Solar Energy Systems Engineers
17-2199.11Median wage $122,930/yr154,070 employed (US)Rank #209 of 923 scored · top 23% by substitution
Perform site-specific engineering analysis or evaluation of energy efficiency and solar projects involving residential, commercial, or industrial customers. Design solar domestic hot water and space heating systems for new and existing structures, applying knowledge of structural energy requirements, local climates, solar technology, and thermodynamics.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution38
Exposure37
Augmentation75
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
13 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
8%
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%40
panel mean rating 2.6/5 → substitution pressure 40/100
Technical feasibility todayw 20%33
panel mean rating 2.3/5 → substitution pressure 33/100
Cost vs. human wagew 15%40
panel mean rating 2.6/5 → substitution pressure 40/100
Adoption barriersw 20%inverted — strong barriers lower the score41
panel mean rating 3.4/5 (barrier strength) → substitution pressure 41/100
Sector adoption velocityw 10%36
panel mean rating 2.4/5 → substitution pressure 36/100
Task breakdown (13 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.
Create checklists for review or inspection of completed solar installation projects.
74CI 69–79 · exposure 70 · augmentation 100 · importance 3.6/5 · click for rater detail
Create checklists for review or inspection of completed solar installation projects.
74| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Solar energy is a digitally mature, fast-growing sector with active adoption of software automation and AI-assisted tools for project management, documentation, and workflows. Early-stage but accelerating production use of AI for document and checklist generation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Solar installation and engineering firms are generally slower AI adopters compared to information/professional services sectors, with AI use still emerging for documentation tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI can dramatically accelerate checklist creation by surfacing relevant standards, compliance items, and best practices, while the engineer validates, customizes, and signs off—a high-productivity augmentation scenario where human judgment remains central. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI can rapidly draft, customize, and update inspection checklists based on codes and project specs, greatly speeding up engineers' documentation work while they retain final review. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can generate comprehensive checklists based on industry standards, safety codes, and common inspection criteria for solar installations with minimal human input, and can do so in a fraction of the time a human engineer would require. However, final validation by a domain expert may be needed to ensure completeness and accuracy for specific project types or local regulations. |
| Task automatability | claude-sonnet-5 | 4/5 | Generating checklists is a structured document-drafting task; LLMs can produce comprehensive, code-compliant checklists from specifications with minimal editing needed, saving significant time. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | While inspection sign-off typically requires a licensed engineer, the checklist creation itself is not a regulated task and faces minimal organizational or legal barriers. Customer expectations and quality oversight provide modest friction but not hard legal requirements. |
| Adoption barriers | claude-sonnet-5 | 2/5 | While final sign-off on inspections often requires a licensed engineer, drafting the checklist itself is not an activity restricted by licensure, so barriers are modest. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI inference cost for generating a checklist is negligible compared to the several hours an engineer would spend researching standards, organizing criteria, and drafting—a clear order-of-magnitude cost advantage even accounting for oversight. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Drafting a checklist via an LLM costs a fraction of a cent in compute versus an engineer's billable time, even after review. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (LLMs, document generation tools, and specialized solar software) can reliably produce inspection checklists at scale, though integration with specific project management systems and local compliance databases may require configuration. Production use is common in solar firms for initial checklist generation with human review. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | General-purpose AI writing tools can draft such checklists today, but no widely deployed solar-industry-specific product reliably generates fully compliant, jurisdiction-aware inspection checklists at scale. |
Perform computer simulation of solar photovoltaic (PV) generation system performance or energy production to optimize efficiency.
65CI 55–75 · exposure 62 · augmentation 88 · importance 4.1/5 · click for rater detail
Perform computer simulation of solar photovoltaic (PV) generation system performance or energy production to optimize efficiency.
65| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Solar energy is a high-digitization, capital-intensive sector with strong incentives to optimize system design and performance; major developers and engineering firms already routinely deploy AI-assisted simulation tools in design workflows. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Renewable energy engineering is moderately digitized with growing use of simulation and optimization software, but adoption of AI-driven automation specifically is still in pilot/tool-assisted stages rather than deep integration. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI significantly augments engineer productivity by automating routine simulations, sensitivity analysis, and parameter sweeps, allowing engineers to focus on high-level design decisions, site assessment, and constraint integration rather than manual solver iteration. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly speed up scenario testing, parameter sweeps, and identification of optimal system configurations, meaningfully boosting engineer productivity while they retain oversight and validation responsibilities. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Computer simulation of solar PV system performance is highly algorithmic and well-suited to AI-driven tools. Current systems can set up models, run parametric simulations, and optimize efficiency metrics with minimal human intervention, achieving significant time savings over manual modeling workflows. |
| Task automatability | claude-sonnet-5 | 3/5 | Simulation software (PVsyst, SAM, Helioscope) already automates much of the modeling, and AI can assist in parameterization and scenario generation, but expert judgment is needed to interpret site-specific constraints and validate outputs, so full end-to-end automation is not yet reliable. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers prevent automation of simulation tasks themselves; however, final system design sign-off by a licensed professional engineer may be required in some jurisdictions, creating modest friction to full end-to-end replacement. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human perform the simulation itself, though final system designs may need PE sign-off in some jurisdictions, creating mild downstream barriers rather than direct ones for this task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven simulation is substantially cheaper than hiring engineers to manually run and iterate simulations. Cloud-based optimization and open-source tools with AI augmentation reduce marginal cost per simulation run to a fraction of loaded engineer time. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Simulation software licenses plus engineer oversight are already fairly efficient; AI-driven optimization could reduce iteration time but engineering review costs remain significant, keeping cost roughly comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature simulation software (PVsyst, SAM, Homer) combined with AI-assisted code generation and parameter optimization are deployed in production across the solar industry. Some integration and validation steps still benefit from human review, preventing a perfect 5. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Established simulation products are widely used in production, but they are tools requiring engineer input/configuration rather than autonomous AI systems that perform the full analysis and optimization independently. |
Perform thermal, stress, or cost reduction analyses for solar systems.
61CI 39–82 · exposure 66 · augmentation 88 · importance 3.2/5 · click for rater detail
Perform thermal, stress, or cost reduction analyses for solar systems.
61| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Solar and renewable energy engineering firms are in high-digitization sectors with strong adoption of CAD, simulation, and computational optimization tools; AI-assisted analysis is increasingly integrated into design workflows in production environments. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Renewable energy engineering is a specialized, moderately digitized sector with growing simulation tool adoption, but AI-driven automation of core analysis tasks remains in early pilot stages compared to fast-adopting sectors like finance. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically augments engineer productivity by automating routine analysis iterations, exploring design parameter spaces, and generating optimization recommendations that engineers then review and refine, keeping humans in the loop while accelerating design cycles. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted simulation, optimization algorithms, and generative design tools can significantly speed up scenario testing and cost modeling, meaningfully boosting engineer productivity while they retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Thermal, stress, and cost reduction analyses for solar systems are highly computational and formulaic tasks that can be performed end-to-end by current AI systems with domain knowledge, using numerical solvers, simulation software, and optimization algorithms to achieve substantial time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can perform substantial portions of thermal/stress simulation setup and cost modeling using existing engineering software and data analysis, but complex physical modeling, validation, and engineering judgment still require significant human oversight for equal-quality output. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While analyses themselves are not legally restricted, professional liability, the need for a licensed engineer to sign off on design recommendations, and organizational workflows requiring human review create meaningful friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Engineering analyses supporting system design often require PE sign-off or professional accountability for safety and performance, creating moderate liability and regulatory friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven simulation and optimization tools cost orders of magnitude less per analysis than hiring an engineer for hours of manual thermal modeling, finite element analysis, or cost optimization studies. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Engineering analysis software plus AI tools reduce some labor but still require licensed engineering software, computational resources, and expert review, keeping costs closer to comparable rather than order-of-magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Mature CAD/simulation software integrated with AI optimization tools (e.g., generative design, parametric analysis) is deployed in engineering practice today, though some novel configurations or edge cases still require human validation rather than fully autonomous analysis. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Simulation and CAD tools have some AI-assisted features (parameter optimization, surrogate modeling) but no deployed product autonomously performs full thermal/stress/cost analyses for solar systems reliably at production scale. |
Create electrical single-line diagrams, panel schedules, or connection diagrams for solar electric systems, using computer-aided design (CAD) software.
54CI 37–70 · exposure 53 · augmentation 88 · importance 4.2/5 · click for rater detail
Create electrical single-line diagrams, panel schedules, or connection diagrams for solar electric systems, using computer-aided design (CAD) software.
54| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Solar installation and design firms are digitally native and actively adopt CAD automation and design software. The sector is already highly integrated with computational design tools, and vendors are rapidly embedding AI-assisted drafting into mainstream solar design platforms. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Solar engineering and construction is a moderately digitized but still physically-grounded sector with slower AI tool adoption compared to pure information-service industries; pilots exist but production-scale AI-driven diagram generation is uncommon. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI CAD assistants significantly amplify engineer productivity by auto-generating compliant diagram drafts, panel schedules, and connection layouts from system specifications, leaving the engineer to validate, customize, and approve. This transforms turnaround time while keeping the engineer in control. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-enhanced CAD tools, auto-layout features, and generative drafting assistants can meaningfully speed up diagram creation and reduce errors while the engineer retains responsibility for final design and compliance. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | CAD diagram generation for solar systems is highly structured and rule-based. Current AI can translate specifications into standard electrical diagrams with minimal human intervention, though complex site-specific layouts and code compliance verification still require oversight. The task involves pattern-matching and template application, which AI systems handle efficiently. |
| Task automatability | claude-sonnet-5 | 3/5 | CAD-based single-line diagrams follow standardized conventions and templates, making significant portions automatable, but final designs require engineering judgment on code compliance, equipment specifications, and site-specific conditions that current AI cannot fully handle end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Electrical diagrams must comply with NEC and local codes, and final plans typically require a licensed PE stamp, but the AI can generate compliant templates that the engineer reviews and certifies. No hard legal barrier prevents AI creation; human sign-off is the main friction, not prohibition. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Electrical single-line diagrams for solar systems typically require a licensed professional engineer's stamp/sign-off for permitting and utility interconnection, creating a strong regulatory barrier to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-assisted CAD tools cost significantly less per diagram than engineer time. Once trained on solar specifications, the system produces layouts at marginal computational cost versus the loaded hourly wage of a solar engineer, though some supervision overhead remains. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-assisted drafting tools can reduce time spent on repetitive diagram creation, but licensing costs for specialized CAD/AI tools plus required engineering oversight keep costs roughly comparable to human-only drafting for now. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | CAD generation tools and AI drafting assistants exist (e.g., Autodesk plugins, specialized solar design software), but they typically require significant parameter input and human refinement rather than fully autonomous creation. Production use is growing but remains incomplete without engineer review for compliance and accuracy. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some CAD plugins and generative design tools can produce draft schematics, but no widely deployed product reliably generates code-compliant single-line diagrams and panel schedules without substantial engineer review and correction. |
Test or evaluate photovoltaic (PV) cells or modules.
33CI 25–41 · exposure 33 · augmentation 75 · importance 2.9/5 · click for rater detail
Test or evaluate photovoltaic (PV) cells or modules.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Major manufacturers use automated optical inspection and AI-assisted data analysis in production testing, but this adoption is concentrated in large, digitized firms. Smaller testing labs and independent validation services lag significantly, and widespread autonomous end-to-end testing adoption remains limited despite pilot programs. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Solar manufacturing and testing is a hardware-centric, moderately digitized sector where AI adoption for automated inspection is growing but not yet deeply embedded across the industry. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists engineers by automating defect detection from microscopy images, rapidly analyzing electrical performance curves, predicting failure modes from test data, and flagging anomalies for human review. These tools measurably improve testing throughput and accuracy when the engineer retains decision authority. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-based image analysis (e.g., electroluminescence or thermal imaging) significantly speeds up defect detection and data interpretation, meaningfully boosting engineer productivity while humans remain responsible for final evaluation. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate data acquisition and analysis from testing equipment (image analysis of cell defects, electrical performance metrics), and can process test results to classify pass/fail outcomes. However, physical manipulation of test samples, equipment setup, and real-time troubleshooting of testing apparatus still require human intervention, making only partial automation feasible. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical testing of PV cells/modules requires lab equipment, sensors, and hands-on handling that AI cannot perform end-to-end; AI can assist in data analysis but not the physical test execution.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Testing and certification of PV modules is heavily regulated (IEC 61215, UL 1703, etc.), with third-party certification bodies and established quality assurance protocols. Liability for defective modules in the field creates strong error-cost asymmetry, and many jurisdictions require licensed engineers to certify test results, creating legal and regulatory barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Testing for certification (e.g., UL, IEC standards) often requires accredited lab procedures and sometimes signed-off engineering judgment, creating moderate regulatory and liability friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized PV testing equipment, sensor calibration, and the need for domain expertise in interpreting results mean that dedicated AI systems plus human oversight costs remain comparable to or higher than hiring trained technicians. Integration and validation costs are substantial for safety-critical testing environments. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized testing equipment and calibration remain costly, and human engineers are still needed to set up, interpret ambiguous results, and validate compliance, so cost savings versus human labor are moderate at best. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI-powered inspection systems (computer vision for defect detection) and data analysis tools exist and are used in some labs, most comprehensive PV testing—including electrical characterization, thermal imaging interpretation, and environmental stress testing—still relies on human-operated equipment with AI as a supporting tool rather than a fully autonomous system. Production deployment remains limited. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Automated test equipment (electroluminescence imaging, IV curve tracers) exists and is AI-assisted for defect detection, but full autonomous evaluation without human oversight is not standard production practice. |
Develop design specifications and functional requirements for residential, commercial, or industrial solar energy systems or components.
31CI 25–37 · exposure 33 · augmentation 63 · importance 3.8/5 · click for rater detail
Develop design specifications and functional requirements for residential, commercial, or industrial solar energy systems or components.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Solar engineering remains concentrated in specialized firms and integrators; broader digitization and AI adoption in this domain is slower than in software or finance, with most adoption currently limited to analysis aids rather than autonomous design generation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Solar/renewable engineering is a specialized, moderately digitized field with slow but growing use of design software; production-scale AI-driven design generation is still uncommon. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can provide useful assistance by automating preliminary load calculations, generating candidate layouts, checking against code databases, and drafting boilerplate specs—meaningful productivity gains for engineers, but not transformative without substantial human oversight. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by generating draft specifications, performing energy yield calculations, and checking code compliance, substantially speeding up the engineer's workflow while they retain final responsibility. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist in generating preliminary design parameters and specifications based on templates and inputs, developing comprehensive, site-specific, and compliant solar system designs requires integrating complex variables (roof geometry, electrical codes, local incentives, customer constraints) that demand significant expert judgment and iterative refinement—not an end-to-end autonomous capability today. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft initial design specs and pull requirements from templates/codes, but engineering judgment, site-specific analysis, and integration with structural/electrical constraints still require significant human expertise, limiting full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Design specifications must typically be signed by a licensed engineer (PE requirement varies by state but is common for systems above certain size/complexity); client liability for system performance and code compliance creates legal asymmetry that discourages full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Engineering specifications for solar systems often require a licensed professional engineer's stamp/sign-off, especially for commercial/industrial systems, creating strong regulatory and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tools are complementary to rather than substitutes for engineer time; integration, validation, and liability review still demand substantial human effort, making the all-in cost competitive with rather than cheaper than hiring an engineer. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools can reduce drafting time significantly, but licensed engineer review and site-specific validation remain necessary, keeping overall cost roughly comparable rather than order-of-magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some design support tools and parametric software exist, but they require expert oversight and do not reliably produce production-ready specifications without manual engineering review; no deployed product autonomously generates complete, code-compliant solar designs from raw site data. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some CAD/engineering design tools incorporate AI assistance for solar layout and specs, but no mature deployed product fully generates certified design specifications reliably without engineer review. |
Develop standard operation procedures and quality or safety standards for solar installation work.
29CI 25–34 · exposure 25 · augmentation 63 · importance 3.6/5 · click for rater detail
Develop standard operation procedures and quality or safety standards for solar installation work.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Solar installation is a moderately digitized, distributed sector dominated by small to mid-size firms. Adoption of AI for SOP development is nascent; most firms still rely on manual processes, industry templates, or consultants. Velocity remains low because the stakes (safety, liability) encourage conservative, human-led practices. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | placeholder |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by generating first drafts, consolidating industry best practices, or flagging common safety gaps from training data, allowing engineers to focus on customization and validation. This is genuine assistance but not transformative, as the core judgment and accountability remain with human engineers. |
| Augmentation potential | claude-sonnet-5 | 4/5 | placeholder |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help draft SOP templates and identify safety standards from existing frameworks, developing *standard* procedures requires domain expertise, liability consideration, and organizational validation that current AI cannot reliably perform end-to-end. AI might accelerate 20–30% of the writing process but cannot replace the engineering judgment needed for site-specific safety protocols. |
| Task automatability | claude-sonnet-5 | 2/5 | Drafting SOPs and standards requires synthesizing engineering judgment, site-specific electrical/safety codes, and organizational risk tolerance, which AI can assist but not fully originate reliably.″}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong barriers exist: SOPs and safety standards must be authored or signed off by qualified engineers; liability for injuries or failures traces back to procedure adequacy; regulatory bodies (OSHA, local building codes) often implicitly require human professional accountability. Legal risk heavily discourages full automation or AI-only development. |
| Adoption barriers | claude-sonnet-5 | 3/5 | placeholder |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted drafting may reduce writing effort, but the cost of oversight, regulatory validation, liability review, and revision by qualified engineers means the total cost per final SOP remains close to or exceeds the cost of human-led development, especially given the mission-critical safety implications. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | placeholder |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably generates organization-specific SOPs and safety standards from scratch. AI writing tools can draft boilerplate text, but they lack the contextual understanding of installation environments, local regulations, and equipment-specific hazards that real solar firms require in production systems. Human review and revision remain substantial. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | placeholder |
Conduct engineering site audits to collect structural, electrical, and related site information for use in the design of residential or commercial solar power systems.
28CI 25–30 · exposure 25 · augmentation 75 · importance 4.5/5 · click for rater detail
Conduct engineering site audits to collect structural, electrical, and related site information for use in the design of residential or commercial solar power systems.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Solar installation is a growing but fragmented industry with many small and mid-sized firms; adoption of AI-driven site auditing is still nascent, with most companies relying on traditional on-site engineer visits and rudimentary digital tools rather than AI-based automation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Solar installation is a physically-oriented, moderately digitized field where AI adoption for site assessment (via drones/software) is emerging but not yet deeply embedded in most firms. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist engineers by automating data organization, generating initial design drafts from collected measurements, comparing site data against historical or regional baselines, and preparing preliminary reports—allowing the engineer to focus on judgment calls and site-specific optimization while remaining in the loop. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered aerial imagery, LIDAR analysis, and design software (e.g., Aurora Solar) significantly speed up site data processing and preliminary system design, meaningfully boosting engineer productivity even though on-site physical inspection remains manual. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can process some structural and electrical data remotely (via satellite imagery, documents, or uploaded plans), the task critically requires on-site physical inspection to collect unmeasured data, assess structural integrity, and verify conditions that cannot be reliably captured without direct observation. Current AI cannot conduct the full audit end-to-end with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical site audits require in-person inspection of roofs, structural elements, and electrical panels that current AI cannot autonomously perform; only data analysis portions after collection could be assisted by AI. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory and liability barriers are substantial: structural and electrical safety assessments typically must be signed off by a licensed engineer, and liability for incorrect site assessment falls on the professional. This requirement to have a qualified human verify and certify findings creates a hard adoption barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no strict licensing mandates AI cannot inspect, liability for structural/electrical safety assessments and reliance on human judgment for code compliance create moderate barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | An on-site audit still requires a qualified engineer to visit the property, measure, inspect, and document findings; AI tools can assist with data synthesis and report generation but do not eliminate the labor cost of the site visit itself. All-in costs remain comparable to or exceed the cost of a human engineer performing the work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI-assisted imagery analysis can reduce some labor, but the physical site visit and electrical inspection still require a human engineer or technician, keeping overall cost similar to human-only approaches. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some AI tools exist for analyzing satellite imagery and building plans, and LLMs can help organize and synthesize audit data, but no deployed product reliably performs the complete site audit task—which demands real-time structural assessment, electrical measurement, and local hazard detection. Products are narrow in scope and require heavy human oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some drone/imagery-based roof assessment tools exist (e.g., aerial solar design software) but full structural and electrical site audits still require human on-site inspection in production workflows. |
Provide technical direction or support to installation teams during installation, start-up, testing, system commissioning, or performance monitoring.
28CI 25–30 · exposure 25 · augmentation 63 · importance 4.3/5 · click for rater detail
Provide technical direction or support to installation teams during installation, start-up, testing, system commissioning, or performance monitoring.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | The solar installation sector remains fragmented with many small regional contractors; while some larger firms use monitoring software, production adoption of autonomous technical direction remains minimal and adoption velocity is slower than in purely digital sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Construction and renewable energy installation sectors have low digitization and slow AI adoption relative to information/finance sectors, with most AI use limited to monitoring software rather than field engineering support. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist engineers through real-time data visualization, predictive diagnostics, and checklist automation during testing and commissioning, meaningfully raising their oversight efficiency without removing them from decision-making. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-based monitoring systems, predictive analytics, and remote diagnostic tools meaningfully assist engineers in identifying issues and guiding commissioning decisions, improving productivity while humans remain on-site. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate technical documentation and remote monitoring insights, the task requires real-time on-site decision-making, safety oversight, and troubleshooting during active installation—activities that demand human judgment and presence that current AI cannot reliably substitute end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires on-site physical troubleshooting, hands-on judgment, and real-time coordination with field crews that current AI cannot perform end-to-end; only ancillary documentation or data analysis pieces are automatable. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Safety regulations, electrical licensing requirements, and warranty/liability concerns in solar installations typically mandate that qualified engineers physically oversee and sign off on critical commissioning activities, creating hard legal barriers to substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing mandate for this specific role, but safety codes, electrical work regulations, and liability for faulty commissioning create meaningful friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted monitoring and documentation tools reduce costs for engineers, but the loaded wage of experienced solar engineers is already modest for specialized field oversight; full automation would require extensive infrastructure investment with uncertain ROI. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human engineers remain necessary for physical site support and liability-bearing decisions, so AI can only reduce some analysis/reporting time, not replace the bulk of labor cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed products can provide remote monitoring dashboards and basic diagnostic support, but no mature product reliably performs the core function of directing installation teams through complex, real-time system commissioning with consistent quality and safety assurance. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | No deployed product provides real-time technical direction to installation crews; monitoring dashboards and remote diagnostics exist but the supervisory/support role itself is not delivered by AI products today. |
Create plans for solar energy system development, monitoring, and evaluation activities.
26CI 25–28 · exposure 25 · augmentation 75 · importance 4.4/5 · click for rater detail
Create plans for solar energy system development, monitoring, and evaluation activities.
26| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Solar installation companies are increasingly using design and simulation tools, but plan generation remains primarily human-led with AI in a supporting role. Adoption of AI-assisted planning is growing in the sector but has not reached the scale or depth of replacement seen in information-intensive industries. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Renewable energy engineering is a specialized, less digitized sector with slower AI tool adoption compared to software or finance, though modeling tools are gradually being enhanced with AI. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI tools for solar system design, feasibility analysis, and performance modeling demonstrably assist engineers in exploring options, running simulations, and analyzing site data faster than manual methods. These augmentations can significantly accelerate planning while keeping the engineer in control of final decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by generating initial designs, running simulations, analyzing site data, and drafting monitoring/evaluation frameworks, significantly speeding up the planning process while engineers retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Creating comprehensive solar energy system plans requires significant domain expertise, site-specific analysis, regulatory compliance, and stakeholder coordination. While AI can assist with data gathering and preliminary design iterations, current systems cannot reliably generate end-to-end plans meeting engineering standards and legal requirements without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Planning solar energy system development requires site-specific engineering judgment, integration of regulatory, structural, and electrical constraints that AI cannot fully replicate end-to-end today, though it can draft components of plans.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Solar system plans must often be signed off by licensed Professional Engineers (PE) and comply with building codes, electrical standards, and grid interconnection requirements. Liability for system performance and safety creates a hard regulatory barrier requiring human professional accountability. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Professional engineering licensure, permitting requirements, and liability for system safety and grid interconnection create strong barriers requiring a licensed engineer to sign off on plans. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for solar design and planning (simulation software, design assistants) are available but still require significant human engineering time for validation, customization, and sign-off. The all-in cost (software, integration, engineer review) remains comparable to or exceeds the cost of an engineer building plans directly. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Engineering oversight, liability, and site-specific customization still require significant human engineering time, so AI cost savings are moderate rather than order-of-magnitude given required verification. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably generates complete solar system development plans independently. Existing tools support design assistance or simulation, but planning—which integrates technical, financial, regulatory, and operational dimensions—remains human-driven with AI serving only as a helper for specific sub-components. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some design-assist and energy modeling tools (e.g., PVsyst-like software with AI features) exist but full autonomous planning products for solar system development are not deployed at scale in engineering firms. |
Review specifications and recommend engineering or manufacturing changes to achieve solar design objectives.
25CI 25–25 · exposure 25 · augmentation 63 · importance 3.9/5 · click for rater detail
Review specifications and recommend engineering or manufacturing changes to achieve solar design objectives.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Solar energy engineering remains concentrated in specialized firms with legacy design processes; while some adoption of CAD and simulation tools is underway, AI-driven design recommendation systems are still in pilot/early adoption phases in the broader industry, particularly for manufacturing change recommendations. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Renewable energy engineering is a specialized, moderately digitized sector with growing but still limited AI tool adoption compared to fast-moving software/finance sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by automatically checking specifications against design standards, suggesting parametric optimizations, and highlighting trade-offs between objectives, which would speed up a human engineer's review cycle. However, augmentation is bounded by the need for expert judgment on feasibility and cost–benefit trade-offs. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by running simulations, flagging spec inconsistencies, and suggesting design alternatives, significantly speeding up the engineer's review process while they retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with reviewing specifications and identifying potential design improvements by analyzing technical documents and flagging common optimization patterns, but the task requires domain expertise, creative problem-solving, and accountability for engineering recommendations that AI systems cannot reliably provide end-to-end. Humans remain essential for validating recommendations against physical constraints and project-specific objectives. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires deep engineering judgment integrating physics, manufacturing constraints, and site-specific tradeoffs that current AI can partially assist with but not reliably execute end-to-end at equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Professional engineering changes typically require a licensed Professional Engineer (PE) or senior engineer to sign off on recommendations, and clients/manufacturers often require human accountability for design modifications that affect system performance, safety, and warranty. Liability asymmetry and regulatory expectations create strong adoption friction. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Engineering sign-off often requires licensed professional engineers, and errors in solar system design carry safety and financial liability, creating strong barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | An experienced solar engineer's loaded cost is substantial ($80–120k+ annually), and AI tools for this task remain niche and require integration, customization, and expert oversight, making total cost roughly comparable to or exceeding human-alone labor for high-stakes recommendations. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Engineering review requires significant human validation and liability coverage, so AI assistance reduces some cost but doesn't yet replace the engineer, keeping costs comparable rather than drastically lower. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can perform partial specification review (e.g., flagging inconsistencies in documents or suggesting optimizations from training data), no deployed product reliably recommends manufacturing or engineering changes across the full scope of solar design without significant human verification. Current tools are narrow (e.g., specific design parameters) rather than holistic design recommendation systems. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI tools exist for design analysis and simulation but no deployed product autonomously reviews specs and issues validated engineering change recommendations in production without expert oversight. |
Design or coordinate design of photovoltaic (PV) or solar thermal systems, including system components, for residential and commercial buildings.
23CI 21–25 · exposure 25 · augmentation 75 · importance 4.4/5 · click for rater detail
Design or coordinate design of photovoltaic (PV) or solar thermal systems, including system components, for residential and commercial buildings.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Solar engineering firms use simulation and CAD aids widely, but these are assistive tools rather than autonomous design systems. Adoption of AI-driven autonomous design is at the pilot/research stage in the industry; most firms still rely on engineer-led workflows, indicating slow transition to AI-primary design coordination in production settings. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Construction and energy engineering sectors have historically been slower to adopt AI compared to information/finance industries, though design software increasingly includes optimization features. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists PV design engineers through fast parametric exploration, automated layout generation, shading analysis, and performance simulation. Tools like NREL's PVWatts and commercial CAD plugins measurably improve designer productivity and exploration speed while engineers retain judgment on final configuration, making this a strong augmentation case. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered design tools significantly speed up load calculations, panel layout optimization, shading analysis, and component selection, meaningfully boosting engineer productivity while the engineer retains final design responsibility. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with parametric calculations, component selection, and simulation of system layouts, the task requires integrating site-specific constraints (roof geometry, shading, structural load), building codes, and client requirements into a coherent design. End-to-end automation would need to reduce design time by ≥50% while maintaining quality, which is not yet demonstrated for novel installations; most AI tools are narrow calculators rather than full design coordinators. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can assist with calculations, sizing, and layout optimization, but full system design requires site-specific judgment, code compliance verification, and integration with structural/electrical systems that current AI cannot autonomously handle end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Solar system design for buildings is governed by electrical codes (NEC), building codes, and structural safety standards, and must be signed off by a licensed professional engineer in most jurisdictions. Liability for system failure (fire, electrical hazard, structural damage) rests on the responsible engineer, creating a hard licensing and accountability barrier that prevents full substitution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Solar system designs for buildings typically require a licensed professional engineer's stamp/approval for permitting, creating a hard regulatory barrier to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | Current AI tools require significant engineer time to set up, validate, and iterate on results. The loaded cost of a solar engineer (high technical skill, liability exposure) is substantial, and AI-assisted design still demands expert review. The AI cost savings do not yet offset the human labor required to supervise the design output and ensure regulatory and performance compliance. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools reduce time on modeling and sizing calculations but licensed engineering oversight, site visits, and liability sign-off still require substantial human labor, keeping costs comparable to traditional engineering costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | CAD and simulation tools exist (e.g., PVsyst, NREL tools) but these are calculation aids, not autonomous design systems. No production system reliably generates a complete, code-compliant, client-approved PV system design without substantial engineer oversight and iteration; deployed products address subcomponents rather than the full coordinated design workflow. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some design software incorporates AI-assisted optimization and layout tools, but these are decision-support aids used by engineers rather than autonomous design systems deployed at scale. |
Design or develop vacuum tube collector systems for solar applications.
19CI 7–30 · exposure 13 · augmentation 63 · importance 1.8/5 · click for rater detail
Design or develop vacuum tube collector systems for solar applications.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Solar engineering is specialized and concentrated in mid-sized engineering firms and corporate R&D; adoption of AI design tools is still pilot-stage. Unlike software or finance, the sector is not yet experiencing rapid AI agent deployment in production design workflows. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Solar/renewable energy engineering is a moderately digitized but physically-oriented sector where AI adoption for core design tasks remains in early pilot stages rather than widespread production use. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist engineers by automating parametric simulations, generating design variants, optimizing tube geometry and materials, and rapidly iterating on thermal performance—all while the engineer maintains control and judgment. This represents significant productivity enhancement even if full automation is not feasible. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can assist with simulation setup, literature review, thermal modeling code, and design iteration suggestions, meaningfully speeding up parts of the engineering workflow while humans retain overall design responsibility. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Designing vacuum tube collector systems requires domain expertise, physics-based modeling, and iterative optimization that current AI can assist with but not fully replace. AI tools can help with CAD drafting, thermal simulations, and literature synthesis, but the creative design decisions, trade-off analysis, and validation against real-world constraints remain fundamentally human-driven. |
| Task automatability | claude-sonnet-5 | 1/5 | Designing vacuum tube solar collector systems requires original engineering design, thermal analysis, materials selection, and iterative physical prototyping/testing that current AI cannot perform end-to-end without substantial human engineering judgment. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Professional engineering liability and potential safety/performance requirements in solar installations create meaningful barriers; many jurisdictions require licensed engineers to sign off on system designs. Customer risk aversion and the need for accountability further protect this work from full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no license is strictly mandated for this specific task, engineering sign-off, safety certification, and liability for physical systems create meaningful organizational and professional friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference and simulation tools are inexpensive, but integration into an engineering workflow, validation, and human oversight to ensure correctness are substantial. Overall system cost remains comparable to a domain expert's hourly rate because the human time required for validation and refinement remains high. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Full engineering design of a physical thermal collector system still requires costly human expertise, testing, and validation, so AI does not yet reduce all-in costs below the human engineering baseline. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably designs complete vacuum tube collector systems end-to-end. CAD software and simulation tools exist but require expert human direction; current AI agents lack the specialized thermal physics knowledge and ability to synthesize multi-constraint engineering tradeoffs at production reliability. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product autonomously designs vacuum tube collector systems; this remains a specialized mechanical/thermal engineering task performed by human engineers using CAD and simulation tools with AI playing only a minor supporting role. |
Related occupations — Architecture & Engineering
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.