Insurance Appraisers, Auto Damage
13-1032.00Median wage $78,240/yr11,560 employed (US)Rank #265 of 923 scored · top 29% by substitution
Appraise automobile or other vehicle damage to determine repair costs for insurance claim settlement. Prepare insurance forms to indicate repair cost or cost estimates and recommendations. May seek agreement with automotive repair shop on repair costs.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution35
Exposure32
Augmentation71
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
7 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
0%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%33
panel mean rating 2.3/5 → substitution pressure 33/100
Technical feasibility todayw 20%30
panel mean rating 2.2/5 → substitution pressure 30/100
Cost vs. human wagew 15%34
panel mean rating 2.3/5 → substitution pressure 34/100
Adoption barriersw 20%inverted — strong barriers lower the score40
panel mean rating 3.4/5 (barrier strength) → substitution pressure 40/100
Sector adoption velocityw 10%44
panel mean rating 2.8/5 → substitution pressure 44/100
Task breakdown (7 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.
Prepare insurance forms to indicate repair cost estimates and recommendations.
57CI 45–70 · exposure 58 · augmentation 88 · importance 4.7/5 · click for rater detail
Prepare insurance forms to indicate repair cost estimates and recommendations.
57| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Insurance companies are piloting AI damage assessment and form-assist tools, but deployment remains cautious and patchy; adoption is accelerating in large carriers but remains limited in smaller insurers and regional markets. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Auto insurance is a fast-moving, digitized sector with major insurers actively deploying AI damage estimation tools at scale. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI damage detection from photos, automated cost lookups, and form field pre-population significantly reduce appraiser effort on routine inspections; the human appraiser benefits from faster data gathering and initial estimates, allowing focus on complex cases and final judgment. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI substantially speeds up estimate preparation and form-filling for appraisers, who then verify and finalize the output, transforming their workflow. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can extract damage information, populate standardized form fields, and generate cost estimates from images and repair databases; however, final forms typically require human judgment on coverage decisions, liability assessments, and client-specific adjustments, making full end-to-end automation difficult without material quality loss. |
| Task automatability | claude-sonnet-5 | 4/5 | AI vision and estimation tools can analyze damage photos and generate structured repair cost estimates and forms with substantial time savings, though some edge cases still need human review. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Insurance appraisals are often required by regulation to be performed or certified by licensed adjusters or qualified appraisers; liability for underestimation and coverage disputes creates strong disincentive to remove human sign-off, and organizational risk-aversion limits substitution. |
| Adoption barriers | claude-sonnet-5 | 2/5 | Some regulatory oversight and insurer sign-off requirements exist, but no licensing mandate requires a human to prepare the estimate itself in most jurisdictions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI labor (vision analysis, form population, integration overhead) is becoming cost-competitive with appraiser time on routine cases, but the ongoing need for human review and liability oversight keeps total cost roughly on par with manual work rather than dramatically cheaper. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-driven photo estimation is dramatically cheaper per claim than dispatching a human appraiser for routine damage assessment and form completion. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist for damage assessment and form field population (e.g., computer vision for damage detection, repair cost lookup systems), but they require human review and correction; no mature system replaces the full form-preparation process reliably without significant oversight. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Tractable and CCC exist and are used by insurers, but accuracy on complex or ambiguous damage still requires human adjuster verification. |
Estimate parts and labor to repair damage, using standard automotive labor and parts cost manuals and knowledge of automotive repair.
51CI 46–56 · exposure 50 · augmentation 88 · importance 4.7/5 · click for rater detail
Estimate parts and labor to repair damage, using standard automotive labor and parts cost manuals and knowledge of automotive repair.
51| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Insurance and automotive sectors are moderately digitized; some large insurers pilot AI-assisted estimating, but widespread production adoption is still emerging. Small insurers and independent appraisers lag significantly, limiting sector-wide velocity. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Auto insurance is a digitized, competitive industry with fast adoption of AI photo estimating tools by major carriers, though full replacement of human appraisers remains partial. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist appraisers by auto-populating parts lists from damage photos, cross-referencing labor rates, and flagging potential missed damage categories, thereby accelerating estimate generation while the appraiser retains oversight and judgment on complex or ambiguous cases. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI tools significantly speed up estimate drafting by auto-detecting damage and pulling parts/labor costs, letting appraisers review and adjust rather than starting from scratch. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can assist with estimating parts costs using manuals and databases, but labor estimation requires judgment about repair complexity, technician skill, and shop rates that vary regionally and by shop. A hybrid approach where AI generates initial estimates that require human review and adjustment is feasible, but full end-to-end automation meeting the 50% time-savings bar is unclear without significant setup. |
| Task automatability | claude-sonnet-5 | 3/5 | AI vision models combined with parts/labor databases can generate preliminary damage estimates from photos, but complex or ambiguous damage still requires human verification and judgment, limiting full end-to-end automation today.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | No hard legal requirement for a licensed human to perform the estimate, but insurance companies and repair shops prefer human appraisers for liability and customer trust reasons. Regulatory oversight of claim accuracy and organizational friction around liability mean adoption requires buy-in from underwriting and claims management. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement mandates a human for estimate generation, but insurers often require appraiser sign-off due to liability, fraud risk, and customer disputes, creating moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference for parts lookup and labor table matching is cheap, but the cost of integration with multiple repair databases, ongoing manual quality oversight by appraisers, and error correction makes the all-in cost per estimate substantial relative to a human appraiser's time on straightforward cases. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | AI-based photo estimating tools are substantially cheaper per estimate than dispatching a human appraiser for straightforward claims, though complex cases still need costly human oversight. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Products exist (e.g., AI-powered estimating platforms integrating with repair databases), but they still have material error rates when assessing damage severity, hidden structural issues, or non-standard repairs. Most require human appraiser validation and adjustment rather than operating independently in production. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Tractable and CCC exist and are used by insurers to generate estimates from photos, but accuracy varies with damage complexity and they typically require human review before finalization. |
Determine salvage value on total-loss vehicle.
33CI 25–41 · exposure 33 · augmentation 75 · importance 4.1/5 · click for rater detail
Determine salvage value on total-loss vehicle.
33| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Insurance is moderately digitized, but adoption of autonomous salvage valuation remains limited; most insurers use AI as a tool to assist licensed appraisers rather than replace them, reflecting both regulatory caution and organizational preference for human sign-off. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Auto insurance is adopting AI estimating and valuation tools moderately, with pilots and partial integration in claims processing, but full production autonomy is still uncommon. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered damage detection and market-comparable tools substantially assist appraisers by accelerating data gathering and providing baseline valuations, allowing appraisers to focus on complex judgment while maintaining quality and compliance oversight. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools that pull comparable salvage values, market data, and damage estimates significantly speed up the appraiser's determination process, even though final judgment remains human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can extract data from vehicle images, VINs, and market comparables to estimate salvage value, but requires human verification of condition assessment and final valuation decisions. Partial automation is feasible but reaching 50% time savings with equal quality requires careful setup and human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Requires physical inspection and judgment about damage extent, market conditions, and salvage auction values that current AI cannot reliably perform end-to-end without significant human verification.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Insurance regulations and liability frameworks typically require a licensed appraiser to certify total-loss valuations, and insurer risk tolerance for autonomous valuation errors on high-value claims creates strong organizational and legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement mandates a human specifically for salvage valuation, but liability, insurer policy requirements, and need for physical/photo verification create moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI tools (vision + valuation APIs) cost $10–50 per assessment plus oversight labor, while an experienced appraiser costs $40–100 for the same task; total integration cost remains comparable or higher than human-only workflows. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI-assisted valuation tools can lower some cost but still require human inspection, data verification, and adjuster oversight, keeping the all-in cost close to human-level rather than order-of-magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision and valuation APIs exist to assist with salvage estimation, but no mature end-to-end product reliably handles the full assessment—which requires nuanced judgment on hidden damage, parts marketability, and regional demand—without material human intervention. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some products use photos and databases to estimate salvage/ACV values, but reliability is limited and insurers still require human appraiser sign-off for total-loss determinations. |
Evaluate practicality of repair as opposed to payment of market value of vehicle before accident.
31CI 25–36 · exposure 25 · augmentation 75 · importance 4.7/5 · click for rater detail
Evaluate practicality of repair as opposed to payment of market value of vehicle before accident.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Insurance companies are piloting AI for damage assessment and cost estimation, but actual adoption of autonomous repair-vs.-total decisions remains limited; most insurers still rely on appraisers in the loop, with AI as a supporting tool rather than a replacement. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Insurance is a digitizing sector with growing use of AI for damage assessment and estimating, but full decision automation for total-loss determinations remains in pilot/hybrid stages. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can materially assist by providing rapid vehicle valuation lookups, comparable market data, and repair cost estimates, allowing appraisers to focus on the judgment call itself; this is a strong use case for augmentation where the human makes the final decision faster and more confidently with AI support. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can quickly pull comparable market values, estimate repair costs from photos, and flag total-loss thresholds, significantly speeding up the appraiser's evaluation process. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with cost estimation and market value lookup, but the core judgment—whether repair is practical given safety, durability, and residual value considerations—requires contextual reasoning about specific vehicle condition, local repair capacity, and insurer guidelines that current systems struggle to perform reliably end-to-end without human review. |
| Task automatability | claude-sonnet-5 | 2/5 | This requires synthesizing repair cost estimates, market value data, and judgment calls about diminished value and edge cases, which current AI can partially support but not fully execute end-to-end reliably today.true.true.true.true. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Insurance adjusters and appraisers must typically be licensed in their state, and the final repair-or-total determination carries liability for underpayment or improper valuations; regulatory and contractual requirements mean an appraiser must sign off on the decision, not delegate it fully to AI. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not always legally requiring a licensed appraiser, insurance regulations, dispute liability, and customer trust create meaningful friction against fully automating this financial determination. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI inference for valuation and cost lookups is cheap, but integration into appraisal workflows and the overhead of human review to validate the decision means all-in costs remain significant relative to the time savings on a task still requiring licensed appraiser sign-off. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI-assisted estimation tools can reduce appraiser time on data-gathering, but human review and final judgment remain necessary, keeping costs roughly comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI tools exist for vehicle valuation and repair cost estimation, no deployed product reliably makes the repair-vs.-total-loss decision independently; appraisers use multiple proprietary databases and judgment calls that products handle only as inputs to human decision-making, not autonomous outputs. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some insurtech tools estimate repair costs and vehicle values, but a deployed product that autonomously makes the totaled-vs-repair decision reliably in production is not yet standard practice. |
Examine damaged vehicle to determine extent of structural, body, mechanical, electrical, or interior damage.
30CI 28–32 · exposure 25 · augmentation 75 · importance 4.7/5 · click for rater detail
Examine damaged vehicle to determine extent of structural, body, mechanical, electrical, or interior damage.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Large insurers are piloting AI-assisted damage assessment (photo triage, supplementary estimates), but production adoption remains limited to mobile damage-capture workflows and preliminary screening. Full replacement of in-person structural appraisals is rare, and adoption velocity is moderate—not yet the deep, rapid pattern seen in back-office processing. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Auto insurance is a digitized, high-volume sector with active AI pilots (e.g., Lemonade, Tractable) but full inspection automation remains limited to simpler claims, indicating middling adoption depth. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI image analysis can assist appraisers by pre-processing photos, flagging damage patterns, and organizing findings before or during physical inspection, significantly raising documentation speed and consistency. However, the human remains essential for final structural and mechanical judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered image analysis significantly speeds up preliminary damage assessment and triage, letting appraisers focus on complex or disputed cases, meaningfully boosting productivity. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI vision systems can analyze photographic damage in controlled settings, the task requires hands-on inspection of structural integrity, hidden damage, and mechanical/electrical systems that demand physical access and tactile assessment. Current AI cannot reliably replace this comprehensive physical inspection with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Physical inspection of a damaged vehicle requires visual, sometimes tactile, assessment of structural and mechanical components that current AI cannot fully replicate without human-captured imagery and judgment, though AI can assist with photo-based damage estimation for straightforward cases. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Insurance appraisals carry regulatory and liability exposure; errors in damage assessment directly affect claim payouts and customer disputes. Most jurisdictions require licensed appraisers to sign off on determinations, and insurers face reputational and legal risk from automation errors, creating high barriers to unsupervised AI substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | Insurance appraisers are often licensed in various jurisdictions and appraisals can have liability implications for claims payouts, creating moderate regulatory and error-cost friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI vision tools for damage assessment cost less than a human appraiser per claim, but integration, training, oversight, and the need for human verification of complex cases keep all-in costs comparable or higher than traditional human appraisers in most deployments. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI photo-estimation tools are cheap per claim, but the need for human verification, on-site inspection for complex cases, and error correction keeps blended costs closer to comparable rather than order-of-magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI-assisted damage assessment tools exist in some insurers' workflows (e.g., mobile apps with computer vision), but they narrow the scope to visible body damage and require human validation. No deployed product reliably performs the full end-to-end inspection task (structural, mechanical, electrical, interior) without significant human expert review and physical inspection. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some insurers deploy photo-based AI damage assessment tools, but these are narrow in scope, struggle with hidden/structural/mechanical damage, and typically require human appraiser verification for complex claims. |
Review repair cost estimates with automobile repair shop to secure agreement on cost of repairs.
29CI 25–32 · exposure 25 · augmentation 63 · importance 4.7/5 · click for rater detail
Review repair cost estimates with automobile repair shop to secure agreement on cost of repairs.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Insurance claims processing has moderate digital adoption for data entry and routing, but human appraisers remain central to cost negotiation in most insurers' workflows; adoption of AI for autonomous negotiation with repair shops is minimal and largely experimental. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Insurance is adopting AI steadily for damage assessment and estimating, but the negotiation/agreement step with third-party shops remains less automated, reflecting mid-tier adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist appraisers by extracting and comparing estimate details from repair quotes, flagging outliers, and summarizing cost justifications, which speeds review preparation and reduces manual data handling before the human engages in negotiation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered estimating tools and data comparisons significantly help appraisers prepare for and support negotiations, even though the human still finalizes agreement with the shop. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can analyze repair estimates and identify cost discrepancies, the task fundamentally requires negotiation and relationship management with repair shops—elements that demand human judgment, flexibility, and interpersonal dynamics that current AI systems cannot reliably execute end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | This involves negotiation and relationship-building with a repair shop to reach mutual agreement, which requires real-time judgment, persuasion, and dispute resolution that current AI cannot fully replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Insurance regulations typically require a licensed insurance adjuster or appraiser to authorize claim decisions and cost agreements; liability for settlement errors and the requirement for human sign-off on final repair authorizations create significant legal and regulatory barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While not always legally requiring a licensed appraiser, insurers often require human sign-off for disputed estimates, and repair shops may resist dealing with automated negotiation systems, creating moderate friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI-assisted review of estimates is cost-effective for data extraction, but the human appraiser must still conduct the negotiation, limiting overall labor displacement and making full-task automation economics unfavorable compared to human appraisers. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply generate cost estimates, but the negotiation component still requires human oversight and back-and-forth, keeping all-in costs closer to human-comparable rather than an order of magnitude cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed products reliably perform autonomous negotiation of repair estimates with shops; existing AI can extract and compare cost data from documents, but cannot independently secure agreement through dialogue or handle the contextual, relational aspects of this negotiation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some insurers use AI-assisted estimate generation and comparison tools, but no deployed product autonomously negotiates and finalizes repair cost agreements with shops at scale. |
Arrange to have damage appraised by another appraiser to resolve disagreement with shop on repair cost.
6CI 5–7 · exposure 0 · augmentation 25 · importance 3.4/5 · click for rater detail
Arrange to have damage appraised by another appraiser to resolve disagreement with shop on repair cost.
6| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Insurance appraisal remains a human-intensive, relationship-dependent process with limited automation adoption. Dispute resolution and third-party coordination are handled by licensed adjusters and appraisers, not by algorithmic systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Auto insurance claims adjustment is a moderately digitized sector, but dispute resolution processes remain human-centric with slow AI penetration into arbitration-type tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could help draft communication templates or identify available appraisers in a database, but it cannot meaningfully augment the core task of arranging and negotiating appointments or resolving professional disagreements. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help schedule appointments or summarize appraisal discrepancies, but it offers limited assistance in the core negotiation and arrangement of an independent review. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires initiating contact, negotiating scheduling, and managing interpersonal disagreement resolution—all deeply human-relational work. Current AI cannot autonomously arrange appointments, contact external parties, or mediate disputes with binding authority. |
| Task automatability | claude-sonnet-5 | 1/5 | This task is fundamentally a coordination and negotiation activity between humans (arranging a second appraiser, resolving disputes) rather than an analytical or generative task AI could perform end-to-end.4o |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Insurance appraisal disputes involve contractual authority and fiduciary responsibility; the appraiser must legally and professionally represent the insurer's interests. External appraisers expect direct human communication and professional accountability that AI cannot provide. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Disputed appraisals often require licensed, independent appraisers and may involve contractual or regulatory processes (e.g., appraisal clauses in insurance policies) that mandate human sign-off. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The human appraiser's loaded wage (typically $25–35/hr for administrative/coordination work) is substantially lower than the infrastructure and oversight cost required for an AI system to handle scheduling, communications, and conflict management. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot substitute for the human coordination and judgment involved, so there is no comparable AI cost basis; a human must still perform this task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can reliably perform this task end-to-end. While AI can draft communications, it cannot actually initiate calls, negotiate schedules, or legally represent an insurance company in dispute resolution. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product arranges independent appraisals or manages appraiser dispute resolution; this remains a human administrative/interpersonal function. |
Related occupations — Business & Financial Operations
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.