Artificial intelligence (AI) is far from new in the insurance industry. For decades, it has powered critical functions such as pricing and risk assessment through machine learning and statistical modeling. These early applications helped insurers improve underwriting accuracy and optimize premium pricing by harnessing historical data patterns.
Today, however, we are witnessing a paradigm shift. The emergence of generative AI (GenAI) has broadened the potential of AI across the insurance value chain. Insurers are no longer limiting AI to technical or actuarial functions. Instead, they are increasingly integrating it into customer service, policy administration, and claims processing. This evolution marks a significant departure from traditional use cases and signals a new era of innovation, growth and operational transformation.
Challenges in scaling AI across the enterprise
Despite growing enthusiasm, the widespread adoption of AI in insurance is not without its hurdles. Chief among these challenges is data — its availability, quality and structure. AI systems are only as effective as the data they are trained on, and many insurers struggle with legacy systems, fragmented data sources, and inconsistent data governance.
Structured, accessible and scalable data infrastructure are essential to realize the full potential of AI. In fact, according to the EY 2025 Global Insurance Outlook, insurers with successful data and analytics strategies could expect to see a 10% to 15% increase in operating profits. Without robust data infrastructure, insurers risk underutilizing powerful AI tools. GenAI offers promising advancements in underwriting and claims processing. Underwriters often sift through voluminous data sets, from inspection reports to legal documents, and GenAI can synthesize this information quickly, enhancing both speed and decision quality.
In property and casualty (P&C) insurance, this capability is especially valuable. Claims teams can use AI to triage incidents, generate documentation, and identify patterns of fraud or inconsistencies. However, without solid data foundations, these benefits remain out of reach.
Understanding and mitigating AI risks
As AI use proliferates, so do the associated risks. One of the most pressing concerns is regulatory compliance as EY research found that 61% of insurers cite evolving regulatory requirements as their top operational challenge. AI models must be explainable and auditable, particularly as regulators increase scrutiny over how decisions are made, especially when they impact consumers directly. This requires careful attention to algorithmic transparency and governance.
Customer service is another area where AI must be handled with care. While AI-powered chatbots and virtual assistants can increase efficiency and availability, they also carry brand and reputation risks if the technology malfunctions or delivers incorrect information. Transparency in these interactions is essential to maintain customer trust.
Cybersecurity and data privacy are also top-of-mind. AI systems introduce new cyber vulnerabilities, particularly when they interface with external data sources or third-party platforms. At the same time, insurers must ensure compliance with increasingly stringent data protection regulations, both domestic and international. Responsible AI practices, including robust privacy controls and continuous monitoring, are no longer optional but essential.
Embracing agentic AI and enterprise integration
While GenAI has already shown great potential in optimizing routine tasks and creating efficiencies for knowledge workers, agentic AI, a distinctive new version of AI, is on the horizon and set to unlock new levels of AI capability, autonomy, growth and efficiency across the insurance industry in the years ahead. Agentic AI can assist with more complex decisions and tasks, adapt to changing conditions, and collaborate with both humans and other AI systems, acting as a nonhuman workforce that enhances traditional teams and drives goal-oriented outcomes.
Agentic AI presents several transformative use cases within the insurance industry. In underwriting, it enables autonomous data analysis for dynamic risk assessment, enhancing accuracy and efficiency. For claims processing, agentic AI can orchestrate end-to-end handling of low to medium complexity claims, leveraging image and video analysis to streamline resolution. In the realm of risk management, the technology supports continuous scenario analysis and real-time risk monitoring. Lastly, for regulatory compliance, Agentic AI automates monitoring and reporting, ensuring adherence to evolving regulatory requirements with greater speed and precision. The industry should be focused on how to continue to build out appropriate risk frameworks to manage risks both to their business and their customers.
Insurers are beginning to think beyond use cases and pilot programs. Unlike isolated AI applications, agentic AI signals a shift toward enterprise-wide transformation where systems can initiate actions, learn from outcomes and collaborate with other AI agents to achieve goals. This enterprise approach creates AI ecosystems where multiple agents interact, learn and improve operations in concert.
A future defined by personalization and prevention
Looking ahead, the role of AI in insurance will deepen, moving from reactive processes toward proactive engagement. Insurers will be able to further offer hyper-personalized products based on real-time data, from wearable devices to connected cars incorporating AI. This will not only enhance customer experience but also enable dynamic pricing models that reflect individual behaviors and risk profiles.
A key transformation will be the industry’s shift from risk prediction to risk protection. Instead of solely assessing historical data to forecast risk, insurers will begin leveraging AI to proactively assess risk through real-time alerts, automated maintenance scheduling, or proactive health interventions. This shift redefines the insurer’s role from a reactive payor to an active risk partner.
Autonomy is another frontier. AI will underpin emerging areas such as autonomous vehicles, smart homes, and robotics in workplaces, fundamentally altering how risk is assessed and transferred. In this future, AI won’t just enhance existing processes; it will enable entirely new models of insurance and create unprecedented opportunities for efficiency and growth.
The regulatory path toward responsible AI
As insurers push forward with AI adoption, regulators are responding with frameworks designed to ensure accountability and consumer protection. Explainable AI, systems that are transparent, traceable and understandable, is critical to this effort. Insurers must be prepared to demonstrate how AI models reach conclusions, particularly in underwriting and claims.
The regulatory landscape is still evolving. From global AI acts to regional data protection laws, insurers face a complex web of compliance requirements. To navigate this, many are building internal governance structures focused on privacy, auditability and responsible AI. Public-private partnerships will also be key to shaping policies that support innovation while safeguarding consumer interests.
Ultimately, trust will be the foundation of AI’s success in insurance. The ability to explain, justify and monitor AI systems will not only satisfy regulators but also foster confidence among customers and stakeholders.
The insurance industry stands at a pivotal moment in its digital evolution. While AI has long been a tool for risk assessment and pricing, its future lies in enterprise transformation, unlocking growth opportunities, personalized service delivery, and proactive risk management.
Realizing this future will require more than technology, it will demand robust data strategies, careful risk management and oversight, and thoughtful engagement with regulators. With a clear vision and a responsible approach, insurers can harness AI to drive growth, better serve customers and strengthen the resilience of the industry.
