By Sonny Patel, Chief Product and Technology Officer

Insurers have long pursued modernization—digitizing paper-based processes, automating workflows, and moving to the cloud. Now, insurers are looking towards generative AI as the next step forward in their data and technology efforts. 

Deploying generative AI, however, requires more than adopting new tools. It demands rethinking core systems, unifying fragmented data, and addressing organizational and ethical challenges. Two use cases—intelligent underwriting and natural language analytics—highlight where generative AI can create massive value if certain challenges are overcome.

Intelligent Underwriting: From Document Chaos to Rapid Decisions

Underwriting remains one of the most manual, labor-intensive processes in insurance. Submissions arrive in a wide range of formats (emails, PDFs, spreadsheets, scanned documents, etc.), forcing underwriters to re-key data, interpret broker notes, and stitch together incomplete information.

Previous waves of technological advancement have promised to solve this issue, but failed—now, generative AI changes that. Paired with intelligent document processing, it can be successfully deployed in real-world use cases to:

• Summarize long broker submissions and extract key risk data,
• Flag anomalies or information gaps,
• Prioritize submissions by complexity, and
• Recommend pricing tiers based on historical patterns

These capabilities prepare submissions with speed, accuracy, and greater insight, empowering underwriters to focus on high-value judgment and broker collaboration.

Insurers must be cautious, however, because in underwriting, explainability is a mandate. Black-box recommendations can’t be blindly trusted. Bias in training data can influence decisions, especially in niche markets. And regulatory scrutiny is increasing.

To succeed, carriers must implement transparent model governance, human-in-the-loop reviews, and clear audit trails. Without this, efficiency gains risk becoming compliance liabilities.

Natural Language Analytics: Unlocking Insights Across the Organization

Insurers are data-rich but insight-poor. Accessing simple metrics—like quote-to-bind ratios or customer churn—often requires technical teams to write SQL or generate static reports. And that’s assuming the data is accessible and clean enough to query, which it often is not.

Generative AI changes that dynamic by enabling business users to ask open-ended questions about their enterprise data using plain English and receive answers and visualizations. This is more than a convenience; it turns analytics into a real-time feedback loop for product design, customer service, and operations. For example:

• A COO can track premium growth by channel and product
• An underwriting leader can assess net business hit ratios and identify pricing issues
• A product manager can explore retention rates, cohort behavior, and customer lifetime value

This isn’t just self-service BI—it’s generative analytics: faster insights, broader access, and smarter decisions across the organization.

But cultural and technical barriers remain. Cultural inertia can stall progress, especially if business users distrust AI outputs or IT fears loss of control. Fragmented infrastructure further limits AI’s value. If data lives in silos, AI can’t generate meaningful insights.

Overcoming Implementation Challenges 

While generative AI promises to deliver evolutionary progress and high-value use cases, insurers must first overcome technological and organizational challenges to deploy the technology. Insurers must invest in:

• A modern and API-first core system that supports integration and real-time data accessibility
• Unified and governed data estate that allows AI to reason across policy, claims, and customer data
• Cross-functional alignment across business, IT, compliance, and executive leadership

Many insurers are already taking steps to improve their IT infrastructure and company culture. These companies understand that the future of insurance isn’t coming. It’s already here.

About Sonny Patel

Sonny Patel is the Chief Product and Technology Officer at Socotra, where she leads our Product and Engineering teams and owns and executes on Socotra’s product strategy. She is a recognized thought leader in AI with over 20 years of experience building and launching products at Fortune 500 companies. Prior to Socotra, Sonny was an integral leader at Dell, Microsoft, Amazon, and LivePerson. She holds an MBA in Strategy & Entrepreneurship from the Haas School of Business at the University of California, Berkeley and a Master’s in Computer Science from Texas A&M University.