Manufactured Building and Mobile Home Installers
49-9095.00Median wage $45,990/yr3,020 employed (US)Rank #848 of 923 scored · top 92% by substitution
Move or install mobile homes or prefabricated buildings.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution13
Exposure4
Augmentation27
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
14 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%4
panel mean rating 1.2/5 → substitution pressure 4/100
Technical feasibility todayw 20%4
panel mean rating 1.2/5 → substitution pressure 4/100
Cost vs. human wagew 15%5
panel mean rating 1.2/5 → substitution pressure 5/100
Adoption barriersw 20%inverted — strong barriers lower the score46
panel mean rating 3.2/5 (barrier strength) → substitution pressure 46/100
Sector adoption velocityw 10%3
panel mean rating 1.1/5 → substitution pressure 3/100
Task breakdown (14 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.
List parts needed, estimate costs, and plan work procedures, using parts lists, technical manuals, and diagrams.
34CI 30–39 · exposure 25 · augmentation 50 · importance 3.8/5 · click for rater detail
List parts needed, estimate costs, and plan work procedures, using parts lists, technical manuals, and diagrams.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Manufactured housing installation remains a relatively labor-intensive, site-specific sector with lower overall digitization; adoption of AI-driven planning tools is still in pilot stages rather than production deployment at scale. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | placeholder |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by rapidly parsing manuals, suggesting standard parts lists, and auto-populating routine cost templates, allowing installers to focus on site-specific adjustments and procedure validation rather than manual data entry. |
| Augmentation potential | claude-sonnet-5 | 3/5 | placeholder |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist in reading technical manuals and generating parts lists from diagrams, but estimating costs accurately and planning procedures requires contextual judgment about site-specific conditions, supplier availability, and labor logistics that current systems handle inconsistently. |
| Task automatability | claude-sonnet-5 | 2/5 | Parts of listing and cost estimation could be assisted by AI given structured technical manuals, but planning physical work procedures requires site-specific judgment AI cannot fully replicate today.} ... |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While not legally restricted, the installer retains responsibility for accurate cost and procedure planning; material errors carry financial and safety liability, creating organizational friction against full automation and requiring human sign-off. |
| Adoption barriers | claude-sonnet-5 | 2/5 | placeholder |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI document analysis and cost estimation systems still require significant human oversight and correction, making the all-in cost (including quality assurance labor) comparable to or exceeding direct human estimation in this domain. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | placeholder |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While document parsing and basic parts enumeration tools exist, no deployed product reliably estimates costs across regional suppliers and develops work procedures that account for site-specific constraints and installation variability at the quality expected for professional use. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | placeholder |
Confer with customers or read work orders to determine the nature and extent of damage to units.
28CI 23–33 · exposure 25 · augmentation 50 · importance 3.7/5 · click for rater detail
Confer with customers or read work orders to determine the nature and extent of damage to units.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mobile home and manufactured building installation remains a fragmented, regional sector with limited digitization. Adoption of AI tooling is minimal; most installers rely on field notes, photos, and verbal communication rather than integrated AI-driven assessment systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufactured housing installation is a small, physically-oriented trade with low digitization and minimal reported AI adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI could usefully assist by auto-transcribing work orders, flagging common damage patterns in photos, or generating preliminary scope summaries for the installer to review. Such tools raise efficiency on documentation and reference checking without replacing the customer interaction or expert judgment required. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help installers organize work orders, generate damage-assessment checklists, or draft customer communication, providing moderate assistance despite not replacing the core task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could assist in analyzing photo/video evidence of damage, the task fundamentally requires direct customer communication to understand context, negotiate scope, and build trust—elements that current AI systems handle poorly. End-to-end automation would require real-time physical inspection plus complex interpersonal negotiation. |
| Task automatability | claude-sonnet-5 | 2/5 | Requires physical inspection of a home/unit and often in-person conversation with customers, which AI cannot perform directly; only the information-processing portion (reading work orders) is automatable today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Manufacturing and mobile-home installation is regulated; damage assessment often feeds into warranty claims, liability documentation, and contractual obligations. Customers expect human judgment and accountability, and errors in scope determination directly affect downstream repair costs and legal exposure. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for this diagnostic conversation, but practical barriers exist since physical presence and trust-building with customers are hard to replace. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI vision and NLP for damage analysis may reduce inspection time modestly, but the core task involves a licensed installer conferring with customers—a human-labor activity where oversight and liability costs offset AI savings, making the solution comparably priced or more expensive than hiring the human directly. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Some cost savings possible for transcription/summarization, but the core damage-assessment and customer-interaction work still requires a human on-site, limiting overall savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs customer-facing damage assessment and scope negotiation at production scale in this sector. Vision systems can detect damage in controlled settings, but the customer-conferencing component and judgment about 'nature and extent' remain human-dependent in practice. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI products can summarize written work orders or transcribe customer calls, but no deployed system reliably assesses physical damage extent or conducts the full customer conference in this trade. |
Inspect, examine, and test the operation of parts or systems to evaluate operating condition and to determine if repairs are needed.
18CI 5–30 · exposure 13 · augmentation 38 · importance 4.0/5 · click for rater detail
Inspect, examine, and test the operation of parts or systems to evaluate operating condition and to determine if repairs are needed.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | The manufactured building sector remains fragmented, with many small and medium installers operating with traditional practices. Adoption of AI-based inspection tools is still in early pilot phases; most companies rely on manual inspection protocols. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufactured housing installation is a low-digitization, physically-oriented trade sector with minimal AI/robotics adoption in the field to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI vision tools can assist technicians by flagging potential defects or areas requiring closer attention, reducing the time spent on routine scanning. However, the requirement for hands-on testing and system-level diagnostics limits the scope of meaningful augmentation. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with checklists, documentation, or diagnostic data logging, but it offers limited direct assistance to the physical inspection and testing process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI-powered visual inspection systems can detect some defects in images, this task requires hands-on testing of mechanical and electrical systems under real conditions, which demands physical presence and integration with complex parts. Current AI cannot reliably perform end-to-end testing that achieves the 50% time-saving threshold for manufactured building systems. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical inspection, hands-on testing, and manipulation of parts/systems in a mobile home installation context, which current AI cannot perform without robotic embodiment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Manufactured homes and mobile homes are subject to federal safety standards and state regulations; inspections often must be documented and signed off by certified technicians. Liability and legal requirements for inspection records create strong barriers to full automation without human validation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no strict licensing mandates AI cannot do this specific inspection, there are practical liability concerns, safety codes, and the inherent physical nature of the work that create real barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inspection systems require expensive hardware (cameras, sensors), integration with specialized equipment, and significant human oversight to validate findings. The total cost per inspection remains comparable to or higher than a technician performing the work directly, especially when factoring in rework from false negatives. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any AI-based approach (e.g., robotics) would be far more expensive than a human technician today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision tools exist for some visual inspections, but deployed products struggle with the full scope of testing operation across diverse manufactured systems and environmental conditions. Reliable testing of parts and systems in situ remains largely dependent on human technicians; AI applications are narrow and experimental. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical inspection and testing of manufactured home systems; this remains a human field task requiring physical presence and tactile/visual assessment on-site. |
Seal open sides of modular units to prepare them for shipment, using polyethylene sheets, nails, and hammers.
15CI 15–15 · exposure 0 · augmentation 13 · importance 4.4/5 · click for rater detail
Seal open sides of modular units to prepare them for shipment, using polyethylene sheets, nails, and hammers.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mobile home and modular building manufacturing is a capital-intensive, regional industry with low digital maturity and slow technology adoption compared to information and financial sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufactured housing and construction trades are low-digitization, physically dominated sectors with minimal AI/robotics adoption for such tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could potentially assist via computer vision for layout planning or defect detection, but the core physical sealing task offers limited augmentation opportunity for the human worker in situ. |
| Augmentation potential | claude-sonnet-5 | 1/5 | AI offers essentially no assistance for this hands-on manual sealing and nailing work. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Sealing modular unit sides requires physical manipulation in 3D space—positioning polyethylene sheets, driving nails with hammers, and adapting to irregular surfaces. Current AI lacks embodied robotics capable of this task reliably without human oversight and setup. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a manual physical task requiring positioning sheeting, nailing, and manipulating materials on-site; no current AI/robotic system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While no explicit licensing requirement restricts automation, the task occurs on construction/manufacturing sites where safety liability, worker presence requirements, and quality inspection create practical friction against full substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically, but physical dexterity and variable site conditions create practical barriers to automation rather than regulatory ones. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized mobile home installer labor is relatively low-cost ($40–50k annual wage), and any robotic system capable of this task would require significant capital, infrastructure, and maintenance exceeding the per-task savings. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI or robotic substitute for this physical task, so any hypothetical automation would require expensive custom robotics far costlier than a human laborer. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product reliably performs this physical sealing task autonomously. Robotics for construction-adjacent tasks exist in limited, highly structured settings but not for the variable, on-site conditions of mobile home preparation. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs mobile home sealing/wrapping tasks; this remains purely manual labor with hand tools. |
Refinish wood surfaces on cabinets, doors, moldings, and floors, using power sanders, putty, spray equipment, brushes, paints, or varnishes.
15CI 15–15 · exposure 0 · augmentation 25 · importance 3.4/5 · click for rater detail
Refinish wood surfaces on cabinets, doors, moldings, and floors, using power sanders, putty, spray equipment, brushes, paints, or varnishes.
15| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Manufactured building and mobile home installation remains a fragmented, small-firm, on-site physical sector with low overall digitization and minimal adoption of robotics or autonomous systems for finishing tasks. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufactured housing and construction trades are among the least digitized sectors with minimal AI/robotics adoption for physical finishing work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist via computer vision inspection systems to identify defects or recommend sanding patterns, but most of the task—actual material removal and finish application—requires direct human control and skill that current AI augmentation tools do not meaningfully enhance. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI offers limited assistance, perhaps in color-matching or planning finishes, but does not meaningfully enhance the hands-on sanding, spraying, and finishing process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Refinishing wood surfaces requires fine motor control, real-time visual feedback, and adaptive decision-making based on wood grain, surface irregularities, and finish quality. Current AI systems cannot handle the spatial reasoning, material physics, and physical precision needed to sand, apply putty, and spray finishes end-to-end with equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual craft task involving sanding, puttying, spraying, and finishing wood surfaces on-site, requiring dexterity and physical presence that current AI cannot replicate. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Although there are no strict licensing requirements for this task, the need for custom problem-solving on each surface, client quality expectations, and the physical complexity of setup create moderate friction to automation, but these are primarily economic rather than regulatory barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing or legal requirement mandates a human specifically, but physical presence, tool manipulation, and quality judgment on-site create strong practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of robotic systems capable of wood refinishing, combined with setup, maintenance, and per-task integration overhead, far exceeds the loaded wage of a skilled installer performing the work manually. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic system to compare cost against; a human worker with hand tools remains the only functional option, making AI substitution infeasible and thus costlier in practice. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs wood refinishing end-to-end. While robotics research exists for some sub-tasks, production systems that independently handle variable wood conditions, surface preparation, and finish application at acceptable quality do not exist at scale in the industry. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical wood refinishing; robotics for this specific unstructured, variable task remain research-stage at best. |
Install, repair, and replace units, fixtures, appliances, and other items and systems in mobile and modular homes, prefabricated buildings, or travel trailers, using hand tools or power tools.
13CI 10–15 · exposure 0 · augmentation 25 · importance 3.7/5 · click for rater detail
Install, repair, and replace units, fixtures, appliances, and other items and systems in mobile and modular homes, prefabricated buildings, or travel trailers, using hand tools or power tools.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mobile home and manufactured building installation remains a labor-intensive, physical-work sector with limited digitization and low capital intensity per installer; adoption of advanced automation is minimal in production today. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and manufactured housing trades are among the least digitized, physically-oriented sectors with minimal AI/robotics adoption in the field. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools could assist with minor tasks such as diagnostic guidance, parts sourcing, or work documentation, but the core physical work of installation and repair remains inherently manual and difficult to augment meaningfully with current systems. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can assist with diagnostics, ordering parts, referencing manuals, or troubleshooting guidance via mobile apps, but offers minimal help with the physical installation and repair actions themselves. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical dexterity, spatial reasoning in real-world environments, and adaptation to non-standardized conditions—capabilities far beyond current AI or robotic systems in general deployment. Physical installation and repair of diverse fixtures and systems in mobile homes cannot achieve 50% time savings with current technology. |
| Task automatability | claude-sonnet-5 | 1/5 | This is physical, hands-on installation and repair work requiring manipulation of tools, fixtures, and structures in varied real-world conditions; no current AI system can perform this end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While there are no strict licensing barriers for the task itself, customer preference for human presence during installation/repair and the liability risks of autonomous systems in homes create some friction to automation, though not hard legal requirements. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not licensed in the way electricians/plumbers sometimes are, work often must meet building codes and safety standards, and physical presence and liability for faulty installation create real friction against any automated substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The labor cost of a trained installer is modest compared to the capital, software, and human oversight required for robotic or autonomous systems capable of on-site physical installation and repair work in uncontrolled environments. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical labor, so AI cost is effectively infinite relative to human labor for the core task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product today performs this task end-to-end. While industrial robotics exists for narrow, controlled manufacturing, general-purpose on-site installation, repair, and fixture replacement in mobile homes remains research-stage or limited to highly controlled scenarios. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical installation/repair of home fixtures and systems; robotics for such unstructured, varied physical tasks remains research-stage. |
Reset hardware, using chisels, mallets, and screwdrivers.
13CI 10–15 · exposure 0 · augmentation 13 · importance 3.6/5 · click for rater detail
Reset hardware, using chisels, mallets, and screwdrivers.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Manufactured housing and mobile home installation is a traditional, non-urban sector with low digital infrastructure adoption and limited mechanization; robotics deployment in this domain remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufactured housing installation is a low-digitization, physical trade sector with minimal AI/robotics adoption to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 1/5 | Current AI offers no meaningful assistance for physical hardware resetting with hand tools; augmentation would require embodied or vision-guided systems that do not exist in practical deployment for this task. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with instructions, diagnostics, or documentation, but offers little direct help with the physical act of resetting hardware using hand tools. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Resetting hardware using hand tools (chisels, mallets, screwdrivers) requires fine motor control, spatial reasoning, and real-time physical manipulation in variable structural contexts. Current AI systems cannot operate these tools or perform precision physical adjustments in the field. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a fine-motor physical manipulation task requiring hand-tool use on-site in varied physical conditions; no current AI system (software or robotic) can perform this end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While not legally licensed, the task occurs in construction/installation contexts where site safety protocols and on-site supervision create moderate friction; however, there is no explicit licensing requirement preventing automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing mandates a human specifically for hardware resetting, but physical dexterity, unstructured environments, and liability for property damage create practical barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A skilled installer's loaded wage is modest, and the infrastructure cost for a robotic system capable of precise hardware reset in field conditions vastly exceeds the labor cost for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute, so the comparison defaults to AI being effectively infinitely costlier or simply unavailable relative to a human installer. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs physical hardware resetting with hand tools on manufactured buildings or mobile homes. This is a physical task requiring embodied automation (robotics), which remains nascent in construction settings. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that resets hardware on manufactured homes using chisels, mallets, and screwdrivers; this remains purely manual skilled trade work. |
Open and close doors, windows, and drawers to test their operation, trimming edges to fit, using jackplanes or drawknives.
13CI 10–15 · exposure 0 · augmentation 13 · importance 3.4/5 · click for rater detail
Open and close doors, windows, and drawers to test their operation, trimming edges to fit, using jackplanes or drawknives.
13| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Manufactured building installation is a laggard sector: small firms, on-site physical work, low digitization, and high variability in job conditions make adoption of advanced automation slow and shallow. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufactured housing installation is a low-digitization, physical trade sector with minimal AI/robotics adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools could assist with design or virtual testing of fit, but the core task—hands-on trimming and operation testing—offers limited room for meaningful augmentation without the human still performing the physical work. |
| Augmentation potential | claude-sonnet-5 | 1/5 | Current AI offers no meaningful assistance for this hands-on carpentry and fitting task; it is not a knowledge-work component amenable to digital tools. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of doors, windows, and drawers in varying conditions, precise manual trimming with hand tools (jackplanes, drawknives), and real-time judgment about fit. Current AI lacks embodied manipulation and fine motor control to perform this reliably. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation, fine motor skill, and tactile judgment to test operation and trim edges with hand tools—no current AI system can perform this physical task. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While no hard legal licensing barrier exists for automation itself, on-site work in manufactured homes involves customer presence, quality verification by inspectors, and organizational reliance on skilled tradecraft, creating moderate friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing specifically bars automation of this task, but it requires a skilled tradesperson on-site with tools and physical dexterity, creating practical (not regulatory) barriers to substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Custom robotics capable of handling variable manual trimming would cost far more than the loaded wage of a skilled installer, and integration costs for custom fitting scenarios would be prohibitive. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute performing this task, so any hypothetical automation would require expensive custom robotics far exceeding human labor costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products perform hands-on trimming and fitting of building components. Robotic systems for such tasks remain experimental and confined to research or highly specialized manufacturing, not production at scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products exist that perform physical fitting, testing, and trimming of doors/windows/drawers on manufactured homes; this remains firmly in the physical robotics research realm at best. |
Move and set up mobile homes or prefabricated buildings on owners' lots or at mobile home parks.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.4/5 · click for rater detail
Move and set up mobile homes or prefabricated buildings on owners' lots or at mobile home parks.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task belongs to construction and manual trades, sectors with low digitization and slow AI adoption. Physical work requiring equipment operation and site-specific customization shows minimal automation pressure in practice. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and manufactured housing installation is a low-digitization, physically intensive sector with minimal AI or robotics adoption for on-site placement tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Limited augmentation potential; AI could assist with site planning or route optimization via software tools, but the core physical work of moving and positioning structures offers little room for AI-assisted productivity gains while the human remains in the loop. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with logistics planning, permit paperwork, or site measurement analysis, but offers little direct help with the physical setup and leveling process itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of large structures in variable outdoor environments, positioning on specific lots, and precise alignment—capabilities far beyond current AI robotics. The task involves heavy equipment operation, site assessment, and real-time problem-solving that demands embodied intelligence. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a heavy physical task requiring transport, crane/hydraulic jack operation, leveling, and anchoring of large structures on-site; no AI or robotic system performs this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Significant regulatory barriers exist: licensed installers are often required by state/local codes, liability for property damage and safety is substantial, and homeowners expect human oversight of work on their property. Insurance and legal accountability also depend on human responsibility. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier for AI specifically, but the task requires physical presence, specialized equipment operation, and compliance with site/utility hookup codes that demand human oversight and liability accountability. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of specialized heavy equipment, robotic systems, and the integration required to perform this task would far exceed the labor cost of skilled installers, especially for one-off jobs across different sites. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for the physical labor and equipment operation involved, so AI cost comparison is moot; human labor and heavy machinery remain the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can autonomously perform end-to-end mobile home setup today. While robotics exists for narrow industrial tasks, no product reliably handles the full scope of moving and positioning entire structures on varied terrain and lots. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product does mobile home or prefab building placement; this remains entirely manual work performed by skilled crews with specialized equipment. |
Connect water hoses to inlet pipes of plumbing systems, and test operation of plumbing fixtures.
7CI 5–10 · exposure 0 · augmentation 25 · importance 4.0/5 · click for rater detail
Connect water hoses to inlet pipes of plumbing systems, and test operation of plumbing fixtures.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Manufacturing and installation of mobile homes remains a traditional, physically-intensive sector with low digitization. Adoption of advanced automation in this subsector is minimal; the work is site-specific, demand is geographically fragmented, and labor costs remain lower than automation investment. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufactured housing and on-site trade installation is a low-digitization, physical-labor sector with minimal AI/robotic adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide instructional or diagnostic support (e.g., visual guidance, pressure-test interpretation), but the core task—physically connecting hoses and testing—offers limited opportunity for meaningful augmentation without full robotic capability. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostic checklists, documentation, or troubleshooting guidance, but offers little direct help with the physical connection and testing steps themselves. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of hoses and pipes in variable, site-specific configurations, plus hands-on testing of fixtures. Current AI lacks embodied robotics capabilities to reliably perform these physical operations at scale, making end-to-end automation with 50% time savings unachievable today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical manual trade task requiring hands-on manipulation of hoses, pipes, and fixtures in varied field conditions; no current AI system can perform physical connection and testing work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory and safety requirements for plumbing work in manufactured housing are stringent; improper connections create liability and water damage risks. Many jurisdictions require licensed or certified installers to perform or sign off on plumbing connections, creating a legal barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not always requiring a specific plumbing license depending on jurisdiction, there are code compliance, safety, and inspection requirements that create moderate friction against non-human or automated substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Specialized mobile or industrial robots capable of plumbing work would cost orders of magnitude more than the loaded wage of a trained installer, and integration costs would be prohibitive for a task performed in highly variable on-site environments. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this physical task, so any AI-based approach (e.g., robotics) would be far more costly than a human installer today. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed commercial product today performs physical plumbing connections and fixture testing autonomously. This remains a skilled manual labor task with no production-stage AI or robotic systems in real-world use for this specific work. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical plumbing connections or fixture testing; this remains purely a human manual labor task with no robotic deployment at scale. |
Remove damaged exterior panels, repair and replace structural frame members, and seal leaks, using hand tools.
7CI 0–15 · exposure 0 · augmentation 25 · importance 3.8/5 · click for rater detail
Remove damaged exterior panels, repair and replace structural frame members, and seal leaks, using hand tools.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mobile home and manufactured building installation is a physically dispersed, small-firm-dominated trade with minimal digital integration; adoption of advanced automation in this sector has been negligible, and physical on-site work remains resistant to remote AI systems. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Construction and manufactured housing trades are a low-digitization, physical-labor sector with minimal AI/robotics adoption for hands-on repair work. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | Limited augmentation potential exists; AI could assist with diagnostics or documentation via image analysis, but the hands-on repair work itself—which dominates the task—offers few touchpoints for meaningful AI assistance to a human worker in the field. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with diagnostics, sourcing replacement parts, or generating repair instructions, but offers little assistance for the physical execution of removing panels, repairing frames, and sealing leaks. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of building components, precise spatial judgment, and structural assessment in varied, unstructured environments—capabilities far beyond current AI systems. Removal, repair, and replacement of structural elements demand embodied dexterity and real-time adaptation that no deployed autonomous system can perform reliably today. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical repair task requiring manual dexterity, tool manipulation, and situational judgment in varied physical environments; no AI system can perform this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong barriers exist: building code compliance and structural integrity certification typically require a licensed contractor or engineer sign-off; liability for structural failures is high; and human judgment on material condition and safety is legally and practically irreplaceable. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing strictly requires a human, but practical barriers like variable job sites, physical dexterity needs, and safety liability make automation impractical rather than legally prohibited. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The specialized robotics, computer vision, and mechanical systems required to perform structural repair would be prohibitively expensive compared to a skilled technician's loaded wage, with integration and maintenance costs adding further burden. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI/robotic substitute for this physical labor, so the human remains the only cost-effective option; deploying robotics for this bespoke, mobile task would be far costlier than a human worker. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs structural repair and panel replacement on manufactured buildings autonomously. This work remains entirely dependent on human skilled trades; no commercial AI agent or robotic system operates at production scale for this task. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed robotic or AI product performs structural repair, panel replacement, and sealing on manufactured homes; this remains far beyond current robotics capability outside narrow lab demos. |
Locate and repair frayed wiring, broken connections, or incorrect wiring, using ohmmeters, soldering irons, tape, and hand tools.
5CI 0–10 · exposure 0 · augmentation 25 · importance 3.5/5 · click for rater detail
Locate and repair frayed wiring, broken connections, or incorrect wiring, using ohmmeters, soldering irons, tape, and hand tools.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Manufactured building installation and repair remains a low-digitization, small-firm, field-based sector with slow capital investment in automation. No measurable adoption of AI agents for electrical diagnostics and repair is evident in industry data. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufactured housing installation and repair is a low-digitization, physical trade sector with minimal AI/robotics adoption in production settings. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could marginally assist by identifying likely fault locations from photos or logged sensor data, but the core task—soldering and hands-on diagnosis—remains inherently manual. Augmentation is limited because the technician must still perform the majority of high-skill work. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostic guidance, wiring diagrams, or troubleshooting reference lookup, but offers limited help with the physical location and repair work itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of fragile electrical components, diagnosis via hands-on testing with specialized equipment (ohmmeters, soldering irons), and real-time judgment in confined spaces. Current AI systems cannot physically perform soldering, handle delicate wiring, or reliably diagnose and repair electrical faults in the field. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a physical diagnostic and repair task requiring hands-on manipulation, probing with meters, soldering, and fine motor work in variable real-world conditions that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Electrical work on manufactured homes is often subject to building codes, licensing requirements, and third-party inspection; a licensed electrician or qualified technician must sign off on repairs. Liability and safety standards create hard legal barriers to full automation or unsupervised AI substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not formally licensed like electrician trade in many jurisdictions, safety and liability concerns around electrical repair create meaningful friction against unsupervised automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current AI and robotics capable of soldering and electrical repair are experimental, expensive, and require significant setup. The loaded cost of a skilled technician ($25–$50/hour fully loaded) remains far cheaper than the capital and integration cost of any attempted automation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI-driven physical repair system, so a human technician remains the only cost-effective option for this hands-on task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs electrical diagnostics and repair in mobile homes or manufactured buildings end-to-end. While computer vision can identify some visible damage, the soldering and hands-on repair work remains exclusively manual; no autonomous or semi-autonomous system operates at scale in this domain. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously locates and repairs frayed wiring or connections in mobile homes; this remains beyond current robotic and AI capabilities in unstructured field environments. |
Repair leaks in plumbing or gas lines, using caulking compounds and plastic or copper pipe.
3CI 0–5 · exposure 0 · augmentation 25 · importance 3.5/5 · click for rater detail
Repair leaks in plumbing or gas lines, using caulking compounds and plastic or copper pipe.
3| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | The manufactured housing and mobile home installation sectors are traditionally labor-intensive, geographically dispersed, and slow to digitize. Adoption of advanced automation in plumbing repair is negligible; most work remains manual and performed on-site by licensed technicians. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufactured housing and field trades are low-digitization, physically-grounded sectors with minimal AI/robotics adoption for hands-on repair tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI tools could provide limited assistance—such as diagnostic imaging analysis or material selection guidance—but the core physical work remains wholly human-dependent. Augmentation potential is minimal because the task is inherently manual and site-specific. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help with diagnostics (e.g., leak detection sensors, troubleshooting guides) or documentation, but offers little assistance for the physical repair itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of pipes, caulking, and sealing in complex spatial configurations inside structures. Current AI systems cannot perform end-to-end physical repair work; the task demands manual dexterity, real-time sensory feedback, and adaptation to variable structural conditions that exceed current robotic and autonomous capabilities. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical repair task requiring manipulation of pipes, caulking, and tools in variable field conditions; no current AI system can perform this physical work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong regulatory and liability barriers exist: building codes require licensed plumbers to sign off on gas and plumbing work in most jurisdictions, and incorrect installation carries safety and legal consequences. Human certification and local authority approval are typically mandated. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Gas line work typically requires licensed/certified installers due to safety and liability concerns, and building codes often mandate qualified human inspection and sign-off. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The capital cost of specialized robotic systems capable of pipe repair, combined with integration and maintenance overhead, far exceeds the loaded wage of a skilled plumber for the same work. Current technology is prohibitively expensive relative to human labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing the physical repair, so AI cost is not comparable—human labor remains the only option. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed products reliably perform plumbing leak repair autonomously today. While some inspection robotics exist, the full workflow—diagnosis, component selection, cutting, fitting, sealing, and testing—remains research-stage and requires human technicians. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs physical plumbing/gas line repairs; robotics for such unstructured physical tasks remain research-stage at best. |
Connect electrical systems to outside power sources and activate switches to test the operation of appliances and light fixtures.
0CI 0–0 · exposure 0 · augmentation 25 · importance 3.5/5 · click for rater detail
Connect electrical systems to outside power sources and activate switches to test the operation of appliances and light fixtures.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Mobile home and manufactured building installation is a physical, on-site trade with low digitization. Adoption of robotics or autonomous systems in this sector remains minimal and experimental. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Manufactured housing installation is a low-digitization, physical trade sector with minimal AI or robotics adoption to date. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with diagnostic checking or documentation of test results, but the core physical tasks of connecting systems and activating switches offer limited opportunity for human-in-the-loop augmentation with current tools. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with diagnostic checklists, wiring diagrams, or troubleshooting guidance via mobile apps, but offers little help with the core physical connection and testing work. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires physical manipulation of electrical connections and switches in variable on-site environments, followed by safety-critical testing. Current AI systems cannot perform end-to-end electrical connections or physically activate switches with reliable quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical manipulation of electrical connections, on-site wiring, and hands-on testing of appliances/fixtures in variable field conditions—no current AI system can perform this physical work. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Electrical work is heavily regulated; most jurisdictions require licensed electricians to connect power sources and certify installations. Liability for electrical failures is severe, creating a hard legal and safety barrier to automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Electrical work is heavily regulated, typically requires licensed electricians or certified installers, and carries significant safety/liability risk, creating hard legal barriers to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI systems capable of electrical work would require specialized robotics, vision systems, and safety certification—far more expensive than hiring a trained electrician or installer for this task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute for this physical electrical work, so the human remains the only viable cost option; robotics for this task do not exist commercially. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI product reliably performs electrical system connections and appliance testing in the field. This requires physical dexterity, site-specific problem-solving, and safety compliance that are not yet automated in production systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product exists that connects electrical systems in mobile homes and physically tests appliance operation; this remains purely human physical labor. |
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.