Generative AI is reshaping industries, and insurance is no exception. This rapidly evolving technology promises to revolutionize underwriting, claims processing, customer service, and beyond—offering insurers unprecedented efficiency and decision-making power. But how should companies adopt generative AI, and what challenges do they need to address?
Recently, during the Unstructured Unlocked podcast, David Moorhead, an information technology executive at Ernst & Young, shared deep insights into generative AI’s game-changing role in the insurance industry. Here, we highlight key takeaways from this enlightening discussion with Moorhead as well as podcast hosts Michelle Gouveia, VP at Sandbox Insurtech Ventures and Tom Wilde, CEO of Indico Data.
Listen to the full podcast episode here: Unstructured Unlocked Podcast
Generative AI and the insurance transformation
The insurance industry thrives on making complex decisions. Whether it’s underwriting a policy or processing claims, the ability to make informed and timely choices hinges on the availability of reliable data. Generative AI is elevating this decision-making process by automating workflows, analyzing vast datasets, and improving accuracy at an unprecedented scale.
Moorhead emphasized that generative AI is not just a “Swiss Army knife” tool that fits every scenario. Instead, its value lies in tailored use-case applications. Insurers can unlock significant benefits by introducing generative AI incrementally into specific processes, rather than overhauling their entire operations at once.
Why use cases matter
According to Moorhead, insurers should focus on addressing specific problems through defined use cases to realize measurable success. This approach allows organizations to break complex systems into actionable components and adopt AI step by step.
For example, generative AI can significantly streamline underwriting. Consider a scenario where an underwriter deals with thousands of pages of documents. With AI-powered solutions, those documents can be converted into readable, organized summaries, allowing the underwriter to make better, faster decisions. Similarly, in claims processing, generative AI can ingest various data types—from accident reports to telematics data—and automatically compile the information for adjusters, reducing manual effort while enhancing accuracy.
“The key,” Moorhead noted, “is not removing the human element, but rather enabling professionals to scale their expertise and focus on higher-value tasks.”
Efficiency gains across the value chain
Generative AI is transforming nearly every aspect of the insurance value chain. Moorhead outlined several key areas where insurers can harness its capabilities for maximum impact:
- Underwriting: Automate manual tasks like reviewing documents and assessing risk factors. By doing so, underwriters can focus on strategic decision-making and process more policies at scale.
- Claims processing: Reduce turnaround time by streamlining workflows, automating document ingestion, and using AI to analyze claims data with unparalleled accuracy.
- Customer service: Enhance customer experiences with AI-driven chatbots and virtual assistants capable of understanding complex queries and resolving issues in real time.
- Renewals and pricing: Identify opportunities for better policy terms or pricing adjustments using predictive analytics. AI can analyze renewal patterns and recommend changes that improve profitability and customer satisfaction.
Generative AI doesn’t just automate processes—it enhances effectiveness and scalability. By focusing on these targeted applications, insurers can achieve tangible, measurable outcomes.
Related Content: Insurance submission triage: Leveraging AI to streamline workflows and boost efficiency
Balancing innovation with governance
The adoption of generative AI comes with its share of risks. One of the key challenges, Moorhead pointed out, is governance. Insurers must ensure that AI models are thoroughly tested, monitored, and aligned with regulatory compliance requirements.
A significant concern with generative AI is the potential for “hallucinations,” or fabrications of data that could erode trust. Organizations must address this challenge by implementing safeguards. For instance, any information generated by AI should be traceable, with sources cited to verify its accuracy.
Additionally, Moorhead explained how insurers can build trust during implementation by starting small. Rather than aiming for fully automated decision-making right out of the gate, insurers should empower professionals—like underwriters and adjusters—to oversee and validate AI outputs. By incorporating feedback from users, companies can refine AI solutions and accelerate technology adoption across teams.
“Keeping humans at the center matters,” Moorhead said. “It’s not about replacing professionals; it’s about giving them better tools for smarter and faster decision-making.”
The road to enterprise-scale AI
One of the most exciting opportunities for generative AI in insurance is its scalability. Moorhead noted that many insurers are moving beyond pilot projects and use-case demonstrations to implement AI solutions at scale.
Scaling AI across an organization requires robust processes, centralized governance, and a well-defined operational model. Insurers must ensure every function—from underwriting to claims to customer service—operates cohesively within a broader AI platform.
For instance, automating claims processing across multiple departments can lead to significant efficiency gains. However, without consistent standards or monitoring protocols, scaling too quickly can introduce risks. Moorhead emphasized the importance of taking a balanced approach where scalability is driven by measurable outcomes and continuous improvement.
Schedule a 1-on-1 demo of Indico Data’s Decision Automation Platform to see how AI integration can fortify your claims management systems.
The future of AI in insurance
The future of generative AI in insurance is both exciting and unpredictable. Moorhead highlights several trends to watch over the next three to five years, including the potential for generative AI to replace outdated data entry systems, enabling real-time processing powered by conversational interfaces.
He predicted that insurers who move quickly to adopt and scale AI will secure a competitive edge by reducing costs, enhancing speed to market, and improving customer satisfaction. Moorhead also emphasized the role of first movers in driving innovation, with insurers that adopt AI early likely reaping the greatest rewards.
However, Moorhead cautioned that with such rapid advancements, insurers must remain vigilant in managing emerging risks. Transparency, accountability, and a strong governance framework will be crucial for ensuring long-term success.
Key takeaways
The insurance industry is entering a new era of innovation powered by generative AI. Here are the critical insights from David Moorhead’s discussion on the Unstructured Unlocked podcast:
- Generative AI offers transformative potential, but its effectiveness depends on tailored use-case applications.
- Insurers can achieve measurable efficiency gains in key areas, including underwriting, claims processing, and customer service.
- Robust governance and human oversight are essential to address risks like bias and inaccurate outputs.
- Scaling generative AI across the enterprise requires thoughtful planning and operational discipline.
- The insurers that adopt generative AI early will lead in profitability, efficiency, and customer satisfaction.
Related Content: Transforming insurance claims: insights from Ian Thompson, Strategic Advisor and Former Zurich Insurance Executive
Generative AI is more than just cutting-edge technology—it’s a strategic tool that enables insurers to make better, faster decisions at scale.
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Frequently asked questions
- What types of data privacy concerns arise when integrating generative AI into insurance workflows, and how can insurers mitigate them? The blog touches on governance and hallucinations but doesn’t directly address data privacy. Generative AI systems often process sensitive customer data, including medical histories, financial records, and personal identifiers. This creates risks around data leakage, improper access, and compliance violations, especially under regulations like GDPR or HIPAA. To mitigate these issues, insurers need to implement strict data access controls, encrypt data in transit and at rest, and ensure that AI models are trained with privacy-preserving techniques like differential privacy or federated learning. Regular audits and documentation also help maintain transparency and compliance.
- How can generative AI impact insurance fraud detection, and is it currently being used for that purpose? While the blog focuses on underwriting, claims, and customer service, it doesn’t explore fraud detection—one of AI’s most impactful use cases in insurance. Generative AI can identify fraud patterns by analyzing structured and unstructured data from past claims, social media, and call transcripts. By generating summaries, anomaly reports, or even simulating suspicious claim narratives, AI can flag potentially fraudulent activities more efficiently than traditional systems. Some insurers are already piloting such tools, using generative AI to support SIU (Special Investigations Unit) teams and reduce false positives in fraud alerts.
- What roles or job functions in insurance are most likely to evolve—or disappear—due to generative AI? The blog emphasizes that AI is a tool to assist humans, not replace them, but it doesn’t directly tackle which roles will change the most. Roles focused on repetitive, document-heavy tasks—like junior underwriters, claims intake specialists, or call center agents—are likely to evolve significantly. These jobs will shift from manual execution to oversight and validation of AI outputs. Instead of being eliminated, these functions may require new skills like prompt engineering, AI auditing, or systems analysis. Companies that invest in reskilling will retain talent while modernizing their workforce.
