Generative AI for Insurance: Claims, Underwriting, and Customer Service

I’ve watched six technology revolutions reshape industries — the web, search, social media, mobile, blockchain, and AI. And insurance, an industry built on risk models and document processing, is sitting on the perfect use case for generative AI. The question isn’t whether it works. It’s whether your organization can move it from pilot to production.

Where Generative AI Is Already Changing Insurance Workflows

Generative AI fits insurance because insurance runs on three things: documents, decisions, and conversations. Claims adjusters read reports. Underwriters assess risk from applications. Customer service reps answer policy questions. All three workflows involve unstructured data. All three are ripe for AI augmentation.

The early wins are in claims processing. GenAI can summarize accident reports, extract relevant details from photos, and draft initial assessments in seconds. It doesn’t replace the adjuster. It gives the adjuster a head start. Underwriting is next. Instead of reading through pages of submissions, underwriters get AI-generated risk summaries with flagged anomalies. Customer service sees similar gains. Chatbots trained on policy language can handle routine questions, escalate complex ones, and generate personalized responses faster than any template library.

These aren’t theoretical. They’re happening now. The difference between pilot and production is what separates organizations testing AI from those scaling it.

The Infrastructure Gap Most Insurers Underestimate

Most insurance companies I talk to have run a pilot. Fewer have a plan to scale. The gap isn’t technical. It’s operational. Generative AI doesn’t work in a vacuum. It needs clean data, clear workflows, and people who trust it enough to use it.

Start with data. If your claims data is scattered across legacy systems, your AI will be too. GenAI can read messy documents, but it performs best when it has structured context. That means investing in data integration before you invest in models. Next is workflow design. AI works when it fits into existing processes, not when it replaces them overnight. The best implementations I’ve seen treat AI as a co-pilot, not autopilot. Adjusters still make final calls. Underwriters still own risk decisions. AI just makes them faster and more informed.

Then there’s trust. If your team doesn’t understand how the AI reached a conclusion, they won’t use it. Explainability isn’t a nice-to-have. It’s a requirement. That’s where governance comes in, and it’s where most pilots stall.

Why Governance and Training Unlock Scale

Governance sounds boring. It’s also the reason some insurers are scaling AI while others are stuck in pilot purgatory. Governance answers three questions: Who owns the model? Who audits the output? Who decides when AI is wrong?

Without clear answers, you get risk. Legal risk if AI generates biased recommendations. Compliance risk if outputs aren’t documented. Operational risk if no one knows how to override the system. The organizations moving fastest have governance frameworks in place before they scale. They define roles. They build audit trails. They train teams not just to use AI, but to challenge it.

Training matters more than most leaders expect. Your adjusters and underwriters need to understand what generative AI can and can’t do. They need to know when to trust the output and when to dig deeper. This isn’t technology training. It’s decision-making training. And it’s what separates a tool from a transformation.

What This Means for Your Organization

If you’re testing generative AI in insurance, ask yourself: do we have the infrastructure to scale this? Do we have governance to manage it? Do we have training to build trust? Those three questions determine whether your pilot becomes a product or a PowerPoint slide.

The Generative AI keynote conversations I have with leadership teams focus less on what’s possible and more on what’s practical. Start small. Pick one workflow. Build the plumbing. Train the people. Then scale. AI doesn’t disrupt industries. Organizations that implement it well do.

Joel Comm is a New York Times bestselling author, internet pioneer, and AI keynote speaker. He has spoken for Microsoft, IBM, Cisco, the United Nations, and Harvard Business School. Learn more at joelcomm.com.