What happens when the people who know your business best are ready to move on? For insurers, the answer could define not just the next year, but also the next generation.
It is true that insurers, like in many industries, are experiencing a wave of retirements among seasoned professionals, with the so-called "Silver Tsunami" bringing decades of experience and knowledge to a natural inflection point. The departure of these valued colleagues is felt across organizations, particularly in areas that rely on legacy technical skills, deep institutional understanding and long-standing client relationships.
However, the industry has a long history of adaptability, finding new ways to deliver value and maintain continuity even as the workforce evolves. Even as insurers navigate these real and complex challenges, promising opportunities are on the horizon.
The scale and scope of the reality
According to the
If current retirement trends continue, the industry could see about five percent of its most experienced specialists retire each year over the next decade, along with the wealth of knowledge they've built that can be difficult to preserve or pass along.
As these seasoned professionals move on, this generational transition presents insurers with a unique opportunity to proactively capture and transfer the leadership, mentorship and practical wisdom that have shaped their organizations. While bringing in new talent is essential for future growth, it's equally important to document the insights, best practices and unwritten rules that have guided firms for decades. With thoughtful planning and the right tools, insurers can preserve their unique practices and expertise for years to come.
Insurers and specialization
Human expertise in insurance is multifaceted and includes a surprising number of subjective factors, where intuition or "gut instinct" are layered atop clear rules and data. For example, insurers grapple with:
- Specific risk expertise: The nuances of employer liability coverage, for example, are quite different from those in the inland marine space, which are different from mortality issues in life insurance and so on. An expert in one subdomain cannot easily be replaced by an expert in another.
- Regulatory context: State by state, and line by line, underwriters and adjusters have detailed rules they must follow. Perhaps more important is the ability to discern where the prevailing regulatory winds are blowing, based on trends and communications from regulators.
- Unwritten practices, especially around channels. New underwriters can learn the company's underwriting manual and claims executives have a set of rules to follow. But they also need to know how written rules are interpreted in practice, and how channel dynamics are factored into their decision-making.
- Understanding the long-term evolution of the firm. Underwriting and claims decisions do not take place in a vacuum. The phrase, "Let experience be your guide…" is tailor-made for the insurance industry. But in an industry where the top 20 firms write almost 60% of all premiums, scale and organizational complexity make it challenging to track relevant experiences and place them in historical perspective.
These are the areas of risk for any insurer that thinks its past is worth preserving.
An AI-driven approach to retaining institutional knowledge
We are now learning at an accelerated pace what AI can do. The combination of massive computing power and the LLM revolution has launched an arms race among AI platforms, hyperscalers and software vendors. Consumers and businesses are knee-deep in AI solutions.
AI excels at processing data at scale and discovering relationships between data elements, often revealing insights that would be difficult for humans to detect. This ability opens new possibilities for critical insurance tasks such as underwriting, identifying fraud and applying policy language to complex claims scenarios.
The catch is that all three of these areas today are also heavily dependent on the successful application of institutional knowledge, as problem solving in a vacuum invariably creates new problems.
A sensible path forward is applying AI directly to the problem of retaining institutional knowledge. Insurers can bring their newfound AI skills to bear on three key tasks:
- Rethinking knowledge capture: Some sources of an insurer's institutional knowledge are obvious, like the product definitions, active and historical policy records, and the database of claims experience. Others, like the millions of emails produced by firms and their partners each year, are not top of mind because the industry has never before had the tools to do anything meaningful with them, other than retain them for regulatory reasons. The challenge across these and many other data sources is intelligently aggregating them into a repository that can be analyzed later. AI is perfect for this task, as it can automate knowledge extraction, indexing and organization to make reams of data more useful downstream.
- Powering up as-needed knowledge delivery: History has taught us that the creation of a massive, poorly constructed data store that almost nobody uses is wasteful. Fortunately, one of AI's superpowers is giving users the ability to express themselves in natural language, which can make the user interface a selling point of a solution rather than a reason for its failure. AI-powered chatbot guides can find answers to asked questions, but even more importantly, they can be tuned to help users make logical jumps to related topics, elevating users well beyond their inherent level of knowledge.
- Creating knowledge transfer and training tools: The same repository used to answer user queries can be applied to the task of onboarding new hires or training existing staff, with the express purpose of ensuring that the firm's knowledge lives on. Case studies, training modules and even simulations—based on the firm's actual experiences—can be created to foster continuity as senior leaders retire.
Taking on these tasks will require focus and funding. The technological and cultural impediments to success are formidable. But for an industry built on managing risk and thinking three steps ahead, addressing the loss of institutional knowledge more effectively by leveraging AI is a clear imperative.
