Simon Glairy stands at the intersection of traditional risk management and the high-speed evolution of artificial intelligence. As a recognized authority in Insurtech, he has spent years advising global carriers on how to move beyond “innovation theater” and into the realm of measurable, scalable technology. His insights come at a pivotal moment, just as the 2026 Evident AI Index highlights a hardening divide between the industry’s digital elite and those still struggling to find their footing. This conversation explores the strategic shifts separating the winners from the pack, the rise of specialized AI agent networks, and the new financial accountability that is finally pinning a dollar value to AI investments.
The discussion centers on the widening chasm between top-tier insurers and the rest of the market, the transition from experimental AI pilots to compounding financial returns, and the evolution of governance as a tool for speed rather than a regulatory burden. We also explore how specific platforms like Allianz’s Nemo and Zurich’s ZurichIQ are redefining the claims and underwriting landscape, shifting the industry from point solutions toward integrated, end-to-end automated workflows.
The latest industry data suggests that the gap between AI leaders and the rest of the sector is not just present, but actually “hardening.” How are the top-tier insurers moving from the experimental phase into a stage of compounding returns that leaves their competitors behind?
The shift we are seeing is essentially the difference between participating in a race and actually owning the track. For the past two years, the industry focused on laying the groundwork, but now the Evident AI Index reveals a sobering reality: the leaders are pulling financial levers that the mid-table carriers haven’t even touched yet. We are seeing companies like Allianz and AXA move beyond mere investment into a phase where every AI deployment feeds into the next, creating a velocity that is incredibly difficult to replicate. This isn’t just about having a larger budget; it’s about a trajectory where the leaders are moving faster because their existing AI infrastructure simplifies the rollout of new capabilities. When you look at the 30 largest insurers globally, the top five are now operating with a level of maturity where AI is no longer a “technology initiative” but a core driver of their competitive advantage.
Allianz has recently claimed the top spot in global rankings, largely due to its massive focus on talent and innovation. What does a “broad-based improvement” look like in practice for a carrier of that size, and how does their approach to human capital differ from the rest of the industry?
Allianz’s ascent to the number one position is a masterclass in scaling a digital culture across a massive, legacy organization. Their talent base for AI-specific roles is now roughly 28% larger than AXA’s, which was the previous leader, showing a relentless commitment to hiring the right technical minds. But the real magic is in their “AI Run” initiative, an enterprise-wide training program that has reached over 150,000 employees across 70 different countries. This means the person adjusting a claim in a regional office has the same baseline understanding of AI tools as the developer in the home office, reducing the friction that usually kills new tech deployments. With more than 900 use cases currently registered worldwide, they are blanketing every department—from underwriting and claims to fraud detection—with smarter, faster processes.
We are hearing a lot about the Nemo platform and its use of specialized AI agents. Could you walk us through how this type of coordinated system changes the traditional, manual experience of filing a claim for something like food spoilage?
The Nemo platform, which Allianz launched in late 2025, represents the true frontier of claims management because it eliminates the clunky hand-offs that define most insurance workflows. Imagine a customer takes a photograph of a spoiled shipment; Nemo doesn’t just “see” the photo, it triggers a coordinated system of seven specialized AI agents that act like a digital pit crew. One agent verifies the weather or storm data to confirm the cause of loss, while another checks the specific policy coverage and a third assesses the fraud risk. Simultaneously, other agents execute the payment and log every single decision for regulatory compliance, leaving only the final authorization to a human reviewer. This removes the “wait time” sensory experience for the customer, turning a process that used to take days or weeks of manual intervention into a near-instantaneous settlement.
Zurich Insurance has seen one of the most dramatic climbs in the rankings this year. What specific strategic moves, particularly regarding their ZurichIQ platform, allowed them to jump eight places in a single year?
Zurich’s move from 12th place to 4th is the single most striking jump we’ve seen in the 2026 data, and it was fueled by a very deliberate shift in their talent mix. They didn’t just hire more people; they ensured that 44% of their entire AI talent base is now focused specifically on AI development roles. This internal engine allowed them to build ZurichIQ, a modular generative AI suite that isn’t just one tool, but a collection of specialized applications like AgentIQ and ClaimsIQ. For example, PolicyIQ and ProgramIQ allow their teams to compare complex policy wordings in seconds, while VoiceIQ is actively monitoring and improving service center performance. By embedding these tools into the daily functions of legal, underwriting, and claims, Zurich has turned AI from a high-level concept into a granular, everyday utility for their staff.
There is a growing sentiment that responsible AI governance is no longer just a “compliance exercise” but actually a “deployment enabler.” How do established guardrails and transparency measures help an insurer move faster rather than slower?
In the past, governance was often viewed as the “brakes” on a car, but the top-performing insurers now realize that better brakes allow you to drive faster. When an insurer has established model validation standards and a clear transparency framework, they can approve and scale new AI use cases in a fraction of the time because the “safety check” is already built into the process. We see this with Allianz’s partnership with Anthropic, where they are building traceability and compliance directly into the automation at the point of construction. This is critical as regulators in 23 U.S. states and the EU step up their oversight, requiring carriers to explain exactly how AI factors into a pricing or underwriting decision. By being transparent and ranking high in governance—as Zurich does in the Transparency pillar—insurers avoid the “retrofitting” nightmare that stalls their less-prepared competitors.
For the first time, we are seeing a few insurers disclose quantified, enterprise-level returns on their AI investments. What do the numbers from companies like Manulife and Generali tell us about the future financial expectations for the industry?
We have finally crossed a major threshold where AI is being held to the same financial accountability as any other core business unit. Manulife, for instance, reported CA$300 million in enterprise value generated from AI in 2025, and they are projecting that figure to hit CA$1 billion by 2027. Generali is on a similar path, disclosing €100 million in bottom-line impact and aiming for over €350 million within the next two years. Even Intact Financial is showing significant muscle, reporting CA$200 million in annual benefits with a goal to exceed CA$500 million by 2030. These aren’t just “pilot project” savings; these are massive, bottom-line shifts that prove AI can move the needle on loss ratios and underwriting margins in a very volatile global economy.
What is your forecast for the insurance industry’s AI landscape over the next twelve months?
I expect to see a “great consolidation” of use cases where insurers stop chasing hundreds of small, disconnected point solutions and instead focus on end-to-end automated workflows. Currently, 49% of disclosed use cases are still just point solutions for single activities, but the leaders are proving that the real money is in the 8% of cases that connect the entire chain from pricing to claims. We will likely see more carriers forced to disclose hard ROI numbers to satisfy investors, and those who cannot show a clear path from AI investment to bottom-line impact will see their valuations suffer. By 2027, the gap we see today won’t just be a technological one; it will be a structural separation where the top five or six carriers operate at a cost-to-income ratio that the rest of the industry simply cannot match.
