Simon Glairy is a cornerstone of the modern insurance landscape, a recognized expert whose career has bridged the gap between traditional risk management and the high-velocity world of Insurtech. With a specialized focus on AI-driven risk assessment, he has spent years navigating the complex intersection of data science and underwriting. Currently, he is at the forefront of the industry’s most significant pivot: the transition from simply proving that technology works to ensuring it can be woven into the very fabric of global markets. His insights reflect a deep understanding of how legacy systems can be modernized without losing the foundational principles of risk transfer.
This conversation explores the fundamental shift from “innovation for innovation’s sake” to a disciplined, adoption-focused strategy. We delve into how insurers are moving beyond the role of a silent payer to become active partners in risk prevention, the critical layering of real-time granular data over traditional catastrophe models, and the rigorous selectivity now required to bridge the gap between a successful pilot and market-wide implementation. Glairy also sheds light on the vital role of collaborative ecosystems like Lloyd’s Lab in funneling capital toward solutions for modern threats like infrastructure decay and cyber vulnerabilities.
The insurance industry has spent the better part of a decade proving that digital tools can actually function in a legacy environment, but we seem to be entering a much more demanding phase. How do you characterize this shift from mere invention to the challenge of market-wide scaling?
The excitement of the “Proof of Concept” era was infectious, but the industry is now waking up to the reality that a successful pilot is only 10% of the journey. We have solved the problem of whether the tech works; now we have to solve the problem of whether the market will actually use it. This shift from proof of concept to proof of adoption is the hardest hurdle we have ever faced because it requires changing human behavior and embedded workflows across thousands of brokers and carriers. It is a transition from a laboratory setting to the messy, high-pressure reality of daily underwriting where any new tool must prove its worth immediately or be discarded. We are seeing a more mature discipline where “cool” technology is no longer enough; it must be practical enough to be adopted at scale to create any real impact.
We are hearing more about a “prevention-first” approach where insurers help policyholders identify hazards before they turn into claims. What does this change look like for the average business owner, and why is this partnership becoming so vital now?
The relationship between the carrier and the policyholder is undergoing a profound transformation from a transactional one to a collaborative one focused on resilience. Today, we have the sensors and the analytics to identify a potential leak or a structural vulnerability before a catastrophic failure occurs, which is a far better outcome for everyone involved. For a business owner, this means their insurer is no longer just a name on a policy document they see once a year, but a partner providing real-time insights to keep their doors open. We are seeing a massive demand for this because the frequency and severity of natural catastrophes, like wildfires and floods, have made traditional “wait and see” insurance feel insufficient. It’s about strengthening the resilience of the business itself, which ultimately narrows the protection gaps that have left so many communities vulnerable in the past.
There is often a debate about whether new, granular data sources will eventually make traditional catastrophe models obsolete. How do you see the relationship between localized, real-time data and the established modeling frameworks we have relied on for decades?
It is a mistake to view this as a zero-sum game where new data replaces the old; rather, it’s a sophisticated layering process that gives us a much higher resolution of risk. Traditional catastrophe models provide a vital broad-view framework, but they often lack the “boots on the ground” detail that local, real-time data can provide. Especially in regions like the US, where the volatility of weather events is increasing, underwriters need to see the specific vulnerabilities of a single building or a local infrastructure point alongside the broader historical trends. This layering of granular insights earns its place by making models more actionable, allowing for more precise pricing and better-informed risk selection. We aren’t discarding what works; we are supplementing it with environmental monitoring that alerts us to shifting hazards as they happen.
With so many startups entering the space, insurers have become much more selective about their partnerships. What are the specific benchmarks a new technology must hit before a major carrier considers moving it out of the pilot phase?
The bar for entry has been raised significantly, and today, selectivity is the defining constraint on our innovation cycle. We look for technologies that don’t just promise a digital future but produce measurable improvements in loss prevention, safety, and resilience right now. A tool can be brilliant in a vacuum, but if it doesn’t fit into the existing workflows of brokers and carriers, it will fail to gain any traction. We use structured pilots to test products in real-world settings, specifically looking for how easily the technology integrates with the systems underwriters use every single day. The focus is no longer on the “magic” of the AI, but on whether that AI addresses a specific, identifiable business problem without adding unnecessary complexity to the user experience.
The collaboration between established carriers and innovation hubs like Lloyd’s Lab seems to be a major driver of this new era. What kind of tangible impact are these accelerators having on the capital flowing into the sector?
The impact of these collaborations is substantial and serves as a powerful proof point for the industry’s direction. For instance, Lloyd’s Lab has built incredibly deep links with the US technology sector, with 47 US startups participating in their accelerator program and collectively raising over $600 million. This isn’t just about money; it’s about mentorship and market access, such as The Hartford providing 11 mentors to 15 different accelerator teams through Syndicate 1221 since 2020. These startups are tackling the most pressing issues of our time, from hurricane and wildfire exposure to the deterioration of critical infrastructure and complex cyber threats. This volume of activity shows that when you combine deep insurance knowledge with specialist tech expertise, you create a fertile ground for solutions that are actually “market-ready.”
Looking at the current landscape of flood monitoring, infrastructure sensors, and cyber detection, what is the biggest challenge these startups face once they have proven their tech works in a controlled environment?
The “valley of death” for these companies is no longer the technology itself, but the move toward wide-scale commercial traction and market embedding. Many of these startups are already gaining traction with individual pilots, but the next chapter is about convincing the broader market to adopt these tools as standard practice. This requires a level of collaboration that we haven’t seen before, involving not just the tech companies and insurers, but also brokers, capacity providers, and investors. They have to prove that their products can deliver consistent, reliable results in the unpredictable environment of the open market, far away from the safety of a controlled pilot. Ultimately, innovation is only successful when it is tested against real market needs and proves it can survive the rigors of scale.
What is your forecast for the insurance industry over the next five years as these technologies move from being “extra” features to core components of the business?
I forecast that by the end of this decade, the distinction between “Insurtech” and “Insurance” will completely disappear because the technology will be so deeply embedded in the workflow that it becomes invisible. We will see a shift where real-time risk mitigation data is treated with the same weight as historical claims data, leading to a much more dynamic and responsive pricing model. The industry will move from being a reactive safety net to a proactive shield, where the majority of our value is found in the losses we help our clients avoid rather than just the checks we write after a disaster. This evolution will be driven by those who prioritize adoption and practical integration over the pursuit of the next shiny object, creating a more resilient global economy in the process.
