Scout InsurTech Spotlight with Chetan Kandhari

Chetan Kandhari is the Chief Innovation and Digital Officer at Nationwide, a Fortune 100 company based in Columbus, Ohio and one of the largest and strongest diversified insurance and financial services organizations in the United States. Chetan was interviewed by Anthony Habayeb, CEO and Co-Founder at Monitaur.

How would you characterize the current status of AI at Nationwide?

“We’ve been working with AI for about a decade, focusing initially on traditional predictive AI—advanced analytical models, statistical modeling and programming languages like Python. Over the past couple of years, with the rise of ChatGPT and large language models, we’ve intentionally ventured into generative AI.

We’ve been on this journey for a while now, experimenting with pilots and proof of concepts, and we’ve implemented a few generative AI solutions across various parts of our property-casualty and financial services businesses. While we’re still in the early stages and there’s a long road ahead, compared to our industry peers, I’d say we continue to blaze the trail in adopting these technologies.”

What are the key stages for introducing and institutionalizing AI capabilities within an insurance company? 

“I think of this journey in three phases: One is scaffolding. This involves building the foundation—ensuring you have the right talent and people who understand how to integrate AI into business processes. It’s also about identifying where to apply AI. At Nationwide, we’ve taken a customer-centric approach by mapping customer journeys, including intermediaries and partners, to prioritize where AI can deliver the most value.

The second phase is building momentum. Once the foundation is in place, the focus shifts to experimenting with use cases, selecting technologies and making informed bets. It’s important to differentiate between reversible decisions (two-way doors) and irreversible ones (one-way doors). This stage involves pilots and proof of concepts to get the flywheel moving.

The final phase is driving meaningful business value through scalable solutions. This requires being outcome-focused, ensuring that AI is embedded into value chains and delivering tangible results rather than pursuing AI for its own sake.

These phases are iterative—you revisit each stage as you grow and adapt.”

Do you envision AI becoming as normalized as coding with Python—a tool or methodology everyone understands and uses in their daily work?

“Yes, but it’ll take time. Artificial Intelligence will become pervasive, embedded into the background where its contributions are seamless. I’m optimistic that AI will enhance financial services and insurance, helping us serve our mission at Nationwide: protecting people, businesses and futures with extraordinary care. Our ‘Human in the Loop’ approach will play an important role in this transition, ensuring that AI is continuously monitored and refined by human experts to maintain our high standards of service.”

Let’s talk about your Center of Excellence (COE) for AI. How has the COE approach helped, and how does it transition to an embedded strategy?

“The COE approach was necessary for us early on. It allowed us to centralize learning, avoid repetitive mistakes and address the scarcity of talent. The COE also provided economies of scale, making it easier to drive innovation.

However, as we mature, federating AI capabilities becomes essential to avoid bottlenecks and empower business units. The transition to an embedded strategy is gradual but crucial for scaling impact.”

What’s your perspective on the idea that data governance must be fully solved before implementing AI?

“It’s not a binary choice. You can tackle data issues and implement AI simultaneously. Structured data is often more manageable, but unstructured content—documents, manuals, marketing materials—requires AI-ready preparation, including metadata tagging and clear risk classifications.

We’ve taken a use-case-driven approach, prioritizing high-value applications and aligning data preparation efforts accordingly.”

By focusing on demand-driven use cases, you’re able to prioritize your data governance work effectively. Have you seen success with this approach?

“Yes, it helps triage and focus on the most critical areas. We’ve been able to prioritize data quality and definitions where they’re most impactful, while deferring less immediate concerns.”

What does success look like a year from now for Nationwide’s AI journey?

“Success would mean having multiple scalable AI solutions in production that deliver measurable business value—whether it’s improving productivity, enhancing underwriting or driving revenue through intermediary support. It’s about tangible outcomes powered by AI.”

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