Get a demo
Select the type of demo you'd like to experience with Indico Data.
Select the type of demo you'd like to experience with Indico Data.
In this episode, Tom Wilde, CEO at Indico Data, and Michelle Gouveia, VP at Sandbox Insurtech Ventures, join Rory Yates, Chief Strategy Officer at EIS Ltd to discuss navigating AI adoption in insurance and harnessing generative AI for growth.
Michelle Gouveia: Hi everybody. Welcome to a new episode of Unstructured Unlocked. I’m your co-host, Michelle Gouveia.
Tom Wilde: And I’m your co-host, Tom Wilde.
Michelle Gouveia: We are thrilled today to be joined by Rory Yates, the Chief Strategy Officer—oh boy, let me restart that. Take two. We are thrilled today to be joined by Rory Yates, the Chief Strategy Officer—oh my God.
Rory Yates: It’s going to be one of those days. The outtakes will be great.
Michelle Gouveia: That’s right. Take three. We are thrilled to be joined today by Rory Yates, the Chief Strategy Officer of EIS. Rory, welcome to the podcast.
Rory Yates: Thank you.
Michelle Gouveia: Before we jump into our questions, would you mind telling us a bit about your background and your current role?
Rory Yates: Sure. For those who don’t know, EIS is a software platform that allows you to run an entire insurance business. We have about 29 customers worldwide and are based in the U.S. My background is a bit of a love-hate relationship with insurance—I keep coming back. I’ve been involved in the industry through capital investment turnarounds, startups, and various insurance-related roles. As a strategist, I spend a lot of time studying anthropology, psychology, and neurology, looking at external market trends to bring insights back into our business. I engage extensively with customers, prospects, and analysts to stay ahead of industry developments.
Tom Wilde: Excellent. Rory, let’s start with a common question across industries: Would you describe EIS as a policy administration system, or do you prefer a different definition?
Rory Yates: We’re built around the customer, so in some ways, we offer a PaaS (Platform-as-a-Service). One key differentiator is that we focus on the customer journey, rather than just policies, because traditional policy structures can be limiting. We provide flexibility within the insurance ecosystem.
Tom Wilde: Your product relies heavily on structured data. Insurance companies need a source of truth—what coverages they’ve written, what risks they’ve taken on, how they priced it. Then generative AI enters the scene, disrupting structured systems with interpretative capabilities. How are you and your customers thinking about blending structured data with generative AI?
Rory Yates: This challenge predates generative AI. Insurers needed more data fluidity even before AI-driven automation became prevalent. Many processes, such as fraud detection, require adaptive systems that can ingest large volumes of structured and unstructured data. At EIS, we’ve built our platform to be event-driven, allowing structured policy management while also enabling flexibility in areas like eligibility and claims processing. We designed the system for seamless integration with AI, ensuring it can support automation and intelligence-driven decision-making.
Michelle Gouveia: Before we started recording, we discussed how quickly AI is evolving. Some insurers risk implementing AI solutions that could become obsolete in a year. The insurance industry has a reputation for being slow to adopt new technology. How can insurers align their teams and strategies to implement AI effectively, despite its rapid advancements?
Rory Yates: Great question. This isn’t just a technology challenge—it’s an enterprise design challenge. Adaptability will be the defining competitive advantage in insurance. Insurers need to develop structures that allow them to pivot quickly. AI can be a massive tool for adaptivity, but many companies still need to lay the foundational groundwork. A multi-agent strategy is key—rather than relying on a single AI solution, insurers should leverage multiple technologies based on use case and risk level. This approach ensures they remain agile as AI continues to evolve.
Tom Wilde: Many insurers get stuck in the “infinite POC loop,” testing solutions without ever fully implementing them. How does EIS help customers break out of this cycle and make confident decisions?
Rory Yates: Pilot purgatory is a real issue in insurance. We encourage customers to focus on high-value, low-risk use cases first. For example, AI-driven analytics tools can identify target customers and product propensities without regulatory concerns. By mapping use cases against a risk-value framework, insurers can prioritize the most impactful applications and avoid getting bogged down in perpetual testing.
Michelle Gouveia: I love that framework. Many AI projects stall because of competing business priorities or lack of IT support. Your approach helps insurers implement AI in a way that delivers real value across different functions like underwriting, claims, and customer support.
Rory Yates: Exactly. Another example is AI-powered call center scripting. We’ve worked with clients to implement AI-driven prompts for customer service reps. The AI provides relevant information in real time, streamlining responses without taking decision-making away from the human agent. High value, low risk. These types of solutions help build confidence in AI adoption across the enterprise.
Tom Wilde: There’s ongoing debate about whether companies need to prepare their data for AI or whether AI can work with existing, unstructured data. What’s your take?
Rory Yates: It’s an important discussion. AI can technically process unstructured data, but the question is: should it? The risk of AI misinterpreting data is real, and there are regulatory implications. The best approach is to start with the assumption that AI can do it all—then work backward to determine where human oversight is still necessary.
Tom Wilde: That’s a great perspective. Instead of proving AI can do something, start by proving it can’t—it forces a re-examination of assumptions and challenges legacy thinking.
Rory Yates: Absolutely. AI’s role isn’t just about automation—it can also enhance regulatory compliance, detect fraud, and support vulnerable customers. These high-value applications often get overlooked but should be prioritized.
Michelle Gouveia: Speaking of overlooked applications, what are some lesser-known high-impact AI use cases you’ve come across?
Rory Yates: Fraud detection is a big one. AI can analyze voice modulation to detect deception—something intelligence agencies have used for years. This is now being tested in insurance claims. Another area is synthetic data generation, where AI creates realistic data models for product testing and risk assessment. AI can also improve image anomaly detection in claims fraud. These are all high-value applications that insurers should explore.
Tom Wilde: Fascinating. Any final thoughts before we wrap up?
Rory Yates: AI is reshaping insurance, but success depends on adaptability. Insurers must build systems and teams that can pivot as technology evolves. The goal should be to harness AI to create better experiences, improve decision-making, and enhance efficiency.
Tom Wilde: Great discussion. We’ve been speaking with Rory Yates, Chief Strategy Officer at EIS. I’m Tom Wilde, your co-host of Unstructured Unlocked.
Michelle Gouveia: And I’m Michelle Gouveia.
Tom Wilde: Rory, thanks so much for joining us.
Rory Yates: Thanks for having me.
Michelle Gouveia: Thank you!
Check out the full Unstructured Unlocked podcast on your favorite platform, including:
