I’m thrilled to sit down with Simon Glairy, a trailblazer in insurance technology and risk management, whose expertise in Insurtech and AI-driven solutions has helped countless insurers navigate the complex landscape of digital transformation. With a deep understanding of how technology can reshape risk assessment and operational efficiency, Simon offers invaluable insights into the seismic shifts happening in the insurance industry. Today, we’ll explore the urgent need for core system overhauls, the barriers legacy platforms pose to innovation, the integration of AI and real-time data, and the evolving demands for trust, transparency, and customer-centric solutions across various insurance sectors. Let’s dive into how these trends are setting the stage for the future of insurance in 2026 and beyond.
How do you see the insurance industry’s shift toward a ‘foundational reset’ in 2025 impacting long-term strategies, and what’s driving this urgent move away from patching legacy systems?
Thanks for having me, Abigail. The ‘foundational reset’ we’re seeing in 2025 is really about insurers recognizing that years of Band-Aid fixes on legacy systems just aren’t cutting it anymore in a world of heightened volatility and customer expectations. The drive behind this pivot is multifaceted—think escalating climate risks, capital market fluctuations, and customers who now expect Amazon-like responsiveness. Patching old platforms has led to fragmented data, slow product rollouts, and spiraling integration costs. I remember working with a mid-sized property insurer a few years back; they spent nearly 18 months trying to integrate a new claims module onto a 15-year-old system, only to see a 30% drop in processing efficiency due to data silos. Rebuilding the core isn’t just a tech upgrade—it’s a survival strategy to achieve agility and risk-adjusted profitability. Insurers are finally seeing that without a resilient backbone, innovations like AI or real-time analytics remain pipe dreams.
What specific challenges have you observed with ‘modern-legacy’ systems—those built in the last 10 to 15 years—that are now holding insurers back in areas like product development or customer engagement?
That’s a critical point, Abigail. These modern-legacy systems, often built on monolithic designs, were never architected for today’s needs like API-first distribution or dynamic product configuration. One major challenge is their rigidity—product development cycles often stretch into months because these systems can’t handle rapid iteration or complex ecosystem integration. I’ve seen this firsthand with a life insurer trying to launch a customizable term policy; they were stuck for over a year because their platform couldn’t support real-time data flows or integrate with external partners for personalized offerings, ultimately losing market share to a nimbler competitor. Customer engagement suffers too—without fluid data, insurers can’t deliver the contextual, seamless experiences policyholders now demand. It’s like trying to run a smartphone app on a dial-up connection; the infrastructure just can’t keep up with the vision.
Can you elaborate on how siloed architectures are stifling AI’s potential in core insurance processes like underwriting or claims, and what steps are insurers taking to overcome this?
Absolutely. Siloed architectures are like trying to solve a puzzle with half the pieces missing—AI needs clean, contextual, real-time data to work effectively, and these fragmented systems can’t deliver that. In underwriting, for instance, batch-based processing means risk assessments are outdated by the time they’re finalized, and in claims, disjointed data slows down automation and decision-making. I recall a regional insurer I advised who piloted an AI tool for claims processing; it failed to scale because their legacy core couldn’t feed real-time customer data, leading to inaccurate fraud detection and frustrated adjusters. Now, forward-thinking insurers are embedding intelligence at the platform layer—think end-to-end data traceability and AI orchestration built directly into the core. They’re ripping out old systems and replacing them with platforms that support natural-language workflows and explainable models, ensuring AI isn’t just a shiny toy but a true operational game-changer.
With static pricing models becoming obsolete, how are event-driven core platforms enabling insurers to shift toward real-time risk intelligence and proactive mitigation?
Static pricing tied to annual cycles is a relic in today’s fast-moving, volatile market—think sudden climate events or economic swings that can shift risk profiles overnight. Event-driven core platforms are a game-changer because they treat data as a live asset, not a historical snapshot. They ingest continuous signals—whether it’s weather alerts or market data—and dynamically recalculate exposure, allowing insurers to adjust pricing or coverage in real time. I worked with a commercial insurer who adopted such a platform after a major storm event; they were able to reassess portfolio risks within hours, not weeks, and steer away from potential losses by tweaking terms proactively. It’s like moving from checking a rearview mirror to having a live dashboard—insurers can finally anticipate and mitigate risks before they snowball. This isn’t just about precision; it’s about building trust with policyholders by showing you’re ahead of the curve.
Trust and transparency have become non-negotiable in 2025 with increasing regulatory scrutiny. How are insurers grappling with these governance demands, and why is core system governance such a priority for decision-makers now?
Trust and transparency aren’t just buzzwords anymore; they’re measurable metrics that can make or break an insurer, especially with regulations like the EU AI Act demanding accountability. Insurers are struggling with legacy systems that lack data lineage or audit trails—how do you explain an AI-driven decision when the underlying data is a black box? I’ve seen CIOs sweat over this; one I worked with had to delay an AI rollout because their platform couldn’t meet bias monitoring requirements, risking hefty fines and reputational damage. Core system governance is now a top buying criterion because it’s the foundation of trust—modern platforms must offer full traceability, explainable AI, and audit-ready workflows. It’s not just about compliance; it’s about proving to customers and regulators that you’re a steward of data, not just a user. The emotional weight of getting this wrong—losing customer faith or facing legal backlash—is pushing leaders to prioritize governance like never before.
Climate volatility is reshaping property risk assessment. What hurdles do insurers face in integrating diverse data sources like IoT telemetry or satellite imagery, and how are modern platforms addressing these?
Climate volatility is a beast, and insurers are finding that integrating diverse data—like IoT sensor feeds or satellite imagery—into workflows is no small feat with legacy systems. The hurdles are often technical and cultural; old platforms can’t handle the volume or variety of real-time data, and teams aren’t always ready to trust predictive models over gut instinct. I remember a coastal insurer I consulted for—they struggled to merge flood telemetry with claims data because their system choked on the input, delaying payouts during a critical hurricane season and frustrating policyholders. Modern platforms flip this on its head by enabling unified data orchestration; they ingest real-time signals, run predictive resilience models, and automate responses like pre-loss warnings. The process starts with a cloud-native core that supports multi-source integration, then layers in AI for pattern detection, and finally ties it to operational workflows—all while keeping human oversight in the loop. It’s like upgrading from a paper map to Google Earth; suddenly, you see the full picture and can act before disaster strikes.
Group benefits are evolving into personalized wellbeing ecosystems. How are traditional systems falling short, and what does a modern platform bring to the table in terms of adaptability?
Traditional systems in group benefits are product-centric relics—they’re built to administer static plans, not to adapt to the personalized, portable wellbeing ecosystems employees and employers now crave. These systems can’t handle the flexibility needed for benefits that adjust to life events or integrate with multi-party ecosystems, leaving insurers unable to meet rising expectations. I recall an employer client who wanted benefits that could shift based on employee wellness data—like increasing mental health support during high-stress periods—but their outdated platform couldn’t support real-time personalization, leading to disengaged staff. Modern platforms change the game by orchestrating outcomes, not just policies; they enable continuous data flows, contextual journeys, and automatic adjustments—like boosting coverage when an employee has a baby. Picture an employee logging into a portal and seeing benefits tailored to their current needs, all powered by a core system that talks to wellness apps and HR systems seamlessly. It’s a shift from rigid administration to a living, breathing support system that builds loyalty.
Pet insurance is emerging as a testing ground for customer-centric transformation. What unique challenges are insurers facing in this space, and how are modern platforms turning pet health data into proactive care?
Pet insurance is fascinating because it’s becoming a microcosm of broader Insurtech trends, but it comes with unique pain points like the lack of standardized veterinary coding and wild cost variability across services. These make underwriting and pricing incredibly tough—unlike human health, there’s no universal framework to lean on, so insurers often guess at risk profiles. I worked with a pet insurer who nearly lost a key market because their legacy system couldn’t process wearable data from pet collars, missing out on early health warnings that could’ve reduced claims by preempting issues. Modern platforms are stepping in by ingesting real-time telemetry—like activity levels or vet diagnostics—and integrating with care networks to dynamically adjust coverage or suggest preventive plans. Imagine a platform alerting a pet owner to a potential joint issue based on reduced movement data, then connecting them to a vet for early intervention while tweaking premiums to reflect the risk—all in a day. It’s about moving from reactive payouts to being a trusted partner in a pet’s life, and that emotional connection with owners is where loyalty is won.
What is your forecast for the role of AI and core platform modernization in shaping the insurance industry over the next few years?
Looking ahead, I believe AI and core platform modernization will be the twin engines driving insurance into a new era of intelligence and adaptability over the next three to five years. We’re moving beyond pilot projects—by 2026, AI will be deeply embedded in underwriting, claims, and customer service, but only for insurers who’ve modernized their cores to handle real-time data and governance demands. I foresee a widening gap between leaders who invest in event-driven, AI-ready platforms and laggards stuck with legacy constraints; the former will pioneer proactive risk mitigation and hyper-personalized experiences, while the latter risk obsolescence. The emotional stakes are high—insurers who get this right will build unshakeable trust with policyholders, becoming partners in navigating life’s uncertainties. My gut tells me we’ll see a wave of consolidation as smaller players struggle to fund these transformations, but those who endure will redefine what insurance means. It’s an exciting, if daunting, horizon.
