal or regulatory barriers to automation, but practical constraints are significant: the task occurs in varied physical environments, requires real-time human coordination, and involves unpredictable tool sequences and spatial demands that create substantial operational friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically, but the physical nature of the task and need for on-site presence create a practical barrier to any digital automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A robotic system capable of holding tools, monitoring spatial positioning, and responding to worker cues would require substantial hardware investment, integration, and maintenance—far exceeding the loaded wage of a helper worker for this straightforward support task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI system capable of performing this physical task, so the AI cost is effectively infinite or nonexistent compared to a low-wage human helper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products perform this task autonomously in production settings. While robotics research explores tool-fetching scenarios, reliable real-world deployment in active repair or installation sites remains at research stage with significant reliability gaps. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that can physically hand tools and parts to workers on job sites; this is purely a physical labor task with no AI product addressing it. |
Transfer tools, parts, equipment, and supplies to and from work stations and other areas.
15CI 15–15 · exposure 0 · augmentation 13 · importance 3.7/5 · click for rater detail
Transfer tools, parts, equipment, and supplies to and from work stations and other areas.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of physical automation for helper tasks in maintenance and repair remains minimal, with most sectors relying on human workers due to the unstructured and varied nature of work environments. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Installation and repair trades are physical, low-digitization sectors with minimal AI/robotic adoption for material handling tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | AI provides no meaningful assistance for physical material transfer; the task is purely logistical movement with no analytical or decision-making component that AI could enhance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with logistics planning, inventory tracking, or route optimization for transfers, but offers little direct help with the physical act of transferring items. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of objects in real-world environments, including moving tools and parts between locations. Current AI systems cannot physically handle, grasp, or transport items without specialized robotics infrastructure that remains far from general deployment. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical materials-handling task requiring locomotion, object manipulation, and navigation of dynamic work environments, which current AI systems cannot perform without embodied robotics far beyond off-the-shelf availability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no formal licensing requirements, the physical nature of the task and integration challenges with existing worksite layouts create modest friction. Human workers are currently preferred for flexibility and adaptability in variable environments. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal barrier exists, but practical barriers like unstructured environments, safety requirements around tools/equipment, and lack of infrastructure limit substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital and integration costs of robotic systems capable of autonomous parts transfer vastly exceed the loaded wage of a helper worker, making economic substitution infeasible at current technology costs. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute at scale, so any hypothetical automation (custom mobile robots) would cost far more than a helper's wage for this task alone. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs autonomous physical transfer of tools and equipment in general workshop or field environments. While robotics exist in controlled factory settings, they are highly task-specific and not deployed for this general helper function. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously transfers tools and parts between varied work stations in installation/repair contexts today; warehouse robots exist but not for this flexible, unstructured helper role. |
Position vehicles, machinery, equipment, physical structures, and other objects for assembly or installation, using hand tools, power tools, and moving equipment.
13CI 10–15 · exposure 0 · augmentation 25 · importance 3.7/5 · click for rater detail
Position vehicles, machinery, equipment, physical structures, and other objects for assembly or installation, using hand tools, power tools, and moving equipment.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Physical installation and assembly work remains dominated by human labor; adoption of automation is slow due to the variability of job sites, objects, and conditions, with most sectors lagging in deployment of mobile manipulation agents. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Installation and repair trades are physical, low-digitization sectors with minimal AI/robotics adoption for this kind of manual positioning work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools offer minimal assistance here; power tools can be electrically enhanced, but current systems provide little augmentation for the spatial reasoning and object-positioning judgment central to this task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with planning or guidance (e.g., AR overlays, checklists) but offers little direct assistance to the physical act of positioning objects using tools. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of large objects in real-world environments with precise spatial positioning—capabilities that current AI systems and mobile robots cannot reliably perform end-to-end without extensive human supervision and intervention. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires physical manipulation of heavy objects using hand tools and moving equipment in variable environments, which is far beyond current AI systems that lack embodied physical capability at scale.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no formal licensing requirements for helpers performing this task, workplace safety regulations, insurance liability concerns, and the need for human judgment in dynamic assembly/installation environments create moderate organizational friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically blocks automation, but physical safety regulations around heavy equipment operation and liability for accidents create some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Mobile manipulation systems capable of this work remain expensive capital investments with high integration costs, far exceeding the loaded wage of a helper. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Physical robotic systems capable of this variable, unstructured task would require expensive custom hardware and setup far exceeding the cost of an entry-level helper worker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products today can autonomously position vehicles, machinery, or structural elements reliably across the variety of settings and object types implied by this task statement. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs general-purpose positioning of vehicles, machinery, and structures for assembly; robotic solutions exist only in narrow, fixed industrial contexts, not for helper-level general tasks. |
Adjust, connect, or disconnect wiring, piping, tubing, and other parts, using hand or power tools.
10CI 5–15 · exposure 0 · augmentation 25 · importance 3.9/5 · click for rater detail
Adjust, connect, or disconnect wiring, piping, tubing, and other parts, using hand or power tools.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of robotics in this sector remains minimal; most installation and maintenance work occurs in small firms, on-site in variable conditions, and under regulatory supervision—all laggard adoption environments for automation technology. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Installation, maintenance, and repair trades are physical, low-digitization sectors with minimal AI/robotic deployment for hands-on tasks like this. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Current AI offers minimal assistance on the core task of physically adjusting connections; diagnostic tools and planning aids exist but provide only marginal support to the hands-on adjustment and troubleshooting work itself. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostics, instructions, or visual guidance via AR/manuals, but offers limited direct help with the physical act of connecting or adjusting parts. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires precise physical manipulation in variable environments, dexterous use of hand and power tools, and real-time spatial reasoning to connect/disconnect components. Current AI systems lack embodied robotics capable of reliable end-to-end performance on diverse wiring and piping configurations in field conditions. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manipulation task requiring hands-on dexterity to connect wiring, piping, and tubing in varied real-world environments; no current AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: many jurisdictions require licensed electricians or plumbers to perform or sign off on wiring and piping work; liability for incorrect connections (safety hazards, equipment damage) creates high error costs; and on-site physical presence is legally mandated in regulated contexts. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for this specific task, but physical presence, tool handling, and safety concerns in maintenance environments create moderate practical barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Physical manipulation robots capable of handling tools and working in varied field conditions are extremely expensive to deploy and maintain, far exceeding the loaded cost of a skilled helper or technician performing the task manually. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven robotic substitute deployed at scale for this task, so any hypothetical robotic solution would be far more expensive than a helper's wage today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial products reliably perform this task autonomously. Robotics research exists but lacks the flexibility, safety certification, and real-world robustness needed for general installation and maintenance work across different equipment and environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs general physical adjustment/connection of wiring and piping; humanoid/robotic manipulation for such varied unstructured tasks remains research-stage. |
Disassemble broken or defective equipment to facilitate repair and reassemble equipment when repairs are complete.
10CI 5–15 · exposure 0 · augmentation 25 · importance 3.8/5 · click for rater detail
Disassemble broken or defective equipment to facilitate repair and reassemble equipment when repairs are complete.
10| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This occupation is heavily weighted toward small firms, field service, and physical work in low-digitization environments; AI adoption in repair-helper roles remains minimal and pilots are rare, reflecting the technical difficulty of automating manual dexterity tasks. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Installation/maintenance/repair trades are physical, low-digitization sectors with minimal AI/robotic deployment for manual disassembly tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist by diagnosing what needs disassembly or providing visual guides, but current systems offer only marginal support for the core physical work itself, and most helpers rely on experience and technical manuals rather than AI tools. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with diagnostic guidance, repair manuals, or visual identification of parts via computer vision, but it offers no direct help with the physical act of disassembly and reassembly. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Physical disassembly and reassembly of diverse, often irregular equipment requires dexterous manipulation, spatial reasoning, and real-time problem-solving in unstructured environments—capabilities far beyond current AI robotic systems in production. Modern AI cannot reliably handle the variability of broken equipment geometries, fastener types, and assembly sequences across different devices. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of tools and equipment components, which current AI systems cannot perform without embodiment; no off-the-shelf system disassembles/reassembles physical equipment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | There are significant organizational and technical barriers: repair work often requires human judgment about what broke and how, warranty and liability concerns tie work to licensed technicians, and the physical unpredictability of each job creates friction against automation substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing typically required for helper-level disassembly work, but physical presence, tool handling, and workplace safety practices create moderate practical friction against remote or software automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized robotics capable of dexterous disassembly and reassembly remain expensive to deploy, program, and maintain, with high capital and integration costs that far exceed the loaded wage of a skilled repair helper for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any hypothetical robotic solution would require far more capital and engineering investment than the low-wage helper labor it would replace. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | While research prototypes exist for simple robotic disassembly, no deployed commercial product reliably disassembles and reassembles arbitrary broken equipment at scale with acceptable error rates. Current robotic systems require extensive per-task programming and controlled environments, not the adaptability needed for repair work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical disassembly/reassembly of varied equipment; robotics for this remains research-stage or narrowly confined to controlled manufacturing lines, not general repair helper tasks. |
Diagnose electrical problems and install and rewire electrical components.
7CI 0–14 · exposure 8 · augmentation 50 · importance 3.8/5 · click for rater detail
Diagnose electrical problems and install and rewire electrical components.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The electrical installation and repair sector remains heavily dependent on skilled trades with high barriers to digitization and roboticization; adoption of autonomous AI systems in production is negligible. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Skilled trades and physical installation/repair work are among the slowest sectors to adopt AI, with minimal automation of hands-on tasks in production. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by helping diagnose faults from descriptions, suggesting solutions based on circuit information, and providing real-time guidance, but the human electrician remains essential for decision-making and physical execution. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with diagnostic reasoning (e.g., analyzing symptoms, referencing wiring diagrams, troubleshooting guides) and documentation, providing moderate support even though the physical work remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with some diagnostic steps (e.g., analyzing circuit diagrams, identifying common faults from descriptions), the task requires hands-on physical work (rewiring, component installation) that current robots cannot reliably perform. No current system achieves the 50% time-saving threshold end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of wiring, tools, and equipment in real-world environments, which current AI systems cannot perform; diagnosis also often requires physical inspection and testing. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Electrical installation and repair work is heavily regulated; most jurisdictions require a licensed electrician to legally perform or sign off on electrical work for safety and liability reasons, creating a hard legal barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Electrical work is often subject to safety codes, licensing/certification requirements, and liability concerns, creating substantial barriers to non-human execution even if diagnostic AI existed. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current AI diagnostic tools require significant oversight and cannot perform the physical labor component, making the all-in cost (software + hardware robotics + human supervision) substantially higher than a trained electrician's loaded wage. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical labor, so AI cost comparison is moot; a human helper must be paid to do the physical work. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably diagnoses electrical problems and performs physical installation/rewiring in real-world conditions. Vision-based fault detection exists in research, but production systems that autonomously handle this task at scale do not exist. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously diagnoses and repairs electrical systems physically; this remains firmly in the domain of human technicians with hands-on tools. |
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.