Simon Glairy is a distinguished strategist in the insurance and Insurtech sectors, renowned for his deep understanding of how emerging technologies reshape risk management and distribution. With a career dedicated to navigating the intersection of traditional underwriting and artificial intelligence, he provides a unique perspective on the digital transformation of the industry. His insights are particularly timely following the market volatility of early 2026, where he has been a leading voice on the structural shifts moving the industry toward platform-driven ecosystems. In this discussion, we explore the evolving role of the broker, the impact of AI-native platforms on market valuations, and the strategic pivot required for firms to survive an increasingly transactional landscape.
The following conversation examines the recent investor anxiety surrounding AI-powered insurance apps and the resulting fluctuations in brokerage stocks. We delve into the diverging paths of personal and commercial lines, the necessity of API connectivity for modern carriers, and how the definition of a “valuable” asset is being rewritten in an era of massive consolidation.
In early 2026, insurance indices and brokerage stocks saw sharp single-day drops following news of AI-powered quote apps. Why did investors react so aggressively to this specific shift in distribution, and what does it reveal about the perceived stability of traditional brokerage revenue streams?
The market reaction we witnessed on February 9 was a visceral response to the realization that the “front door” of insurance is moving. When Tuio announced a ChatGPT-powered app for home insurance quotes, the S&P 500 Insurance index didn’t just stumble; it fell 3.9%, while a basket of major brokerage stocks plummeted by roughly 9% in a single day. This wasn’t a reaction to poor quarterly earnings, but rather a structural narrative taking hold in the minds of investors who now see a credible threat to the broker’s role as the primary intermediary. Two weeks later, those stocks were still down about 11%, proving that the fear of disintermediation is no longer a theoretical “someday” problem but a priced-in risk. It reveals a growing skepticism toward the long-term stability of commission-based revenue if an AI platform can provide the same access and personalization with a fraction of the friction.
Digital adoption for personal lines like auto and home insurance currently sits between 55% and 65%. How will the “front door” of insurance change for these customers compared to complex commercial clients, and which specific advisory tasks remain safest from platform-driven disintermediation?
The transition for personal lines is moving toward a platform-led ecosystem where the customer never actually visits a broker’s website or office. For the 65% of auto insurance customers and 55% of homeowners who are already comfortable with digital interfaces, the shift will start with conversational AI acting as a concierge that gathers data, compares prices, and issues policies in one seamless flow. However, for complex commercial clients, the process remains anchored in human expertise because the risk profiles are not yet commoditized. Advisory tasks involving layered programs, bespoke risk assessments, and navigating regulatory mazes are the safest from this trend. You cannot easily replace the nuanced negotiation of a multi-million dollar industrial policy with a chatbot, as these require a level of claims advocacy and specialized knowledge that current AI models cannot replicate.
Tech brokerages are now securing spots on major AI marketplaces and adopting new interaction models. How should traditional carriers balance experimental platform adoption with their existing agent networks, and what specific steps are required to ensure these new interfaces actually convert leads into policyholders?
Traditional carriers find themselves in a delicate balancing act where they must embrace API connectivity without alienating the agents who still drive a massive portion of their volume. We are seeing tech-forward firms like Insurify secure placement on OpenAI’s marketplace, which should serve as a signal for carriers to start investing in AI-native models like the Model Context Protocol or Claude Cowork. To ensure these interfaces actually convert, carriers need to move beyond simple “rate and quote” functionality and integrate deep, real-time data exchange that allows for instant binding. The goal is to create a frictionless handoff where the AI handles the discovery phase, but the carrier’s back-end systems are robust enough to finalize the transaction without forcing the user to start over. It is about being present where the customer is already spending their time, whether that is a search engine or a specialized AI assistant.
Massive brokerage mergers and planned IPOs suggest a race for scale, yet AI-native firms are commanding premium multiples. How is AI shifting the definition of a “valuable” asset during an acquisition, and what happens to mid-tier firms that lack either specialized expertise or high-tech distribution?
The definition of value is shifting rapidly from the size of a book of business to the quality of the data assets and the efficiency of the servicing economics. While we see massive consolidation through deals like Gallagher’s acquisition of AssuredPartners or AON’s move for NFP, the firms commanding the highest multiples are those with AI in their DNA. For mid-tier firms that lack a clear technological edge or a highly specialized niche, the future looks increasingly grim as they are squeezed between the massive scale of global brokers and the extreme efficiency of AI-native startups. These “generalist” firms often compete solely on access, but when access is democratized by a platform, their primary value proposition disappears. This environment will likely trigger an even more aggressive wave of consolidation as these firms scramble to find a partner who can provide the technological “air cover” they lack.
As simple risk becomes leaner and more transactional through AI, the industry faces a potential squeeze on economics. How can firms pivot their talent strategy to focus on bespoke risk and claims advocacy, and what metrics should they use to track the success of this transition?
Firms must fundamentally rethink their talent strategy by moving away from hiring “order takers” and focusing instead on high-level risk consultants and specialized advocates. As the transactional side of the business becomes a low-margin race to the bottom, the real economic value will migrate toward handling complex claims and managing specialized risks that require a human touch. Success in this transition should be measured by metrics such as “advisory revenue per employee” and the percentage of the portfolio tied to non-commoditized, bespoke products. We want to see our teams spending less time on data entry and more time on high-stakes advocacy where their expertise directly impacts the client’s bottom line. It is a shift from being a vendor of policies to becoming a partner in resilience, and that requires a workforce that feels comfortable operating alongside AI rather than competing with it.
What is your forecast for the future of AI-driven insurance distribution?
I anticipate that by the end of the decade, the industry will have split into two distinct tiers: a highly automated, low-margin “utility layer” for personal and small business lines, and a premium “advisory layer” for everything else. We will see the emergence of a few dominant AI marketplaces that act as the primary gatekeepers for insurance, effectively replacing the traditional search for quotes with a continuous, personalized risk-management dialogue. While this will lead to a leaner, faster, and more transactional market for simple risks, it will also elevate the importance of specialized brokers who can navigate the complexities that AI still finds daunting. Ultimately, the winners will be those who view AI not as a tool for cutting costs, but as a bridge to a deeper, more frequent connection with the policyholder.
