Many insurance agencies have assumed that their core customers, longtime policyholders, homeowners, and small business owners prefer human-only service and would be reluctant to engage with AI. The data tells a different story.

A recent survey from Sonant, polling more than 1,000 consumers, found that more than 70% of Gen X consumers (ages 45–60) are comfortable engaging with AI assistants, with nearly half reporting that they are very comfortable. These comfort levels are higher than any other age group surveyed.

And Gen X is not a fringe audience for most agencies. They represent valuable customers that have multiple policies, higher coverage limits, and long-standing relationships. They are also decision-makers, balancing busy professional and personal lives, which makes convenience and responsiveness especially important. The data flips the assumption. For many agencies, voice AI may not be a risk to the relationship. It may be a way to meet the service expectations of the customers they value most.

Convenience Wins

Today, consumers are used to interacting with AI in other sectors such as retail, banking, and travel. It is not a novel experience any more. They are not evaluating AI based on whether it replaces a human interaction. They are evaluating whether it helps them get what they need quickly and accurately. The same survey found that over 65% of consumers are willing to use a voice AI assistant if it helps them get answers faster, reinforcing that speed is one of the biggest drivers of adoption.

For Gen X customers, that expectation is shaped by experiences far beyond insurance. Waiting on hold or navigating multiple phone menus can feel outdated when customers are used to faster, more intuitive service in other parts of their lives. For agencies, this creates a shift in mindset. The question is no longer whether AI will feel too impersonal. It is whether traditional service models are keeping up with what customers now expect.

Considering a New Front Door

With a large faction of insurance customers already comfortable with voice AI, the opportunity for agencies is not just to adopt the technology, but to rethink how the customer journey is structured around it. That often starts at the very first touchpoint: the phone call.

Instead of calls going unanswered after hours or being routed through multiple menus, a voice AI assistant can immediately engage the caller, understand the request, and take action. A policyholder calling about a billing question can get an instant answer. A prospective client can provide quote details without waiting for a callback. A customer reporting a claim can share key information right away, even if human follow-up is needed.

Voice AI can also act as a triage layer throughout the customer journey. Routine service requests, such as policy changes, document requests, coverage questions, can be handled or pre-processed automatically, allowing agents to focus their time on higher-value conversations like advising on coverage, resolving complex claims issues, or strengthening client relationships.

AI Is Only as Good as the Execution

Agencies that are successful with voice AI tend to treat it as an extension of their service model, not a replacement for it. That starts with designing the experience around how customers actually communicate. Interactions should feel natural and conversational, allowing callers to explain what they need in their own words rather than forcing them into predefined paths.

Equally important is ensuring that AI responses are grounded in real agency operations. Systems need to be trained on the types of questions customers actually ask and the workflows staff follow every day. Without that alignment, even sophisticated technology can quickly fall short.

Clear escalation paths are another critical component. Customers should never feel stuck in an AI interaction. When a situation becomes more complex or sensitive, transitioning to a human should be immediate and seamless.

Another common misstep is over-automation. Not every interaction should be handled entirely by AI. Complex conversations, emotionally sensitive situations, and high-value client interactions still require a human touch.

Failing to address concerns around accuracy and data security can also undermine adoption. Customers need confidence that the information they receive is correct and that their personal data is protected.

For agencies, the takeaway is clear: Gen X customers, who are the backbone of many books of business, are not resisting AI. They are ready to use it when it delivers meaningful value. The agencies that succeed will not be the ones that simply add AI to their operations. They will be the ones that use it to provide more on-demand, faster, and efficient services that elevate the insurance customer experience to the same level clients receive in other industries.

The AI that survives contact with insurance production isn't the one that claims to handle everything. It's the one that defers to human experts on configuration — and tells them when it isn't sure.

It's 4:45 p.m. on a Friday. An underwriter is staring at a 50-page submission that just landed in her inbox. Half the fields on the broker's application are blank, the loss runs are scanned PDFs with blurry text, and the broker's note says if she can quote it before Monday the business is hers.

A few years ago, that submission either gets the rest of her week or it gets a polite "thanks but no thanks." Today, in a well-run shop, an AI document pipeline pre-processes the package overnight. By Monday morning the structured fields are sitting in her workflow tool, except for three highlighted fields in yellow, where the model wasn't sure. She spends 20 minutes verifying those three against the source PDFs, corrects one, accepts the others, and quotes the deal before lunch.

That highlighted-in-yellow moment is the entire game. It's the difference between AI that gets adopted and AI that gets quietly abandoned six months in. And it doesn't come from the model being smarter. It comes from two design choices most vendors are reluctant to lead with: the AI defers to a human expert on protocol and configuration, and it tells that human when it isn't confident in its own answer.

I've been building document AI workflows for insurance carriers, MGAs, and reinsurers for several years. The pattern that separates the systems that scale from the ones that get shelved isn't subtle. The systems that scale behave like an apprentice. The ones that get shelved behave like an oracle.

The apprentice mindset

Nobody hands a first-year underwriter the keys to a renewal book on day one. The new hire shadows a senior, learns the carrier's appetite, sees how the desk handles a tricky loss run, and runs every recommendation past someone with 20 years of context before it goes out the door. The expectation isn't that the apprentice arrives knowing everything, it's that they get faster, more accurate, and more independent through repeated cycles of review and correction.

That's what AI in insurance needs to look like. Not a system you switch on, but a system you train, configure, and refine with human expertise as the central input.

The work that this requires is real, and most vendors undersell it. The carrier has to define which document types matter, which fields the model needs to extract from each, which business rules govern acceptance, where the human handoff points sit, and what the escalation path looks like when the model is unsure. None of that is the AI's job. It's the expert's job. AI is only as useful as the configuration the experts give it.

This is one reason BCG found that 77% of insurance carriers are piloting AI but only 7% have scaled it. The model accuracy on isolated test sets is rarely the bottleneck. The configuration work, the continuing protocol-setting, review, and refinement that turns a generic model into a trusted production tool, is what most programs underinvest in. The pilot will look great in a sandbox but quietly underperform in the wild.

The "I don't know" feature

Most modern AI tools were built first for consumer use cases, where a confident wrong answer is a small cost. In insurance, a confident wrong answer is a mispriced policy, a wrongly denied claim, or a compliance exposure that surfaces 18 months later when a regulator asks how a decision got made.

That changes the design priority. A production-grade insurance AI doesn't just need to be accurate — it needs to know when it isn't. Field-level confidence scoring isn't a nice-to-have feature; it's the trust infrastructure that makes the whole apprentice model work.

When the system can tell a reviewer "I extracted this date of birth with 99% confidence and this loss history with 62%," three things change. Review goes from "re-read everything to catch errors" to "check the flagged ones" — the only review pattern that actually saves human time at scale. Reviewers build calibrated trust over time, learning which extractions they can skim and which need a careful look at the source. And the system produces an audit trail that regulators are increasingly going to require. The NAIC Model Bulletin on AI, New York DFS Circular Letter No. 7, Colorado's Regulation 10-1-1, and the EU AI Act's high-risk obligations all share a through-line: AI used in insurance decisions must include meaningful human oversight, and carriers must be able to show their work. A system that flags its own uncertainty produces that documentation natively. A system that doesn't is one your compliance team will spend a year retrofitting.

One important caveat. Confidence scores are only useful if they're calibrated — meaning when the model says it's 90% confident, it should actually be right roughly 90% of the time. An overconfident model with a meaningless score is worse than no score at all, because it teaches reviewers to ignore the signal. That's something buyers should test for, not assume.

What carriers should ask before they buy

Most RFPs for insurance AI ask the wrong questions. They focus on benchmark accuracy, model size, and end-to-end automation claims — easy questions to answer in a slide deck, but not the ones that predict whether the system will still be in production a year from now.

The questions that predict adoption are about the apprentice posture. Can the system expose field-level confidence scores, and are they calibrated against actual accuracy? Can our experts configure new document types and business rules without filing a vendor ticket? When a reviewer corrects the model's output, does the system actually learn from the correction or just log it? Does the human review queue route work by confidence level, or dump everything into one bucket?

The carriers that ask these questions tend to end up with AI that gets used. The carriers that buy on autonomy claims tend to end up in the 60% of organizations that, per BCG, generate no material value from their AI investment despite continued spending.

The bet worth making

The right AI for insurance isn't the one that claims to do everything. It's the one that knows the expert is still in charge — and acts like it. It asks the expert for configuration. It defers when it isn't sure. It gets better when it's corrected. That posture isn't a limitation of the technology. It's the reason the technology survives contact with production.

The Friday afternoon submission is going to keep arriving. The question is whether your AI is going to help your underwriter quote it by lunch on Monday — or just give her a different kind of mess to clean up.

Insurance distribution is entering a new phase defined less by the products sold and more by the systems that support their delivery. For years, agencies have expanded their offerings to meet evolving customer needs, but the foundational systems have not always kept pace to do so.

As a result, many organizations are now confronting a disconnect between how they want to grow and their operational capabilities, and that gap is becoming increasingly difficult to ignore as agencies look to scale across multiple product lines and deliver more cohesive, long-term value to their clients.

Demand Is Strong, But Execution Has Fallen Short

This challenge is particularly visible in life insurance. Despite strong and sustained consumer demand, life insurance products often remain underused. In fact, approximately 102 million American adults report needing life insurance or more coverage, yet many agencies still struggle to fully integrate it into their day-to-day operations. The issue is not a lack of opportunity or awareness, but the complexity of the systems required to support life insurance distribution, which have historically been fragmented and difficult to navigate.

For many agents, selling life insurance involves working across multiple carrier portals, managing varying underwriting requirements, and reconciling separate commission structures. These disconnected processes create inefficiencies that slow down workflows and make it harder to build consistent, repeatable practices. Over time, this operational burden limits an agency's ability to scale life insurance as a primary offering, leaving it positioned as an add-on rather than an integrated part of a broader client strategy.

Health Insurance Modernization as the Blueprint

Health insurance distribution has already solved many of the infrastructure challenges that continue to limit life insurance today. Over the past decade, it has undergone significant modernization, driven by regulatory complexity, rising consumer expectations, and the need for greater efficiency. In response, agencies have invested in more unified systems for enrollment, billing, claims management, and data integration, creating a more consistent and scalable operating environment.

While these systems did not completely eliminate complexity, they made it manageable by standardizing workflows. This evolution demonstrates how aligned systems can support growth without introducing additional friction, providing a clear model for how other product lines, including life insurance, can evolve.

The Shift Toward Multi-Line Distribution

At the same time, infrastructure is improving, and agency strategies are also changing. There is a clear shift toward multi-product distribution, driven by a focus on lifetime customer value. Agencies are moving beyond transactional relationships and instead looking to support clients across different stages of their lives with a broader range of solutions.

Life insurance plays an important role in this approach, complementing health and supplemental offerings and enabling more comprehensive client conversations. Agencies that can deliver across these touchpoints are better positioned to strengthen relationships, improve retention, and create more sustainable growth. However, realizing these benefits depends on the ability to operate across product lines without introducing additional complexity.

When Infrastructure Becomes a Constraint

As agencies expand, the limitations of fragmented systems become more pronounced. Disconnected platforms often require duplicate data entry, introduce process inconsistencies, and make it difficult to maintain a clear view of the overall business. These inefficiencies affect both day-to-day productivity and long-term scalability, as growth introduces more operational friction rather than greater efficiency.

Over time, this creates a structural challenge. Agencies may have the demand and strategic intent to grow, but without the right infrastructure, execution becomes increasingly difficult. In this environment, operational complexity can quietly limit progress, even as market opportunities continue to expand.

Integration as a Path to Scalable Growth

Integrated infrastructure offers a more effective path forward by bringing multiple products into a single operational framework. When systems are aligned, agencies can standardize workflows, centralize data, and reduce the administrative burden placed on agents and staff. This creates a more consistent experience across the organization while enabling greater visibility and control.

These gains do not come from selling more, but from making it easier to deliver more value within the same workflow. By removing unnecessary friction, agencies can improve efficiency and scale more effectively without fundamentally changing how they engage with clients.

Elevating the Role of the Agent

As operational barriers are reduced, the agent's role becomes more focused and impactful. With less time spent navigating systems and managing administrative tasks, agents can dedicate more attention to advising clients and guiding them through complex decisions. This is particularly important in life insurance, where trust and context play a central role in the decision-making process.

Technology supports this shift by streamlining processes, but it does not replace the need for human expertise. Instead, it enhances the agent's ability to deliver meaningful guidance, strengthening the overall client experience and reinforcing the value of the advisor relationship.

The Future Is Integrated and Adaptive

Looking ahead, insurance distribution is moving toward more integrated and flexible models. The traditional approach of managing each product line through separate systems is becoming increasingly unsustainable in an environment that demands both efficiency and adaptability. Agencies need infrastructure that supports multiple products within a single framework while accommodating diverse sales channels and evolving client expectations.

This shift reflects a broader trend across financial services, where integrated ecosystems are replacing siloed solutions. Clients expect a more seamless experience, and agencies are responding by rethinking how they structure their operations to meet those expectations.

A Practical Path Forward

For agencies looking to expand into life insurance or strengthen their multi-line capabilities, the most practical starting point is often the systems they already have in place. By building on existing investments in health and supplemental distribution, organizations can create a more cohesive system that supports growth without introducing unnecessary complexity.

This approach allows agencies to move forward in a way that is both efficient and scalable, while aligning more closely with how clients engage with insurance products in real life. It also provides a clear framework for turning strategy into execution, ensuring that growth is supported by the systems required to sustain it.

As the industry continues to evolve, infrastructure will play an increasingly central role in determining which organizations can grow effectively. Agencies that prioritize integration will be better positioned to expand their offerings, improve operational efficiency, and deliver more consistent value to their clients. Those who continue to rely on fragmented systems may find that growth becomes more difficult to manage over time. Simply put, by approaching distribution through an infrastructure lens, organizations can move beyond incremental improvements and build a foundation for more intentional, scalable, and sustainable growth.

In the global insurance landscape, the "last mile" of policy distribution has long been a notorious bottleneck—particularly in compulsory schemes where regulatory compliance is the primary driver. However, a significant shift is occurring in Southeast Asia that offers a road map for how national health mandates can be digitized at scale.

The evolution of Malaysia's SPIKPA (Skim Perlindungan Insurans Kesihatan Pekerja Asing) insurance framework represents more than just a local policy update. It is a masterclass in how government-led digital mandates, specifically through the Foreign Worker Centralized Management System (FWCMS), are forcing a legacy industry to migrate from manual brokerage to API-native, embedded ecosystems. For senior insurance executives worldwide, this transition provides critical insights into the future of regulatory technology (regtech) and the inevitable shift toward embedded insurance.

The Death of Manual Compliance and the Rise of "Data Events"

Historically, migrant health insurance in Malaysia was a fragmented market, heavily reliant on traditional agent networks and manual verification. While functional, this system was riddled with systemic risks. For insurers, the lack of real-time visibility into policy issuance led to high friction costs and a persistent threat of certificate fraud. For employers, the manual nature of the process meant that a simple renewal could take days or even weeks of administrative back-and-forth.

Since the recent overhaul in the Malaysian regulatory framework, the government has effectively ended this era of ambiguity. By mandating that all foreign worker permits be processed via the FWCMS, the state has fundamentally altered the value chain. Central to this digital transformation is the Insurance Transaction Reference (ITR) number.

By requiring a real-time digital handshake between the insurance purchase and the government's centralized database, the regulator has effectively turned insurance from a "stand-alone financial product" into a "synchronized data event." In this new paradigm, a policy does not legally "exist" until it is validated by the FWCMS server. This move toward real-time validation is a global trend that senior leaders must prepare for, as it shifts the insurer's role from a mere risk-bearer to a critical component of national infrastructure.

The Strategic Pivot: Why Specialization Wins in RegTech

For senior insurance executives, the Malaysian shift illustrates a critical competitive trend: the decline of generalist aggregators in favor of specialized, workflow-integrated intermediaries. In a commoditized market, the ability to offer a "pretty UI" is no longer a competitive advantage. The real value now lies in the depth of technical integration into the state's regulatory rails.

When evaluating the market, it becomes clear that the leaders are those that have anticipated this "API-first" requirement. For instance, companies that provide a dedicated SPIKPA Insurance portal have emerged as specialized leaders by building their entire infrastructure around the FWCMS ITR workflow. Unlike general insurance portals that treat foreign worker coverage as just one item in a broad catalog, these specialized platforms treat the regulatory workflow as the core product.

These platforms are solving three high-level executive challenges that traditional distribution models cannot:

1. Systemic Fraud Mitigation

   In many emerging markets, "grey market" policies—where agents issue certificates without properly registering them with the carrier or the state—have plagued loss ratios and damaged industry reputation. By providing native ITR verification tools, specialized platforms allow for independent, real-time confirmation that a policy is genuinely linked to a valid government record. This transparency protects the brand equity of the underwriting carriers and ensures that the "protection gap" is effectively closed.

2. Operational Resilience and Zero-Latency

   In a high-volume environment where millions of migrant workers require annual renewals, even a 1% error rate in manual entry can lead to thousands of hours of administrative waste. Native API integration allows for "same session" certificate issuance. This reduces the operational burden on the primary insurer's back office, allowing them to focus on underwriting and risk management rather than data entry and customer support for administrative errors.

3. Maintaining Underwriting Integrity through Strategic Partnerships

   Digital transformation does not mean a race to the bottom in terms of quality. The most successful digital conduits in Malaysia have maintained high standards by partnering with Ministry of Health (KKM) approved carriers such as Tokio Marine, Allianz, and Chubb. This ensures that while the delivery is modernized, the product remains backed by the financial strength of global insurance leaders.

The Macro-Economic Impact: Insurance as a Catalyst for Digital Economy

The implications of this shift extend far beyond the insurance sector. When a country successfully digitizes a compulsory insurance scheme, it creates a "trust layer" for the entire digital economy.

In the Malaysian context, the integration of SPIKPA into the FWCMS has streamlined the labor supply chain—a critical factor for sectors like manufacturing, plantation management, and construction. By removing the friction from insurance compliance, the state is effectively lowering the cost of doing business. For insurers, this means the "total addressable market" (TAM) is no longer just the premium collected, but the data and insights gathered from being an integral part of the national labor ecosystem.

Looking Toward 2026: The Global Blueprint

As we look toward 2026 and beyond, the Malaysian model suggests that the future of compulsory insurance lies in embedded regulatory compliance. We are moving away from a world where a customer "goes out to buy insurance" and into a world where insurance is a silent, automated step within a larger regulatory or commercial journey.

For global carriers, the strategic takeaways are clear:

• Infrastructure is the New Distribution: The next decade of growth will not come from more agents, but from better APIs. Carriers must invest in middle-office technology that can speak to government databases in real-time.
• The Power of the Niche: In complex, regulated markets, the "one-stop-shop" aggregator is often too shallow. There is immense value in the "specialized intermediary" that understands the deep, granular pain points of a specific regulatory mandate.
• Regulation as a Channel: Instead of viewing new mandates as a compliance hurdle, forward-thinking executives should view them as a digital distribution channel that provides a "captive" audience with a mandatory need.

Conclusion

The story of the SPIKPA transition in Malaysia is a microcosm of the broader digital transformation occurring in the insurance industry. It proves that when the public and private sectors align through shared digital standards, the result is a more transparent, efficient, and resilient market.

For the senior executive, the lesson is simple: The "best online platform" is no longer just a website—it is a bridge. The winners in the 2026 insurance market will be those who stop selling policies in isolation and start providing the digital infrastructure that makes compliance invisible and instantaneous.

Key Takeaways for Senior Leadership:

• Shift to API-first: Real-time validation via government systems is the new standard for compulsory products.
• Specialization vs. Aggregation: Deep integration into specific regulatory workflows offers a higher barrier to entry than generalist digital storefronts.
• Brand Protection: Digital transparency is the most effective tool for eliminating fraudulent policies and protecting carrier reputations in emerging markets.

An April 2026 AM Best survey of more than 150 rated insurers and MGAs found that 45% still cite data readiness as the top barrier to AI deployment, even among analytically mature organizations.

Insurance carriers have invested years building trusted analytics platforms. Data warehouses and governed metrics now deliver consistent loss ratios, premium trends and claims data that actuaries, underwriters and executives rely on. Yet when these same carriers launch AI initiatives, results often disappoint. Underwriting assistants hallucinate risk scores, fraud models produce unexplainable flags and catastrophe projections miss critical context buried in broker narratives and loss-run documents.

Your Analytics Layer Works - For What It Was Designed For

Analytics-ready data serves human decision-makers asking backward-looking questions. Actuaries calculating reserves, underwriters reviewing portfolios, compliance teams preparing filings and executives setting strategy all need consistent, summarized and explainable information. The central question is always the same: what happened?

The semantic layer defines business metrics, dimensions and access rules. It creates a single governed version of the truth so an actuary in New York and an underwriting director in London both get the same answer to "What was our Q3 combined ratio?" This layer excels at aggregation and stability. It compresses complex reality into clear, auditable numbers that humans interpret using their own judgment and domain knowledge.

AI Systems Are a Different Consumer Entirely

Large language models, reasoning engines and autonomous agents do not aggregate and summarize. They traverse, reason and act. They consume tokens, embeddings and context windows. They need properties that analytics infrastructure was never designed to provide:

• Full context and completeness behind every number.
• Provenance and trust: every fact carries its source, confidence level and resolution history for audit and regulatory defense.
• Semantic richness: named, typed relationships between policies, claims, insureds and risk factors so agents can reason without custom code.
• Conflict handling: the system preserves multiple sources, flags discrepancies and tracks resolutions rather than forcing a single version of the truth.

The enabling structure is an ontology - a knowledge layer that makes the domain understandable and navigable for machines.

Key Structural Differences

Analytics-ready data answers "what happened" by compressing thousands of transactions into governed numbers. AI-ready data answers "what should happen next" by preserving the full evidence trail behind every fact. A semantic layer translates business questions into consistent reports. An ontology maps the domain so machines can traverse it, weigh evidence and explain conclusions. Both are necessary. They serve different audiences through different infrastructure over the same foundational data.

The Right Architecture: Parallel Layers, Shared Foundation

Leading insurers build both capabilities over the same foundational event data (policies, claims, submissions). The semantic layer powers governed dashboards and regulatory reporting. The ontology gives AI agents the structure to reason, track provenance and produce explainable decisions.

The layers reinforce each other. Unified governance (lineage, confidence scoring, conflict rules and human review) serves both without compromise.

Why the Stakes Are Higher in Our Industry

Regulators demand explanations that dashboards cannot provide. The NAIC AI Model Bulletin and Colorado SB21-169 require clear, contemporaneous provenance for AI-influenced decisions. Most underwriting intelligence lives in unstructured documents that analytics systems were never designed to handle. Renewal workflows are change-detection exercises that benefit enormously when both layers work together.

Where to Start

Insurers with mature analytics platforms do not need to start over, but they must:

1. Capture foundational events at the atomic level before aggregation.
2. Protect your existing analytics layer. It works for actuaries and regulators.
3. Build the ontology around one high-value use case, such as underwriting submission intake.
4. Apply governance that serves both layers.
5. Measure success separately: analytics by dashboard trust and reporting accuracy; AI by reduced hallucinations, explainability and business impact.

The Bilingual Insurer

The carriers that will lead the next decade are fluent in both data languages: one that helps humans trust and explain the past, the other that enables machines to reason about the future with transparency and confidence.

Your current analytics investment remains essential. It is also insufficient for AI. Building a parallel ontology capability is one of the highest-leverage moves you can make today. Closing the gap will unlock better risk selection, stronger regulatory resilience, lower fraud losses and genuinely intelligent operations.

The insurance industry is one built on history and resilience, but it's also one where the future is facing immense uncertainty.

By the end of 2026, an estimated 400,000 insurance professionals will have retired in the U.S. since the beginning of 2021, according to the Bureau of Labor Statistics. At the same time, nearly one-third of the current global population is Gen Z. And yet, 79% say they've never considered working in insurance due to perceptions of the industry being "boring" or too corporate. Right now, the industry is facing a major workforce shortage that could have the same consequences for our stability as any underwriting cycle or catastrophe trend.

This disconnect is a structural risk to the industry's ability to operate, innovate and respond to crises in the years ahead.

The perception problem is now a workforce problem

For decades, insurance has struggled with an image issue. Despite the passing of the so-called "Great Resignation," the insurance industry continues to face significant workforce challenges.

In an industry survey, Gen Z was asked to identify business sectors that they found the most appealing to work in, and insurance came in last.

Not to mention that this generation is bringing a new meaning to work. One of those is finding a greater purpose in the work itself. However, Gen Z doesn't associate the insurance industry as one that could provide that purpose. Not to mention they want to work in a fun, social environment ... and the perception of the industry is the opposite for most.

It's fair to say that these perceptions are working against the industry.

In reality, insurance is one of the most human industries that exists. It shows up at the most critical moments in people's lives. Most people don't picture it in this way, but insurance is the industry that helps families rebuild after disasters, enables small businesses to survive disruptions and plays a major role in addressing systemic risks like climate change. Yet we continue to present it externally as a series of processes rather than outcomes.

At the same time, there's also an expectation that work environments are dynamic and technology-forward. When those expectations collide with outdated perceptions of insurance, the result is simple. The talent looks elsewhere.

Why talent gaps are becoming operational risks

What makes this moment different is not just the scale of retirements but the nature of the skills leaving the industry.

Insurance has always been a knowledge-driven business. Institutional expertise is the foundation of underwriting decisions, claims handling and client relationships. Still, as experienced professionals exit the workforce, much of that knowledge is at risk of being completely lost or only partially transferred.

The industry is also being asked to evolve faster than ever. Volatility just keeps happening. For example, climate-driven events are increasing in frequency and severity, not to mention cyber risk, supply chain disruption and emerging technologies are introducing new categories of exposure. Meanwhile, customers are expecting faster and more transparent service in real-time.

This is where the talent gap becomes a direct threat to performance.

Without a steady pipeline of new talent, insurers are facing three immediate challenges. First, claims handling capacity becomes strained during surge events, leading to slower response times and diminished customer trust. Second, the adoption of technologies like AI and advanced analytics slows, not because the tools are unavailable, but because the workforce lacks the capacity or skills to implement them effectively. Third, innovation stalls, as fewer cross-disciplinary thinkers enter the industry to challenge legacy approaches.

Because of this, workforce shortages are no longer an HR issue. They become core drivers of operational risk.

Reframing insurance careers for a new generation

If the problem is misalignment between perception and reality, then the solution starts with how we present the industry and how we design the actual employee experience behind that message.

The first shift is reframing the purpose of the work. Insurance organizations need to move beyond describing roles in terms of tasks and instead clearly articulate the impact. Processing a claim is not an administrative function; it's helping someone recover from loss. Underwriting isn't just risk selection; it's enabling economic activity and resilience. If we can't clearly communicate why the work matters, we shouldn't expect younger generations to see its value.

The second shift is making the modern reality of the work visible. Inside many organizations, workflows are already evolving. Automation reduces manual processes. AI is supporting decision-making. Data is becoming central to operations. Yet externally, candidates picture fax machines and cubicles. Bridging this gap requires intentional storytelling and transparency about how the work is actually being done today.

The third is creating clearer and faster paths for growth. Gen Z isn't interested in climbing the ladder and waiting more than 10 years to "become important." They want to understand how they can develop skills and take on new responsibilities within the first one to two years. This requires developing more structured progression frameworks, exposure to different parts of the business and earlier involvement in meaningful decision-making.

Finally, the industry needs to address its reputation directly. There's skepticism from younger generations around complexity, transparency and claims outcomes. Ignoring it will only reinforce distrust. Organizations that acknowledge these concerns and demonstrate how they're improving will be the ones that win over talent, and even customers.

At the end of the day, this all boils down to an alignment problem, not marketing. If your employer brand says "innovative, flexible, purpose-driven," but the actual experience feels slow and transactional, Gen Z will spot that immediately and opt out.

Workforce planning is risk management

All of this leads to a broader point that the industry has not fully embraced. Workforce planning should be treated as a risk management priority.

In insurance, we are disciplined about identifying and managing exposures. We model catastrophe risk. We monitor market volatility. We stress-test portfolios. Yet we've historically approached workforce planning as a functional responsibility rather than a strategic one.

This approach no longer works and cannot continue.

Everything in insurance (i.e., growth targets, service commitments, etc.) ultimately depends on having the right people with the right skills. When that foundation weakens, the impact is immediate. Service levels decline. Innovation slows. Risk exposure increases.

Forward-looking organizations are beginning to recognize this and are integrating workforce considerations into broader risk frameworks. They're mapping critical roles against future business needs. They're identifying where skills gaps are likely to emerge and invest ahead of them. They're rethinking talent models, including how and where work gets done, to ensure resilience at scale.

The bigger picture is ensuring the business can operate reliably under pressure.

The path forward

The insurance industry doesn't have any issues being relevant. What it does have an issue with its perception.

The opportunity in front of us is significant. We have a chance to redefine how a new generation sees this industry and, more importantly, how it experiences working within it. Doing so will not only address current workforce challenges but also position insurers to be more adaptive, innovative and resilient in the face of future risks.

The organizations that move first will have a distinct advantage. They will attract the talent that others struggle to reach. They will build the teams capable of navigating increasing complexity. And they will be better equipped to deliver on the fundamental promise of insurance: stability in an uncertain world.

In 2026, changing the narrative around insurance jobs is not optional. It is a prerequisite for the industry's long-term growth and stability.

The conversation around legal AI usually follows a predictable script about BigLaw billable hours, the democratization of small firms, or whether an LLM can pass the bar. These debates focus on the visible front lines—the lawyers and the courtrooms. But focusing there means you're watching the wrong game. The real transformation is happening deeper within the economic engine of civil litigation, driven by the insurance carriers. And on the other side, the plaintiff bar is arming up just as fast.

McKinsey estimates generative AI could unlock $50–70 billion in insurance industry revenue. Bain found that 78% of P&C insurers are already using generative AI—though only 4% have scaled it meaningfully. The arms race is underway. Most litigators just haven't noticed the battlefield has moved.

From Colossus to LLMs

Carriers have used algorithmic case valuation for 30 years. The best-known tool, Colossus, is a rules-based system with over 10,000 decision rules, relying on structured inputs, including ICD codes, CPT codes, and severity ratings. If something wasn't in a form field, the algorithm was blind to it. What many practitioners don't realize is that Colossus is reportedly still used by over 70% of insurers. If you've negotiated a bodily injury claim in the last decade, your demand was likely run through it or a similar tool on the other side of the table.

Colossus generated over $293 million in class action settlements and sustained NAIC scrutiny over the "black box" problem of algorithmic valuation. That history matters, because the next generation of these tools is far more powerful and far less transparent.

LLMs are the leap. They don't need structured fields. They ingest the entire case file, medical records, deposition transcripts, and police reports, and spot nuance a rules engine never could. The gap between what a carrier knows about a case and what a plaintiff's attorney knows has always been a matter of leverage. That gap is narrowing fast. Vendors such as Shift Technology, CLARA Analytics, DigitalOwl, and Wisedocs are deploying LLM-driven analysis at scale across the carrier ecosystem. Meanwhile, carriers from Allstate to Chubb are building proprietary tools internally.

The Data Moat Is Eroding

The carrier's deepest advantage isn't computing, it's context. Carriers sit on millions of closed claims, private settlements, and internal outcomes that never see a public docket. A carrier AI doesn't just know what a jury in Cook County did last week; it knows what the carrier paid to settle 10,000 similar cases over the last decade without a trial. That training data is unique.

But the moat is narrowing. CLARA Analytics operates a contributory database trained on millions of closed claims across its carrier clients. On the plaintiff side, EvenUp, now valued at over $2 billion, has crowdsourced actual settlement data from over 2,000 plaintiff firms processing roughly 10,000 cases per week. The information asymmetry that defined carrier leverage for decades is real, but both sides are now building proprietary data assets. The gap is closing.

When Models Argue With Models

This isn't hypothetical any more. In January 2026, a startup called Mighty launched a platform that acts as an AI agent negotiating personal injury settlements against carrier AI on behalf of consumers. Its CEO stated plainly: the company gives consumers AI to negotiate with the insurance company's AI. This builds on decades of automated dispute resolution. Cybersettle alone has facilitated roughly 200,000 claims totaling $1.4 billion using algorithmic double-blind settlement since the late 1990s.

Now imagine the next step. A plaintiff firm's AI evaluates a case at $850,000 based on crowdsourced settlement data. The carrier's AI, trained on 40 years of internal claims history, pegs it at $320,000. Does a shared analytical baseline strip away posturing and accelerate resolution? Or does it entrench positions because each side treats its own model as truth? We risk moving from a world of legal judgment to a world of model drift, where outcomes depend less on case facts and more on whose training data runs deeper.

Regulators Are Already on the Case

When a carrier's AI determines a claim is worth zero, how does a plaintiff challenge that logic? Regulators have been working on this since at least 2021. As of early 2026, at least 25 states plus D.C. have adopted the NAIC's Model Bulletin on AI, requiring written governance programs, consumer notice when AI affects decisions, and bias testing. Colorado has gone further, SB 21-169 requires quantitative bias testing for AI used in claims handling, with enforcement tools including civil penalties and license revocation. The black box problem is real, but it's an active regulatory battleground, not an open question. Practitioners who don't understand the compliance landscape their opponents operate under are leaving leverage on the table.

Nuisance Value

If carrier AI gets better at early case triage, the economics of "nuisance value" - paying $5,000 to make a weak claim go away rather than litigating - could shift. Claims that used to settle for small sums may face an automated "no." But let's be honest: there is no published empirical evidence that AI triage is currently eroding nuisance settlement patterns. This is a plausible hypothesis, not an observed trend. And the counter-argument has merit, if AI reduces per-claim evaluation costs, carriers might become more willing to pay small amounts quickly, not less. Conversely, if a model flags a case as high-exposure early, carriers have every incentive to settle fast rather than lowball a claim they're likely to lose at trial.

A New Equilibrium?

Law is an adversarial system. When one side upgrades, the other responds. Carriers are deploying AI across claims processing, litigation prediction, and settlement valuation. The plaintiff bar is responding in kind. Contributory databases are eroding data monopolies. Regulators are imposing transparency requirements that may force carriers to show their work in ways they never have before.

The question for litigators isn't whether AI will change how cases are valued. It already is. The question is whether you understand what's in the black box on the other side of the table—and whether you have your own.

In recent years, a discontinuity has been emerging in health insurance related to when the need for healthcare becomes observable and manageable.

The evolution of clinical practice—across early diagnosis, continuous monitoring, and risk management—is shifting the point at which the insured enters the care pathway, introducing an intermediate phase between a state of health and a manifest clinical event.

This shift transforms need from a discrete event into a process distributed over time, characterized by progressive signals and increasingly frequent follow-up activities.

Impact on the Insurance Model

The health insurance model remains built around formal triggers and codified benefits. It works when need translates into clearly identifiable actions—procedures, hospitalizations, therapies—but is less structured in the phase where need is still forming. This creates a misalignment: medicine generates demand for care earlier and continuously, while coverage activates when that demand takes on a defined and reimbursable form.

The anticipation of need emergence is not neutral for the portfolio. The increase in screening and monitoring translates into higher frequency of services in early stages: more visits, tests, and follow-ups. The critical issue is not only the increase in frequency, but its nature: it is more difficult to model, as it is linked to distributed behaviors rather than discrete events. The phenomenon is already observable in recurring signals within portfolio data: increased outpatient frequency, longer claims sequences, and greater dispersion between frequency and severity. In this context, leakage phenomena also emerge; services that are in fact preventive are formally classified, through prescription, as reimbursable visits or diagnostic tests. This is not fraud, but a dynamic that shifts volumes into covered areas, making frequency more difficult to interpret.

Diagnostics and Predictive Models

Diagnostic and therapeutic innovations are reducing the informational value of point-in-time measurements used at underwriting (such as BMI, blood glucose, and cholesterol), which on their own are no longer sufficient to represent risk. With the introduction of new therapies, the risk profile assessed at policy inception can change significantly over time: it may decrease rapidly in some cases, or increase, especially in the presence of treatment discontinuation.

Diagnostics and predictive models increase information asymmetry: insured individuals have better knowledge of their own risk than the insurer. Direct-to-consumer tests, such as genomic testing, may lead higher-risk individuals to insure themselves more (anti-selection) and at the same time make premiums less sustainable for these segments.

This is compounded by growing economic pressure: some innovative therapies have costs so high that they challenge the ability of traditional insurance models to absorb them.

Consistency Between Offering Model and Portfolio Impact

Finally, this shift introduces an issue of consistency between the offering model and technical sustainability, which already emerges at the level of portfolio analysis. If an increasing share of activity takes place in this intermediate phase and becomes reimbursable through formal classification as covered services, the risk is that part of the cost is already reflected in the loss ratio without being explicitly recognized as such. This can contribute to a gradual erosion of technical balance and make pricing more complex. For the insurer, the issue is not only whether to extend coverage, but whether the portfolio is already exposed to these dynamics without having been designed and governed accordingly.

This misalignment translates into a gap between the moment when need emerges and the activation of coverage: a space in which need is already clinically relevant but not yet insurable.

In this context, prevention takes on a different role. From an ancillary lever, it can become a point of activation of the insurance relationship along the healthcare pathway, intercepting needs that are not yet formalized. Its integration, however, introduces technical complexities. In particular:

• the different time horizon between prevention and policy: economic benefits are distributed over multi-year periods and often exceed the contractual duration.
• variability of engagement: effectiveness depends on the insured's adherence, often heterogeneous.
• dispersion of economic return: ROI varies by condition, target population, and intensity.

In this context, the design of health coverage cannot ignore the point at which need emerges along the care trajectory, because it is in that phase that a growing share of costs is generated, often already present in the numbers but not explicitly recognized. The issue is not whether to intercept these needs, but whether the portfolio is already exposed to such dynamics without having been designed and governed accordingly. From this perspective, prevention services are not an extension of the offering, but a potential lever to manage frequency and usage dynamics that are already underway.

Let's be honest: Consulting—my trade for over two decades—has historically been a business of selling inputs while clients absorb the risk. Expertise, frameworks, transformation road maps, all cleanly packaged. Outcomes? Implied, not guaranteed.

AI is changing that, and fast. Insights are suddenly cheap, free even. Sophisticated technology skills are becoming commoditized.

Enterprise buyers are, understandably, thrilled. For the first time, they have the leverage to demand what they always wanted: not recommendations, not capability decks, but results. Measurable, contracted, financially enforced results. No more change theater. Actual change.

More control. Less risk. Pay for outcomes, not effort. What's not to like?

Here's what: The moment you demand outcomes, you force the people selling them to build machines that produce those outcomes, reliably, repeatedly, and at scale. And those machines don't just outperform bad consulting. Eventually, they may outperform you.

If we're being honest about consulting, let's be equally honest about enterprise inertia. Organizational drag, legacy processes, fragmented systems, incentives that reward preservation over performance. In some corners of the enterprise, inertia isn't a problem to solve; it's an asset to protect. Entire roles and hierarchies exist to navigate systems that no one has true incentives to fix.

Demanding guaranteed outcomes implicitly invites someone else to build a better version of your operation. Cleaner, faster, more instrumented, continuously improving, and unencumbered by your history.

Consulting firms that embrace this shift won't look like consultants for long. They'll look like operators running increasingly sophisticated, domain-specific machines, systems that improve with every client, every dataset, every cycle.

And when that machine exists, when it can process claims faster, manage revenue cycles more accurately, underwrite risk more profitably, the boardroom conversation changes. Not "should we take their advice?" but "why are we still doing this ourselves?"

Boardroom decisions will shift from sourcing to sovereignty as the definition of "core" narrows and operational borders shrink.

The consulting business has always been episodic, project to project, relationship to relationship. Promise to promise, you could say. But the model that let a thousand PowerPoint decks bloom while accountability stayed conveniently offshore is getting a reckoning. What's emerging is the opposite, and it has real teeth: firms that own problems, scale solutions, and compound returns with every engagement.

Selling recommendations is out, and delivering results is in. "Trusted advisor" either earns its keep or gets exposed as a title on a lanyard. The firms that win won't just be smarter. They'll be true operators, builders, partners with skin in the game, working alongside clients to eliminate the bureaucratic friction that everyone always knew was there and nobody had the incentive to touch.

The insurance industry keeps asking whether artificial intelligence will replace brokers. I think that is the wrong question. The better question is why does the market still need brokers in the first place?

I work with brokers every day across submissions, renewals, and placements. One thing becomes obvious very quickly: two similar risks rarely look identical to an underwriter.

Same operations. Similar exposures. Comparable loss history.

Yet one submission moves. The other stalls.

That difference is not always the risk itself.

It is how the risk is represented.

THE ROLE WE DON'T TALK ABOUT

The industry often describes brokers as intermediaries—connecting agent/clients to carriers, negotiating terms, and moving submissions through the process.

That description is not wrong. It is simply incomplete.

Brokers are the risk-representation layer of the insurance market.

They take something messy and real operations, exposures, controls, loss history, client explanations, incomplete documentation — and turn it into something a carrier can evaluate, price, and act on.

Carriers do not underwrite reality directly.

They underwrite representations of reality.

WHAT THIS LOOKS LIKE IN PRACTICE

We see it in everyday submission work. One account arrives clear, structured, and coherent. The exposure story is easy to follow. The loss history makes sense. If there were prior issues, they are explained with context: what happened, what changed, and why the account should now be viewed differently.

Another account may involve a very similar risk, but the submission feels fragmented. Details are missing. Insurable values do not fully line up. Losses are listed, but not explained. The story is not necessarily wrong, but it is not complete enough for the market to fully trust.

Both accounts go to market. They do not get treated the same.

Not because the underlying risk is always different, but because the representation of the risk is different.

And someone must bridge that gap. That someone is the broker.

WHY THIS LAYER EXISTS

If risk could be captured once, structured consistently, and reused across carriers and renewals, this layer would shrink. But that is not how the market works.

The same risk is re-described again and again. Information is re-entered, reformatted, and reinterpreted. Loss narratives shift depending on who prepares them. Each carrier may see a slightly different version of the same account.

Every submission becomes a reconstruction.

Brokers absorb that complexity. They align the client's reality with what each carrier needs to see. They fill gaps, reconcile inconsistencies, and shape the narrative so it is credible and complete.

That is not just intermediation. It is translation.

WHY AI HAS NOT REPLACED THIS

AI is already helping the industry move faster. It can extract data from documents, pre-fill applications, flag inconsistencies, summarize files, and support communication.

But speed is not the same as understanding.

If the underlying risk representation is incomplete, inconsistent, or poorly structured, AI simply processes that weakness faster. It may move the submission along, but it does not necessarily make the risk clearer, more credible, or more accurately understood.

That is the layer many technology conversations miss.

The problem is not only that insurance work is manual. The deeper problem is that risk information is often reconstructed from fragments every time it moves through the market.

"AI can improve the workflow around that problem, but it does not solve the problem itself"

THE REAL DIVIDE HAPPENING NOW

What is changing is not whether brokers matter, but which brokers matter.

Some brokers operate transactionally. They move documents, chase quotes, and respond to carrier requests as they come.

Others are much more deliberate about how risk is presented. Their submissions are structured. Their narratives are consistent across cycles. They understand how underwriters interpret information, and they build credibility through clarity, accuracy, and context.

The difference shows up quickly. As underwriting becomes more data-driven, the quality of what enters the system matters more, not less.

THE HIDDEN ASSET

The strongest agencies are not just relationship driven. They are consistent in how they present risk. When a well-prepared submission comes in, you can feel it. It is easier to understand, easier to trust, and easier to price. That does not happen by accident. It is built over time through disciplined data collection, clear documentation, and repeatable submission quality.

You could call this risk representation capital — the accumulated ability to present risk in a way the market can reliably interpret.

That kind of capital is becoming more important.

Not because brokers need to resist technology, but because technology makes weak representation more visible.

WHAT THIS MEANS

The future of brokerage will not be decided only by who adopts AI first. It will be decided by who controls the quality of risk representation.

Because that is what determines how risk is understood, how quickly it moves, and how confidently it is priced.

Until the insurance market has a stable, trusted way to represent risk across time, carriers, and renewals, brokers will remain essential.

"The broker of the future will not simply place risk. They will shape how the market understands it."