Simon Glairy is a seasoned strategist at the intersection of risk management and digital innovation, bringing decades of insight to an industry currently facing a demographic turning point. As the “Silver Tsunami” approaches, the insurance sector finds itself at a critical juncture where the departure of veteran professionals threatens to erase years of specialized institutional wisdom. Simon’s expertise lies in leveraging emerging technologies to transform traditional knowledge transfer, ensuring that the intuitive mastery of senior adjusters and underwriters is not lost but instead hardcoded into the digital DNA of the firm. In this conversation, we explore the nuances of specialized risk, the hidden logic of insurance culture, and the role of artificial intelligence in safeguarding the future of the industry.
This interview explores the looming challenges of a retiring workforce, the difficulty of capturing subjective decision-making, and the strategic deployment of AI to preserve institutional memory. We discuss the specific gaps in subdomains like employer liability, the art of sensing regulatory trends, and the necessity of executive buy-in for technological transformation.
With over a fifth of the insurance workforce nearing retirement, what specific gaps appear in subdomains like employer liability or inland marine? How does the loss of these specialists threaten institutional memory, and what are the immediate consequences for maintaining long-standing client relationships?
We are currently standing at a demographic inflection point where approximately 22% of the insurance workforce is aged 55 or older, representing a massive concentration of vital expertise. In highly technical subdomains like employer liability or inland marine, the gap isn’t just about losing a pair of hands; it’s about losing the “feel” for risks that don’t fit into a standard box. An expert in inland marine understands the physical and logistical nuances of cargo and transit in a way a generalist simply cannot, and if these specialists retire at the projected rate of 5% per year, those specific insights vanish. The immediate consequence is a cooling of long-standing client relationships, as clients often stay with a firm because they trust the specific person who understands their unique business history. Without a proactive plan to capture this institutional memory, firms risk a “knowledge vacuum” where new hires may follow written manuals but lack the historical context to manage complex, multi-decade accounts.
Beyond standard manuals, insurance relies heavily on intuition and unwritten rules regarding channel dynamics. How can organizations effectively capture this subjective decision-making logic? What specific steps allow a firm to document how written policies are interpreted in practice by its most experienced adjusters?
The true craft of insurance lies in the “unwritten rules”—those subjective factors and gut instincts that underwriters layer on top of cold data. To capture this, firms must move beyond static underwriting manuals and start mapping the actual “work-around” logic and channel dynamics that senior adjusters use daily. We can begin by conducting deep-dive “shadowing” sessions where we record the internal dialogue of an expert as they navigate a difficult claim or a sensitive broker relationship. By documenting these deviations from the standard rulebook, we can create a secondary repository of “practice-based interpretations” that explain why a certain risk was accepted despite a red flag. This turns fleeting intuition into a structured narrative, allowing the next generation to see the logic behind the decision rather than just the final, often unexplained, outcome.
Regulatory environments shift state-by-state, often requiring practitioners to sense emerging trends rather than just follow static rules. How can firms teach new hires to discern these regulatory “winds”? What methods ensure that a firm’s historical evolution remains a functional guide for navigating complex, modern claims?
Teaching a new hire to sense “regulatory winds” is one of the most difficult challenges in our industry because it requires a historical perspective that young professionals simply haven’t lived through. We encourage firms to treat their history not as a dusty archive, but as a living laboratory where new hires can track how specific state regulations have evolved line-by-line over several decades. By analyzing past communications from regulators and the subsequent adjustments the firm made, juniors can start to recognize the patterns that signal an impending shift in the legal climate. This method of “retrospective training” ensures that historical evolution becomes a functional compass, allowing a new adjuster to predict a regulator’s move before the official bulletin is even published. It transforms the firm’s past experiences from a static record into a predictive tool for navigating the complexities of modern, state-specific claims.
AI can now aggregate millions of emails and historical policy records into searchable repositories. What are the logistical challenges of indexing such a massive volume of informal communication? How can automated extraction transform these raw data points into actionable insights for the next generation of leadership?
The sheer volume of informal data—specifically the millions of emails generated between firms and partners annually—presents a logistical nightmare for traditional indexing, as this information was never intended to be “useful” beyond its immediate purpose. However, the revolution in Large Language Models (LLMs) allows us to intelligently aggregate these disparate records into a repository that understands the context of a conversation rather than just keywords. The real transformation happens when automated extraction identifies the “logic jumps” in an email thread, revealing why a specific claim was settled or why a premium was adjusted mid-term. For the next generation of leadership, these raw data points become a searchable “oracle” of company wisdom, providing the historical “why” behind every major move the firm has made. This prevents the loss of crucial context that usually disappears the moment an executive deletes their inbox or walks out the door for retirement.
AI-powered chatbots and simulations are increasingly used to bridge the knowledge gap for new hires. How do you design a simulation based on actual firm experiences to ensure operational continuity? What specific metrics should leadership track to confirm that these tools are successfully accelerating the onboarding process?
To ensure true operational continuity, a simulation must be built using the firm’s actual “battle-scarred” data—real past claims, actual policy disputes, and genuine loss scenarios that the company has faced. We design these environments so that a new hire can “play through” a decade’s worth of claims history in a few months, receiving real-time feedback from an AI tutor that mimics the firm’s specific underwriting philosophy. Leadership needs to look past basic completion rates and instead track metrics like “time to competency” and the accuracy of “logical jumps” made during these simulations. If a trainee can navigate a complex, multi-layered liability scenario with the same logic as a 20-year veteran, you have empirical proof that the tool is successfully transferring the firm’s unique intellectual DNA. These metrics provide a clear window into how quickly the “knowledge gap” is closing across the organization.
Implementation of these technologies requires significant funding and a shift in corporate culture. What are the primary hurdles to securing executive buy-in for knowledge preservation projects? How can firms balance the cost of these tools against the long-term risks of losing decades of practical wisdom?
The biggest hurdle is the “invisible” nature of the risk; it is very hard to put a price tag on a loss that hasn’t happened yet due to a mistake a veteran wouldn’t have made. However, when we remind executives that the top 20 firms write almost 60% of all premiums, the scale of the competitive landscape makes it clear that even a small dip in institutional wisdom can lead to a massive loss in market share. We have to frame these AI projects not as a technological “nice-to-have,” but as a fundamental risk-management strategy for the firm’s own survival. By balancing the upfront cost of AI implementation against the catastrophic risk of a “brain drain” that could cripple underwriting accuracy, the investment becomes a clear imperative. It’s about convincing leadership that the most expensive thing a company can own is a database of experience that no one knows how to use.
What is your forecast for institutional knowledge preservation in the insurance industry?
I forecast that within the next five to seven years, we will see a Great Divergence in the industry between “Static Insurers” and “Learning Insurers.” The firms that treat their history as a stagnant cost center will find themselves unable to compete, as their technical proficiency withers with each retirement party. Conversely, “Learning Insurers” will use AI to create a “permanent expert” at the core of their business—a digital repository of every email, decision, and intuition their best people ever had. This won’t replace humans, but it will create a “bionic” workforce where a junior adjuster with six months of experience can leverage the collective wisdom of sixty years. Ultimately, the industry’s future belongs to those who realize that their most valuable asset isn’t their capital, but the preserved, searchable intelligence of the people who built the firm.
