Are Insurance Appraisers, Auto Damages at Risk Due to AI?

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.

The occupation "Insurance Appraisers, Auto Damage" has an automation risk of 56.4%, which closely aligns with its base risk of 57.1%. This risk stems from the fact that many tasks performed by auto damage appraisers are procedural and grounded in clear, rules-based decision-making, making them more susceptible to automation. Technology has enabled various aspects of damage evaluation, cost estimation, and documentation to be handled by advanced artificial intelligence and computer vision systems. For example, automated image analysis can assess the extent of vehicle damage, while software can quickly calculate costs based on standardized pricing databases. These trends contribute to the generally high risk level for automation in this occupation. The top three most automatable tasks for this role include: (1) evaluating the practicality of repairs as opposed to paying out the market value of the vehicle before the accident, (2) reviewing repair cost estimates with automobile repair shops to secure agreement on costs, and (3) physically examining the damaged vehicle to determine the extent of both structural and component-level damage. These processes heavily rely on standardized procedures and logical comparisons, which can be easily replicated by algorithms or machine learning models. Automated systems can now process photographic evidence, access vehicle history and cost databases, and even communicate directly with repair shops, minimizing the need for human involvement in these specific tasks. However, certain responsibilities within this occupation are more resistant to automation due to their complex or judgment-based nature. For example, arranging for a secondary appraiser to resolve disputes with auto shops, determining salvage value on total-loss vehicles, and estimating repair parts and labor costs using manuals and hands-on expertise are tasks that require negotiation, contextual judgment, and practical experience. These activities depend on interpersonal skills, flexibility, and the application of nuanced professional knowledge that current AI systems struggle to replicate effectively. Furthermore, creativity and originality are identified as bottleneck skills, albeit at relatively low levels (2.4% and 2.6%), indicating that while the occupation may require some degree of out-of-the-box thinking, it is not the primary barrier to automation. Thus, the automation risk is moderate rather than extreme, as human involvement remains valuable in resolving complex or contested claims and providing expert assessments.