Bain: AI Adoption Expands in P&C Claims, but Full Scaling Remains Rare

Scaled artificial intelligence (AI), particularly generative AI, is beginning to impact property and casualty (P&C) claims processes, but widespread adoption remains limited. According to Bain & Company’s 2025 Claims Maturity Assessment, which surveyed 81 global P&C insurers, while 78% are experimenting with generative AI in some form, only 4% have scaled these technologies enterprise-wide. Current AI applications focus on specific tasks such as document summarization, customer communications support, and fraud detection to streamline manual work in claims handling. This cautious adoption aligns with findings from Morningstar DBRS, which shows insurers expanding AI use across underwriting and back-office functions to drive efficiency and cost savings amid heightened governance and regulatory attention. Bain's report suggests that substantial value lies not just in piecemeal AI implementation but in reengineering the entire claims workflow around AI capabilities. At scale, generative AI could accelerate settlement times, reduce home insurance claims processing duration by up to 50%, and boost claims productivity by approximately 35%. However, barriers remain, with only 27% of insurers pursuing full AI-driven claims transformation. Key challenges include limited internal AI expertise, concerns over model accuracy, and issues related to data security and privacy. The report identifies five characteristics common among insurers moving beyond pilot projects, including setting a clear strategic vision for end-to-end claims redesign, having a technology roadmap balancing build versus buy decisions, and fostering close collaboration between claims and technology teams. Supporting workforce readiness by engaging claims employees in AI innovation and developing future skills in data analytics and digital tools is another critical factor. Deloitte research indicates insurers foresee a two-to-four-year horizon for AI initiatives to yield meaningful returns, a timeframe longer than typical IT projects, possibly contributing to resistance against extensive claims overhauls. Early adopters that have scaled AI report measurable improvements in speed, cost efficiency, and productivity across multiple claims lifecycle stages. These operational efficiencies, such as faster quote generation and shortened claims cycles, represent the near-term value insurers are capturing as they move from experimentation toward broader AI integration in claims management.