Simon Glairy has spent years at the intersection of risk management and insurance technology, witnessing firsthand the industry’s shift from rigid legacy frameworks to the current era of intelligent automation. As a leading voice in Insurtech, he understands that the real challenge of digital transformation isn’t just about the software—it’s about managing the human and regulatory anxieties that come with it. In this conversation, we explore the uneven landscape of AI adoption, the critical difference between surface-level chatbots and agentic systems, and how carriers can move past their traditional “wait-and-see” mentality to remain competitive in an increasingly fast-moving market.
Many life insurers remain cautious about AI, particularly regarding underwriting and compliance. How do infrastructure constraints affect the adoption rate, and what specific guardrails should leadership establish to move past this hesitation?
The primary hurdle for many life insurers is a technological foundation built on fragmented data systems and legacy software that simply wasn’t designed for high-speed data processing. This structural rigidity creates a sense of paralysis where leadership fears that introducing AI into underwriting or compliance will lead to uncontrollable outcomes or regulatory friction. To overcome this, organizations must first move away from the idea that they need a “perfect” or all-encompassing system before they begin. I recommend a step-by-step approach that starts with building a secure “sandbox” where AI can be tested against historical data without affecting live policies. By establishing clear guardrails around auditability and traceability, leaders can prove to themselves and their regulators that the technology is reliable. We find that once insurers see reassurance through these controlled proofs of concept, the organizational confidence begins to swell, allowing them to finally address those long-standing infrastructure bottlenecks.
While executive support is vital, frontline employees often dictate whether new technology actually succeeds. How can insurers bridge the gap between leadership’s vision and the day-to-day comfort of the staff?
I recently saw a stark contrast between two clients that illustrates this perfectly: one was fully committed from the top down, while the other was so cautious they wouldn’t even let staff experiment with basic AI tools internally. This second approach often leads to a culture of fear or indifference, where employees see AI as a threat or a “side experiment” rather than a helpful tool. To bridge this gap, AI adoption needs to be treated as a strategic priority that empowers the individual, not just a CIO’s talking point. When teams are given the freedom to explore how these tools can remove the friction from their specific daily tasks, the shift in culture happens organically. You see a lightbulb go off when an underwriter realizes they no longer have to manually cross-reference dozens of documents, and suddenly, the technology is seen as a teammate rather than a replacement. It’s about creating an environment where the people doing the work feel safe enough to discover their own use cases and drive the transformation from the bottom up.
Some carriers have reduced policy administration tasks from forty minutes to just a few minutes using agentic AI. Where else is this technology delivering immediate ROI, and how should companies measure success beyond simple time savings?
The leap from a forty-minute manual process to a three-minute automated one in systems like Equisoft/amplify is the most visible metric, but the true ROI is felt in the reduction of human error and the lowering of operational costs. Beyond just time, carriers should look at “good order” processing rates and the speed of suitability checks as primary indicators of success. We are seeing incredible traction in claims handling and internal reviews, where the AI can flag discrepancies that a tired human eye might miss after hours of repetitive work. Success should also be measured by employee engagement and the ability to reallocate those “saved” minutes toward complex cases that require genuine human empathy and judgment. When you see a reduction in the backlog of pending applications and an increase in the accuracy of underwriting support, you know the agentic AI is doing its job by enhancing the workflow without the user even having to think about it.
Identifying the difference between a simple chatbot and sophisticated agentic AI is critical for maintaining regulatory standards. What specific questions should carriers ask vendors about auditability and domain-specific expertise?
Insurers must be rigorous when vetting vendors, because a generic AI provider often lacks the specialized knowledge required to navigate the nuances of life insurance regulations. You should start by asking if the system is truly “agentic”—meaning, can it autonomously break a complex task into smaller steps, repeat processes when necessary, and take direct actions within the policy administration system? It is also vital to ask about the specific models being used and how data privacy is maintained at every touchpoint. Carriers should demand to see how a decision is traced; if a vendor cannot provide a clear audit trail for why the AI suggested a certain underwriting path, that is a massive red flag. Without deep industry expertise, these tools often miss critical business nuances, which can ultimately make the solution more expensive and far less valuable in the long run.
The gap between AI early adopters and those waiting for “perfect” solutions is widening. What are the long-term consequences of a wait-and-see approach, and how can hesitant carriers safely put tools into employees’ hands to spark innovation?
The most dangerous consequence of waiting is that the technology is evolving much faster than traditional insurance organizations can adapt, meaning the “perfect” solution will always be just out of reach while competitors pull ahead. Carriers that stay on the sidelines risk losing their best talent to more innovative firms and will eventually find themselves operating with a cost structure that is completely unsustainable compared to AI-driven peers. To start safely, leadership should provide secure, enterprise-grade AI tools to their staff in a controlled environment and encourage them to experiment with low-stakes internal tasks. This mirrors the trajectory of cloud adoption from years ago; those who understood the operational benefits early were the ones who successfully scaled when the market shifted. By putting these tools in the hands of the people today, you build the institutional knowledge and the “muscle memory” needed to innovate when the stakes get higher.
What is your forecast for AI adoption in the life insurance industry?
I expect to see a massive acceleration in adoption over the next few years as the “proof of value” becomes undeniable across the sector. We are moving toward a reality where AI is not a separate application but is deeply embedded into every existing system, from claims to underwriting, making it an invisible but essential part of the insurance lifecycle. Carriers will stop viewing AI as a experimental luxury and start treating it as the standard operating engine that drives efficiency, serves customers in real-time, and finally solves the data fragmentation issues that have plagued the industry for decades. The winners will be those who stop waiting for the perfect moment and start building their AI-ready workforce today.
