Introduction
The rise of agentic, autonomous AI agents represents a fundamental shift in how insurance companies will operate, interact with customers and manage risk. Unlike traditional AI, which primarily enhances efficiency through automation and analytics, these next-generation agents can independently make decisions, execute complex tasks and continuously learn from interactions. In insurance, this means policies that dynamically adjust based on real-time risk factors, claims that are processed and settled instantly without human intervention, and customer service that is hyper-personalized, contextaware and available 24/7.
The potential extends beyond operational improvements — agentic AI can redefine entire business models, enabling insurers to proactively mitigate risk, optimize pricing dynamically and provide unprecedented levels of customer engagement. These systems demand vast, high-quality data inputs, meaning insurers must invest in seamless data integration, real-time analytics and ethical AI governance. However, our research finds that insurers are still grappling with legacy operating models, technical debt and linear workflows, which are ill-equipped to handle the dynamic nature of AI innovation. Data is fragmented and often locked in functional specific systems. Rigid hierarchies and siloed functions create choke points that impede cross-functional collaboration, slow decision-making and limit agility.
Concerns about the rapid pace of technology development and caution over the AI-specific risks are causing hesitancy: 75 percent of insurance executives in our survey are concerned that investments they make now may be rendered obsolete in the near future. Insurers are also hesitating when it comes to build or buy decisions and worried that a vendor may release a better version. When coupled with concerns over the unknown risks, AI leaders are unsure where and when to focus their investments.
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