I’m thrilled to sit down with Simon Glairy, a renowned expert in insurance and Insurtech, whose deep expertise in risk management and AI-driven risk assessment has positioned him as a thought leader in the rapidly evolving insurance landscape. With generative AI transforming the industry and trends like embedded insurance gaining traction, Simon offers invaluable insights into how these innovations are reshaping insurers’ operations and customer experiences. In our conversation, we explore the drivers behind AI adoption, its practical applications in key business areas, the challenges insurers face amidst global pressures like climate-related losses, and the exciting growth of new distribution models. Join us as we dive into the future of insurance technology.
How would you describe the key factors pushing 76% of insurance organizations to adopt generative AI at such a rapid pace?
The rapid adoption of generative AI across the insurance sector is driven by a combination of competitive pressure and the urgent need for efficiency. Insurers are facing complex challenges like rising claims costs and shrinking margins, especially with global issues like climate-related losses. AI offers a way to streamline processes, from underwriting to claims handling, while also personalizing customer interactions. Additionally, the technology has matured enough to provide tangible results, and the fear of falling behind competitors who are already leveraging AI is a significant motivator. It’s no longer just about experimenting; it’s about staying relevant in a fast-changing market.
What does the transition from experimentation to full-scale AI implementation look like in the day-to-day operations of insurers?
In day-to-day operations, this shift is quite transformative. Initially, AI was tested in isolated pilots—say, automating a small subset of claims or generating basic customer responses. Now, we’re seeing it integrated across entire workflows. For instance, AI is being used to analyze vast datasets in real-time for risk assessment, predict customer needs during policy sales, and even automate complex claims decisions with human oversight. It’s about embedding AI into the core systems so that it’s not a standalone tool but a seamless part of how business gets done, ultimately saving time and reducing errors.
Historically, the insurance industry has been cautious about new technology. What’s changed to make companies more open to AI now?
The insurance industry’s risk-averse nature stems from the high stakes of getting things wrong—missteps can cost millions or damage trust. But the landscape has shifted dramatically. First, the sheer volume of data insurers handle today demands advanced tools like AI for processing and decision-making; manual methods just can’t keep up. Second, customer expectations have evolved—they want faster, more personalized services, which AI can deliver. Finally, there’s a growing body of evidence showing AI’s reliability and ROI, which has built confidence. The industry is realizing that the real risk lies in not adapting.
Can you share some concrete examples of how AI is being applied in areas like distribution, risk management, and claims handling?
Absolutely. In distribution, AI is powering chatbots and virtual assistants that guide customers through policy selection with tailored recommendations based on their data. For risk management, AI models are analyzing historical and real-time data—think weather patterns or social trends—to predict and price risks more accurately. In claims handling, AI automates initial assessments by processing photos or documents, flagging fraudulent claims, and speeding up payouts for straightforward cases. These applications aren’t just theoretical; they’re actively reducing costs and improving turnaround times for insurers and customers alike.
Looking ahead, are there other business functions where you expect AI to make a significant impact soon?
I see AI making waves in customer service and product development. Beyond basic chatbots, AI can deeply analyze customer feedback and behavior to anticipate needs, offering proactive solutions like policy adjustments before a customer even asks. In product development, AI can simulate market responses to new insurance offerings, helping design products that close protection gaps, especially in emerging areas like cyber risks or AI-related liabilities. These areas are ripe for innovation because they directly tie into enhancing customer trust and expanding market reach.
With climate-related losses and protection gaps hitting $234 billion globally in 2023, how is AI helping insurers address these massive challenges?
AI is becoming a critical tool in tackling climate-related challenges by improving risk modeling and mitigation strategies. For instance, AI can process satellite imagery, weather data, and historical loss patterns to predict where and when disasters might strike, allowing insurers to adjust pricing or coverage proactively. It also helps in closing protection gaps by identifying underserved regions or demographics through data analysis, enabling targeted product offerings. While AI can’t prevent natural disasters, it equips insurers to respond smarter—both in terms of financial preparedness and customer support post-event.
Rate increases are creating affordability issues in markets like the UK and Australia. How can AI help balance profitability with keeping insurance accessible for customers?
AI can play a pivotal role here by optimizing operational efficiency and personalizing risk assessment. By automating back-office tasks and reducing fraud through better detection, insurers can cut costs without passing them onto customers. Additionally, AI enables micro-segmentation of risk, meaning policies can be priced more accurately for individuals rather than applying broad rate hikes. This granular approach helps keep premiums fairer for low-risk customers. It’s about using AI to find savings internally while ensuring pricing reflects true risk, making insurance more affordable without sacrificing profitability.
In the Asia Pacific region, insurers are leading in AI deployment. What’s unique about this area that’s driving such progress?
The Asia Pacific region stands out due to a combination of factors. There’s a high availability of technical talent, especially in countries with strong STEM education systems, which fuels innovation. Culturally, there’s also a greater openness to adopting new technologies among both businesses and consumers, which reduces resistance to AI integration. Additionally, the region’s fast-growing economies and large, diverse populations create a massive demand for scalable insurance solutions—AI fits perfectly as a way to meet that need efficiently. It’s a fertile ground for tech-driven transformation.
How do barriers like data security and privacy impact insurers’ ability to scale AI, and what specific concerns are they grappling with?
Data security and privacy are huge hurdles because insurance relies on sensitive personal information. Insurers are worried about breaches that could expose customer data, leading to legal repercussions and loss of trust. There’s also the concern of complying with varying regulations across regions—think GDPR in Europe or state-specific laws in the US. Another issue is ensuring AI doesn’t inadvertently misuse data in ways that seem invasive to customers, like overly personalized offers that feel intrusive. These concerns slow down scaling because every AI deployment must be rigorously vetted for compliance and security.
What’s your forecast for the future of AI in the insurance industry over the next decade?
I believe AI will become the backbone of the insurance industry within the next decade, not just a tool but the foundation of how insurers operate. We’ll see AI driving hyper-personalized policies, predicting risks with uncanny accuracy, and automating nearly every touchpoint of the customer journey. But the real game-changer will be how AI bridges protection gaps globally, especially in emerging markets, by enabling low-cost, accessible products. The challenge will be balancing innovation with ethics—ensuring transparency and fairness. It’s an exciting time, but insurers will need to stay agile to harness AI’s full potential while navigating regulatory and societal expectations.
