The middle market remains one of the most persistent protection gaps in the insurance industry. Middle-income households face meaningful financial exposure yet remain underinsured across life and annuity (L&A) insurance. Traditional insurance distribution models were designed for high-touch sales, longer underwriting cycles, and relatively high premiums. These models struggle to operate efficiently for lower-margin products that require speed, simplicity, and flexibility. As a result, many middle-market customers remain unserved, not because insurance is irrelevant but because existing distribution models cannot reach them at scale.

Traditional Distribution Model Wasn't Built for the Middle Market

Insurance has long been described as a product that is sold rather than bought. This dynamic becomes especially pronounced in the middle market, where customers often lack the urgency or familiarity that would prompt purchasing through conventional channels. At the same time, the economics of agent-led distribution are poorly suited to products with lower premiums and shorter lifecycles.

Middle-market customers typically require coverage that can be purchased quickly, adjusted over time, and delivered within digital experiences they already use. Traditional models, built around lengthy sales processes and manual servicing, are misaligned with these expectations.

How AI Makes Embedded Distribution Scalable

Embedded insurance is no longer a niche distribution experiment. Recent analysis estimates global embedded insurance sales of $87.4 billion, projected to grow at a 20% CAGR from 2023 to 2032. The channel is maturing quickly, but its strategic significance is not simply reach. It is that embedded models shift how protection is priced, sold, and supported when coverage is delivered inside third-party ecosystems rather than insurer-owned journeys.

As outlined by RGA, embedded programs generally take three forms. Soft-embedded (opt-in) insurance is presented contextually at the point of a primary purchase, such as travel insurance offered during flight booking. Hard-embedded (opt-out) insurance is included by default within a broader transaction, requiring the customer to actively decline coverage. Invisible insurance is embedded so deeply within the primary service that coverage is automatically activated based on participation in the service.

While embedded distribution expands access, it also introduces new operational pressures. Embedded products are high-volume, lower-premium, and event-driven, which compresses decision timelines and pushes servicing costs closer to economic limits. Servicing expectations remain high, even as tolerance for manual intervention declines.

This is where AI plays a critical role. Embedded insurance changes not only where insurance is offered, but how protection is evaluated, quoted, bound, and serviced. For insurers and MGAs, AI is applied across distributed journeys to coordinate decisioning, data access, and workflow execution across underwriting, policy issuance, servicing, and claims.

Applied at the workflow level, AI aligns automated decisions, business rules, and human oversight within a single operating flow. This supports consistency, traceability, and governance as products scale across partners and channels.

Embedded insurance only succeeds when the experience is seamless. For bite-sized products, this translates into near-100% straight-through processing at purchase and a largely touchless claims and servicing experience thereafter. AI enables this by coordinating real-time data access, automated decisioning, and workflow execution across the lifecycle, allowing insurers to scale volume without increasing operational friction.

Embedded Insurance in Practice

These models place different demands on insurers across product design, distribution, servicing, and compliance. They also reset expectations around speed, simplicity, and relevance, particularly in middle market segments where traditional distribution often struggles to operate economically. Boston Consulting Group has cited Prudential Financial’s Simplified Solutions initiative with Neutrinos as an example of how AI-enabled, embedded distribution models can expand access to life insurance in North America.

Market sizing reinforces the upside: Forrester estimates that building new solutions for just 1% of the roughly 4 billion underserved people globally could translate into approximately 40 million new customers, if insurers can deliver simpler products through scalable journeys.

Closing the Gap at Scale

Embedded insurance is increasingly proving itself as a scalable distribution model rather than a niche channel. However, distribution alone is insufficient. Insurers that invest in the operational foundations required to support high-volume embedded products, including AI-enabled workflow orchestration, explainability, and integration with existing systems, are better positioned to improve access and close long-standing protection gaps in the middle market.

70% of vehicle collisions are caused by inattention, including distraction, cell phone usage, and fatigue. Most of this risk develops silently, unseen by fleet managers and insurers alike, only potentially becoming visible once it has resulted in a claim.

That reality exposes a growing structural weakness in commercial motor insurance. If the majority of collision risk forms upstream of loss, underwriting frameworks built primarily on historical claims data are, by definition, incomplete. In a market grappling with rising severity, social inflation, and earnings volatility, this gap is no longer theoretical. It is material.

The industry's next competitive advantage will come from seeing risk earlier and acting on it before loss occurs.

Risk begins long before first notice of loss

For decades, underwriting has relied on lagging indicators such as loss runs, experience modifiers, and aggregated exposure metrics. These tools remain necessary, but they describe outcomes rather than causes. In commercial auto, collisions are rarely random events. They are typically preceded by identifiable behavioral patterns interacting with vehicle dynamics and environmental conditions.

Until recently, those precursors were largely inaccessible to insurers. Risk could be priced after the fact, but not meaningfully influenced in advance.

From descriptive telematics to predictive intelligence

Early telematics solutions represented an important step forward, providing visibility into speed, harsh braking, acceleration, and location. These signals improved transparency and gave insurers better behavioral proxies, but they remained descriptive. They explained what had already happened rather than what was about to happen.

Predictive artificial intelligence fundamentally changes that relationship with time.

By analyzing multiple contextual signals simultaneously, including driver attentiveness, following distance, vehicle movement, and road conditions, advanced AI systems can identify elevated collision risk as it forms. Crucially, this intelligence can be acted upon in real time, alerting drivers in the critical seconds before a potential impact. While that window is narrow, it is often enough to change the outcome entirely.

At Nauto, for example, AI models trained on more than 6 billion miles of global driving data have demonstrated the ability to detect imminent collision risk with over 99% accuracy, validated through independent research. In practice, interventions typically occur two to four seconds before the triggering event. Those few seconds frequently determine whether an incident becomes a near miss or a loss event.

This represents a shift from measuring perceived risk to actively influencing outcomes, and it has profound implications for underwriting.

What this changes for underwriting

Predictive behavioral intelligence introduces a new variable into the underwriting equation, real-time risk quality. Rather than inferring exposure solely from historical outcomes, underwriters can now access leading indicators of attentiveness, distraction, and behavioral discipline.

This capability is particularly valuable in portfolios with limited claims history, rapidly evolving operations, or exposure to emerging risk factors where backward-looking data provides limited guidance. Pricing becomes more responsive and more defensible. Fleets that demonstrate sustained behavioral improvement can be differentiated with greater confidence, while persistent risk signals can be addressed earlier through pricing, terms, or targeted intervention.

The result is a move away from portfolio-level averaging toward a more granular assessment of how risk is actually created.

For MGAs, predictive intelligence enables prevention to be embedded directly into product design, aligning delegated authority with real-world risk outcomes. For brokers, it strengthens the advisory role by grounding renewal conversations in objective, forward-looking evidence rather than retrospective explanation. Across the value chain, assumption is replaced with observation.

The economics of prevention

Prevention does not simply reduce claim counts. It changes claim outcomes.

Across fleets deploying Nauto's predictive AI, collision frequency reductions of between 40% and 60% are consistently observed. Importantly, the effect does not stop there. When collisions are avoided entirely, claims disappear. When incidents do occur, earlier intervention often reduces speed at impact, point of contact, and loss complexity, leading to materially lower claim severity.

This dual effect, fewer claims and less severe claims, alters loss cost trajectories in a way traditional risk controls rarely achieve. Secondary costs decline alongside primary losses. Vehicle downtime is reduced, supply-chain disruption is minimized, litigation exposure falls, and operational volatility softens. Claims that do occur are resolved faster and with greater confidence when supported by contextual video and AI-derived insight, reducing frictional cost and uncertainty.

At scale, these dynamics stabilize combined ratios and improve capital efficiency. In a market where margin expansion through pricing alone is increasingly constrained, prevention offers a durable alternative grounded in operational reality rather than actuarial optimism.

From recovery to partnership

This shift reflects a broader evolution in the role of insurance. Predictive AI does not replace underwriting judgment or risk management expertise. It enhances them. It allows insurers, brokers, MGAs, and fleet operators to share a clearer, real-time understanding of risk as it actually unfolds, rather than reconstructing it after the fact.

The insurers best positioned for the next decade will be those who underwrite behavior rather than history, reward prevention rather than recovery, and treat intelligence as something to be acted on, not archived. In an environment where risk is increasingly dynamic and unforgiving, resilience will belong to those who can see loss forming and intervene before it ever reaches the balance sheet.

Prevention is no longer an aspirational ideal. It is becoming a defining capability, and increasingly, a prerequisite for sustainable underwriting performance.

The hybrid fronting model is gaining traction in the re/insurance market, driven by a shift toward deeper risk alignment and the rapid expansion of MGAs.

As of 2026, approximately 25 major fronting carriers now operate in the U.S., with new ones launching frequently. The surge is tied to the expansion of MGAs, the tightening of reinsurance capacity, the growing need for efficient capital, and the availability of insurtech-enabled data modeling. Gallagher Re reports that hybrid fronting carriers generated nearly $28 billion in gross written premiums by the start of 2025, a significant portion of the total $100 billion-plus MGA market. A 2025 TMPAA survey reveals that 19% of program administrators now use hybrid fronting models for their operations.

What is a hybrid fronting carrier, and why is this model taking off now?

DEFINING A NEW MODEL

Hybrid fronting carriers are fully licensed and regulated insurance entities that provide rated paper to MGAs and program partners while retaining a share of the underwriting risk on their own balance sheets. Their retained portion of risk is typically 5% to 30%, and rated insurance paper is provided to MGAs/MGUs, captives, and programs. Hybrid fronting carriers use reinsurance or alternative capital to cover the remaining risk and maintain strong capital partner relationships with private equity firms or insurance-linked securities (ILS) to fund growth and operations.

Unlike traditional fronting carriers, which pass all risk along to reinsurers, hybrid fronting carriers retain a meaningful portion of exposure. In doing so, they share the risk and the incentives with other stakeholders across the insurance lifecycle. That retained risk drives tighter alignment with reinsurers and capital partners and positions hybrid fronts as not just enablers, but committed participants in the value chain. In other words, hybrid fronting carriers have more "skin in the game" than traditional fronting models. Ultimately, this sharing of risk results in more disciplined underwriting and oversight.

The hybrid fronting carrier model is becoming a more attractive option in the MGA and specialist program space, especially for launching new, niche, or complex insurance products quickly.

KEY DRIVERS OF THE HYBRID FRONTING CARRIER

There are multiple drivers behind the rise of the hybrid fronting carrier model. At the top of the list is capacity. Traditional reinsurers have been reducing capacity in challenging insurance markets, such as California, Texas, and Florida, where NatCat claims, including hurricanes, floods, and wildfires, have led them to reduce their exposure to tail risks. Others are pulling out of these markets entirely. As a result, demand for alternative capacity is rising as traditional insurers shift risk appetite or exit markets.

Hybrid fronting carriers are also filling an expanding gap in the excess & surplus (E&S) insurance market by serving as a bridge between specialized, high-risk, or niche business produced by Managing General Agents (MGAs) and the risk-bearing capital of reinsurers.

Another key driver is the acceleration of MGA growth, not just in the U.S., but globally. MGAs are growing rapidly and seeking flexible partners to develop new products and expand capacity. Hybrid fronting carriers supply MGAs with the rated paper and shoulder some of the risk, enabling them to launch niche or specialist programs, like cyber risks or complex casualty lines, at speeds much faster than traditional carriers with legacy systems. They are essential for navigating "volatile and emerging risks" where traditional insurers may have pulled back. This is crucial to MGAs' success, where the first to market with an innovative product wins the race.

Alternative capital fills these gaps for E&S insurance organizations and MGAs left by shrinking traditional supply. For capital providers, such as private equity and ILS investors, the hybrid model provides an efficient, scalable way to enter insurance markets without partnering with a traditional, full-scale carrier. Additionally, it streamlines cross-border expansion for MGAs by managing complex local licensing and regulatory compliance. Private equity and ILS investors are interested in hybrid fronts as capital-efficient, lower-risk insurance platforms.

BENEFITS FOR ALL PARTIES

Who wins with this model? Basically, all parties involved benefit. Hybrid fronting carriers present significant benefits to MGAs, program managers, and reinsurers. A shared alignment of interests ensures better underwriting and oversight, faster market access, and greater capital efficiency.

Other key benefits include:

• Multi-year capacity stability: While traditional reinsurers are tightening capacity, hybrid fronts can access a broader investor base. MGAs can reduce their reliance on a small group of traditional reinsurers.
• Faster product launches and distribution: Hybrid fronting carriers provide the necessary AM Best or S&P rating and state licensing required for MGAs to write business immediately. This bypasses the multi-year process an MGA would otherwise face to become a standalone licensed insurer.
• Access to reinsurance and capital markets: Unlike traditional insurers, which can face rigid internal governance, hybrid fronts act as conduits to global reinsurance markets. This provides MGAs with a broader pool of capital to back specialized or niche programs.
• Higher transparency and potential for profit-sharing between stakeholders: Unlike traditional models, where data is exchanged in fragmented silos using PDFs or monthly reports, modern hybrid carriers are built on technology and use unified underwriting command centers, making them extremely transparent. Because the hybrid front shares in any losses, it has a financial incentive to perform the same level of due diligence as a standard carrier, including thorough analytic and exposure reviews.
• Lower cost base compared to traditional carriers: The best hybrid fronts operate as lean, technology-driven entities that delegate expensive operational functions to specialized partners.

Fronting carriers play a crucial role in enabling MGA distribution by providing regulatory paper, compliance frameworks, reporting mechanisms, and risk-sharing structures necessary to launch new programs.

TECH-FIRST HYBRID FRONTING CARRIERS WILL WIN

However, with this model comes new complexity. Hybrid fronting carriers often manage numerous MGA relationships, demanding coordination of complex capacity flows, diverse systems, multi-territory compliance, and increasing calls for reporting clarity. These operational and regulatory demands frequently exceed the capabilities of older, legacy carrier infrastructure.

To thrive in this environment, hybrid fronting carriers require more than rated paper, risk, and capital. They'll need modern infrastructure that can support:

• Real-time digital dashboards provide immediate visibility into MGA, program, and portfolio performance, enabling faster, data-driven underwriting decisions and proactive intervention on underperforming segments. Because hybrid fronts have their own capital at stake, they need this transparency to effectively manage their own risk.
• Data transparency for reinsurers, regulators, and capital partners alike fosters trust, ensures compliance, and strengthens long-term capacity relationships.
• Automated bordereaux processing and streamlined delegated authority workflows reduce operational overhead, boost accuracy, and ensure audit readiness. Further, using a single, shared platform eliminates the need for manual bordereaux reporting, giving hybrid fronting carriers better data and a single source of truth for their reinsurance partners.
• Integration with reinsurers, TPAs, and third-party systems that streamline operations, accelerate speed to market, and enhance collaboration across the entire value chain.
• Scalable platforms that accommodate multi-entity, multi-jurisdiction operations — supporting rapid growth across regions, lines of business, and regulatory regimes without losing control.

With hybrid fronting carriers, technology isn't a support function—it's part of their very structure as an organization. It's an enabler of profitable growth, faster onboarding, and regulator-ready reporting. Carriers that invest early in building this connected foundation will be best positioned to scale. And those that can scale will be the most successful as they court private equity and ILS investors while bringing innovative MGAs and risk products to market.

Many insurers are in an AI pilot purgatory, where promising experiments rarely scale into everyday operations. The models perform adequately, and the business cases hold up. The primary barrier is data architecture. Systems built for reporting and analytics simply cannot support the demands of production AI.

Core insurance decisions depend on synthesizing information from multiple sources. Underwriters assess application data, loss histories, external data, and regulations to evaluate risk. Claims handlers review photos, repair estimates, medical notes, and witness statements to settle cases. Investigators pull together scattered, sometimes conflicting information to pursue recovery. Transforming this expertise into AI capability requires data architecture that supports learning, generation, and contextual requirements.

Why traditional architectures fall short

Organizations have long separated day-to-day transaction systems from analytical warehouses. This division supported dashboards and compliance reporting effectively. However, AI blurs these boundaries because it learns from historical patterns to make real-time operational decisions.

When AI evaluates a new claim, it needs current policy data, historical loss patterns, regulatory requirements, and market conditions simultaneously. It needs real-time transactional data integrated with comprehensive historical context.

Unstructured documents create an even larger hurdle. Applications, claims notes, legal filings, and reports hold the most valuable intel for decision making. Many architectures treat this as a mere storage challenge. AI needs to understand documents at a much deeper level - identifying key elements, mapping connections, and pulling meaning in real time alongside structured records.

This matters most for complex workflows. When AI processes legal documents, filings, or investigation files, it can handle work that once demanded years of specialist knowledge. Document intelligence must sit at the same level as core transactional and analytical data in the architecture to enable this.

What AI-ready data architecture requires

Getting data ready for AI means building four specific capabilities that work together. Each closes a critical gap between how traditional systems operate and what AI applications need to work in production.

1. Bridging architecture and data silos

AI applications need access to policy systems, claims platforms, finance, and external data without long delays. This means real-time operational data alongside historical context, structured tables alongside document content, internal data alongside external feeds. This doesn't mean consolidating everything into one repository or platform. Focus on connecting data where it lives, with clear tracking of its path and origins. This architecture enables AI to navigate existing systems securely with proper lineage and control.

2. Capturing and using expert knowledge

Every time an underwriter overrides an AI suggestion or a claims handler adjusts an estimate, that action contains valuable knowledge. The capability to capture, curate, and organize expert feedback into training datasets separates competitive AI from generic tools. Raw corrections alone aren't sufficient. The architecture must support structured approaches that validate expert feedback, enrich it with context and reasoning, and organize it into training datasets that prevent bias while maximizing learning signal.

3. Managing context data for AI

Experienced underwriters or claims adjusters don't evaluate evidence in isolation. They build a growing understanding as new information arrives, drawing inferences, applying rules, and tracking reasoning. AI needs the same ability: to maintain and evolve understanding throughout a process. Context is this accumulated understanding that an AI system builds, stores, and shares as it works through a process. AI requires context as a managed data type with its own lifecycle, access controls, and transformation rules.

4. Creating data environments for AI development and testing

Moving AI from pilots to production deployment requires infrastructure that can provision realistic environments on demand. The data architecture must support replicating production data with appropriate privacy controls and generating synthetic data for edge cases. As AI programs scale across use cases and product lines, the ability to spin up multiple isolated environments lets teams work in parallel without interference. Provisioning environments quickly with realistic data, then tearing them down when complete, becomes critical infrastructure for scaling AI operations.

Competitive reality

AI will transform insurance operations. Organizations that address these data foundations build compounding advantages: faster decisions, greater accuracy, reduced leakage, and teams freed for higher-impact work.

Start with use cases where the business case is clearest. Focus on the data and capabilities those require, and build incrementally on current investments.

Thirty percent of companies will eliminate remote work this year, and 83% of CEOs globally expect a return to full-time office work in 2027, according to two recent reports. Many insurers will be among those heading back to the status quo pre-COVID. 

But a lot of employees are pushing in the opposite direction. They not only want flexibility on where they work. They want flexibility on when they work. 

We hear all the time about the hundreds of thousands of insurance industry employees reaching retirement age and about all the difficulties in attracting the talent needed to replace them, so I suggest we don't dismiss the desire for time flexibility out of hand. Yes, it runs counter to the management reflex that wants to bring everyone back to the office so they can be seen and managed as a cohesive group. But insurance desperately needs an influx of talent, and, as the saying goes, you attract more bees with honey than vinegar — or, more bluntly, beggars can't be choosers.

Clearly, many parts of the insurance process can't happen whenever an employee chooses to work. Agents and brokers need to be available, for instance, whenever a client needs them. But many underwriters and claims representatives could do their work based on a caseload, rather than on office hours, especially now that generative AI can track down so much of the data for them. 

Whether to offer more flexibility, not less, is worth a thought.

My interest in the topic of flexibility was piqued by a smart column by Matthew Fray at Quartz (which supplied the statistics I quoted in the first sentence). It says:

"Work-life balance has overtaken salary and compensation as the leading priority cited by 65% of office workers globally, up from 59% four years ago, said Peter Miscovich, co-author of the book The Workplace You Need Now, and the executive managing director and global future of work leader at JLL, the commercial real estate giant.

"Employees increasingly value control over when they work such as start and stop times, protected focus blocks, and predictable personal-time boundaries, more than additional workplace location choice, Miscovich said."

I realize I have a bias about flexibility, having worked remotely and pretty much on my own schedule since I left the Wall Street Journal in the mid-'90s. The productivity of a writer is also awfully easy to track. You either produce, or you don't. Even at the WSJ, where I worked office hours, if I went a couple of weeks without a byline, I might get a call that began, "Pa-uu-ll, this is your faaaaather. I'm just calling to make sure my son is still employed." (Thanks so much, Dad.)

But I do think flexibility attracts and retains top talent and is possible in many parts of insurance processes. I'm thinking, in particular, of claims and underwriting. An experience manager knows what a claims rep or underwriter should be able to handle, not just based on the number of cases but on their complexity, so it should be possible to let them work largely on their own time in their own place. I'm sure other processes can allow for at least some additional measure of flexibility, too.

People should still come to the office for socialization purposes. Training of newbies probably needs to be largely done side by side. And anything that requires frequent interaction between employees obviously needs to be done in the same place at the same time — Zoom eliminates some of the need for being in the same place but by no means all. 

I realize that, in many types of jobs, there's a fear that employees will slack off if they're not under close supervision, and that surely happens. But we also see how compulsive people can be about keeping up with their email and other work even during off-hours, so I'd bet some people — especially the talented and ambitious — would work even harder if motivated by more flexibility. 

I harken back to an interview I did with Scott McNealy, at the time the CEO of Sun Microsystems, in 2001. In the days before everyone had a laptop they carted to and from work, McNealy had spent quite a bit of money buying home computers for his 40,000 employees. He caught some grief for the expense but seemed to me to have a pretty good justification.

"I do not want somebody at 10 o'clock at night who can't sleep, who wants to work because there's nothing good on TV, to not have the full capability to do everything he needs to do to get the job done," McNealy said. 

Worth a try?

Cheers,

Paul

In recent conversations, Brian Poppe at Mutual of Omaha highlighted AI adoption in four stages – a transition from AI being a fun tool that employees use to a final stage where AI-driven processes are integrated for seamless workflows. And certainly directionally, this appears to be correct – AI use cases have historically been focused on addressing specific inefficiencies and then scaling, which will eventually lead to AI driven processes.

While there is an acknowledgment that AI is a rocket that will move insurance in a truly transformative way, carriers are treating AI as if you are putting a new engine in an old car: it performs better, but it is still driven the same way.

AI-driven processes raise an entirely different question – do legacy operating models make sense for insurance carriers in the age of AI? And the answer is – no, they do not.

When we think of an operating model, definitionally we should think of the way in which people, processes, technology, and capabilities are arranged within an organization to deliver value to customers. The evolution of AI will be AI assisting with a task to developing a process that is driven by AI. But operating models are derived from the assumption that processes are human-driven.

Consider the underwriting process and how the evolution of technology has changed it. Historically, you needed many underwriters to carefully review applications, assess the risk, and then provide a quote on pricing against that risk. RPA reduced the manual effort and increased productivity, but the process remained the same. Then automation and enhanced underwriting (e.g., algorithmic, usage-based, simplified, etc.) were implemented to provide faster underwriting, but the organization did not necessarily change to reflect these changes. Instead, carriers have viewed this from the lens of capacity and workforce management.

But if you were building a new insurance carrier today, would you structure underwriting in the same way that it is today? Most likely, no. And as AI evolves over time, you would certainly design a different operating model.

In other words, processes designed around AI and technology would require a quite different organization than human-driven processes. The more carriers lean into AI-driven processes, the more the legacy operating model makes little sense.

The Legacy Trap: Why Current Models Are Not Changing

If we accept that AI-integrated processes are directionally where the insurance industry is headed, then the question is why haven't carriers designed new models? There are several reasons why organizations are not evolving:

1. Organizational Resistance: AI-driven processes come with an uncomfortable question – what is the role of a human in this new environment? Most assume that it means that AI is "coming for their role," and to some extent, they may be correct. But that assumption hinges on two beliefs – that all capabilities can be automated and that all automated capabilities no longer require people. Neither of these beliefs is true.

2. Lack of Success With AI: There is an often-cited statistic that 95% of all AI projects do not make it from pilot to tangible, measurable ROI. This suggests that although carriers are investing in AI and understand its capabilities, they are not finding success at scale, delaying transition to AI-driven processes and capabilities. While this suggests that the AI-driven process may not be as close as some believe, it would be incorrect to dismiss it as hype. Executives and insurance leaders only need to be directionally right, and innovation in the space should be balanced with an AI strategy on what to invest in and how to prioritize.

3. Unproven Models: Insurance carriers are conservative – an op model built on new technology is a significant risk and has not aligned with traditional automation strategies. Typically, a process is automated and then resources are reallocated or modified once the investment has generated ROI. But there is evidence of carriers operating in dual environments with new operating models, in what some have called a "two highway" approach – a legacy environment for in-force business coupled with a new environment for new products. A new target operating model does not need to be an enterprise effort initially – it may be useful to design a different model in a specific business unit to run in parallel to assess strengths and weaknesses before eventually scaling it.

Building AI-Native Op Models: A Practical Framework

If carriers accept that integrated AI processes creating new workflows is the future, then part of the planning effort must be an exercise developing a new target operating model. As carriers seek assistance with developing these models, there are five key principles that will lead to the greatest chance of success:

1. Realize Directionality Is More Important Than Timing: Carriers do not need to know exactly when a transformation will occur, they only need to think in terms of where AI is moving directionally. Consider various capabilities in insurance. Operational support of the insurance model is likely headed toward significant automation of processes, while sales and marketing are likely to remain less automated in the future. From an operating model perspective, that likely means that AI driven processes will push workflow in the back office (think of new business submission or policy administration), while in the sales capability, you are more likely making the agent/advisor/broker more efficient (e.g., next best actions, generating marketing material with existing pre-approved templates).

2. Ignore Biases and Existing Requirements: One of the most difficult aspects of designing a new operating model in general is getting stakeholders to leave "the way it has always been done" at the door. Remember that this is a white space exercise and should be framed as such. For example, policy servicing should initially be thought of in terms of desired customer/agent experience, not how that service is delivered. When framed appropriately, carriers can focus on what they want to achieve and then assess how they would achieve it.

3. Understand the Hard Lines: For some carriers, there are hard rules that they will not consider. For example, risk appetite in underwriting may make some AI-driven processes impossible, or there may be a decision to create a large case workflow that is human driven to provide white-glove treatment for a particular agent class. Understanding enterprise non-negotiables upfront eliminates downstream decision-making on the op model.

4. Embrace Uncertainty: Carriers must understand they are blazing a new path forward. There is no cookie-cutter approach to a new operating model. While there are proven approaches, the result is that you may no longer have a clear benchmark. AI is introducing uncertainty and the only thing that we know is that it will transform the way that insurance carriers operate. The introduction of AI-driven processes will inevitably create a feedback workflow connecting actuarial product design, underwriting, and claims to create real-time adjustments to initial assumptions. The long-term consequences are unknown, but carriers still have to develop these capabilities to compete in the market.

5. Iterate, Iterate, Iterate: While there is directional design, understand that operating models evolve as new data is presented. While there are assumptions that sales (particularly personal lines) will continue to be driven through agents and brokers, significant change in customer dynamics or technology could change these assumptions. Additionally, end-state operating models make assumptions on where technology will be, not where technology is today. That may mean an agile approach to op model development.

The process of developing these operating models will not be instant. But carriers must begin the process of reassessing how they are organized to meet client needs in the age of AI. Digitally native carriers like MGT Insurance (organization built around AI stack to support small businesses) and Ethos (organization built around underwriting that can be done in five minutes) are already further along in this journey than legacy insurers, and the consequences may mean bloated organizations, reduced profitability, and an inability to compete in the marketplace, particularly in price sensitive markets. Embracing AI while ignoring op model transformation is only delaying the inevitable. As AI evolves, what assumptions in your op model might need rethinking?

Growth no longer arrives quietly. It comes with evolving rules and regulations, higher expectations from consumers for seamless service, and less room for operational error. Expansion puts every assumption about how an agency operates under a spotlight.

The agencies that succeed are not growing faster by accident. They are building from the start with an eye toward what it will take to operate as a next-generation agency.

What that looks like in practice becomes clear when you examine how a handful of fast-growing agencies have approached scale over the past year. After years of working alongside agencies as they grow and change, those patterns are hard to miss.

When Ambition Forces the Issue

Look across agencies at different stages of growth, and a pattern emerges. Ambition is rarely the constraint. Execution is. The divide came into focus when we worked with a newly formed agency that entered the market with clear and aggressive growth objectives.

The founders were not new to insurance, but they were clear-eyed about the risks. Rapid expansion without proper structure would create compliance risk, service inconsistency, and operational drag. Rather than treating those challenges as problems to solve later, they treated them as foundational design requirements from day one.

Designing for Scale Before It Is Required

Instead of layering tools and processes reactively, the agency focused on building repeatable frameworks. Compliance expectations were standardized and ingrained into processes and systems. Service models were defined. Training and onboarding were designed to work across locations and teams.

This approach created clarity early. New offices could launch efficiently, without reinventing how the agency operated. Agents could onboard quickly, without sacrificing quality or oversight. Leadership retained visibility as the organization expanded, and could quickly course correct where needed.

The company is now well positioned for continued expansion without the loss of control that typically accompanies rapid growth. The takeaway is not that speed matters most. It is that discipline and sequencing matters. Infrastructure came first. Scale followed.

Why Growth Exposes Weak Operating Models

Many agencies discover their operational limitations only after growth accelerates. Processes that worked at a small scale begin to break. Informal knowledge becomes a bottleneck. Compliance shifts from manageable to overwhelming.

In response, agencies often add more tools. A system for enrollment. Another for compliance. Another for reporting. Each addition solves a narrow problem but increases fragmentation. Over time, leaders lose a clear view of what is happening across the business. Agents spend more time navigating systems than serving clients. These issues create motion without momentum. Focus on the customer inadvertently wanes. Growth begins to slow, and further scale becomes next to impossible.

The Difference Between Scaling and Expanding

There is a meaningful distinction between expanding and scaling. Expansion adds volume. Scaling adds capacity.

Agencies that scale successfully build operating models that absorb growth without degrading performance. Compliance and quality remain consistent. Service delivery is predictable. Visibility improves rather than erodes as volume increases. This requires standardization without rigidity. Processes must be repeatable, but flexible enough to adapt to different markets and consumer needs. Growth becomes something the organization plans for and manages deliberately, rather than reacting to as problems arise.

Rethinking Revenue and Retention

Growth also forces agencies to confront how they think about revenue.

In the case of another agency we recently worked with, which was entering a growth phase, leadership recognized that focusing on short-term results was creating an unstable foundation. Leadership began to prioritize lifetime customer value and persistence as core performance metrics.

Product strategy was aligned with long-term outcomes rather than immediate payouts. Agents were better educated on how coverage decisions affected customer satisfaction over time. Data was used to reinforce better decision-making at the point of sale, with an intense focus on customer satisfaction as key to an effective lifetime value model. The result was a healthier book of business and more predictable growth. Revenue was no longer completely reliant on obtaining new customers. It was supported by durability and lifetime-value-based business objectives.

What This Means for Agents

When workflows are clear and systems are coordinated, agents spend less time navigating administrative tasks and more time working with clients. Expectations are consistent across the organization, support is easier to access, and day-to-day work feels more predictable.

That stability matters. Growth no longer feels chaotic or dependent on workarounds. Instead, agents operate in environments where processes support them, allowing them to focus on building relationships and growing their business with confidence.

Where Agencies Pull Ahead

Growth itself is not a differentiator. In every thriving business, growth is expected. What separates agencies is whether they can scale without losing control, consistency, or trust. The real challenge is not adding volume but sustaining clarity as complexity increases.

The agencies that succeed will not be defined by how quickly they expand but by how intentionally they build for the future. Compliant growth becomes a foundation rather than a constraint, and processes are designed to repeat and scale instead of relying on individual heroics. Growth is not a moment to chase. It is a test of whether an agency was built to last.

Once the carrier's most trusted ally, mainframe systems/on-prem applications have now become outdated. Their prowess compared with modern technologies such as cloud computing and artificial intelligence (AI) appears bleak at best. They're expensive to maintain. According to a BCG Analysis, global IT spending in the insurance industry was about $210 billion in 2023 and is expected to grow 9% annually through 2027. Plus, evolving customer digital appetite, the competitive landscape, and regulatory complexity put perpetual pressure on traditional insurance businesses to make that ultimate call. Application modernization!

That said, complete legacy rewrites remain too risky and costly. These core systems power mission-critical functions, including underwriting, policy issuance, claims approval, renewals, and compliance. Complete rewrites can lead to the loss of critical business logic, reintroduction of old bugs, and increased security vulnerabilities. Most importantly, rewrites can disrupt operations, affecting existing policyholders' trust, revenue, and regulatory standing.

That's why insurance CIOs today appear to be at a crossroads. How can we accelerate modernization without breaking what works and risking downtime? The key here is moving toward a modern core environment with digital capabilities. In this article, we discuss a strategic, outcome-driven transformation approach that helps CIOs introduce modernization into core insurance processes while increasing customer satisfaction and productivity while cutting costs.

What's Happening in Insurance Businesses Today: The Need to Modernize

For years, insurers have persisted with traditional processes for day-to-day operations that have been heavily reliant on paper-based interactions, on-prem legacy systems, and CRM databases. This means that agents spend more time on administrative tasks and less on risk assessment, actuarial analysis, processing claim submissions or bringing innovation. However, over the last few years, technology has evolved at breakneck speed. Businesses are leveling up innovation by bringing in AI across various functions.

Consumer psychology is also being reshaped with each passing day as AI penetrates different spheres of life. People are using AI/LLM tools to generate content, build apps, get new business ideas, seek therapy, create financial plans, and whatnot. The human brain is being rewired to get personalized information in seconds. And as people get increasingly used to instant gratification, the insurance industry, with its limited customer touchpoints, will find it harder to keep them engaged.

Especially, with legacy systems that have become more prone to downtime as the seasoned professionals who have maintained these are aging or already retired. Even regulations have become an issue, necessitating the need for a robust, modern data security infrastructure.

Lastly, it's also about keeping up with the world. To stay competitive, insurers must adopt cloud and new technologies such as generative AI. And for that, CIOs don't need to face operational disruption, cost overruns, and service degradation. Phased modernization approach can prove to be effective.

Initiating Modernization: How Can CIOs Move Forward

Deciding whether to build custom solutions in-house, upgrade existing systems with modern wrappers, or purchase a ready-made platform is a complex decision. Carriers are contemplating this. In the United States, roughly half of the leading P&C carriers opt to buy and configure systems, while the other half decide to build. Each approach has its merits.

1. Transforming Existing Systems Incrementally

CIOs seeking to revitalize their legacy software systems can choose between less invasive approaches, such as code refactoring and replatforming, and more complex transformation strategies, such as rearchitecting. For less complex applications, former approaches work best. Existing core insurance apps can be moved to modern cloud infrastructure with minor adjustments, improving performance while reducing operational costs. That said, this transition can take months using traditional software development methods. In continuously evolving markets, the cost of waiting is more than what businesses can digest. This often appears as a daunting mountain, making IT leaders abandon the idea altogether. This is where a step-by-step approach, incremental modernization, becomes more effective.

It balances business continuity with technical evolution. It's not about updating one app/core function at a time. Incremental modernization is about identifying modular modernization units: workflows, sub-domains that can evolve independently. That means reshaping the system from within.

2. Using AI Agents as Intelligent Wrappers

CIOs can leverage modern wrappers, such as AI agents and RPA, to improve efficiency. These modern technologies can work alongside your primary heritage applications as digital assistants (modern wrappers) without replacing or changing the system itself. These AI agents interact: observe (read), monitor (listen), and act (share real-time insights) while boosting security and reducing technical debt. This can empower human agents to make more effective decisions as they act on real-time insights, helping boost productivity, increase customer satisfaction, and maximize ROI.

3. Simplifying Workflows with a Unified, Scalable Insurance Software

CIOs can also opt for modern insurance software to facilitate quick deployment, shorten modernization timelines, and meet modern business requirements without heavy customization or a rip-and-replace approach. An AI-enabled insurance management software can streamline an insurance policy lifecycle, including onboarding, underwriting, billing, claims, and servicing. But the main challenge is to select software that can actually become a real growth engine. This is where due diligence counts.

Evaluate the vendor's API capabilities and integration experience. Deduplicate and cleanse data (build a solid data foundation). Run pilot tests and increase stakeholder alignment to facilitate adoption. Include the compliance and information security teams in the evaluation process. These measures can help insurers modernize with greater confidence, reduce execution risk, and move with the agility required in today's insurance landscape.

Alternatively, big insurance firms can benefit more from custom-built software, which offers greater customization to the unique needs of the business.

Conclusion

System modernization is a transformative journey, a meaningful opportunity for insurers to reposition themselves as a modern-day enterprise in the eyes of both the workforce and customers. A business that can skillfully orchestrate complex insurance operations while remaining digitally advanced. And for that, CIOs no longer need to choose between protecting mission-critical core systems or embracing end-to-end digital transformation. Incremental modernization, intelligent wrappers, or a well-evaluated COTS offer a strategic, low-risk path forward.

Don't think of the modernization journey as one that dismantles your most trusted allies, mainframe systems/on-prem applications. Instead, it empowers these systems with modern-day capabilities. It helps consolidate up-to-date data in one place, minimize manual and iterative work, increase customer engagement through personalization, and respond faster to regulatory and market changes.

So, the question is: Are you ready to build a resilient technology foundation that supports sustained, long-term growth and builds a competitive advantage? Those who act now with clarity will gain an advantage tomorrow.

Not long ago, managing millions of documents across dozens of databases was a challenge reserved for the largest enterprises. Today, widespread cloud adoption and a more distributed workforce mean that organizations of all sizes are handling vast, growing volumes of data.

The challenge isn't just scale—it's complexity. Much of this data is unstructured, scattered across systems, and increasingly filled with personally identifiable information (PII) and other sensitive details that are difficult to find, manage, and protect.

Furthermore, the cost of data breaches continues to rise. The 2024 IBM Cost of a Data Breach report states that the global average cost in 2023 reached $4.8 million, marking a 10% increase from the previous year and the highest on record. A significant 75% of this increase is attributed to lost business and activities related to responding to the breach.

Allocating resources to prevent data breaches is already challenging—and it becomes even more complex as privacy regulations like GDPR and CCPA continue to expand. These laws require organizations to maintain greater transparency and stronger protections for sensitive data across its entire lifecycle, covering information collected from customers, patients, employees, and even website visitors, regardless of when or how that data was gathered.

The harsh reality is that data loss has become commonplace. Breaches often go unnoticed for months, and meeting compliance standards is becoming progressively more challenging.

How can businesses confront these challenges? The good news is that the process of identifying, classifying, and fixing sensitive data to mitigate risk can be automated. In fact, as the IBM report states, organizations that heavily use security AI and automation saved approximately $1.8 million compared with those that do not.

How AI Is Transforming Data Privacy

In the age of digital transformation, data privacy is now a fundamental concern for businesses globally. Incorporating artificial intelligence (AI) into data privacy strategies is not just a technological step forward; it is essential.

Like many AI applications, the aim is to boost productivity and minimize human error. In cybersecurity, AI supports a SOC environment by aiding threat hunting, incident response, and daily cybersecurity operations. It enhances value by processing data, offering better context to security teams, and automating routine tasks. The main goal is to use AI to boost experts' productivity, particularly when errors carry high costs.

Primary Functions of AI for Data Protection and Privacy

Proactive data management: Unlike traditional systems that react to issues after they occur, AI adopts a proactive approach. Data security solutions that leverage machine learning to scan, categorize, and monitor data continuously in real time help ensure that PII is securely stored and actively protected.

Deep insights and predictive analysis: AI's strength is in extracting meaningful insights from large datasets. It identifies patterns to forecast potential threats and vulnerabilities, enabling businesses to proactively strengthen their security defenses. Tools that use AI to automatically spot anomalies, such as unauthorized access, risky data sharing, improper permissions, and incorrect locations, facilitate quick responses and corrections.

Adaptive learning: This key feature quickly adapts to changing cyber threats. As threats evolve, AI systems analyze new patterns to enhance security and prevent breaches. Advanced machine learning can scan and classify data, learning from observed patterns. AI-powered risk analysis automatically detects PII, understands its usage, and assesses its risk level. As the system encounters new data formats and usage methods, it updates its knowledge to provide more precise risk evaluations.

The Future of Data Privacy With AI

Data privacy is evolving rapidly, and traditional methods are no longer sufficient. With cloud-first deployments, increasingly stringent regulations, and continuous data growth, protection must keep pace with emerging threats. AI introduces a new pace by detecting risks instantly, adjusting to new data patterns, and making decisions that previously took days within seconds. The future focuses on leveraging AI to enhance data security—making it smarter, more precise, and constantly active.

Here's what AI offers:

Real-time protection: This is crucial as businesses shift to live operations. AI's capacity to process and analyze data instantly makes it essential for immediate protection. By autonomously scanning and classifying data, advanced AI ensures businesses can trust that their PII is continuously protected.

Regulatory evolution: As data privacy challenges increase, regulatory frameworks also expand. AI's flexibility enables businesses to easily adapt to these changing rules while remaining compliant with minimal disruption. AI can adjust its monitoring and protection measures to comply with various regulations.

A collaborative approach: In the future, AI and human expertise will work together. AI will manage real-time processing and threat prediction, while security teams focus on developing and executing long-term data protection strategies. Businesses should seek an AI-driven solution that provides the technical tools and integrates smoothly with human-led strategies and decision-making. This allows businesses to leverage the advantages of both: AI's speed and efficiency, combined with the strategic insight of human experts.