Solar Sales Representatives and Assessors
41-4011.07Median wage $104,920/yr284,800 employed (US)Rank #73 of 923 scored · top 8% by substitution
Contact new or existing customers to determine their solar equipment needs, suggest systems or equipment, or estimate costs.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution52
Exposure47
Augmentation77
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%47
panel mean rating 2.9/5 → substitution pressure 47/100
Technical feasibility todayw 20%47
panel mean rating 2.9/5 → substitution pressure 47/100
Cost vs. human wagew 15%59
panel mean rating 3.3/5 → substitution pressure 59/100
Adoption barriersw 20%inverted — strong barriers lower the score63
panel mean rating 2.5/5 (barrier strength) → substitution pressure 63/100
Sector adoption velocityw 10%49
panel mean rating 3.0/5 → substitution pressure 49/100
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.
Calculate potential solar resources or solar array production for a particular site considering issues such as climate, shading, and roof orientation.
87CI 79–95 · exposure 87 · augmentation 100 · importance 4.5/5 · click for rater detail
Calculate potential solar resources or solar array production for a particular site considering issues such as climate, shading, and roof orientation.
87| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 5/5 | Solar installation is a digitalized, growth sector with rapid AI adoption. Major installers (Sunrun, Vivint Solar, regional players) have integrated automated assessment into their workflows; tools like Aurora are industry-standard and widely deployed. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | The solar industry has rapidly adopted automated design and modeling software as an industry standard for over a decade, reflecting fast, deep adoption of this specific sub-task. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI transforms human productivity by providing instant, data-rich assessment visualizations, shade analysis, and production estimates that sales reps and engineers use to refine designs and close sales. Humans remain in the loop for validation and customer consultation, dramatically reducing time per assessment. |
| Augmentation potential | claude-sonnet-5 | 5/5 | These tools dramatically speed up and improve accuracy of production estimates while sales reps still handle customer-facing interpretation, financing discussions, and final proposal delivery. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | Current AI systems can automatically assess solar resources using satellite imagery, climate data, roof orientation from aerial/3D models, and specialized solar calculators (NREL's PVWatts, commercial tools like Aurora). This achieves >50% time savings by eliminating manual site surveys and calculations, with equal or better accuracy. |
| Task automatability | claude-sonnet-5 | 4/5 | Solar production estimation is a well-defined computational task using satellite imagery, weather databases, and physics-based models that software like Aurora Solar, HelioScope, or PVWatts already handles largely automatically.atab |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No legal requirement for a licensed professional to perform the technical assessment itself; however, some regulatory contexts require a licensed engineer's sign-off on the final system design. The assessment itself faces minimal adoption barriers beyond customer preference to validate results. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human perform this specific calculation, though the sales rep's broader role in customer interaction and system design sign-off creates some organizational friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated solar assessment costs pennies to dollars per site (satellite data + API calls + compute), while a human field surveyor or engineer costs $150–400 per assessment. AI is easily an order of magnitude cheaper end-to-end. |
| Cost vs. human wage | claude-sonnet-5 | 5/5 | Software subscription costs per assessment are trivial compared to a human analyst's time spent manually calculating irradiance, shading, and orientation factors. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 5/5 | Deployed products (Aurora Solar, Sunrun's assessment platforms, NREL tools) reliably perform site assessment, shading analysis, and production estimation in production. These systems are actively used by solar installers at scale with well-validated outputs. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Mature commercial products (Aurora Solar, HelioScope, Google Project Sunroof, PVWatts) are widely deployed in production and used daily by solar sales reps to generate shading and production estimates. |
Take quote requests or orders from dealers or customers.
69CI 59–79 · exposure 62 · augmentation 75 · importance 4.3/5 · click for rater detail
Take quote requests or orders from dealers or customers.
69| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Solar industry has been a fast adopter of digital sales tools and CRM automation; quote request intake is among the earliest and deepest AI integrations in this sector, with widespread production deployment among major installers and dealers. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Solar sales is a mid-digitization sector with growing but uneven adoption of AI chat/CRM tools for lead intake; larger installers use automated systems while many smaller dealers still rely on manual processes. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assistants effectively augment sales representatives by pre-qualifying leads, populating customer data, suggesting relevant quote templates, and flagging follow-up items, allowing reps to focus on relationship-building and technical customization rather than data entry. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools (chat intake, CRM autofill, quote generators) meaningfully speed up capturing and organizing requests, letting reps focus on qualifying and closing rather than manual entry. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can handle the majority of quote request intake through chatbots, web forms, and email parsing, extracting customer information and basic requirements automatically. However, complex site assessments, custom specifications, or technical clarifications often require human judgment, preventing full end-to-end automation from consistently meeting the 50% time-saving bar without significant human oversight. |
| Task automatability | claude-sonnet-5 | 3/5 | Intake of quote requests/orders is a structured data-capture task that chatbots and AI-driven CRM/order forms can largely handle, but qualifying customer needs and finalizing accurate solar quotes often requires human judgment (site specifics, financing options), so only partial time savings are achievable end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal regulatory or licensing barriers exist for automating quote intake itself; no law requires a human to receive the initial request. Organizational friction and preference for personal touch in sales exist but are weak barriers compared to licensed professions, and most dealers are rapidly adopting automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks automating order intake, though customers may prefer speaking with a knowledgeable human for a significant purchase like solar, creating some soft friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven quote intake (via chatbots, automated emails, form processing) costs cents per transaction compared to a solar sales representative's fully-loaded hourly wage ($40–70+), achieving order-of-magnitude cost advantage while reducing labor burden. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated intake via chat/forms/IVR is dramatically cheaper than a human rep taking every call, though some oversight and correction of AI-collected data adds cost, keeping it just below the top tier. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed chatbots and CRM integrations for quote intake are widely available and reliable for standard requests; companies like solar installers actively use AI-powered intake systems. Production systems handle basic lead capture, qualification, and quote request routing effectively, though edge cases and complex technical details still require human review. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Deployed CRM chatbots, voice assistants, and web order forms already take requests and route them in many industries, but solar-specific quoting tools still frequently involve human reps for accuracy and customer trust, so reliability in this narrow domain is moderate rather than mature. |
Prepare proposals, quotes, contracts, or presentations for potential solar customers.
67CI 59–75 · exposure 62 · augmentation 88 · importance 4.8/5 · click for rater detail
Prepare proposals, quotes, contracts, or presentations for potential solar customers.
67| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Solar companies operate in a digitized, competitive sector with strong cost pressure, and many have already adopted CRM-integrated quote and proposal tools with AI components. Industry-specific software vendors actively ship these features, driving relatively rapid deployment. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Solar sales is a moderately digitized but still relationship-driven, physically distributed industry; software tools for proposal generation are common but full AI-driven automation is still emerging, placing it in middling adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI substantially augments solar reps by drafting proposals, generating customized quotes in minutes, and producing visual presentations, freeing reps to focus on site assessment, customer engagement, and closing—while the rep remains fully in control of accuracy and customer relationship. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools significantly speed up drafting of quotes, presentations, and proposal narratives, letting sales reps focus on customization and closing, which is a well-established augmentation use case in solar sales software. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can generate proposal templates, quotes, and presentations automatically from customer data and solar system specifications, achieving significant time savings. However, customization for complex site conditions or customer negotiations typically requires human refinement, preventing a full 5 rating. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft proposals, quotes, and presentation content from structured inputs (site data, pricing, financing terms), but assembling accurate, customer-specific contracts and quotes still requires human verification and system-specific data integration. Overall about half the drafting labor could be automated with setup. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Minimal regulatory or licensing barriers exist for automating proposal and quote generation itself; however, contract review and customer-facing presentation often benefit from human sign-off for liability and customer relationship reasons, introducing modest friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement for drafting quotes/contracts, though contracts may need review by authorized reps or notice of state-specific solar regulations; customer trust in a human salesperson creates some friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI document generation, proposal assembly, and presentation creation cost pennies per task after integration, while a solar rep's fully loaded labor for the same output runs $50–150+. AI costs are an order of magnitude lower for routine proposals. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated proposal/quote generation software is inexpensive per use compared to a sales rep's time spent manually drafting documents, though integration and oversight costs offset some savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products (e.g., solar design software with AI quote generation, document automation tools) reliably produce proposals and quotes at scale in production environments. Minor gaps remain in handling highly bespoke contract terms or complex legal variations across jurisdictions. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | CRM and sales-enablement tools (e.g., proposal generators, AI writing assistants integrated into solar sales software like Aurora or OpenSolar) exist and are used in production, but they still require significant human input for accuracy and customization, so error rates and scope limitations remain. |
Gather information from prospective customers to identify their solar energy needs.
60CI 41–79 · exposure 50 · augmentation 75 · importance 4.5/5 · click for rater detail
Gather information from prospective customers to identify their solar energy needs.
60| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Solar companies, operating in the competitive B2C and B2B energy sector, have rapidly adopted CRM, chatbots, and online intake forms; companies like Sunrun and Vivint Solar use automated lead qualification and assessment tools at scale. The sector shows strong digital adoption patterns. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Solar sales firms increasingly use CRM automation and chatbot lead qualification, though the sector overall shows middling AI adoption compared to fully digital industries. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered intake forms, knowledge bases, and chatbots assist representatives by pre-qualifying leads, suggesting follow-up questions, and auto-populating customer profiles, significantly raising their productivity and allowing focus on relationship-building and closing rather than data collection. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools like chatbots, satellite roof analysis, and automated bill uploads significantly speed up initial data gathering, letting reps focus on tailored consultation. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can effectively conduct information-gathering interviews via conversational agents, forms, and questionnaires to capture customer needs, location, energy usage, and roof characteristics. While some complex follow-up probing may benefit from human judgment, the core information-gathering function can be automated with substantial time savings and minimal quality loss. |
| Task automatability | claude-sonnet-5 | 2/5 | Needs assessment involves live rapport-building, reading customer situations, and adaptive questioning about roofs, energy bills, and financing preferences that current AI cannot fully replicate end-to-end.dusk |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | No legal licensing is required for solar sales representatives to gather customer information, and no regulatory mandate requires human-only contact for the information-gathering phase itself. The primary friction is customer preference for human contact and organizational inertia, both modest barriers. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI from gathering information, though customer trust and preference for a human touch in a big-ticket purchase creates moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | An AI-driven intake system costs far less per interaction (pennies to dollars) than a sales representative's fully-loaded hourly wage ($30–80+), making the ratio highly favorable even with required human review and integration overhead. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-driven intake forms and chatbots are cheap to run, but human sales reps still need to close gaps in understanding, making the cost comparison moderate rather than dramatically favorable. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products (chatbots, intake forms, CRM systems with AI qualification) reliably perform customer information gathering in solar and related B2C sales contexts. Production systems handle lead qualification and needs assessment at scale, though some oversight and human verification typically remains. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Chatbots and lead-qualification forms exist to collect basic customer data, but deployed products rarely conduct the full nuanced needs assessment reliably without human follow-up. |
Generate solar energy customer leads to develop new accounts.
60CI 41–79 · exposure 50 · augmentation 88 · importance 4.4/5 · click for rater detail
Generate solar energy customer leads to develop new accounts.
60| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Solar sales and home-improvement sectors have rapidly adopted CRM, marketing automation, and predictive lead-scoring over the past 5–10 years. Deployment is now common in mid-to-large firms and fast-growing in smaller ones, driven by competitive pressure and clear ROI. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | The solar and broader home-services sales sector has adopted digital marketing and CRM-based AI tools at a moderate pace, with pilots for AI-driven lead scoring and chat-based qualification becoming more common but not yet dominant. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI dramatically augments solar sales reps by surfacing high-probability prospects, automating list filtering, and enabling reps to focus on qualification and closing. The human remains central to relationship and account development while AI multiplies their effective reach. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can meaningfully enhance lead generation by identifying target demographics, personalizing outreach content, and scoring lead quality, substantially boosting a sales rep's efficiency while the human still manages the relationship. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | AI can automate substantial portions of lead generation via web scraping, account data enrichment, predictive targeting, and outbound email/messaging at scale, achieving well over 50% time savings compared to manual prospecting. However, final qualification and conversion typically still benefit from human judgment and relationship-building, preventing a perfect 5. |
| Task automatability | claude-sonnet-5 | 2/5 | Lead generation involves digital marketing tasks (ads, outreach content) that AI can assist with, but qualifying leads, cold outreach, and relationship-building still require significant human involvement, so full end-to-end automation at equal quality is not yet achieved. |
| Adoption barriers | claude-haiku-4-5-20251001 | 2/5 | Few regulatory or licensing barriers exist for automated lead generation itself; the main friction is data privacy (GDPR, CCPA) and internal risk appetite for AI-assisted outreach. Solar firms can legally deploy these systems, and adoption friction is organizational rather than legal. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement blocks AI from generating leads, though local door-to-door sales practices, consumer trust concerns, and canvassing regulations create some friction against pure automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | AI-driven lead generation via existing SaaS platforms or in-house ML models costs a small fraction of hiring and managing dedicated prospectors; the per-lead cost is typically orders of magnitude lower than full-time sales development representative labor. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-driven marketing and lead-scoring tools can reduce some labor costs for identifying prospects, but human-driven canvassing, community engagement, and personalized follow-up remain costly and only partially offset by AI, making costs roughly comparable overall. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Multiple deployed products (predictive lead-scoring platforms, CRM integrations, email automation tools) reliably perform lead generation and enrichment in production for sales organizations. Some error in targeting and list quality persists, but the core capability is demonstrably live and used at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | CRM and marketing automation tools with AI features (lead scoring, chatbots, targeted ad generation) exist and are used in sales, but solar-specific lead generation still relies heavily on canvassing, referrals, and human sales reps closing initial contact. |
Provide technical information about solar power, solar systems, equipment, and services to potential customers or dealers.
56CI 54–59 · exposure 50 · augmentation 75 · importance 4.4/5 · click for rater detail
Provide technical information about solar power, solar systems, equipment, and services to potential customers or dealers.
56| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Solar companies are increasingly deploying chatbots and digital lead-qualification tools, but most still rely on human reps for technical consultations and closings. Adoption is rising in tech-forward firms and large national installers, but remains uneven across the fragmented residential solar market. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Solar sales is a moderately digitized but still relationship- and field-driven sector; AI tools are being piloted for lead qualification and FAQs but full-scale reliance is uneven across firms. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly boost sales rep productivity by instantly retrieving accurate equipment specs, financing options, system configurations, and performance data, allowing reps to focus on customer relationships and site assessment. Knowledge assistants that augment rather than replace reps are already in use in the sector. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially aids reps by providing instant technical specs, comparison data, and customized talking points, boosting productivity while the human closes the sale. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can generate accurate technical information about solar systems, equipment specs, and services reliably, but the task requires significant customization to customer needs, site conditions, and financial situations. Current AI can handle ~50% of the interaction (information delivery) but struggles with real-time site assessment and customer-specific configuration without human oversight. |
| Task automatability | claude-sonnet-5 | 3/5 | AI chatbots and knowledge assistants can answer standard technical questions about solar systems, panels, and financing, but nuanced site-specific assessments and trust-building conversations still require human judgment.Roughly half the informational component is automatable today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Solar installations involve contractual liability and financing decisions; customers often prefer human consultants for final validation. Some jurisdictions require licensed electricians or installers to sign off on system design, though information provision alone faces fewer regulatory barriers than system approval. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement to provide general technical information, though some jurisdictions require licensed professionals for final system design or electrical specifics, creating moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI inference costs for providing technical information are minimal (cents per interaction), while solar sales rep labor costs are substantial ($25–50+/hour loaded). Integration and oversight are low once a knowledge base is built, making AI dramatically cheaper per information-delivery interaction. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-driven informational tools cost far less per interaction than a salaried sales rep answering the same questions, though human oversight and updates are still needed for accuracy. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Chatbots and AI systems can provide solar technical information at scale, and some companies have deployed them for initial customer inquiries. However, accuracy errors on equipment compatibility or system sizing have real financial consequences, so production systems remain limited in scope and typically escalate to human specialists for final recommendations. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Many solar companies deploy chatbots and AI-driven configurators for FAQs and initial technical explanations, but these have narrow scope and often escalate to human reps for complex or high-stakes decisions. |
Provide customers with information, such as quotes, orders, sales, shipping, warranties, credit, funding options, incentives, or tax rebates.
53CI 48–59 · exposure 50 · augmentation 75 · importance 4.6/5 · click for rater detail
Provide customers with information, such as quotes, orders, sales, shipping, warranties, credit, funding options, incentives, or tax rebates.
53| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Solar companies are digitizing sales operations (quote generators, online configurators, CRM systems), but human reps remain central to closing deals and navigating complex financing. Pilots of AI assistants are common; full displacement of information-delivery tasks is slower in the segment than in pure information sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Solar sales remains a relationship-driven, in-home sales process with moderate digitization; AI tools are used for quoting/lead gen but full task automation adoption is still nascent in this sector. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly assist solar reps by instantly pulling quotes, summarizing incentives, comparing financing options, and flagging eligibility for rebates. These assistants materially raise productivity while keeping the human in the loop for negotiation and contract closure. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered quoting software, financing calculators, and chatbots meaningfully speed up how reps generate quotes and explain incentives while the human closes the sale and builds trust. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can automate parts of this task—generating quotes, retrieving warranty information, summarizing tax incentive programs, and presenting funding options—but still requires human judgment for customer negotiation, addressing objections, and understanding nuanced financial situations. The system would need integration with CRM and product databases, limiting true end-to-end time savings to roughly 40–50% of the task. |
| Task automatability | claude-sonnet-5 | 3/5 | Providing quotes, tracking orders, and explaining warranties/incentives is largely informational and can be automated via chatbots and configurators, though customized funding/tax rebate advice often needs human judgment and verification., limiting full automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Sales representatives typically have discretion over quotes and credit terms, but incentive programs, tax rebates, and financing eligibility are heavily regulated. Liability for incorrect warranty or tax information creates meaningful error-cost asymmetry, and many customers still expect human contact before signing contracts. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No strict licensing requirement for providing quotes/information, though tax and financing advice may invite some liability concerns and customers often prefer human reassurance for big-ticket purchases. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-driven quote and information systems cost relatively little to operate per interaction, but customer acquisition, human follow-up for complex financing, and oversight to prevent quote errors raise integrated costs. All-in, the cost is roughly comparable to a junior sales rep's fully loaded wage. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated quoting and chat-based information delivery is far cheaper per interaction than a live sales rep's time, though oversight for compliance keeps it from being a full order-of-magnitude cheaper in all cases. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products like quote generators, chatbots, and CRM assistants can handle information retrieval and basic order processing, but deployed systems often lack the contextual sophistication needed to explain complex financing, incentives, and warranties accurately to diverse customer profiles. Real-world error rates in tax-code interpretation and funding eligibility remain material. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Solar CRM and quoting tools (e.g., Aurora Solar, EverBright) already generate quotes and financing options in production, but nuanced tax rebate guidance and closing sales still typically involve human reps. |
Prepare or review detailed design drawings, specifications, or lists related to solar installations.
47CI 45–50 · exposure 50 · augmentation 88 · importance 4.2/5 · click for rater detail
Prepare or review detailed design drawings, specifications, or lists related to solar installations.
47| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Solar companies are piloting AI-assisted design tools and roof-analysis software, but adoption remains fragmented. Large installers use parametric design and drone-based assessment; small and mid-sized companies lag. Production deployment is emerging but not yet deep or widespread across the sector. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Solar design software adoption is growing steadily in the solar industry but remains uneven across small installers and regions, reflecting a mid-tier digitization sector rather than fast, universal adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI excels at augmenting sales reps and junior designers by auto-generating initial layouts, pulling in site data, and flagging code issues, materially speeding up the prep phase while the human stays in control. This is one of the sector's clearest use cases for human-AI collaboration. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI-powered design tools dramatically speed up drawing generation, permit sets, and equipment lists, letting a single sales rep or assessor produce accurate, detailed proposals much faster than manual drafting. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can draft initial design drawings and specifications using templates and parametric design tools, but current systems struggle with site-specific constraints, local electrical codes, and the final review/sign-off that a qualified engineer must perform. Roughly half the work (initial documentation, preliminary specs) can be automated; the critical review and customization still require human judgment. |
| Task automatability | claude-sonnet-5 | 3/5 | AI design tools can generate solar layout drawings, shading analyses, and equipment lists from site data, but final specifications typically require human review for site-specific engineering and code compliance, limiting full end-to-end automation. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Solar installations typically require licensed professional engineer (PE) sign-off or a qualified installer sign-off depending on jurisdiction, and liability for design errors (roof load, electrical safety, code compliance) creates strong incentive for human accountability. Regulatory coverage of electrical safety standards and building codes effectively mandates human review. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Many jurisdictions require a licensed engineer's stamp on final electrical/structural drawings for permitting, creating moderate regulatory friction even though the drafting itself can be AI-assisted. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI tools for design assistance (software subscriptions, inference costs) are becoming comparable in cost to the time a junior designer or sales rep would spend on initial drafts, but engineering review and customization still demand human labor that dominates total cost. Overall cost is roughly equivalent to hiring human design support. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Solar design software subscriptions are much cheaper than fully manual engineering time, but a licensed reviewer or engineer still must check outputs, keeping overall costs only moderately reduced rather than order-of-magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | CAD-assistive tools and AI-powered design software exist in production (e.g., roof-analysis tools, parametric solar layout generators), but they often require significant manual correction for complex sites and have limited integration with electrical code databases. Products work for straightforward residential cases but show material error rates or narrowness in edge cases. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Aurora Solar, OpenSolar, and Solargraf already generate design drawings and specs in production use, but they still require human input for site verification, permitting nuances, and error correction. |
Select solar energy products, systems, or services for customers based on electrical energy requirements, site conditions, price, or other factors.
41CI 30–51 · exposure 38 · augmentation 75 · importance 4.8/5 · click for rater detail
Select solar energy products, systems, or services for customers based on electrical energy requirements, site conditions, price, or other factors.
41| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Solar sales is moderately digitized but remains relationship-heavy and geographically distributed. Adoption of AI-driven product selection is emerging (configurators, design tools) but not yet standard practice, especially for on-site assessments and complex customer negotiations. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Solar industry has adopted design and CRM software fairly widely, but full AI-driven sales decision-making is still uncommon in this physically-grounded, sales-driven sector. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially augment human sales representatives by rapidly generating product comparisons, calculating system designs, estimating costs, and flagging site constraints, allowing reps to focus on customer communication and deal closure. This is a strong augmentation scenario even if full automation remains limited. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered design and quoting tools significantly speed up system sizing, cost estimation, and proposal generation, meaningfully boosting rep productivity while humans retain final decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can partially automate this task by analyzing electrical requirements, site conditions from data/images, and comparing product specifications against price—potentially covering 40–60% of the selection logic. However, the task requires integrating customer preferences, site-specific nuances, and negotiation context that still demand human judgment and site visits. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can assist with calculations and product matching but final selection requires site-specific judgment, customer relationship management, and integration of physical inspection data that current systems cannot fully replace. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Solar installation is regulated in many jurisdictions and often requires licensed electricians or engineers to sign off on system design. Liability for system performance and safety creates friction, and customer preference for human consultation and site visits is strong. These barriers slow but do not prevent automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing strictly requires a human for product selection, but customer trust, financing decisions, and liability for system performance create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference for product selection is cheap, but integration into CRM, site assessment tools, and ongoing human oversight (validation, customer communication, liability sign-off) adds meaningful cost. The all-in cost is likely comparable to or slightly cheaper than a loaded sales representative hour, but not dramatically cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Software tools reduce time on calculations but still require human oversight, site assessment, and sales interaction, so overall cost savings versus a human rep are modest, not order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Tools exist for solar system design and product recommendation (e.g., PVWatts, design software, configurators), but they are typically used as aids rather than fully autonomous selectors. Deployed systems handle straightforward cases but struggle with complex site conditions, custom requirements, and customer communication nuances. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some solar design/quoting software (e.g., Aurora Solar, EnergySage) assists with system sizing and product matching, but human sales reps still finalize recommendations combining site visits, financing, and customer preferences. |
Develop marketing or strategic plans for sales territories.
34CI 30–39 · exposure 25 · augmentation 63 · importance 3.8/5 · click for rater detail
Develop marketing or strategic plans for sales territories.
34| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Solar sales organizations, while increasingly digitized, have adopted AI-assisted analytics slowly in strategy development; pilots and early tools exist but full production automation of territory planning remains limited, reflecting mid-market or laggard adoption patterns typical of specialized sales environments. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Solar sales is a small, physically-oriented, moderately digitized sector where AI adoption for territory strategy work is still nascent, mostly limited to CRM and basic data analytics tools. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by generating competitive analyses, market data summaries, and scenario modeling that help humans refine strategy faster; however, the core strategic and contextual judgments remain human-driven, making this a useful but not transformative augmentation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by analyzing market data, generating drafts of marketing plans, and providing competitive or demographic insights, boosting the productivity of the human planner substantially while they retain strategic control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate data-driven analysis and template-based plans using historical sales and territory data, the strategic prioritization, competitive positioning, and nuanced territory decisions typically require human judgment about local market dynamics, customer relationships, and organizational context that current systems cannot fully replicate end-to-end at quality parity. |
| Task automatability | claude-sonnet-5 | 2/5 | Strategic and marketing planning for a sales territory requires local market judgment, relationship context, and integration of nuanced business knowledge that current AI cannot fully replace, though it can assist with drafting and analysis.chestrated tasks are only partially automatable today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Territory strategy often involves regional market knowledge, regulatory considerations, and customer relationship factors that benefit from human expertise and sign-off; however, there are no hard legal or licensing barriers preventing AI-assisted or AI-generated plans, creating modest organizational friction. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement exists for this task, but organizational reliance on the sales rep's local market knowledge and relationships creates moderate friction to full replacement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tools for marketing planning still require significant human guidance, validation, and customization, meaning the effective cost (tool cost + expert labor for refinement) remains comparable to or higher than having a skilled sales strategist develop the plan directly. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools can cut research and drafting time significantly, lowering cost for parts of the task, but human oversight, local expertise, and strategic judgment still require substantial paid time, making overall cost roughly comparable to a human-only process. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some AI tools assist with market segmentation and report generation, but deployed products do not reliably produce complete, actionable marketing or strategic plans for specific territories without substantial human oversight and revision; the task remains largely human-led. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some AI-driven marketing planning tools exist (e.g., generative marketing copilots, CRM analytics), but no deployed product reliably creates full strategic sales territory plans autonomously in production. |
Assess sites to determine suitability for solar equipment, using equipment such as tape measures, compasses, and computer software.
33CI 25–41 · exposure 25 · augmentation 63 · importance 4.4/5 · click for rater detail
Assess sites to determine suitability for solar equipment, using equipment such as tape measures, compasses, and computer software.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Solar companies are still largely depot-based with high touch on site visits; while some firms use mapping software to pre-screen leads, widespread displacement of the assessment visit itself remains limited. Adoption of AI-driven assessment tools is in early/pilot phases, not deep production deployment. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Solar sales and design software adoption is growing steadily with tools like Aurora Solar and Google Sunroof widely used for preliminary assessments, though full on-site verification remains largely manual. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by pre-analyzing satellite imagery, identifying likely good sites, calculating roof angles and orientations, and generating preliminary reports that the rep refines on-site. This speeds workflow and reduces manual calculations, but the human expert remains essential for final validation and judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered design and satellite imagery tools significantly speed up and enhance the accuracy of preliminary site suitability analysis, letting human assessors focus on verification and customer interaction. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can analyze some aspects of solar suitability via satellite imagery and software (shading, orientation, roof pitch from aerial data), the task requires physical on-site measurements with tape measures and compasses, direct observation of structural integrity, and judgment calls about site-specific obstacles that current AI cannot reliably perform without human presence. The physical measurement and safety assessment components are not automatable today. |
| Task automatability | claude-sonnet-5 | 2/5 | Site assessment requires physical presence to measure roof dimensions, check structural integrity, assess shading, and identify obstructions, which AI cannot yet perform end-to-end without a human on-site."}, |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Solar installations carry liability and safety risk; customers typically expect and prefer an in-person expert assessment; many jurisdictions require licensed professionals or documented on-site evaluation before permitting. The human-contact requirement and liability asymmetry (errors in suitability assessment can be costly) create significant barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for site assessment, though some jurisdictions require professional sign-off for structural or electrical suitability, creating moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tools (satellite analysis, modeling software) reduce some assessment overhead but cannot eliminate the need for a trained representative to visit the site, take precise measurements, and make binding assessments. The human cost remains dominant because the physical inspection is non-negotiable, keeping AI cost savings marginal relative to loaded labor costs. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Remote assessment software can reduce costs for preliminary screening, but final verification still requires paid human labor, keeping overall cost roughly comparable to a human-only process. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some software tools exist for solar potential estimation using satellite data and property records, but these are partial solutions that still require human site visits to validate measurements, inspect structural conditions, and make final suitability determinations. No deployed AI system reliably completes the full assessment end-to-end without human involvement. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some software (e.g., satellite/aerial-imagery tools like Google Project Sunroof, Aurora Solar) assists in remote assessment, but reliable production-grade full site suitability determination still requires human verification and on-site visits for accuracy. |
Demonstrate use of solar and related equipment to customers or dealers.
33CI 30–35 · exposure 25 · augmentation 63 · importance 3.5/5 · click for rater detail
Demonstrate use of solar and related equipment to customers or dealers.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Solar sales and installation is relatively non-digital; adoption of AI agents for customer-facing demonstration tasks remains minimal. Most solar companies still rely on field representatives and traditional sales methods rather than deploying AI systems for equipment demonstrations. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Solar sales is a small-business, field-based sector with modest digitization; AI tools are used for design/proposal generation but adoption for live demos is minimal. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist sales reps by generating talking points, visual aids, performance calculators, and customer-specific ROI analyses. However, the human must deliver the demonstration and engage the customer directly, making this a supportive role rather than a transformative productivity multiplier. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered solar design and visualization tools (e.g., satellite imagery-based savings estimators, AR overlays) meaningfully enhance a rep's ability to demonstrate value and customize pitches during in-person interactions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Demonstrating equipment requires hands-on physical interaction, customer engagement, and real-time responsiveness to questions. While AI could generate explanation content or videos, the live interactive demonstration and relationship-building elements cannot be fully automated by current systems. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical/in-person demonstration of solar equipment involves hands-on interaction, site-specific context, and relationship building that current AI cannot perform end-to-end; AI can support with visuals or virtual walkthroughs but not replace the live demo. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While there are no strict legal licensing requirements for equipment demonstrations, sales roles have moderate organizational friction: customer preference for human contact, the need for relationship-building and trust, and liability concerns around product advice and performance claims. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement specifically for demonstrations, but customer trust, safety of showing physical equipment, and sales relationship dynamics create moderate organizational friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Creating and maintaining AI systems to generate custom demonstrations, handle objections, and manage customer relationships would likely cost more than paying a sales representative, especially given the need for integration with inventory and customer management systems. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Software-assisted visualization is cheap to run, but full demonstration still requires human presence, travel, and equipment handling, keeping overall cost comparable to or above human-led demos. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs live equipment demonstrations with full customer engagement. Pre-recorded videos and chatbot explanations exist but lack the dynamic interaction, problem-solving, and persuasion required for effective in-person or remote product demonstrations. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some AI-driven visualization tools (AR/3D solar design software) exist to show projected energy savings or panel placement, but no deployed product autonomously conducts customer-facing equipment demonstrations. |
Create customized energy management packages to satisfy customer needs.
28CI 25–30 · exposure 25 · augmentation 63 · importance 4.0/5 · click for rater detail
Create customized energy management packages to satisfy customer needs.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Solar sales remains a relationship-driven, locally-variable business with relatively low digital penetration for AI automation; while some companies use AI for lead scoring and initial assessments, deployment of autonomous package creation remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Solar sales is a relatively small, regionally fragmented, in-person sales industry with limited but growing use of AI design/quoting tools; broad production-scale AI adoption is still limited. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by rapidly generating baseline energy models, comparing financing options, and surfacing relevant incentives, allowing sales reps to spend more time on customer engagement and refinement rather than calculation, but human expertise in local code, customer negotiation, and deal structure remains essential. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools (e.g., automated solar design software, satellite-based roof analysis, financing calculators) significantly speed up creating customized packages, letting reps focus on customer interaction and closing. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help generate energy efficiency recommendations and model outputs, creating truly customized packages requires understanding specific customer constraints (budget, roof conditions, existing infrastructure, local incentives), which demands human judgment and iterative dialogue that current systems struggle to execute reliably end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | This task requires site-specific assessment, understanding customer priorities/budget, and relationship-based negotiation that current AI cannot fully replicate end-to-end, though AI can assist with proposal generation and system sizing calculations. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Solar sales packages involve contractual commitments, financing structures, and performance guarantees that create liability concerns if generated without licensed professional review; customer trust and direct relationship-building also remain critical to deal closure in the solar market. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement for the sales role itself, but customer trust, financing complexity, and utility/permitting interactions create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tools for energy assessment and recommendation generation still require significant human oversight, customer consultation, and custom configuration, keeping total AI-assisted costs comparable to or higher than direct human sales consultation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI tools can cut down design/proposal time, the need for site visits, human consultation, and trust-building keeps overall labor cost savings modest compared to a fully human-driven process. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some tools exist for energy modeling and recommendation generation, but no deployed product reliably performs full customized package creation—balancing technical, financial, and customer-preference factors—without substantial human rework and verification. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some solar sales software includes AI-assisted design and quoting tools, but full customized package creation still relies heavily on human assessors visiting sites and tailoring financial/technical solutions. |
Related occupations — Sales & Related
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.