How to Succeed with AI

Insurance organizations manage vast datasets pertaining to claims, policy details, or customer correspondence. Document data is heavily relied upon for quoting, binding, servicing, renewal, and back-office transactions, and the data is often unstructured with varying levels of quality and accessibility. While data may drive AI projects, over-focusing on data harmonization can delay progress. Instead of striving for “perfect” data alignment, insurance leaders should emphasize the business problems AI is intended to solve, keeping end-user needs at the forefront.

This approach allows teams to tackle data issues in stages while the AI project itself progresses, improving both the data quality and AI model incrementally. For instance, by focusing on the immediate needs of claims processing or fraud detection, insurers can start seeing results sooner rather than delaying projects for full data harmonization.

Successful AI projects require more than technology. It is essential to have organizational cultures that support digital transformation.

“Our team can be skeptical of new technology,” admits one early AI adopter at an insurance brokerage. “But after seeing other team members leveraging AI for policy checking, they couldn’t wait to get their hands on it.”

Investing in AI leadership can help cultivate the culture essential to success, with roles such as “translators” to bridge business and technical teams, and “evangelists” to promote AI initiatives across departments.

Large insurance organizations can benefit from adding data governance and ethics roles, as many state and national-level organizations, such as the National Association of Insurance Commissioners continue to release new guidance and frameworks to safeguard consumers, promote fairness, and uphold the highest standards of integrity within the industry. Building new roles focused on responsible governance, risk management policies, and procedures to ensure accurate and fair outcomes for consumers fosters a forward-thinking, insight-driven culture that embraces AI.

While subject matter experts and key decision-makers in different organizational silos have unique processes and perspectives, AI demands more collaborative approaches. Where an AI initiative resides within an insurance organization can determine its success. Some industry leaders, like large banks, have created AI Centers of Excellence (CoE), to ensure their AI efforts align with business needs across the organization. AI CoEs are independent units that operate much like a shared service.

The CoE sets the AI vision, immerses itself in different departments to understand processes, and collaborates with internal and external resources. This centralized but accessible approach helps the CoE share success stories and best practices organization-wide, strengthening AI adoption and execution. In insurance, this means the CoE can embed within departments like underwriting, claims, and customer service, making sure that each team benefits from AI’s insights and advancements.

Maintain AI project momentum by identifying quick win opportunities. Start by identifying smaller, high-impact use cases that demonstrate AI’s potential, such as policy checking. Launching a focused pilot for this area allows insurers to showcase AI’s capabilities, secure buy-in from leadership, and pave the way for further investment in larger initiatives.

“We’re able to immediately cut our policy checking efforts in half for simple policies,” shared an early AI adopter whose organization’s pilot focused on policy checking. “On larger policies, we’ve reduced our manual efforts by 80-90%,” they confirmed.

Pilots should ideally produce a minimum viable product (MVP) that demonstrates the AI model’s impact. For instance, an MVP that ensures the accuracy and quality of bound policies claim not only proves AI’s value but also boosts confidence in larger AI projects that require more resources.

AI is a developing field and small and mid-size insurance organizations may find it challenging to build expertise in-house. Seeking guidance from industry experts, universities, or technology partners can speed up project timelines and enhance the learning curve. Partnerships can provide unique insights, reduce time-to-market, and ensure that best practices are followed, especially for insurance leaders new to AI.

Insurance leaders who apply these strategies will be better positioned to realize AI’s potential, improving not only operational efficiencies but also customer experiences. AI can reshape the insurance industry, and with thoughtful planning, organizations can lead the way toward a smarter, data-driven future.