Augmenting Insurance with AI

AI, data mining, and automated tools and technology are transforming the insurance sector. How are you thinking about prioritizing that transformation and what criteria define success?

We approach transformation and innovation through the lens that technology should help our agents, customers, and employees work together more effectively. We prioritize these ideas through benefits realization, looking across the value chain to invest in areas where we believe we have a strategic advantage. Today, much of that focus has been on the front end of our business: strengthening how we connect with agents, improving information flow, organizing and triaging work across quoting, servicing, and claims, and equipping our employees with the tools necessary to deliver the best outcomes through problem solving and great service.

Success is measured through tangible business outcomes: are we helping agents serve customers more effectively and growing their customer base? Are we improving underwriting and claims outcomes? And, most importantly, are we creating a better experience across the value chain for our agents and policyholders? If we’re achieving those outcomes, then we know we’re investing in the right areas.

What have been the biggest challenges in training AI tools to effectively interpret and structure complex insurance data across underwriting, claims, and service?

One of the biggest challenges is the nature of insurance data itself. We rely on a large volume of data, often in diverse and unstructured formats. To create value, AI must ingest this information at scale, synthesize it across multiple contexts, turn it into something structured and actionable, and then triage it so we can move through the process more quickly. Throughout this process, it’s critical human judgment remains at the forefront to evaluate AI outcomes and identify where prompts or LLMs may need adjustment, ensuring continuous improvement.

Another challenge is thinking about AI as an enterprise capability rather than a series of isolated use cases. We’re focused on creating reusable AI agents for underwriting, claims, and service, using a common set of capabilities to analyze, summarize, and triage information across the value chain.

Data exchange has been identified as a major friction point. What tangible improvements have you seen for agents and customers as AI is deployed?

One of the most promising areas for AI is improving the flow of data. The data exchange from customer to agent to carrier and back again can create significant friction, as information is touched and processed multiple times. When information moves seamlessly, agents spend less time managing transactions and more time focusing on decisions, service, and outcomes that create value. Similarly, robust data management is critical in coordinating organizational alignment. Having the ability to look through the insurance value chain and establish shared definitions enables AI systems to operate more consistently.

For agents, that means faster responses and less administrative effort while keeping in sync with the carrier on servicing, billing, and inquiries. Any way that we, as the carrier, can help enable agents to deliver better outcomes for customers is a win-win.

For customers, we’re seeing opportunities to reduce complexity, particularly in claims. As automation capabilities evolve, they have the potential to deliver faster assessments, quicker resolutions, and better customer experiences. Customers are looking for self-service tools, with human support when it matters most.

As AI reshapes workflows, how are you supporting employees in adapting to new roles while maintaining engagement and trust in the transformation?

Change management is as important as the technology itself. Employees are far more engaged when they understand the vision, see why the change is happening, and can be excited about how their roles are evolving. Successful transformation requires balancing speed, innovation, and a thoughtful, people-centered approach.

We position AI as a tool that augments, not replaces, human expertise. While AI can automate tasks, surface insights, and improve efficiency, employees remain responsible for exercising judgment, discernment, and decision-making, as well as delivering the skills AI cannot replicate, such as empathy, relationship-building, and critical thinking.

Leaders play a critical role by articulating the vision, building skills, and creating opportunities for employees to contribute to the transformation. By enhancing the employee experience and expanding what people can accomplish, we can help our workforce thrive alongside these new technologies.