Developing a Practical, Value-Driven GenAI Strategy for Insurance

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Insurance CIOs stand at a pivotal crossroads. Insurance data is vast, complex, and deeply intertwined with risk. This makes it both a prime candidate for AI transformation and a cautionary tale of regulatory, ethical, and operational challenges. Generative AI (GenAI) has moved beyond theoretical experiments, and now CIOs are expected to deliver concrete business value through demonstrated use cases. But how can they do so while ensuring compliance, scalability, and real-world applicability?

Selecting the Right Use Cases

In “How Insurance CIOs Can Develop a Successful Generative AI Strategy” (13 December 2024), and in their Key Findings section, Gartner^® states:

• “The top factor among insurers in selecting generative AI (GenAI) use cases is business outcomes; however, Gartner research has found that this is often difficult to quantify.”
• “GenAI is pushing beyond experimentation, adding pressure on insurance CIOs to formalize their validation and selection processes to target those that can deliver measurable business results.”
• “To be effective, while adhering to compliance and regulation, insurers must consider four key dimensions to GenAI use case selection: risk appetite, organizational readiness focused on process and employees, business value and metrics, and vendor/solution.”

At Denodo, we agree with the comments from Gartner and furthermore find that organizations are focused on horizontal applications—like IT code generation and call-center summarization—rather than higher-value, domain-specific applications such as underwriting, policy servicing, or broker communications.

To break through this barrier, insurance CIOs must rethink how they prioritize GenAI implementations. The goal is to strategically balance risk with reward, experimenting where safe, and doubling down on strategic value where there is transformative potential.

The Critical Insurance Use Cases: Rethinking Data Sharing Between Brokers and Underwriters

One of the most immediate, high-impact areas for GenAI in insurance lies in improving data sharing between brokers and underwriters. This relationship has long been hindered by fragmented data, slow manual processes, and inconsistencies in policy documentation. GenAI presents a game-changing opportunity to streamline this critical exchange. Here’s how:

1. Intelligent Policy Summarization and Risk Evaluation

Underwriters are often bogged down by long, unstructured broker submissions, requiring them to manually sift through hundreds of pages of documents, emails, and attachments. GenAI-powered summarization can extract key risk indicators, flag inconsistencies, and present structured insights—cutting down underwriting turnaround time from days to minutes.

2. Automated Broker Queries and Responses

GenAI can act as an intelligent intermediary, automatically handling common broker queries and requesting missing information. Instead of back-and-forth email chains, an AI-powered assistant can dynamically engage with brokers to clarify ambiguities, suggest risk classifications, and collect all required data points upfront.

3. Real-Time Market and Risk Analysis

Brokers need fast, data-driven insights to offer competitive quotes to clients. By leveraging GenAI-driven natural language queries over aggregated underwriting data, they can instantly generate insights into risk trends, pricing models, and policy exclusions—enhancing their ability to negotiate effectively with insurers.

4. Personalized Client Recommendations

With the rise of embedded insurance and tailored risk solutions, brokers are under increasing pressure to deliver hyper-personalized policy recommendations. GenAI can analyze past claim histories, real-time market conditions, and individual customer risk profiles, to suggest optimal coverage options—enhancing both customer satisfaction and sales conversion rates.

Balancing Innovation with Governance: The CIO’s Mandate

Despite these compelling use cases, CIOs must approach GenAI adoption with a clear-eyed view of risk, compliance, and execution challenges.

• Regulatory Compliance & Ethical AI: The European AI Act, NAIC guidelines, and state-specific AI regulations will require insurers to maintain transparency in AI-driven decision-making. Any underwriting-related AI models must be explainable, bias-tested, and compliant with data privacy laws.
• Human-in-the-Loop Design: The underwriting and brokerage process remains deeply human-driven. GenAI should empower—not replace—decision-makers by offering insights while allowing human experts to apply judgment.
• Enterprise Data Readiness: I feel that Gartner suggests that data quality and AI governance remain barriers to GenAI success. For this reason, CIOs should invest in robust data virtualization and logical data management solutions for seamless, trusted data access, accuracy, and security.
• Scalability & Vendor Strategy: The question of “buy vs. build” looms large. While hyperscalers and insurtech vendors offer plug-and-play GenAI solutions, insurers must assess their long-term control over AI models, data ownership, and integration complexity.

Unlocking GenAI’s Full Potential in Insurance

Insurance runs on vast, scattered, and fast-moving data—broker submissions, underwriting systems, claims records, and regulatory feeds. Without logical data management capabilities, GenAI is flying blind. The Denodo Platform, a logical data management solution, seamlessly connects fragmented data in real time, eliminating duplication and costly integrations, and provides GenAI applications with trusted, AI-ready data from across the organization. The result? Smarter underwriting, automated broker interactions, and instant risk assessments—all while facilitating compliance, scalability, and AI-driven transformation.

A Final Thought: Moving from AI Experimentation to AI Differentiation

Insurance CIOs must move beyond pilot programs and isolated use cases toward a holistic GenAI strategy that drives measurable business outcomes. The winners in this space will not be those who merely experiment but those who integrate AI deeply into their core operational workflows—redefining how data is shared, analyzed, and acted upon across the insurance value chain.

The time to act is now. The future of insurance will be written by those who successfully bridge the gap between GenAI’s potential and its practical, real-world impact.

Gartner, How Insurance CIOs Can Develop a Successful Generative AI Strategy, Kimberly Harris-Ferrante, 13 December 2024

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