Simon Glairy is a distinguished authority in the insurance and Insurtech sectors, renowned for his strategic focus on risk management and the integration of artificial intelligence into traditional insurance workflows. With years of experience bridging the gap between legacy operations and cutting-edge technology, he has become a leading voice on how carriers can move beyond pilot programs to achieve true enterprise-level transformation. In this conversation, we explore the shift from minor use cases to functional overhauls, the cultural evolution required to treat AI as a teammate, and the rigorous governance structures that turn technological promise into sustained performance.
Many firms are pivoting from chasing hundreds of minor use cases to transforming entire functions like claims or underwriting. How do cross-functional teams of actuaries and tech experts decide which core functions to prioritize, and what specific indicators distinguish a functional overhaul from a series of isolated wins?
The decision-making process has shifted from looking for “quick wins” to identifying where AI can solve deep-seated business problems across the entire value chain. We bring together business leaders, actuaries, and tech experts to look for areas where data density meets high-frequency decision-making, such as claims processing or underwriting. A functional overhaul is distinguished by its ability to redefine the core workflow, such as compressing claims timelines from days to minutes while simultaneously improving accuracy. You know you’ve moved beyond isolated wins when the technology creates a more agile, connected enterprise rather than just fixing a single, siloed pain point. It’s about building a calculated, long-term play that changes how the organization operates at its foundation.
Adopting an “AI as teammate” model requires moving employees away from mundane data gathering toward high-value critical thinking and relationship-building. What practical steps are necessary to build this culture of adoption, and how does freeing up staff time directly translate into a better experience for the end customer?
Building this culture starts with the philosophy that AI should be implemented with employees, not to them, ensuring they feel empowered rather than replaced. Practically, this involves providing hands-on training and engaging teams early to understand their specific needs so the tools we deploy are actually relevant to their daily tasks. When we automate the mundane data gathering that previously took hours, we allow our professionals to reinvest that time into complex judgment calls and relationship-building. For the end customer, this translates into faster response times and more personalized service, as the human representative is no longer bogged down by paperwork and can focus entirely on the claimant’s needs. It creates a more empathetic and efficient service model that honors the customer’s time and emotional state.
In a highly regulated environment, clear governance and data quality standards act as enablers for scaling technology rather than barriers. How should organizations design responsible use frameworks that build internal trust, and what role do these guardrails play in securing a long-term, sustainable return on investment?
Organizations must view governance not as a series of “no” markers, but as the essential foundation for confident, wide-scale adoption across the firm. A responsible use framework should prioritize data quality and transparent decision-making, ensuring that every AI-driven output can be audited and understood by the human “teammate.” These guardrails are vital for ROI because they prevent the costly reputational and regulatory setbacks that occur when technology is deployed without oversight. When leaders and employees trust the controls behind the tools, they are more willing to integrate AI deeply into the business, which is the only way to achieve a sustainable return. Without this trust, the technology remains a peripheral novelty rather than a core driver of value.
Moving AI from a novelty to an enterprise discipline involves embedding it directly into daily workflows to increase organizational agility. Can you provide a step-by-step example of how this integration changes the decision-making process for an underwriter, and what technical or cultural hurdles usually arise during this transition?
In a transformed underwriting workflow, the process begins with AI instantly synthesizing vast amounts of external and internal data that an underwriter would have previously spent hours collecting. The AI then flags specific risk factors or anomalies, presenting the underwriter with a curated summary and a suggested risk profile rather than a raw stack of documents. The primary hurdle here is often cultural; many veteran underwriters may initially feel that the machine is overstepping or that they are losing their “gut feel” for risk. To overcome this, the transition must be positioned as a way to enhance their expertise, moving their role from data hunter to high-level decision-maker. Technically, ensuring that these AI insights are delivered seamlessly within the existing software environment is critical to avoiding “tool fatigue” and ensuring the process remains agile.
Achieving a sustainable return on investment often depends more on workforce transformation than on the technology itself. When adopting a skills-first approach, which specific analytical or collaborative traits should leaders look for, and how can training programs be structured to ensure employees work with AI instead of around it?
When we look at workforce transformation, we prioritize adaptability, analytical thinking, and the ability to collaborate across different disciplines. It isn’t just about technical coding skills; it’s about finding people who can interpret AI outputs and apply human judgment to edge cases that the machine might not fully grasp. Training programs should be structured to be immersive and continuous, moving away from one-off seminars to integrated learning paths that happen within the flow of work. By teaching employees how the AI arrives at its conclusions, we ensure they understand how to “co-pilot” the technology rather than trying to find workarounds. This alignment ensures that the workforce and the technology evolve in tandem, which is the true driver of long-term performance.
What is your forecast for AI in the insurance industry?
I believe we are entering a period of disciplined execution where the initial hype is being replaced by measurable accountability and performance. Over the next few years, we will see the “AI as teammate” model become the standard operating procedure for leading carriers, effectively separating the market into those who integrated AI and those who merely experimented with it. Success will no longer be measured by the number of pilots launched, but by the tangible impact on loss ratios, customer retention, and operational speed. The winners will be the insurers who treated AI as an enterprise discipline, aligning their digital strategy with their broader business objectives to turn promise into performance. Ultimately, AI will not replace the insurer, but it will fundamentally redefine what it means to be an insurance professional in a data-driven world.
