AI is already making waves in vehicle damage reporting, but not everyone is tapping into its full potential. Those who embrace it can unlock new opportunities and streamline their operations. Here are four real-life examples of how AI is transforming vehicle damage reporting and impacting the industry.
Amazon partnered with UVeye to implement its AI-powered Automated Vehicle Inspection (AVI) system, which automates daily inspections of delivery vehicles. The system scans vehicles through a tunnel of cameras and sensors, detecting internal and external damage such as bumps, dents, leaks, window cracks, as well as brake- and exhaust-system issues. This reduced the average inspection time for delivery service partners (DSPs) from about five minutes to less than a minute.
A BMW dealership in Hawaii introduced the world’s first fully automated car inspection system, similar to an MRI scan. The system captures high-resolution images from multiple angles, using advanced image recognition software to detect and assess any damage, including scratches, dents, and other wear. This offers a 360° examination of vehicles without human intervention and cuts inspection times in half for dealerships.
Deloitte Luxembourg's AI recognition accurately identifies car damages to streamline insurers' claims processes and enhance customers' experiences. The AI uses deep learning to analyse vehicle photos, automatically detecting damage, assessing its severity, and instantly providing repair cost estimates by connecting to a repair cost database. This benefits insurers by accelerating claims processing and ensuring faster, more accurate claim settlements.
Liberty Mutual's AI-enabled service, developed by Solaria Labs, assesses vehicle damages and provides accurate evaluations and repair estimates based on anonymised claims photos. The system uses these images to make a comparative analysis of uploaded images and provides an exact repair cost estimate, enabling faster and more accurate claims processing for insurers.