With a wealth of experience in Insurtech and AI-driven risk management, Simon Glairy is a leading voice guiding the insurance industry through its digital transformation. He joins us to cut through the hype and discuss how insurers can move beyond viewing AI as a buzzword and start implementing it as a core driver of business value. Our conversation will explore how to build a culture of AI adoption from the ground up, the foundational importance of data, and the strategic steps necessary to transition from simple efficiency gains to true revenue growth. We’ll also touch on how leadership can sustain momentum by tying AI initiatives directly to transformative business goals.
Thinking about how most of us learned Excel—as users, not experts—how can a leader cultivate a similar culture of confident AI experimentation? What practical first steps can they take to encourage use without overwhelming teams who aren’t tech-savvy?
That’s the perfect analogy because it removes the fear factor. Nobody expected you to be a pivot table wizard on day one; you learned what you needed and asked for help when you got stuck. Leaders need to frame AI in the same way. It’s not this intimidating force that’s going to replace everyone. It’s a tool, and the goal is proficiency, not universal expertise. The first practical step is to make it feel safe and accessible. Start with broad training on general-purpose tools like Microsoft Copilot. Show teams how it can help with tasks they do every day, like summarizing long documents or drafting emails. This builds a baseline of confidence and proves the immediate utility, making people feel empowered to play around with it in their own workflows rather than feeling like they’re being forced into a complex, high-stakes tech project.
Given that messy, siloed data can derail any AI project, could you outline the first few critical steps a company must take to get its “data house in order”? What is the single most important role for non-IT leadership in supporting that foundational effort?
Before you can even think about sophisticated AI models, you have to face the data reality. The first critical step is an honest audit of your data infrastructure. Where does your data live? Is it clean, organized, and, most importantly, accessible? If you have critical information locked away in legacy systems or departmental silos, your AI initiatives will fail before they even begin. The next step is establishing clear governance and a unified strategy. But the single most important thing non-IT leadership can do is champion this as a business-wide priority, not just an IT problem. When leaders from sales, operations, and even HR are at the table, they ensure the data strategy aligns with actual business goals. Their involvement sends a powerful message that this is a collective effort to build a foundation for future growth, not just a technical cleanup exercise.
When rolling out general tools like Microsoft Copilot, what specific programs or team rituals have you seen successfully move employees beyond basic tasks? Share an example of how a team transformed a real workflow, going from initial curiosity to full adoption.
Moving from basic use to true transformation requires intentional effort. I’ve seen teams succeed by creating “AI sandboxes” or holding regular “workflow workshops” where they specifically brainstorm how a tool like Copilot can address a bottleneck in their process. For instance, a claims processing team I know of started by simply using it to summarize initial incident reports—a small but real time-saver. Seeing that win, their manager encouraged them to push further. They began using it to analyze claim documents to pull out key details, which drastically cut down their manual review time. This success built the confidence and the business case to adopt a more specialized AI tool that now helps them predict claim costs with greater accuracy. They went from curiosity about writing faster emails to fundamentally changing how they manage reserves, all because they were encouraged to start small and continuously ask, “What else can this do for us?”
The insurance industry is ripe for AI-driven growth, not just efficiency. Can you describe a real-world example of an insurer using AI-driven risk intelligence to create new revenue? How did they use it to proactively identify client needs and boost upsell opportunities?
This is where AI gets truly exciting because it shifts from a cost-saving tool to a value-creation engine. One of the most powerful examples is in commercial lines. A brokerage I’m familiar with implemented a system that uses dynamic AI models to assess client risk in real time. Instead of just looking at a client’s claims history, the model ingests a constant stream of external risk signals—things like localized weather forecasts for property risk, new cyber threats for liability, or global supply chain disruptions. This AI-driven intelligence doesn’t just sit on a dashboard; it proactively flags emerging needs. For example, the system might identify that a client in the manufacturing sector is suddenly exposed to a new supply chain vulnerability. This prompts the broker to proactively reach out with tailored coverage recommendations, creating significant cross-sell and upsell opportunities that simply wouldn’t have existed with a traditional, static risk assessment.
Enthusiasm for AI can fade if it’s only seen as a tool for faster emails. What’s a key strategy for tying AI initiatives to bigger business goals that create real value? Describe how a leader can communicate this vision to keep both their teams and clients engaged.
The key is to relentlessly focus the narrative on transformation, not just automation. If the only wins you celebrate are faster meeting notes, you’re right, the momentum will die. A leader must articulate a clear vision of how AI will help the company differentiate itself, create a superior customer experience, and unlock new avenues for growth. This means tying every initiative to a broader business goal that resonates with everyone. For example, instead of saying, “We’re using AI to process claims 20% faster,” a leader should say, “We’re using AI to get our clients the support they need in record time, reinforcing our promise to be there when it matters most.” This frames AI as a strategic enabler for delivering on the company’s core mission. It engages teams by giving their work greater purpose and excites clients by demonstrating a commitment to better service, ensuring that enthusiasm is sustained for the long haul.
What is your forecast for the future of AI in the insurance industry?
My forecast is that we are at a critical inflection point where the gap between the leaders and the laggards will widen dramatically and quickly. The future isn’t about humans versus machines; it’s about humans with machines, working together to manage risk in ways we never thought possible. However, this future won’t just happen on its own. Companies that sit back with passive optimism, assuming adoption will unfold naturally, will miss one of the greatest business opportunities of our time. The winners will be those who demonstrate bold leadership and take decisive action now. They will treat this not as a series of small efficiency projects, but as a relentless, top-to-bottom transformation of their business. The future is bright, but it belongs to the intentional and the brave.
