I’m thrilled to sit down with Simon Glairy, a renowned expert in insurance and Insurtech, whose deep knowledge of risk management and AI-driven risk assessment has shaped industry perspectives. With a keen eye on emerging technologies, Simon offers invaluable insights into how the insurance landscape is evolving as we approach 2026. In this interview, we explore the transformation of the industry, the pivotal role of AI and advanced analytics, the integration of modern connectivity solutions, and the future of data-driven decision-making in insurance.
How do you see the insurance industry evolving as we head into 2026, particularly in terms of its approach to innovation?
The insurance industry is undergoing a significant shift as we approach 2026. A few years ago, the focus was on rapid disruption, with startups pushing boundaries at breakneck speed. Now, there’s a pivot toward sustainability and compliance. This change is partly driven by financial realities—global insurtech funding has dropped sharply, so companies must prove profitability with solid economics. We’re seeing less of the “move fast and break things” mentality and more emphasis on building solutions that integrate with existing systems and deliver long-term value for carriers and brokers.
What are some of the key factors pushing the industry toward this more measured, sustainable mindset in insurtech?
Financial pressures are a big driver. With funding down by nearly half in 2025, startups can’t afford to burn cash on flashy marketing or unproven ideas. Investors are pickier, focusing on ventures that show a clear path to profitability. Beyond that, there’s a growing recognition that collaboration, rather than disruption, is the way forward. Insurtechs are now designing tools to help traditional players achieve efficiency and growth, rather than trying to replace them. It’s a pragmatic shift that aligns innovation with real-world needs.
Why do you think AI remains such a dominant force in the insurance space for 2026?
AI’s dominance comes down to its ability to transform complex, manual processes into streamlined, efficient workflows. By 2026, it’s no longer just a buzzword—it’s a core tool for insurers. From handling customer interactions to automating document-heavy tasks, AI is proving its worth in delivering measurable returns. What’s exciting is how it’s evolving beyond basic applications into more sophisticated uses like generative AI and autonomous agents, which tackle end-to-end processes while still allowing for human oversight to ensure accuracy.
Can you share how generative AI has transitioned from experimental projects to practical, everyday use in insurance?
Generative AI started as a bit of a novelty, with lots of pilot programs but hesitation around issues like inaccuracies or ethical concerns. Over time, the underlying technology, like Large Language Models, has improved dramatically, reducing errors and building trust. Now, it’s embedded in daily operations—think customer service chatbots that feel almost human, or tools that generate policy documents and marketing content for review. The return on investment is clear: better customer experiences, faster product rollouts, and boosted staff productivity.
How is something like Intelligent Document Processing making a difference in day-to-day insurance operations?
Intelligent Document Processing, or IDP, is a game-changer for efficiency. It uses AI to scan and extract key information from unstructured documents, turning chaos into usable data. For example, in underwriting or claims, IDP can pull critical details from submissions, validate them in real time, and route them to the right systems or people. This cuts down on tedious manual work, letting staff focus on strategic decisions rather than data entry. It’s all about speeding up processes while maintaining accuracy.
What role does human oversight play when leveraging AI tools like IDP in insurance workflows?
Human oversight is still crucial, even with advanced AI tools like IDP. While these systems can handle a lot autonomously, there’s always a risk of errors or misinterpretations—think of AI “hallucinations” where it might misread context. Having a person in the loop to review outputs, validate data, and manage exceptions ensures trust and reliability. It’s about striking a balance: letting AI do the heavy lifting while humans provide the critical judgment needed for accuracy and compliance.
How are insurers taking the vast amounts of data they’ve collected and turning it into actionable insights?
Insurers are sitting on mountains of data, and the next step is using advanced analytics to extract real value from it. By 2026, we’re seeing predictive analytics move from niche experiments to widespread adoption across entire organizations. The focus is on dynamic models that update in real time as new data comes in. This allows insurers to assess risk more accurately, price policies better, and improve profitability. It’s about making data work harder and faster to inform decisions.
Why are APIs becoming so critical for insurers, especially those dealing with outdated systems?
APIs, or application programming interfaces, are a lifeline for insurers stuck with legacy systems that don’t play well with modern tech. They act as a bridge, connecting old platforms to new tools and enabling seamless data flow across the insurance value chain. This is huge for processes like quoting or issuing policies, which can now be embedded directly into workflows. APIs also pull in external data—like property records—without manual effort, saving time and reducing errors. It’s a practical fix for a persistent problem.
How do you envision the combination of real-time data from sources like telematics and IoT devices shaping the future of risk assessment?
Combining real-time data from telematics and IoT devices with broader customer information is revolutionizing risk assessment. Imagine having a 360-degree view of a policyholder’s risk profile updated in the moment—whether it’s driving behavior from a car sensor or property conditions from connected devices. This allows insurers to segment risk more precisely and adjust pricing dynamically. It’s not just about better accuracy; it’s about creating sustainable models that benefit both insurers and customers through tailored offerings.
What is your forecast for the role of technology in shaping the insurance industry over the next five years?
Over the next five years, I see technology becoming even more integral to every facet of insurance, but with a focus on balance. AI will continue to mature, automating more complex tasks while refining its accuracy with human collaboration. Advanced analytics and real-time data will drive hyper-personalized products and pricing, making insurance more responsive to individual needs. Meanwhile, connectivity solutions like APIs and cloud systems will break down silos, enabling seamless integration across the industry. The key will be adopting these tools thoughtfully—prioritizing efficiency, trust, and sustainability to build resilient business models that serve both companies and policyholders.
