Simon Glairy is a distinguished authority in the insurtech sector, renowned for his deep expertise in the intersection of insurance law and artificial intelligence. With a background rooted in risk management and AI-driven risk assessment, he has dedicated his career to solving the “bottleneck” of policy interpretation—the transition from artisanal, human-led judgment to scalable, machine-enhanced intelligence. His insights focus on how organizations can move beyond simple automation to develop true coverage intelligence, ensuring that the billions of dollars governed by contract language are managed with precision and consistency.
Insurance policies are often stored as unstructured PDFs where small wording changes or cross-references alter everything. How does standard document parsing fail to capture the relationship between tables and conditions, and what steps are necessary to make these documents truly machine-readable for complex reasoning?
Standard document parsing and generic optical character recognition often fail because they treat an insurance policy like a flat narrative rather than a multi-dimensional legal map. In these legacy formats, tables and schedules frequently house critical conditions that modify the entire agreement, yet a basic parser might see them as isolated data points or, worse, skip them entirely if they are poorly scanned. To make these documents machine-readable, we must first implement a structural ingestion layer that recognizes the specific hierarchy of insuring agreements, definitions, exclusions, and endorsements. This involves a step-by-step approach: first, using advanced extraction to preserve the spatial relationships of tables; second, mapping cross-references to ensure the AI understands how a definition in section A limits a coverage in section B; and finally, creating a structured data foundation where every clause is tagged by its functional role. Without this specific architecture, any downstream reasoning is built on a shaky foundation, leading to hallucinations or critical omissions in policy analysis.
Professional judgment involves understanding a “tree” of precedents and edge cases branching out from a single clause. How can organizations centralize this lived experience into an AI framework, and what specific interpretive layers are required to distinguish meaningful language differences from mere noise?
Centralizing lived experience requires moving beyond the “text” to the “meaning,” which involves capturing the interpretive “trees” that seasoned professionals carry in their heads. An expert doesn’t just read an exclusion; they see the 10 claim scenarios it triggers and the negotiation history that led to its specific phrasing. To replicate this, organizations must build an interpretation layer that combines structured policy data with attorney-trained AI frameworks enriched by real-world annotations and edge cases. For example, in a complex liability dispute, a professional knows that a change from “arising out of” to “caused by” is a significant shift in scope, not just a stylistic choice. By feeding these specific linguistic nuances and their historical outcomes into the AI, we transform the system from a simple text-retrieval tool into a repository of organizational wisdom that makes expert judgment reproducible.
Consistency is critical in regulated environments, yet AI model behavior can shift over time. What specific benchmarking strategies ensure that a system’s interpretation of a contract remains stable, and how should practitioners design tests to monitor for drift?
In high-stakes insurance environments, the most damaging failure is not just an error, but an inconsistent conclusion that erodes trust with regulators and clients. We must implement constant benchmarking against domain-specific questions that reflect actual policy work to ensure that if a question is asked today, it yields the same result six months from now. Practitioners should design evaluation sets that include “golden cases”—complex policy scenarios with known, expert-verified outcomes—and monitor for drift using metrics like interpretive stability and accuracy against these benchmarks. When underlying large language models are updated, the system must be retested against these 100 or 500 core scenarios to ensure the logic remains intact. This proactive monitoring ensures that the AI’s “coverage intelligence” doesn’t degrade, maintaining the reliability required for hundreds of billions of dollars in premium and claim management.
Misinterpreting contract language at scale creates a significant “economic tax” through slow claims and avoidable disputes. Beyond simply increasing processing speed, how does making policy language legible at scale impact an organization’s bottom line, and what are the first steps to implementing this level of coverage intelligence?
When policies live in fragmented PDFs and interpretation is trapped in individual heads, the resulting friction acts as a massive economic tax, leading to leakage and avoidable disputes that can be measured in billions of dollars. Making policy language legible at scale directly impacts the bottom line by reducing this leakage and ensuring that claims are settled with 100% consistency, rather than varying based on which adjuster happens to pick up the file. The first step toward implementing this is to treat policy understanding as critical infrastructure rather than a back-office task. Organizations should begin by auditing their most complex lines of business, identifying where “artisanal” interpretation causes the most delays, and then deploying specialized AI tools to structure those documents. By establishing a reliable, machine-readable library of policies, an organization can finally achieve a repeatable operating model that systematizes expertise without turning professional judgment into a mere box-checking exercise.
What is your forecast for the future of AI-driven policy interpretation?
I believe we are moving toward a future where the “insurance policy as a document” will be replaced by the “policy as code,” where coverage intelligence is baked directly into the transaction layer of the industry. In the next five to ten years, I forecast that AI will not just interpret these contracts, but will act as a real-time bridge between policy wording and claims reality, virtually eliminating the “economic tax” of ambiguity. We will see a shift where insurers no longer compete on their ability to read a PDF faster, but on how effectively they can apply centralized, high-fidelity judgment to every single risk they underwrite. Ultimately, this will lead to a more transparent and efficient market where disputes are minimized because the contract language is finally as legible to the systems managing it as it is to the experts who wrote it. This transition will redefine the role of the insurance professional, moving them away from document retrieval and toward high-level strategic risk management.
