I’m thrilled to sit down with Simon Glairy, a trailblazer in the insurance and Insurtech space, whose expertise in risk management and AI-driven risk assessment has positioned him at the forefront of industry innovation. With a career dedicated to reimagining how insurance brokers operate in a digital age, Simon has been instrumental in driving transformative change at his organization. Today, we’ll dive into his insights on redefining client experiences, tackling internal workflow challenges, harnessing the power of AI, and the critical role of communication during digital overhauls. Our conversation explores how technology and strategy intersect to shape the future of insurance brokerage, with a focus on practical solutions and real-world outcomes.
How did your journey toward digital transformation begin, and what was a defining challenge in those early days that shaped your approach?
Thanks for having me, Abigail. Our digital transformation journey kicked off about two years ago with a bold vision to become the broker of the future. We started with a formal process, bringing in consultants to help us pinpoint what that future looked like for us. One of the early challenges was confronting the reality that our client experience wasn’t as unique as we’d hoped—it felt very much like what everyone else was offering. I remember sitting in a meeting, reviewing client feedback, and seeing the same phrases over and over: “it’s fine,” “it’s what I expected.” That mediocrity stung, and it pushed us to rethink every touchpoint. We tackled this by forming dedicated teams, including our innovation group and IT department, to map out where we could differentiate. That early discomfort became a catalyst for us to focus not just on tech, but on how clients truly feel when they interact with us.
What led you to the realization that client experiences in insurance often lack distinction, and how did you start to address that gap?
I’ve long held the belief that client experience in our industry can feel like a carbon copy across brokerages. This came from years of hearing similar stories at industry events—clients describing their interactions as predictable, even mundane, regardless of the provider. One specific instance hit home when a long-standing client casually mentioned during a renewal meeting that they couldn’t tell us apart from a competitor based on service alone. That was a wake-up call. We dug deeper, analyzing feedback and observing pain points in the client journey, and decided to redesign how we deliver value. For example, we revamped our onboarding process to be more consultative, focusing on personalized risk education rather than just policy details. Seeing a client’s face light up when they understood a complex coverage gap we’d identified—that’s when I knew we were on to something. It’s an ongoing effort, but those small, human moments of connection are what we’re chasing now.
Your approach to testing new technologies involves running multiple proof-of-concept trials each year. Can you walk us through how you decide what to test and share a standout result from one of those experiments?
Absolutely. We’re in a unique spot where we see hundreds of potential solutions annually through our connections in the Insurtech ecosystem, and we typically pilot 6 to 10 of them each year. Choosing what to test comes down to alignment with our biggest pain points—whether it’s client-facing or internal efficiency—and ensuring the business unit leaders who’ll ultimately use the tech are involved from the get-go. A typical POC runs fast, often just a few months, where we test the solution in a controlled environment with real data and real users. One memorable trial was with a little-known startup offering a data integration tool. Honestly, we didn’t expect much, but after rolling it out to a small team, we saw a 30% reduction in time spent on manual data entry for a specific workflow. I’ll never forget the relief on our associates’ faces when they realized they could focus on strategy instead of grunt work. That success shaped how we prioritize integration-focused tools in our strategy moving forward.
You’ve spoken about the frustration of disconnected tech stacks creating inefficient workflows. Could you describe a specific instance of this at your organization and how you worked to fix it?
Oh, disconnected tech stacks are a nightmare we’ve lived with for too long in this industry. One glaring issue was a workflow where associates had to bounce between 15 different pieces of technology just to complete a single client analysis. Imagine the frustration—clicking out of a data platform for energy sector analytics, then logging into a separate cyber risk tool, all while juggling spreadsheets. It was like watching someone run a marathon with ankle weights. We knew this was unsustainable, so we prioritized integrating key platforms into a more seamless system, starting with a unified dashboard that pulled data from multiple sources. The fix wasn’t overnight—it took months of testing and feedback loops with the team—but once rolled out, the response was overwhelmingly positive. Associates told me it felt like they’d gotten hours back in their day, and I could sense the tension lift during team meetings. That kind of relief is why we push so hard to streamline.
With AI generating so much buzz, how do you help your teams move from vague ideas to concrete plans, and can you share an example of turning an AI concept into something actionable?
AI is the buzzword everyone loves, but clarity around it is rare. Many folks come to us saying they need “more AI,” without a clear picture of why or how. To bridge that gap, we set up an internal Technology Advisory Group to filter proposals, requiring teams to get local leadership buy-in and articulate specific business value before anything moves forward. It’s about grounding the hype in reality. One example that stands out is when a team approached us wanting AI for client communications but couldn’t define the scope. Through workshops with the Advisory Group, we honed in on automating follow-up emails based on client interaction data, setting a goal to cut response times by 20%. I remember the skepticism at first—people thought it wouldn’t feel personal—but when we piloted it and saw clients appreciating the timely replies, attitudes shifted. That process taught us to always tie AI to measurable outcomes, not just flashy promises. It’s a discipline we’ve stuck with since.
You’ve emphasized the power of clear communication during transformation. How have you ensured consistency in messaging, and what’s an example of a tough message you had to deliver?
Communication is the glue that holds a transformation together, and I can’t overstate its importance. We’ve worked hard to ensure everyone hears the same story, whether they’re in the C-suite or on the front lines, by aligning messaging across all channels—town halls, emails, even casual check-ins. If people hear different things from different leaders, trust erodes fast. One tough message I had to deliver was about pausing a popular but underperforming tech initiative. I stood in front of the team, explained the hard data showing it wasn’t delivering ROI, and shared why we needed to redirect resources. I could feel the disappointment in the room—some had poured months into it—but by being transparent about the ‘why,’ I saw nods of understanding by the end. That honesty shifted the mindset from frustration to focus on what’s next, and it reinforced for me that clarity, even when it’s hard, builds resilience in a team.
Given your exposure to countless Insurtech solutions, how do you identify the ones with real potential, and can you tell us about a surprising discovery that made a big impact?
Sifting through hundreds of Insurtech solutions each year is both a privilege and a challenge. We narrow it down by focusing on what solves our most pressing issues—client experience or operational bottlenecks—and by trusting the gut instincts of our operational leaders who’ll use these tools. We also look for scalability and cultural fit, not just shiny features. One surprising discovery was a small startup with a workflow automation tool that didn’t initially seem groundbreaking. During a pilot, though, it reduced processing time for a niche client segment by nearly 25%, which was huge for our team’s bandwidth. I remember walking past a desk and hearing an associate chuckle, saying they finally had time for a coffee break. That unexpected win reminded me to never underestimate the underdog solutions—they often pack the biggest punch when tailored to a specific need.
You’ve been clear that AI should augment rather than replace humans, with investments tied to measurable returns. How do you evaluate that return, and can you share a specific AI project where the numbers told a compelling story?
I’m a firm believer that AI’s role is to enhance human work, not eliminate it, and every investment has to justify itself financially. We evaluate return by looking at operational efficiency gains or how much hiring offset we can achieve—basically, can we redeploy human capital elsewhere for the same cost? It’s a math problem at the end of the day. One AI project that comes to mind involved automating data analysis for risk assessments. We projected a specific time saving for our team, equating to about two full-time positions worth of hours annually, and tied the investment to that benchmark. When we rolled it out, the numbers actually exceeded our expectations—associates reported feeling less buried in mundane tasks, and we redirected those saved hours into client-facing strategy work. I could see the energy shift in the room during updates; people felt empowered rather than threatened. However, I’ve also seen projects where the math didn’t add up, and we had to pull the plug early—those are humbling moments that keep us grounded in reality.
What is your forecast for the role of AI in insurance brokerage over the next few years?
Looking ahead, I think AI will become an indispensable partner in insurance brokerage, but only if we stay disciplined about its application. I foresee it taking on more of the heavy lifting in data processing and predictive analytics, freeing up brokers to focus on relationships and complex problem-solving. The challenge will be avoiding the temptation to over-rely on it—human judgment in risk assessment isn’t going away, and I believe the brokers who thrive will be those who master the balance of tech and touch. My forecast is that within five years, the firms leading the pack will be the ones who’ve woven AI seamlessly into workflows without losing the personal connection clients crave. We’re already seeing early adopters pull ahead, and I’m excited to see how this space evolves with smarter, more intuitive tools.
