Vertical AI agents, tailored for industries like insurance, healthcare, etc., are redefining how organizations handle complex, open-ended, and multi-step workflows. 

By combining domain-specific knowledge graphs, advanced retrieval methods such as GraphRAG, and orchestrator-worker agentic patterns, these agents can reason, decide, and act with transparency. When paired with hardware such as AR/VR devices, SmartGlass, telematics, and wearables, they enable contextual understanding of the environment and real-time decision support. 

This article explores why such agents are well-suited for scenarios such as risk assessment and long-term care adjudication.

Problem Statement: What?

Insurers have often leveraged robotic process automation (RPA) for efficiency, and this approach has reached a saturation point. Moreover, risk postures and customer needs are changing dynamically, and insurers require capabilities to improve customer experience and provide differentiated services. In this context, let us consider the following two scenarios.

Long-term care (LTC) claims - With the rise in U.S. aging population and shortage of skilled workers such as registered nurses (RN), licensed practical nurses (LPN), certified nursing assistants (CNA), etc., the burden on nursing staff increases in terms of documentation of care and services with respect to activities of daily living (ADLs), evaluation and approval of care plans, health status, etc. This information is critical for adjusters to evaluate policyholders' eligibility and medical assessments, along with reports from facilities and monitoring devices, all while complying with HIPAA and insurance regulations.

Auto-insurance claims – With advancements in vehicle technology such as advanced driver assistance systems (ADAS), software-defined vehicles (SDV), telematics, etc., there is also increased need to assess behavioral patterns, dynamic risk posture, liability, and fraud risks.

These workflows demonstrate the following traits:

• Heterogeneous data - comprising structured data such as policy and activities of daily living (ADL) scores, and unstructured data such as provider notes, reports, and images/videos (facility inspections, etc.).
• Domain-specific logic – requiring specialized skill sets to reason and infer care eligibility criteria, ontologies, risk posture, policy clauses, and regulatory compliance.

Solution - Agentic AI: Why?

The dynamic and variable nature of the above workflows requires knowledge workers with the ability to understand the context to plan, act, and reflect on the chosen path. This complex task is best suited for agentic AI (orchestrator-worker agentic pattern), as it is an open-ended problem that needs specialized skills to learn and adapt to the environment. At a high level, it involves the following components:

• An orchestrator to analyze the request, plan and decompose tasks into sub-tasks/workers, orchestrate the workflow, and synthesize the results.
• Workers/specialized agents – such as wearable worker to process mobility data and generate structured events, telematics worker to normalize trip/vehicle signals into standardized event schema, knowledge graph query agent to map intents to graph queries – patients' history, claims history, prior similar incidents, etc.
• Knowledge graph and graph database – canonical domain model, enabling reasoning and inference. Schema: ontology (OWL/SHACL), nodes for entities (patient, policy, device, trip, event), edges for relations (caused_by, observed_at, claimed_in)
• RAG + LLM service – assemble knowledge graph-grounded context and retrieve documents from vector database to produce answer/plan

Long-Term Care Claims Adjudication: How?

Consider a scenario where a policyholder files a claim for in-home care services. Wearable devices track their mobility and heart rate patterns, and a care provider uploads ADL assessment forms and daily care logs.

The following is the conceptual flow:

1. Wearable device/event triggers a request, wherein wearable data worker ingests mobility and physiological data, runs on-device pre-processing to compute ADL scores, and flags anomalies (e.g., sudden decline in mobility).

2. Data is transmitted securely to the orchestrator, where it analyzes and routes to appropriate workers such as:

• Policy knowledge graph query agent to match ADL scores against benefit triggers in LTC policy
• Document worker to parse care provider notes for evidence supporting claim eligibility

3. Knowledge graph encodes the domain ontology (policies, events, claims, etc.) and GraphRAG starts with semantic retrieval, then expands context through relationships for multi-step reasoning to ensure outputs are grounded on facts.

4. Compliance worker validates whether the recommendation meets both insurer policy and local regulatory guidelines.

5. Orchestrator then synthesizes the results/decision and sends to a claims adjuster for review and approval.

6. All decisions and their sources/origins are appended to knowledge graph with audit trail for regulatory/ML training.

Potential Benefits

Agents help alleviate the burden on knowledge workers by augmenting them and orchestrating and synthesizing complex processes such as claims through specialized workers. This enables delivery of the following benefits:

• Faster, evidence-driven claims processing
• Improved quality of care by synthesizing real-time/near-real-time information from edge devices, wearables, etc.
• Reduced false positives/fraud rates through correlation/identification of fraudulent rings
• Grounded response that is explainable and traceable to improve trust

The Way forward

Graph-based retrieval, structured communication protocol, IoT/wearables/edge devices, and multi-agent orchestration are converging into a practical toolkit for industry-specific AI.

For organizations to scale responsibly, the path forward is:

• Problem awareness and choosing the right high-impact tasks/processes for Agentic AI. It is not a silver bullet for all problems. Tasks that are repeatable, generic/not specialized with fixed/pre-defined paths, etc., are not ideal candidates for Agentic AI to justify the ROI.
• Define and embrace domain ontology to capture knowledge in knowledge graphs to power the LLMs with grounded context.
• Implement GraphRAG retrieval with provenance support. This ensures transparency, accountability, and trustworthiness in decision making.
• Iterate and integrate with environmental data such as wearables, facilities, provider networks, etc.
• Iterate by adding specialized workers as the workflow expands or needs change.
• Measure the outcomes to demonstrate the value and recalibrate/adapt to changing needs.

Vertical AI agents are no longer a research-only concept. If harnessed at the appropriate value chain, the power and benefits they unleash will be a game changer for any industry.

For years, the dominant question in insurance IT has been: Which option, buy or build, will cause fewer headaches? But as software capabilities become more commoditized, and as AI and low-code tools put more power directly in the hands of teams, a simpler question is emerging:

Who can get this done?

The tools and capabilities all work. Everyone has application programming interfaces (APIs) you can connect to. However, while it's possible to stand up a customized experience for a new product in a matter of weeks, shipping something truly integrated, flexible, and usable is a different story.

And here's the truth: Either a product fits you, or you need to fit the product.

Most packaged software products assume the latter. You're expected to adapt to their processes, compromise your priorities, and sacrifice competitive edge just to match their system. But no single technology platform fits every insurer. Forcing fit often means slowing down or giving up what makes you different.

The real challenge is less about choosing the right technology and solutions; it's how they get delivered.

And delivery only works if you have the people to do it. Insurance talent is aging out. A significant portion of the insurance workforce is nearing retirement, and with it goes deep domain knowledge of both the systems and logic behind them. As these experienced professionals exit, many insurers are left with delivery and IT gaps that they can't easily fill.

Where Execution Breaks Down

Insurers and MGAs consistently voice the same frustrations:

"We didn't realize how many things would be out of scope."

"We're paying for change requests for things we thought were standard."

"The vendor team we thought we'd be working with disappears once we sign the contract."

Too often, delivery is handed off to offshore teams who were never part of planning and won't be around for iteration. Or it's handed over to internal IT groups already stretched across 10 priorities. Or, in the worst case, it's controlled by product vendors who monetize lack of flexibility, turning every change into a new contract.

This is what happens when product and delivery get disconnected: Timelines slip, no one owns the outcome, and every small change turns into a negotiation. It's not always a technology problem. It's an execution and fit problem.

What Execution-First Looks Like

Execution-first delivery means your partner goes beyond standing up a platform; they work with your team to engineer outcomes. More than just handing over a minimum viable product (MVP) that checks the boxes, think about how to build processes where your partner can stay close to the business, adapting in real time, and delivering something that actually works in the field.

In the best cases, that looks like:

• Engineers who understand the business logic
• Iteration without change request delays
• The same team staying accountable post-launch

Execution-first partners are those who stay close to the work and ship with you, not ones who add layers between planning and delivery.

Insurers that succeed do so not just because they chose the right platform. They built the right team and partners around it. Execution is what determines whether your road map turns into reality, or a backlog of change requests and mounting frustration.

The real question is no longer what to choose. It is who can deliver.

Insurance, like many industries, is in full sprint toward artificial intelligence. Conferences are packed with AI demos. Strategy decks are flooded with automation goals. And boardrooms are asking "Why not AI?" 

Instead, many might want to ask, "Why now?" or even, "Are we ready?"

From where I sit, too many carriers are chasing AI before they've laid the groundwork. They're eager to run with advanced tools, but their operations are still learning to walk.

Let's be clear: AI and automation are powerful upgrades, like switching from hand tools to power tools when building a house. But it doesn't matter how advanced your tools are if the foundation is cracked. In the same way, if your core systems are fragmented, inefficient, or poorly integrated, AI won't fix them. It will just amplify what's broken.

The Risk of Skipping the Basics

I've seen this happen more than once. A carrier gets excited about AI-powered underwriting or virtual claims assistants. They invest in the tech, build a team and expect results. Immediately. But within months, the project stalls. Budgets balloon. Stakeholders lose faith. Or worse, the tool works, but they produce outputs that are unusable because the surrounding systems aren't connected.

AI doesn't work in isolation. It needs clean, structured, reliable data. It needs integrated workflows. It needs clear visibility into the customer journey. And it needs all of that before you turn on your first model.

Too many insurers are trying to build a smart home, installing smart bulbs, thermostats, and locks, without fixing the faulty wiring behind the walls. Layering AI on top of outdated systems, manual workarounds and siloed data means you're not innovating. You're firefighting.

What Are the Facts?

According to a 2024 Deloitte survey, between 70% and 80% of U.S. insurers have implemented generative AI in at least one business function, such as claims, customer service, or distribution. That aligns with broader findings that indicate that by the end of 2025, around 91% of insurance companies worldwide will have adopted some form of AI technology. Some AI-powered claims automation is already cutting processing time by as much as 70%.

But that adoption isn't without friction. According to another recent survey, 74% of insurers still rely on outdated legacy systems for critical operations like pricing, underwriting and rating.

That gap reveals the heart of the issue: Enthusiasm for AI is real and fast, but operational maturity often isn't keeping pace.

The Must-Haves Before You Automate

If you're an insurer considering AI, there are ways to implement it. Before doing that, I caution you to take a hard look at your operations first. Ask:

• Are our core systems integrated?
• Is our data clean, consistent and accessible in real time?
• Do we have automated workflows that allow AI to act, or do we still depend on email and spreadsheets to get things done?
• Can we trace and audit every customer touchpoint across systems?

If the answer is no to any of the above, AI won't help you, at least not yet. And that's not a critique on AI. It's a call to action for operational readiness.

Modernization First, Then Automation

The insurers seeing real results from AI are the ones who took the time to modernize their business first. They invested in workflow automation. They connected their systems. They focused on data quality and governance. They created operational environments that are scalable, transparent and efficient.

Only then did they start exploring AI, not as a gimmick but as an extension of the maturity they'd already built. The difference is obvious. Their projects hit milestones. Their tools integrate seamlessly. And their teams actually trust and use the outputs.

AI Can't Fix Ops, But Ops Can Make AI Work

AI adoption feels inevitable (maybe even urgent), with pressure coming from all sides. But urgency without readiness doesn't lead to progress. It leads to wasted time, money and trust. You wouldn't run a marathon if you hadn't tried a 5K first, right? The real opportunity in insurance isn't just about being early to AI, it's about being ready for it.

Operational maturity isn't glamorous. It's not as flashy as a chatbot demo or as headline-grabbing as an AI-powered claims system. But it's the difference between innovation that sticks and ideas that quietly fail.

AI will transform insurance; there's no question about it. But it's not a quick fix. It's a multiplier, not a miracle. And only those who've done the hard work of modernization will see it pay off.

Recently, third party administrators (TPAs) have experienced inorganic growth through acquisition. Acquiring new lines of business or further expanding existing lines of business has been at the core of business growth strategy for several TPAs. TPAs then attempt to reduce cost to improve business unit (BU) margin and overall profitability. While that is a viable growth strategy, TPAs often ignore an obvious growth lever – cross-selling and upselling existing clients.

The value of cross-selling and upselling has not been lost in the insurance space – indeed, carriers have used data- and analytics-driven models to cross-sell and upsell and improve productivity. Those carriers have seen revenue increases of up to 30% increase. But the cross-selling/upselling focus has historically been in the retail markets, driven by financial advisors and brokers. For TPAs, sales strategy is typically fragmented.

Putting the Pieces Together

A TPA sales and renewal strategy typically faces several key obstacles that inhibit cross-selling and upselling:

1. Lack of Financial Alignment Between Performance and Cross-Selling – Most TPAs report on margin at two levels – business unit (BU) margin and enterprise margin. As a result, BUs are given incentives to consider only their own profitability and growth, not the profitability of the enterprise. This results in missed opportunities – for example, a healthcare claims TPA missed opportunities to provide tort claims administration because the client was unaware the TPA provided the additional services.

2. Disjointed Relationship Management – Client relationships are often owned and managed at the BU level. This structure creates a challenge, as leaders of individual lines of business rarely identify or pursue opportunities to offer services from other units. While some TPAs use enterprise-level client relationship managers, these roles often lack the authority or visibility needed to serve as the primary point of contact for all client interactions.

3. Lack of Pricing Discipline – Pricing for both new business and renewals is usually a struggle for several reasons. First, TPAs tend to offer several different pricing models for services. Each of those comes with different cost assumptions, which may or may not accurately reflect the true cost of the service offered. Second, pricing may be done in a shared service model or it may be done at a BU level. At the BU level, cost assumptions may not be the same for each line of business. For example, are corporate functions (e.g., finance) included in costs underlying pricing assumptions? 

Cost allocation mistakes can lead to unprofitable underwriting. In one instance, a TPA made faulty assumptions on labor costs, where claims adjuster productivity was 33% worse than the numbers used in pricing assumptions. Additional claims personnel were needed to meet overall production requirements. The same TPA also failed to allocate enterprise costs when calculating pricing. What appeared to be a 20% margin on paper actually was a single-digit or even negative margin in reality.

These challenges can be overcome, but TPAs must understand what cross-selling and upselling opportunities can mean to both topline revenue and overall profitability. Although TPAs typically have nascent cross-selling capabilities, there is significant upside to TPAs that invest appropriately.

So what should TPAs do if they want to expand their cross-selling and upselling capabilities?

Unlocking Value Through Cross-Selling

For TPAs to unlock value through cross-selling, TPAs need to ensure they have met all the activation requirements:

1. The functional capability to cross-sell and upsell
2. Strong pricing and cost discipline
3. Organizational enablement

Functional Capability to Cross-Sell and Upsell

Cross-selling and upselling for TPAs begins with good-quality data. TPAs need to use their data to understand 1) the factors that drive a customer to purchase additional services and 2) the best timing for making the purchase. This will prove to be a significant pain point for TPAs to overcome, but this will be a critical market differentiator. TPAs often segment customers based on line of business and size of account. Understanding how these interactions and key cross-selling factors will differ based on the type of client is a major opportunity.

In addition to data components, TPAs also need to ensure that there is the necessary technology. Specifically, the use of common CRMs, customer segmentation tools, centralized reporting tools, and leveraging AI for automation purposes are all critical tools that help enable cross-selling and upselling.

Strong Pricing and Cost Discipline

Cross-selling can drive revenue, but the true value is in unlocking additional profitability, both at the BU and enterprise level. To do so, TPAs need to understand a service's cost drivers and more importantly, how cross-selling and upselling can reduce traditional costs. TPA services typically have three cost types – BU labor, BU non-labor, and enterprise/corporate costs. The graphical representation below highlights how disciplined pricing teams will incorporate these three costs (shades of blue), and then adjust pricing to the target profitability (in green).

When TPAs attempt to cross-sell or upsell, they need to ensure their pricing team has accurately included all cost variables in their quote. 

Here's an example:

If a TPA is currently providing P&C claims support to a client, and now can provide cyber claims support, the TPA has to have a strong understanding of cyber claims support pricing. If the TPA is leveraging a claims-driven pricing model (i.e., price to process X amount of claims over Y time leveraging Z individuals with certain processing assumptions), then profitability against the BU labor costs depend on how accurate the processing assumptions are for the TPA. Accuracy promotes stable margins, while inaccuracy invites cost creep. But cross-selling should inspire a reduction in BU non-labor costs. For example, marketing expenses that are typical for the cyber business should not exist in a cross-sale scenario, thus improving profitability both for the BU and the enterprise. But the opportunity does not stop there. Allocating corporate costs ensures profitability against shared resources, but it also helps to benchmark spend against revenue.

In the case of cross-selling and upselling, TPAs have the opportunity to improve the spending ratio between corporate support and the revenue generated, which should lead to improved enterprise margin, assuming scalability.

Organizational Enablement

Last, but certainly not least, TPAs need to integrate the cross-selling and upselling process into their sales dynamic. It is not uncommon to see lines of business, enterprise customer relations teams, and other teams involved in the sales and renewal processes, with significant friction as the usual result.

While the exact method by which a TPA manages these various teams to achieve cross-sale and upselling goals will vary, there are general concepts that can push the organization in the right direction.

1. Tie Financial Incentives to Cross-Sale/Upselling Metrics – TPAs should consider setting enterprise goals that highlight the importance of cross-selling/upselling. Rather than tying any particular individuals' compensation to a specific target, the enterprise could set a target that X amount of revenue is derived from selling new services to existing clients for the enterprise to be eligible for specific incentives (e.g., a minimum amount of cross-sales yields a 5% bonus toward every employee in the company).

2. Automate Where Possible – Too often, insurance carriers and distributors try to consciously engage in cross-selling, rather than trying to embed it into their sales process. For TPAs, the opportunity exists to automate the upselling and cross-selling processes, particularly for small or medium-sized employers or customers. In these situations, where renewals are likely already automated, TPAs should consider redesigning processes and responsibilities to suggest services leveraged by customers of similar size and industry.

3. Emphasize Customer-Centric Relationship Models – For larger clients, TPAs can leverage client-centric customer relationship teams to serve as single points of contact. This approach can reduce friction and encourage a "single TPA" interaction with the customer. The customer relationship management team then triages to the relevant internal teams. In these scenarios, the TPA can much more easily showcase its product shelf to customers and encourage cross-selling/upselling opportunities.

What's Next?

For TPAs, an investment into cross-selling and upselling provides an opportunity for organic growth. While the M&A market remains hot for TPAs, TPAs will eventually reach a point where inorganic growth is no longer a viable long-term strategy. Instead, TPAs will need to modify their growth goals to include customer penetration goals.

To do so, TPAs must act now to develop the organizational muscle necessary to grow this capability.

TPAs that are interested in pursuing cross-selling and upselling initiatives should:

1. Assess their readiness against operational, technical, growth strategy, and organizational dimensions.
2. Identify the factors that lead to opportunities
3. Develop data models and pilots to test these factors
4. Refine these models and roll out for broader application

Doing so will position TPAs to develop deeper relationships with their customers and with that, secure profitable clients for the future.

With about three million "18-wheeler" trucks operating in the U.S. and the demand for timely delivery of cargo, the transportation sector is often defined by its complexity and the speed at which decisions must be made. 

For freight brokers and motor carriers, arranging cargo insurance has, until recently, been a manual and time-consuming process. The value of many shipments is higher than the $100,000 basic cargo insurance coverage carried by most trucking companies. It could take a day or more to secure supplemental coverage for high-value or specialized loads. In an industry where hours can mean the difference between winning and losing a shipment, this lag is more than an inconvenience; it is a money-loser.

Recent advances in embedded insurance technology are beginning to emerge as a game-changer. In consumer transactions, embedded insurance has been commonplace for years. Examples of this are the travel insurance offered during flight bookings or ticket insurance at checkout for major sports events and concerts. But in business-to-business (B2B) transactions, embedded insurance has been limited. Now, that is shifting, and the implications for freight risk management are significant.

Moving Beyond the Consumer Model

Embedded insurance, when you break it down to basics, refers to the integration of insurance products into a broader transaction or workflow. This allows users such as freight brokers, shippers and motor truck carriers to access and bind coverage without leaving their primary platform. In the consumer world, embedded insurance is often a simple checkbox at the point of sale. In the B2B context, the complexity is greater, and the requirements for expertise are much higher.

The challenge in commercial freight has been twofold. First, there is the need for custom coverage based on shipment specifics such as the goods being transported, the value of the load and the points of origin and destination. Second, there has been a lack of digital infrastructure to support instant quoting and policy issuance at the moment a load is arranged. As a result, many freight brokers and motor carriers have relied on time-consuming manual processes such as emails and phone calls. This causes friction, delays and sometimes lost business opportunities.

A Case Study in B2B Embedded Insurance

The sort of system developed at Logistiq Insurance Solutions, called Freight Insurance Fast, addresses these challenges. The program runs on a software application with an application programming interface (API) designed to integrate directly into transportation management systems (TMS), load boards, and other logistics platforms. This enables freight brokers and motor carriers to access high-quality shipper's interest policies instantly, precisely at the point in the workflow when insurance is most often required, which is when the details of a load are being finalized.

Early adopters have seen immediate benefits. Users can secure supplemental cargo coverage in seconds rather than hours or days. This reduces manual administrative steps and keeps shipments moving. This timeliness is especially valuable because rising insurance premiums have led many motor carriers to lower their standard ($100,000 per load) coverage limits, making spot insurance a necessity for higher-value loads. Verisk CargoNet reports 3,625 cargo theft incidents in 2024, a 27% increase from 2023, with the average loss climbing to approximately $202,000 per theft, underscoring the need for adequate insurance on high-value loads.

Efficiency, Transparency, and the Role of Expertise

The integration of embedded insurance into freight workflows does more than save time. It also creates a more transparent and informed decision-making process. By using shipment data already entered into a TMS, the system can accurately price risk and present coverage options for the specific load. This not only reduces errors and gaps in coverage but also helps brokers and carriers make better risk management decisions without leaving their "go-to" workflows.

It's also important to note that embedded insurance in the freight sector is not only about automating transactions. The most effective embedded solutions combine digital convenience with deep insurance expertise. This ensures that users are not simply rushed to a purchase but are guided toward the right coverage for their needs.

The Road Ahead for B2B Embedded Insurance

The adoption of API-based, digitally delivered embedded insurance in B2B logistics is still in its early days, but the momentum is clear. As more TMS providers, load boards, insurance agencies, and vetting companies recognize the value of integrated insurance solutions, we can expect to see broader uptake and continued innovation in this space. For freight brokers and motor carriers, the result is a more agile, efficient, and resilient supply chain, one where risk management keeps pace with the speed of business.

As the insurance industry continues to explore the possibilities of embedded products, the freight sector offers a compelling case study in how the well-thought-out integration of insurance can help to improve operational efficiency and provide better risk outcomes. The lessons here are broadly applicable; when insurance is delivered at the right moment, in the right context, and with the right expertise, everyone from the underwriter to the end customer enjoys the benefits.

As natural perils intensify and become more unpredictable, the imperative to accurately assess, price, and mitigate risk has never been more critical. While catastrophe modeling has long been our industry's bedrock, the sheer volume of unstructured, real-time data presents both a formidable challenge and an immense opportunity. Large language models (LLMs) are poised to unlock this potential, fundamentally reshaping our approach to risk management.

The Unified Risk Picture

The true power of the LLM revolution lies not in isolated capabilities but in their synergy. Consider a widespread, sudden event, such as the 2013 Eastern Canada ice storm or a large-scale power outage in a major European city. Traditionally, assessing the full impact involves sifting through disparate sources post-event. With LLMs, initial signals—from real-time social media chatter, emergency service dispatches, and local news reports—can trigger immediate, cascading actions. The system can instantly identify affected properties, cross-reference policies for specific peril clauses (e.g., wind or ice damage), and flag potential exposures. This shifts us from a reactive, post-event data collation process to a dynamic, pre-emptive risk mapping system.

This unified view offers profound value to underwriters globally. Imagine an alert during the 2021 British Columbia atmospheric river event, or a significant flood in Central Europe. An LLM-powered system could not only identify affected properties but also pinpoint those within a portfolio with a high probability of claims based on a newly identified overland water coverage trigger. It could even enrich property data with nuanced details like "a heritage brick house, built in the 1920s, with a newly reinforced basement, surrounded by mature trees in a high-wind zone"—all extracted from unstructured notes, historical records, or public descriptions. This unified risk picture transcends traditional modeling; it becomes a living, breathing digital twin of our entire exposure, continuously updating as events unfold and new data emerges.

Going Beyond Assessment

This continuous stream of hyper-accurate data ushers in a new era of risk management. Instead of merely assessing losses after they happen, insurers can begin to mitigate them before they become catastrophic. The same LLM-driven system calculating real-time exposure could send targeted alerts to policyholders in the path of a disaster, such as the 2016 Fort McMurray wildfire or large-scale blazes in Australia or California. These alerts could offer tailored advice on securing properties before evacuation, moving beyond generic warnings to actionable, personalized guidance.

This fundamentally changes the insurer's role from a financial backstop to a genuine risk partner. It moves beyond simple risk pricing to genuine risk prevention. For agricultural clients, the system might not just assess hail risk but also provide hyper-local weather alerts combined with tailored advice on securing specific crops or equipment. This shift to pre-loss mitigation is not just about reducing claims; it's about building a more resilient society, one informed decision at a time. The strategic value of unstructured data in enhancing resilience, as highlighted by extensive research in risk management and supply chain logistics, underscores this imperative.

Challenges and the Human Imperative

Of course, this transformative vision is not without its challenges. The most significant is trust. An LLM's ability to extract nuanced insights from complex data is only as good as its underlying training and validation. In a highly regulated industry, we cannot afford for models to "hallucinate" or misinterpret critical policy clauses. The ethical implications are equally immense. The use of public and social media data, for instance, must be handled with extreme care to protect privacy and security, adhering to evolving global data protection regulations like GDPR and Canada's privacy laws.

Academic and industry literature consistently emphasize the necessity of a "human-in-the-loop" model. The future of this technology isn't about replacing human experts but augmenting them. Actuaries, underwriters, and claims adjusters will remain essential, but their roles will evolve. Instead of manually sifting through vast datasets, they will become critical thinkers and validators, leveraging LLM-generated insights to make faster, more precise, and more strategic decisions. This human oversight is the crucial final check, ensuring that while machines process the deluge of data, human expertise guides the way forward, maintaining fairness, explainability, and equitable outcomes.

A Phased Approach to Adoption

Given the transformative potential and the identified challenges, our recommendation for executive leadership is to pursue a phased, strategic adoption of LLM technology. This is not a "big bang" project but a continuous evolution, built on measured steps and clear governance.

1. Phase 1: Pilot and Validation. Initiate small, focused pilot programs to test LLMs on specific, well-defined problems. A strong starting point could be using LLMs to parse and extract key data points from a limited set of complex policy documents or to analyze drone footage from a past significant event (e.g., a regional flood or wildfire) to improve preliminary damage assessment protocols. This phase is critical for proving the concept, building internal trust, and demonstrating tangible ROI.

2. Phase 2: Integration. Once pilot successes are validated, focus on integrating LLM capabilities into existing systems. This involves building robust bridges to connect unstructured data insights from LLMs with the structured data in our core catastrophe models, policy administration systems, and claims platforms. The goal is seamless data flow and enhanced decision support.

3. Phase 3: Scale and Governance. As the technology scales across the enterprise, establish comprehensive governance frameworks. This is a non-negotiable step to ensure data integrity, address potential biases, and maintain strict compliance with all relevant regulatory standards globally. A dedicated, cross-functional team—comprising legal, compliance, IT, and business leaders—will be essential to guide this process, ensuring responsible and ethical deployment.

By taking this measured and strategic approach, we can harness the immense power of LLMs to move from a reactive to a truly proactive model of risk management. This will not only strengthen our organizational foundations but also enable us to provide greater stability, security, and peace of mind to our policyholders worldwide, solidifying our role as essential pillars of societal resilience in an increasingly uncertain future.

The life insurance underwriting landscape is undergoing rapid transformation. Emerging data sources, advanced technologies, and a growing push toward automation are reshaping how risk is assessed. At the same time, medical breakthroughs in early detection, diagnosis, and treatment are improving health outcomes, while rising rates of obesity and diabetes introduce new challenges.

Our Q&A series explores this evolving environment, spotlighting the innovators driving change.

In this edition, we speak with Adnan Haque (AH), founder and team leader of alitheia, Munich Re’s rapid risk assessment and decisioning platform. Adnan leads a multidisciplinary team of data scientists, machine learning engineers, and underwriting specialists who are building cutting-edge tools to enhance both instant decisioning and human review. Their work is redefining what it means to “underwrite” life insurance.

What are some of the key market trends that excite you about where things might be headed?

AH: One of the trends I'm most optimistic about is the adoption of electronic health records, or EHRs. I believe EHRs accelerate two significant improvements in how we assess risk: 

1. Rely on data first, disclosures second. By prioritizing data over self-reported information, we minimize the risk of omissions and inaccuracies that can occur with disclosures.
2. Increase automation rates without impacting mortality cost. EHRs will underpin the next significant increase in automation rates by providing readily accessible and standardized health information. This streamlines operations, reduces application-to-issue times, and enhances customer experience  ̶  all without compromising our mortality assumptions.

Another trend I'm excited about is the rapid development happening in AI. It will impact every part of the insurance value chain, from how we engage with customers to how we manage claims.

What can we do as an industry to react to or stay ahead of these trends?

AH: Ensure you actively engage in evaluating new tools and data sources to understand their impact. Some of these pilots/proof of concepts can be done with very limited resources. I'd also recommend being thoughtful about where we choose to build versus partner. We should build where we can have a strategic competitive advantage, and it directly contributes to our core competency, or if we have very niche requirements. In most other scenarios, we should buy or partner.

What are some of the major challenges you see that the industry is facing or will face in the near term? 

AH: Cybersecurity risk is a major challenge I see the industry facing over the near term. According to Security Magazine, a cyber-attack occurred every 39 seconds in 2023. As life insurers, we have a duty to protect the sensitive personal data that individuals entrust to us. At the same time, insurers’ core systems can be dated, sometimes as old as 50 years, and are less able to adequately protect that data.

Are there any blind spots that worry you in underwriting today or in the life insurance market in general?

AH: While we have many controls around misrepresentation, I've always worried the industry was a few blog posts away from a significant increase in misrepresentation rates. For example, a post with tips about gaming insurance applications could go viral, and third-party data would likely not catch every instance. 

If so, what can we do to fix those blind spots? 

AH: As data sources and our ability to leverage them improve, this risk will naturally be mitigated. In the interim, we should closely monitor our misrepresentation rates through post-issue audits and other tools.

What does it mean to you to “get the basics right” and why would that be important in underwriting and risk assessment? 

AH: To me, getting the basics right means getting the right team together to address the problem at hand. Everything stems from people and how they come together to achieve a goal. As underwriting and risk assessment become increasingly multidisciplinary, it means having underwriters, actuaries, data scientists, and engineers all working in tandem. We’ve written about this topic in more detail here.

Munich Re’s Future of Underwriting Forum Q&A series explores the rapidly evolving risk assessment landscape with industry experts. View the full series here.

Sponsored by ITL Partner: Munich Re

The integration of artificial intelligence (AI) and intelligent decision-making now prevails across the insurance industry. From streamlining underwriting processes to personalizing customer experiences and accelerating claim settlements, AI holds the promise of a more efficient, responsive, and ultimately, more valuable industry.

However, the true potential of this technological revolution cannot be fully realized without a parallel evolution in leadership, one that embraces diversity and recognizes the indispensable contributions of women. As a woman working at the forefront of insurtech, I have witnessed firsthand the transformative power of diverse perspectives, particularly when it comes to navigating the complexities and ethical considerations inherent in AI adoption.

While women make up approximately 59% of the overall insurance workforce, their representation in leadership roles is far lower. A 2024 report by the Triple-I blog states that only about 22% of those in the C-suite are women.

Fostering more female leadership within life insurance is not merely a matter of equity but a strategic imperative for driving truly intelligent innovation and ensuring the long-term success and societal benefit of these powerful technologies.

Mitigating Bias in Algorithmic Systems

One of the most critical areas where women leaders bring a unique and vital perspective to the AI-driven life insurance industry is in mitigating inherent biases within algorithmic systems. AI models are, at their core, reflections of the data they are trained on. If this data is skewed or incomplete, the resulting algorithms can perpetuate and even amplify existing societal biases, potentially leading to unfair or discriminatory outcomes in areas like risk assessment and premium pricing.

Women leaders, with their heightened awareness of equity and inclusion issues, are more likely to critically examine the data sets used to train AI models, ask the challenging questions about representation and fairness, and advocate for the implementation of rigorous ethical frameworks and governance structures. Their focus on ensuring that AI is developed and deployed responsibly is crucial for building trust with a diverse customer base and avoiding the pitfalls of algorithmic bias that could undermine the integrity of the industry.

By championing inclusive data practices and demanding transparency in AI decision-making, women leaders can help steer the industry toward a future where technology serves all segments of the population equitably.

Enhancing Customer-Centricity and Empathy

Beyond the important work of bias mitigation, women leaders also excel at fostering a more customer-centric approach to technological innovation. While AI offers the potential for unprecedented efficiency, it is essential to remember that life insurance is ultimately about people – their security, their families, and their futures.

Women, often recognized for their strong emotional intelligence and empathetic leadership styles, can ensure that the adoption of AI enhances, rather than detracts from, the human element of the insurance experience. They are adept at understanding customer needs and pain points and can advocate for the design of AI-powered tools that prioritize user experience, accessibility, and trust.

Earlier in life, my husband died unexpectedly, and my children and I were left to pick up the pieces without having life insurance in place. My experiences have shaped my approach to this industry, and I look at each and every customer through this type of lens to ensure each family has an opportunity to make the right choice for their individual needs.

Whether it's ensuring that AI-driven chatbots offer genuinely helpful and empathetic support or that automated underwriting processes are transparent and easy to understand, women leaders can champion a vision of technology that empowers and supports customers throughout their journey. Their focus on relationship building and clear communication ensures that the industry's technological advancements translate into tangible benefits and a more positive experience for policyholders.

Driving Innovation and Collaboration

Furthermore, the infusion of more women into leadership roles naturally fosters greater innovation and collaboration within life insurance organizations. Diverse teams, by their very nature, bring a wider range of perspectives, experiences, and problem-solving approaches to the table.

Women leaders often excel at creating inclusive environments where different viewpoints are valued and where interdisciplinary teams can collaborate effectively. The successful integration of AI requires a confluence of technical expertise, actuarial science, underwriting knowledge, and a deep understanding of customer behavior. Women leaders, with their collaborative leadership styles, can break down silos between departments and encourage the cross-functional communication necessary to harness the full potential of AI.

By challenging the status quo and promoting creative thinking, they can drive the development of novel AI-powered solutions that address unmet customer needs and propel the industry forward in unexpected and important ways. Moreover, a visible commitment to gender diversity in leadership serves as a powerful magnet for attracting a more diverse talent pool, including the next generation of data scientists, engineers, and business innovators who will be essential for fueling future technological advancements.

Today's life insurance industry realizes the transformative power of AI that is poised to reshape its operations and customer interactions. To truly capitalize on this opportunity and ensure a future that is both technologically advanced and ethically sound, the industry must cultivate and elevate women leaders.

Their unique perspectives, their focus on mitigating bias and enhancing customer empathy, and their collaborative approach to innovation are not merely supplementary benefits, but essential ingredients for success. By embracing gender diversity at the highest levels, the life insurance industry can harness the full potential of AI to create a more inclusive, efficient, and ultimately more human-centered future for all.

In specialty insurance, software alone can't ensure efficiency, accurate decision-making, and steady business growth. The systems that win are those that deftly balance automation with human touch.

Specialty lines are not a natural fit for wide automation. Their complex perils, bespoke coverage terms, and one-off claim scenarios by design demand human judgment and oversight. For years, carriers have been striving to move standardizable parts of their workflows to digital rails. But the fact that we're still having deep conversations about automating specialty insurance indicates that out-of-the-box tools haven't totally succeeded and that few reusable best practices for custom buildups exist.

Yet the pressure to automate is mounting. In a landscape where artificial intelligence (AI) developments are outpacing specialty operating models and exposures are evolving faster than line-specific product backlog, you can no longer afford to rely heavily on manual routines. Insurers are aware of the gaps and are rushing to seize the opportunities unlocked by new technology, with 74% placing digital transformation highest on their 2025 strategic agenda. For  many chief information officers and chief technology officers I work with, the question today isn't whether to automate, but how to do this right, without compromising critical aspects and waiting another three to five years to see meaningful results.

From my experience with specialty insurers globally, I've learned four dangerous automation pitfalls — often invisible from the outset and costly to escape from. Whether you're deploying point tools or setting out a broad transformation, here are the mistakes you'll want to avoid.

Mistake 1. Underestimating Software Flexibility Needs

Flexibility is the foundational requirement for specialty insurance automation systems. Yet this is often the first thing compromised in the pursuit of fast go-lives and cheap prebuilt workflows. The results are sadly consistent: quick but short-lived automation benefits, high cost of change, and frustrated people who have to revert to manual routines.

The primary reason to prioritize flexibility is that you don't yet know what you'll need to automate next. New risks, products, distribution models, technology — all of these will entail changes to your operational rules and software. Think of how fast-evolving cyber exposures made basic IT hygiene checks that were sufficient three years ago — and the actuarial frameworks layered on them — somewhat obsolete. Or how recent shifts in climate patterns and the rise of parametric models have changed underwriting and payouts in marine, aviation, and agricultural lines. Any legacy systems that failed to adapt basically lost their edge.

The need for flexibility becomes even more urgent when you consider regulatory volatility. An automation system must rapidly accommodate every evolving rule, from jurisdiction-specific Anti-Money Laundering and Countering the Financing of Terrorism (AML/CFT) demands to new reporting standards in specialty finance lines. If changes require extensive coding, you risk violating compliance before fixes in your solution even materialize.

While you can't foresee everything, you can employ an automation solution that assumes change is coming and allows iterative enhancements. Flexible software will let you not just upgrade what you already do but amplify innovation and future-proof your operations.

Here's what securing that flexibility means in practice:

• Hardcoding is out of the question. Business users, not just IT teams, should be able to modify automation logic on the fly. For example, in one aviation insurance software project, an underwriting engine was built where risk engineers could adjust, test, and deploy risk rating and quoting rules mid-cycle without involving developers. This capability ensured quick response to regulatory shifts and emerging risk factors that rapidly altered customers' exposure profiles.

• If you're building a custom solution — like many organizations do, precisely because off-the-shelf tools fail to accommodate specialty insurance nuances — prioritize modular architecture. It can be service-oriented architecture, microservices, or a modular monolith. Modularity will let you design automation around isolated functions (quoting, binding, policy servicing, and so on), evolve each component independently, and easily add new features. Lack of modular architecture may stall feature rollouts for months, which is a quick route to losing a competitive edge in specialty's fast-shifting tech landscape.

• Your automation system must not only connect to data sources you currently use for know your customer processes, underwriting, and claim adjudication but also support smooth integration with new and emerging ones. This is becoming critical as alternative feeds like Internet of Things-enabled data from sensors, drones, and satellites take on a central role in specialty risk assessment. For custom systems, interoperability can be maximized via an application programming interface (API)-first approach, where you plan software around integrations from the ground up. The resulting solution is modular and easy to evolve by design. If you're implementing a ready-made tool, make sure it offers built-in extensibility for future integrations.

Mistake 2. Overrelying on Intelligent Automation

Some of the insurers I talked to believe that AI will soon unlock full automation for specialty products. The rise of generative and agentic AI gave even more hope that we can have specialty automated in a straight-through way, much like personal lines. Studies claim that 99% of insurers around the world are already investing or planning to invest in GenAI, even though 60% of firms haven't yet developed a sharp return on investment model for the technology.

AI does have clear, high-value use cases in specialty insurance. It offers five to 50 times speed gains in data-rich, high-volume scenarios that require quick action, like application processing or claim validation and triaging. Machine learning-supported analytics have proven more than 95% accuracy in dynamically mapping risks, predicting financial performance, and surfacing exposures across complex specialty portfolios. Generative AI came to automate the most time-intensive routines, such as reviewing 100-page submissions, compiling insights, crafting bespoke documents, and delivering basic customer advice. Frontrunners report more than twofold growth in employee productivity and an up to 6% increase in revenue.

But I have to disappoint you: AI, however powerful, can't automate specialty insurance end-to-end.

The reason is the inherent limitations of the tech's predictive, reasoning, and creative scope. Intelligent models draw on historical and current data, meaning that however crafty they are at extrapolating patterns and ideating, they cannot anticipate unprecedented risks and effectively navigate unique context. This makes AI unfeasible as an autonomous decision-maker in novel, low-data environments and "gray zones" with special arrangements — the specialty lines' everyday settings.

Take specialty claim adjudication in multi-tiered areas like aviation liability or marine cargo. AI tools could automatically process claim evidence, spot forged submissions, summarize investigation results, and suggest optimal settlement paths. But human judgement, negotiation, and context-aware decision making remain critical for accurate settlement.

When it comes to underwriting, AI may struggle to predict and score exposures that fall outside historical precedent, like the new energy technology's risks in environmental liability or war-on-terror exclusions in political risk coverage. Similarly, for actuarial modeling, no model can reliably replace expert judgment in niche or emerging segments (think intellectual property insurance or space launch coverage), where actuarial baselines don't yet exist. By trusting specialty actuarial and underwriting decisions to your intelligent solution, you may take more risk than opportunity.

When it comes to generative AI, data analytics experts highlight that it's really strong at summarizing information, reasoning, and concluding. At the same time, the non-deterministic structure of generative AI algorithms makes it hard to achieve repeatable results. In practice, it means that tasks like synthesizing underwriting summaries or claims reports may yield slightly different outputs each time, even when the input data remains unchanged. Combining generative AI with machine learning and independent assessment models drastically enhances consistency, but you'll still need a human in the loop to validate the insights.

Not to mention that AI-fueled automation doesn't guarantee value and, in some scenarios, may lose on cost-benefit to traditional approaches. For one aviation insurer, an underwriting system built on rule-based engines and statistical algorithms, with no AI involved to score risks or compose quotes, delivered accuracy comparable to intelligent models but with better transparency, flexibility, and at a lower cost. The resulting software succeeded because it precisely matched the real business needs.

Mistake 3. Treating Data as a Secondary Success Factor

Too often, insurers approach automation as a purely software play and treat the data that fuels digital operations as a secondary aspect. But like any bad fuel, poor data corrodes everything it touches. And it's inherently easy to misfuel your engine in specialty insurance, where data doesn't come from plug-and-play sources and is rarely standardized.

For advanced analytics and AI, the stakes are even higher. If the data used for model training is inconsistent or lacks depth, the AI solution may miss critical risk signals, overlook fraud, and reinforce biases. In agentic AI, anchoring models on proprietary expertise can become a huge competitive advantage, but that's only possible if your knowledge is well-organized and accessible at scale.

What do you actually need to build strong data foundations?

• Data discipline starts with a robust data governance strategy, which includes defining secure, controlled pipelines and clear standards for data quality, storage, and processing tiers. You also need to map which data can support which decisions, at what levels, and under what conditions. The map lays the basis for a resilient data architecture where new data sources can be seamlessly onboarded into a standardized, governed framework.

• Checking data for duplicates, missing values, and formatting errors and enriching it at ingestion is a must to ensure accurate entries. Data engineers use data integration tools like Azure Data Factory and AWS Glue to automate validation and cleansing routines at scale.

• Consider intelligent image analysis and natural language processing tools to automatically extract and normalize data from unstructured inputs. There are pre-built options, but custom pipelines and algorithms trained specifically on specialty insurance concepts would offer more accurate parsing and classification for your niche use cases.

• To maintain consistent data formats, apply profile and document templates, enforce standard taxonomies for specialty insurance risk classes, and define unified data inputs and outputs across automated processing workflows. 

• Implement centralized, scalable data storages to avoid data silos that hamper automation accuracy and speed. Dedicated repositories benefit specialty insurers from both data accessibility and cost standpoints. For example, you might employ a cloud data lake to store raw risk feeds and insured documents and use a data warehouse for structured data like policy records and claim histories.

• Use database indexing. It is crucial for smooth structured data retrieval operations. In one recent engagement, a managing general agent faced errors and delays in automated report generation. Optimizing and indexing the software's underlying database eliminated accuracy issues and introduced up to 75 times quicker reporting. For another specialty insurance client, database restructuring doubled claim processing efficiency..

The quality of third-party data that feeds automation — think your satellite imagery for agricultural products or telematics for fleet lines — matters a lot. Prioritize reputable data vendors who provide up-to-date datasets backed by clear documentation and offer flexible data integration options (APIs, direct feeds). Also, check for contingency: The vendor must maintain robust backup mechanisms to prevent data delivery disruptions.

You also need the ability to trace every data point used for automation to its origin. This helps you establish auditable data-driven workflows, quickly isolate errors, and achieve explainability in specialty AI models. Popular data integration platforms provide go-to lineage capabilities and allow building tailored metadata logging components into data pipelines.

Mistake 4. Neglecting the Human Take

Specialty insurers often pride themselves on human expertise, and rightly so. Which makes it all the more surprising to me why some embark on automation projects with minimal input from the people who actually carry the knowledge: actuarial, underwriting, and claim experts.

Without involving domain subject matter experts, you risk missing workflow specifics, subtle productivity blockers, non-apparent risk factors, and other contextual nuances critical for a winning automation system design. As a specialty insurance IT consultant, I know firsthand that no automation vendor can intuit these things. Worse, software planned out of touch with business users can lose its credibility at the door, which will inevitably hamper adoption.

I've seen the most impressive automation outcomes where organizations brought subject matter experts in from the start. For example, in one portal development project for a specialty managing general agent, teams worked directly with the underwriters and claims specialists whose data entry routines were to be partially moved to the customer and broker side to alleviate the workload. They involved them in planning portal features, validating automation rules, and testing ready components. Doing this helped optimize portal design, secure logic accuracy, and, most importantly, ensure the delivered functionality eliminates the teams' real operating issues. The result was both more efficient servicing workflows and higher satisfaction of the managing general agent's customers.

Change management is another area where employee participation brings much value. Before any software is rolled out, you need to map, challenge, and optimize every business process subject to automation. Otherwise, you're just automating friction. Engaging specialty insurance teams will help you surface hidden bottlenecks and design workflows for higher efficiency in real-world and digital settings. This move also fosters ownership and enhances employee trust in technology, which is critical for adoption.

Automation is no longer optional in specialty insurance, but it's not a cure-all either. The organizations that thrive will be the ones that treat automation as part of a broad business rewiring, align technology with real operational needs, and respect the irreplaceable value of human expertise. Getting all that right from day one will help you position for efficiency and sustainable growth despite increasing domain complexity.

Contributors: Stacy Dubovik, financial technology researcher, ScienceSoft; Alex Bekker, AI & data management expert, ScienceSoft.

In today's workplace, supporting employee well-being requires more than reactive benefits and one-size-fits-all coverage. With financial stress on the rise and employees increasingly worried about healthcare affordability and long-term security, employers must adopt a more proactive, comprehensive approach to benefits communication. A key opportunity lies in one of the most misunderstood benefits to help employees: life insurance.

Too often, life insurance is seen strictly as a death benefit. However, many modern life policies include living benefits that support employees while they're still alive. These can include accelerated death benefit riders (ADBRs), cash value accumulation in permanent policies, and waiver of premium riders. These features offer financial protection during critical life events—such as illness, disability, or unexpected expenses—and can significantly enhance financial wellness and peace of mind.

Employees Need More Than a Death Payout

According to Morgan Stanley's 2025 "State of the Workplace" study, 66% of employees report that financial stress hurts their work, up 9% from the previous year. HR leaders are noticing, too—83% say financial strain among employees is harming productivity. At the same time, younger workers are entering the workforce without adequate savings or a long-term plan. A 2025 MetLife study found that 60% of Gen Z women and 45% of men feel unprepared to manage their long-term financial future.

Even as the job market has stabilized, employee expectations have changed, and many now look for the right benefits options for long-term security. That's where living benefits can bridge the gap—if HR teams know how to explain them effectively.

Demystifying Living Benefits

Accelerated death benefit riders (ADBRs) are one of the most important, yet under-communicated, features in life policies. These riders allow employees diagnosed with terminal or serious illnesses to access a portion of their life insurance payout while still alive. Funds can be used for medical bills, rent, or family caregiving—all at a time when financial relief is needed most. With healthcare premiums soaring, ADBRs provide necessary liquidity for employees facing devastating diagnoses like cancer or heart disease.

Permanent life policies offer another layer of protection through cash value accumulation, which can be tapped as a tax-deferred loan or withdrawal. Whether used for emergency car repairs or education expenses, this cash value acts as a personal safety net—especially important given that 37% of Americans can't afford a $400 emergency.

The waiver of premium rider is another lesser-known but significant benefit. If an employee becomes disabled and can't work, this rider ensures their life insurance coverage remains active without requiring premium payments.

Why Educating on Living Benefits Matters for Employers

The employer benefits go far beyond compliance or checkbox offerings. When employees understand the full value of their life insurance policy, they feel more financially secure and supported. 

MetLife's 2025 Employee Benefits Trends study found that employees who feel "holistically well" are 67% more likely to be productive and 56% more likely to stay with their employer long-term. Meanwhile, HR Executive reports that 72% of organizations now view financial wellness as a core part of their HR strategy, linking it directly to talent retention and workforce equity.

When employees feel holistically supported—financially, emotionally, and physically—they are more likely to be productive, engaged, and loyal to their organization. Increasingly, companies are recognizing that financial wellness plays a central role in workforce well-being and are incorporating it into broader organizational strategies. By prioritizing benefits that support employees' everyday lives, employers can foster a stronger culture of care while also strengthening talent retention and equity across the workforce.

How to Educate Employees Year-Round

Despite the power of these features, most employees still don't understand them—largely due to how benefits are communicated. Too many HR teams rely on one-time enrollment guides filled with insurance jargon. A better approach requires a year-round, multi-channel education strategy.

First, go beyond the brochure. Use webinars, brief videos, and even real-life (anonymized) stories of employees who benefited from living riders. This makes the concept more relatable. Segment messages by life stage: Younger workers may be interested in cash value savings, while older employees may focus more on critical illness coverage.

Simplify the language. Replace terms like "accelerated death benefit rider" with phrases like "get money early from your policy if you get very sick." Consider monthly reminders or Q&A office hours where benefits administrators walk employees through what's available and when to use it.

Keep it proactive and personal. Personalized benefits statements or calculators can help employees understand exactly what's included in their coverage. Moreover, don't just educate during open enrollment—life events happen year-round, and communications should reflect that.

Turning a Traditional Benefit Into a Strategic Asset

At a time when employee loyalty is fragile and financial wellness is directly tied to job satisfaction, HR leaders must view benefits education as part of their larger workforce strategy. Life insurance is no longer just a "what if" policy—it's an active tool that can offer relief during life's hardest moments.

Educating employees on living benefits helps reimagine life insurance from a static document into a dynamic financial wellness tool. It empowers workers to make smarter decisions, relieves burdens when it matters most, and reinforces a culture of care and trust. In short, living benefits aren't just a feature—they're a reflection of the value employers place on their people, in life and in legacy.