The U.S. healthcare system spends enormous resources treating conditions that, in many cases, poor nutrition helped create. Diet-related chronic diseases such as diabetes, heart failure, chronic kidney disease, and obesity drive a disproportionate share of total healthcare costs and represent some of the highest-cost members across Medicaid, Medicare Advantage, and commercial populations.

A relatively small group of high-risk members drives a large percentage of total spending. That is not new. What is new is how little progress most organizations have made in addressing one of the most direct drivers of that cost: nutrition.

As medical trend continues to be shaped by these high-cost populations, interventions that meaningfully change outcomes for this group will have an outsized impact on overall cost and premium pressure.

Yet for most patients, nutrition is still treated as an afterthought. It is addressed with a brief counseling note, a handout, or a recommendation that assumes patients have the time, resources, and knowledge to translate advice into action. Most do not.

That gap is no longer acceptable in a value-based environment.

Food as medicine, delivered through medically tailored meals and dietitian support, is emerging as a true clinical intervention. The question is no longer whether food matters. The question is whether the system is willing to treat it as care.

The Data Is Clear. Execution Has Been the Constraint.

A study published in the Journal of the American Medical Association Internal Medicine found that medically tailored meal programs were associated with 16% fewer hospitalizations, 49% fewer skilled nursing facility admissions, and significantly lower total healthcare costs compared to similar patients who did not receive the intervention.

In high-risk populations, those are not incremental improvements. They are material to the cost structure.

Separate modeling from researchers at Tufts University estimates that scaling medically tailored meals nationally for patients with diet-sensitive conditions could generate approximately $23 billion in annual healthcare savings. That level of impact is rare in healthcare.

Additional research across heart failure, chronic kidney disease, and diabetes populations continues to show reductions in emergency department usage and slower disease progression. These are two of the most expensive drivers in any high-acuity population.

The industry has not lacked evidence. It has lacked a way to operationalize nutrition at scale.

That is what is changing.

Medicaid Is Leading. Others Will Follow.

Medicaid beneficiaries sit at the intersection of high rates of diet-related chronic disease, elevated food insecurity, and significant barriers to accessing consistent, appropriate nutrition. They are also among the highest-cost members in any managed care population.

This makes Medicaid the most urgent and logical place to deploy medically tailored meals at scale. It is also where the market is moving fastest.

Section 1115 waivers have created a pathway to fund nutrition as a health-related social need, and states such as North Carolina, California, New York, and Massachusetts are actively integrating food-based interventions into their care models. What was once a niche pilot category is now being implemented at scale across multiple states and national plans.

While Medicaid is leading, the same underlying dynamic exists in Medicare Advantage and increasingly in commercial populations. Chronic disease is rising, nutrition is a primary driver, and traditional care management alone is not bending the cost curve.

Most Plans Cover Nutrition. Very Few Deliver It.

Most health plans already offer some form of nutrition benefit. That is not the issue.

The issue is that these benefits rarely change behavior, improve outcomes, or reduce usage in a measurable way.

Medically tailored meals are different because they function as clinical infrastructure. They can be prescribed based on diagnosis, delivered through repeatable workflows, and measured directly against claims outcomes.

That changes the conversation.

In a value-based model, interventions that cannot be operationalized and measured do not scale. Nutrition has historically fallen into that category. Medically tailored meals move it out of it.

What Happens When Nutrition Is Delivered as Care

In working with high-risk populations across Medicaid, Medicare, and commercial lines of business managing conditions such as type 2 diabetes, hypertension, irritable bowel syndrome (IBS), and kidney disease, a consistent pattern emerges. When nutrition is delivered in a way that is clinically appropriate, convenient, and paired with dietitian support, engagement increases significantly. More importantly, usage begins to shift.

In large Medicaid populations, meals often serve as the front door to broader care management. They create immediate engagement and make it easier to connect members to additional services. Within the first few months, changes in usage patterns begin to appear, particularly among members with poorly controlled chronic conditions.

This is not about sending food. It is about delivering a clinical input that most high-risk patients have never had consistent access to, and measuring what happens when they do.

A Strategic Choice, Not a Pilot Decision

For payers evaluating their value-based care strategies, the question is not whether nutrition affects outcomes. That is already established.

The real question is whether nutrition will be treated as a core component of care delivery or remain a peripheral benefit that produces limited impact.

Medically tailored meals address clinical risk, social determinants, member engagement, and total cost of care in a single intervention. That combination is rare. It is also increasingly necessary as cost pressures and quality expectations rise across all lines of business.

Over the next three to five years, nutrition will move from a supplemental benefit to a standard component of high-risk care strategies, much like care management did a decade ago.

Plans that move early and operationalize nutrition as care will build a measurable advantage in managing their highest-cost populations.

Plans that do not will continue to manage the downstream consequences.

Ignoring food is not a neutral decision. It is a financial one.

The life insurance and annuities industry has spent the past several years accelerating digital transformation. But as we move through 2026, the conversation is shifting from experimentation to execution.

The core technologies driving modernization – artificial intelligence, advanced data infrastructure, and digital distribution platforms – are no longer optional capabilities. They are becoming the foundation of how insurers design products, engage customers, and support advisors.

At the same time, deeper economic forces are reshaping the industry's mission. Millions of Americans remain underprepared for retirement. According to recent BlackRock data, by 2050, the population over age 80 is expected to triple, while median savings rates slip 17% from 2020 levels.

That gap between longer lifespans and the savings needed to support longer retirement is one of today's defining challenges, placing life insurance and annuities at the center of the solution.

Looking ahead, several connected shifts will define how the industry responds.

AI moves from experimentation to embedded capability

Artificial intelligence has been a dominant topic across financial services for several years. In the insurance sector, however, the real transformation is only beginning.

Early AI deployments focused on narrow applications such as fraud detection, chatbots, and basic automation. Those uses delivered incremental efficiencies but left much of the broader value untapped.

In 2026, the industry is moving beyond those isolated pilots as AI becomes increasingly integrated throughout the insurance lifecycle from application intake and underwriting triage to product design, distribution insights, and servicing.

The practical impact is speed.

Processes that historically required multiple handoffs, manual reviews, or weeks of back-and-forth can now be dramatically shortened. Advisors can evaluate product options and compare them more quickly, applications can be pre-populated using verified data sources, and underwriting decisions can be generated faster through advanced data analysis.

But AI's greatest potential is in enabling intelligent decision-making across the entire insurance ecosystem.

That means helping advisors identify the most appropriate product solutions for individual households, enabling carriers to refine risk assessments, and guiding customers through complex financial decisions with clarity and confidence.

Integration across the ecosystem will determine who leads

Despite the excitement around AI, one reality remains clear: technology is only as effective as the broader ecosystem it operates within.

Many insurers still work across fragmented systems built over decades. Customer information, underwriting data, product illustrations, servicing workflows, and distribution tools often exist in separate systems that do not connect as seamlessly as they should.

That lack of integration creates friction across the insurance lifecycle. Even the most promising technologies can only deliver limited value if the systems, partners, and processes around them remain disconnected.

In 2026, the most competitive insurers will prioritize creating more connected ecosystems that bring together internal operations, external partners, and advisor workflows in a coordinated way. That means enabling smoother handoffs across the journey, improving visibility between stakeholders, and ensuring that information flows more consistently from application through underwriting, issuance, and service.

When that level of integration is in place, innovation becomes more realistic and scalable.

In practical terms, this means fewer process bottlenecks, better coordination across the distribution chain, more consistent customer experiences, and a stronger foundation for delivering speed, clarity, and trust at every step.

Personalization will redefine product experiences

Consumers increasingly expect financial services to feel tailored to their individual circumstances.

In industries such as ecommerce retail and media streaming, personalization has become standard practice. Insurance is beginning to follow a similar path.

Advances in analytics and machine learning are allowing carriers to better evaluate demographic, behavioral, and geographic signals when designing products or recommending coverage levels.

Instead of presenting customers with a broad menu of generic options, insurers can guide individuals toward solutions that align with their financial goals, risk tolerance, and life stage.

In the near term, personalization will focus primarily on improving product fit and simplifying decision-making. Customers will encounter clearer choices, better explanations of tradeoffs, and more relevant options while they are evaluating coverage.

Over time, the industry may move toward more flexible product structures that allow coverage elements to be assembled in modular ways. While regulatory considerations will shape how quickly that evolution occurs, the trajectory toward greater customization is clear.

For consumers who have historically found life insurance complicated or intimidating, this shift has the potential to make protection solutions far more accessible.

Data-driven distribution is transforming advisor engagement

Distribution has always been one of the most complex elements of the life and annuities market.

Advisors must navigate product comparisons, regulatory requirements, suitability documentation, and application processes, often across multiple carriers and systems.

As digital capabilities improve, the distribution model itself is becoming more intelligent.

Advanced analytics can help advisors identify households that are most likely to benefit from protection or retirement income products. Instead of relying primarily on broad marketing campaigns or cold outreach, firms can engage potential clients with greater precision.

At the same time, technology is improving operational visibility. Advisors increasingly expect to track applications, underwriting progress, and case status in real time rather than waiting for manual updates.

This transparency is quickly becoming a competitive necessity.

When advisors can move quickly, provide clear updates to clients, and eliminate unnecessary friction in the process, the entire customer experience improves.

Retirement pressures are reshaping demand

Technology may be transforming how insurance products are delivered, but demographic forces are driving why they are needed.

The United States is entering a period often referred to as "Peak 65," when record numbers of Americans reach retirement age each year. At the same time, trillions of dollars are expected to transfer from Baby Boomers to younger generations over the coming decades.

Yet many households remain financially vulnerable.

Research suggests the typical worker's retirement savings are far below recommended targets, and Social Security alone generally replaces only about 40% of pre-retirement income for the average beneficiary.

This reality is fueling growing interest in lifetime income solutions and protection products that can provide greater financial stability.

Younger investors are also approaching retirement planning differently. They expect digital tools, transparent comparisons, and on-demand information, but they still value professional guidance when navigating complex decisions.

That dynamic reinforces the importance of equipping advisors with technology that strengthens their ability to educate clients and provide personalized recommendations.

Digital journeys must match modern consumer expectations

One of the most important shifts underway in insurance is the rising influence of digital commerce standards.

Consumers increasingly evaluate financial experiences through the same lens they apply to online banking or investment platform apps. They expect simple navigation, clear information, and immediate feedback.

For insurers, this means the digital experience cannot stop at the first interaction.

Customers expect continuity from initial research through application, underwriting, policy issuance, and continuing service. Advisors likewise need tools that allow them to guide clients seamlessly across those stages.

Modern electronic applications, automated validation checks, and integrated illustration platforms can dramatically reduce the number of "not-in-good-order" applications mired by incomplete or incorrect information.

Reducing these friction points improves efficiency for carriers and distributors while creating a smoother experience for policyholders.

The industry's next chapter: integration and trust

Taken together, the forces shaping the life and annuities sector in 2026 tell a larger story.

Artificial intelligence is accelerating decision-making. More connected ecosystems are enabling new levels of personalization. Distribution networks are becoming more precise and efficient. Demographic shifts are reinforcing the need for reliable retirement income solutions.

But none of these trends operate in isolation.

The organizations that lead the next phase of the industry will be those that align technology, data, and human expertise into a cohesive operating model.

Modernization is no longer about deploying the latest tool or launching a new digital initiative. It is about creating an ecosystem where every element – from underwriting to distribution to customer engagement – works together seamlessly.

For insurers, advisors, and technology partners alike, the opportunity ahead is significant.

At a time when millions of Americans are searching for greater financial security, the life and annuities industry has the chance to deliver not only innovation, but also clarity, stability, and trust.

And in 2026, those qualities may prove to be the most valuable differentiators of all.

From lightning and downpours to twisters and hailstones the size of tennis balls, severe convective storms (SCS) can manifest in dramatic and destructive ways. A complex cocktail of meteorological factors can lead to their formation, including the rapid ascent of warm moist air into cooler air higher up, and shifts in wind speed or direction.

Unlike regular thunderstorms, SCS must exhibit at least one of the following characteristics: winds exceeding 100kph (62mph), hailstones that are at least inch in diameter, or a tornado. SCS can produce a variety of weather patterns including straight-line winds, derechos, microbursts and macrobursts.

Unlike hurricanes, SCS events can strike with little or no warning, unleashing significant localized damage and triggering knock-on effects such as flash flooding. These unpredictable events have emerged as a major annual loss driver for the insurance industry, accounting for nearly half of all insured natural catastrophe losses last year, totaling over $60 billion.

SCS Losses Increasing

Between 2023 and 2025, losses exceeded $200 billion, according to Gallagher Re. The US is the number one SCS hotspot, accounting for more than 80% of the value of insured losses globally. This trend is reflected in the latest Allianz Risk Barometer where natural catastrophes ranked No. 5, remaining a consistent presence in the annual business risk ranking.

While tornadoes often dominate the headlines, the most significant SCS losses are caused by hailstorms, which are estimated to account for as much as 50%-80% of all losses. Again, the US is the global hotspot for these events, as well as the top loss location for hail claims, but Allianz Commercial analysis shows many other regions have also suffered substantial hailstorm damages.

Building resilience against this peril needs to be on the agenda of every company with exposed assets in high-risk areas. Addressing this peril requires more than traditional scenario planning. New approaches leveraging AI are able to identify physical vulnerabilities in advance, enabling risk mitigation to build resilience.

Inflation and Expanding Footprints Fuel Losses

SCS exposures have been intensified by population growth and development into hazard-prone areas. Rapid urbanization, aging infrastructure and assets, and building codes out of step with current exposures can all heighten the risk and value of losses. The limited spatial footprint and brief duration of SCS belie their capacity for concentrated destruction, particularly in densely populated regions.

After hail, damaging winds are the second major loss driver, consisting primarily of tornadoes and derechos. Severe hailstorms primarily affect buildings – especially roofs – and all kinds of vehicles, which are major drivers of expensive insurance claims. The damage to physical assets from hailstones can be extensive. A baseball-sized hailstone can carry the same kinetic energy as a Major League fastball, reaching speeds of up to 100mph (160kph) or more. Hail-related losses are not only growing in frequency but also shifting in character. What was once considered routine property damage now increasingly involves high-value assets, from aircraft fleets to solar installations, driving claim severity to levels that demand a fundamentally different response, Allianz Commercial analysis shows.

Inflation has driven up the costs of rebuilding and repairing property, an increase that is compounded by supply chain disruptions such as shortages in skilled labor and materials. A clear example of inflation's effect can be seen in the case of roof replacements, which are a significant factor in insured losses from SCS. Since the year 2000, the cost of built-up roof replacements has reportedly surged by 250% in some regions, with costs rising 45% in the last five years alone, according to Willis Re.

Business Must Prioritize Risk Mitigation

Mitigation measures for withstanding SCS will vary depending on the nature of a business's activities and the local weather systems it is subject to. A data center in Tornado Alley in the central US will need different resilience strategies than an automotive dealership in hail-prone northern Spain.

Scenario analysis is essential for assessing risk exposure and building resilience to climate perils. Instead of reacting to losses after a major storm, organizations can now use AI-supported insights to identify weak points in roofs, facades or critical equipment and prioritize upgrades that minimize future damage. This helps organizations understand how different climate futures could affect their assets, operations, and long-term performance.

Scenario analysis can also reveal hidden vulnerabilities, identify possible tipping points, and show how risks may change over time. This forward-looking approach strengthens decision-making by allowing organizations to test adaptation options, focus investments, and design strategies that remain effective under multiple future conditions.

Read the full report at Allianz Commercial Severe Convective Storms.

In many cases, the biggest challenge of delivering a policy administration system has not been a matter of time – but of predictability. With the increasing intricacies of requirements and growing number of dependencies, the task of keeping everything aligned has become much harder. However, based on recent experience, embedding AI into the delivery life cycle appears to help address these concerns.

Why PAS Implementation Has Traditionally Taken Years

Launch and operation of a policy administration system require careful planning, extensive documentation and coordination across various departments of the insurer. The reason is that such a platform supports a variety of activities, including underwriting, policy lifecycle management, quotation, billing and other functions.

Complexity in PAS implementation is rarely linked to any single technical factor. It emerges gradually as a result of expanding requirements and growing number of parties involved. As the result, inconsistencies that would otherwise be small can easily turn into costly issues once development and integration are started.

In traditional PAS implementations, consisting of development, implementation, integrations and testing, the duration is estimated at 18 to 36 months. Usually, delays emerge when information flows slowly between teams, when inconsistency in one phase results in rework in the next, or when documentation phases add to ticket preparation and testing time.

Where Complexity Slows Delivery

There are certain common patterns where complexities occur. First, structuring business requirements into implementable artifacts always poses its own challenges. Requirements tend to be driven by regulatory constraints, product diversity, regional regulations, and other factors. To structure them into technical documentation and validate them is a complex process, which traditionally relies extensively on manual effort.

Documentation is another challenge that must be addressed before proceeding to ticket preparation. PAS initiatives tend to consist of 3,000 tickets, covering development, implementation, integrations, configuration and testing tasks. Each requires proper description, acceptance criteria, and dependencies on related tasks. Once all this documentation work is performed manually, differences in wording and structure lead to inconsistencies, which can slow down the process considerably. Moreover, the resulting fragmented documentation creates serious obstacles when teams realize that certain aspects could have been clarified before entering the implementation stage.

Use Cases Where AI Makes a Difference

Fortunately, AI technologies enable teams to overcome some of the mentioned complexities, especially if applied consistently throughout the delivery lifecycle of a project. In recent experience, the benefits were not produced by adding artificial intelligence as a separate layer but by integrating it into the day-to-day workflows of analysts, developers and QA engineers.

• Requirements analysis

  It is not uncommon for requirement documentation to come from several sources at once – Confluence pages, email threads, discussion minutes, and sometimes even informal chat discussions. In their initial form, requirements are unstructured, repetitive and occasionally contradictory.

  In an AI-driven delivery approach, AI analyzes requirements received by different channels. It structures them according to the domains that they refer to (quotation, underwriting, billing or policy servicing). The tool groups requirements together, detects potential duplications, contradictions between different versions, and identifies missing information.

  Importantly, AI performs requirements analysis, not business analysis. The output generated by AI is carefully validated and reviewed by the analyst team and then used as a basis for further actions. This reduces the time spent on clarifying the contradictions and inconsistencies in requirements before any tickets are created.

• Creation of Jira tickets

  With requirements structured, the next logical step is to translate them into ticket format. This approach relies on Claude to analyze structured requirements and produce a set of Jira tickets. For each ticket, it provides description, acceptance criteria, and dependencies.

  This is still not done in an automated fashion. All tickets prepared by AI undergo validation and adjustment to ensure completeness. The main benefit here is the increase in speed and consistency. Ticket preparation becomes noticeably faster, and developers receive clearer instructions, with less ambiguity and inconsistencies.

• Assisting developers

  During development, AI is used primarily to provide support to developers, rather than making decisions on their behalf. Developers use it to discuss implementation strategies, ask for code suggestions, check changes in the first draft and solve problems that arise during debugging.

  AI does not replace developer judgement or creativity. All its suggestions and recommendations undergo validation and confirmation. However, it substantially reduces the amount of context-switching. Developers spend less time looking for, interpreting and rewriting the same information over and over again. Instead, they are able to focus on solving the problem itself.

• Analyzing releases and detect conflicts

  When multiple developers work in parallel, conflicts may occur. After tasks are assigned, completed and bundled into a release candidate, merge conflicts and overlaps of changes are identified in the version control system.

  AI assists in detecting conflicts and resolving them faster. Change descriptions, notes made by developers, and logs of conflicts in version control are analyzed, and AI summarizes them in clear terms. It suggests possible ways to resolve the conflict and checks whether the change proposed will not cause problems elsewhere. However, the final decision belongs to the developer.

• Generation of test cases and assistance in troubleshooting

  Generating test cases and preparing test scenarios and data is one of the clear-cut use cases where AI helps achieve tangible results. On the basis of structured requirements, AI generates test cases, including rare edge cases that may be overlooked without the help of automation tools. Additionally, for testing APIs and frontend solutions, it prepares test cases and test data.

  Once tests are carried out, another use of AI appears to provide the best results – it analyses test results and helps identify possible root causes of failures. This allows testers and developers to find the root causes much faster and thus avoid wasting time on resolving unrelated problems. While it does not eliminate errors, it helps speed up the test cycle and increase its coverage.

• Code quality check and SonarQube integration

  AI is also used to assist the work of SonarQube in analyzing code and finding issues. Upon scanning the code automatically, SonarQube identifies issues in the code and creates a Jira ticket with an explanation of the problem. Then, it is passed to AI, which provides clear information on the nature of the problem and potential suggestions to deal with it.

  Developers use this information and decide on what suggestions to follow. This creates a semi-automated feedback loop. Code is analyzed automatically, AI helps make sense of the problem, and the issue is closed. The code does not correct itself, but the process becomes much more streamlined and consistent.

Case Study

Typically, a PAS implementation project involving 2,000-3,000 tickets and 10+ specialists has its own peculiarities. Traditional development approach implies that such a project, with development, integration, testing and implementation, will span approximately 18 to 36 months.

In one recent implementation, the same scope took a completely different course. By integrating AI into almost every aspect of the delivery process, the team managed to maintain continuity and prevent accumulation of delays.

Approximately within six months, the core phase of PAS implementation reached its first production-ready release. Notably, this period includes only implementation and does not cover the end-to-end life cycle, including integration and further activities after go-live. Nevertheless, this period can be considered impressive in comparison with conventional delivery methods.

What helped in accelerating this phase? First, preparation processes were significantly shortened. Requirement preparation and validation took less time. Development was more efficient due to lack of unnecessary context switching. Integration and testing also progressed smoothly, without unnecessary interruptions. This translated into less rework and, ultimately, faster development pace.

Key Learnings From Implementation

Here are some key learnings from our experience.

First, complexity should be addressed as a process problem, not necessarily a technical one. Many bottlenecks originate from ineffective information exchange, and well-designed workflows can eliminate much of them.

Second, good documentation practices pay off. While AI helps with achieving more consistency, it is essential to have proper documentation discipline, in order to achieve reliable results.

Third, early-stage clarity pays off exponentially. The clearer and more structured the requirements are, the better and smoother development, integration and testing go.

Managing Complexity as a Strategic Capability

Shorter timelines alone do not define success in PAS delivery. Reliable implementation depends on both speed and predictability. What became clear from this experience is that AI does not simply accelerate development. Its greater value lies in helping teams manage project complexity in a more disciplined and predictable way. When requirements are structured earlier, communication becomes clearer, and dependencies are visible sooner, teams spend less time correcting misunderstandings later. In practice, this translates into less rework, better alignment between business intent and technical execution, and fewer delays caused by issues discovered too late.

For insurers planning modernization efforts, this is perhaps the most practical takeaway. Large PAS initiatives will remain complex by nature. The difference comes from how that complexity is handled. When it is surfaced earlier, structured clearly, and addressed at the right stage, projects become more predictable and easier to control. In that sense, the real contribution of AI is not speed alone, but the ability to manage complexity with greater confidence throughout the lifecycle.

Every regulated email an agent sends carries a signature block your compliance team almost certainly does not control. Here are 10 reasons that gap is becoming a governance problem.

Insurance is a paper-trail business. Every email an agent sends is a regulated communication, a marketing artifact, and a potential exhibit in litigation. Sometimes all three at once.

The one element that appears in every one of those emails: the employee's email signature. This is governed in most carriers, brokerages, and TPAs by tribal knowledge, copy-paste, and the IT helpdesk.

I lead a company that manages email signatures for thousands of regulated organizations, including a meaningful slice of the insurance industry. The patterns are remarkably consistent across carriers, MGAs, and agencies. So are the blind spots.

Here are 10 things insurance leaders consistently underestimate about email signature compliance, and why most of the issues have quietly graduated from an IT housekeeping task into a board-level governance issue.

1. The email signature is a regulated communication, not a design choice

   State departments of insurance, NAIC model rules, GDPR, CCPA, HIPAA, and GLBA all reach into what your outbound business email is allowed to say. The signature is not exempt. Treat it as regulated correspondence. Because that's what it is.

2. Producer license disclosure is a moving target, not a one-time setup

   Most states require an active producer to disclose license number, lines of authority, or both on outbound communications. Numbers expire. Producers change appointments. Agents move states.

   An email signature template that was approved last year is, in practice, out of date by the next renewal cycle. Without a system that updates automatically, every renewal becomes a silent compliance gap.

3. A single tracking pixel can convert an email signature into a privacy event

   Marketing teams love the engagement data signature pixels deliver. But once an email signature image drops a third-party tracker, you have arguably collected personal data under GDPR and CCPA, with no notice, no lawful basis, and no record of consent.

   That exposure scales linearly with your headcount and your email volume.

4. Confidentiality footers are only as effective as the governance around them

   Every compliance team knows the "this email is confidential" boilerplate is hygiene rather than legal armor. The harder question, and the one most carriers can't answer cleanly, is who actually owns the words.

   In practice, the footer was drafted by legal years ago and has not been revisited since, and any producer can edit or delete it from their signature in two clicks. That is the real exposure: drift, inconsistency, and no clear owner.

   Treat the footer the way you treat a policy form. Version it, lock down who can change it, and set a review cadence tied to regulatory updates rather than to whoever last noticed.

5. HIPAA exposure includes the email signature, not just the message body

   Health carriers, supplemental insurers, and third-party administrators routinely embed photos, vCards, calendar links, and direct dial numbers in signatures. Combined with a recipient list or a forwarded chain, those small fields can recreate identifiable PHI.

   Auditors notice. OCR investigators have noticed, too.

6. Marketing banners in signatures can trigger advertising rules

   A producer who appends "Get a free quote, click here" to every email has, depending on the state, just sent regulated insurance advertising. Some states require advertising materials to be filed, retained, and approved before use.

   Most ad-hoc email signature banners satisfy none of those steps. Most carriers do not even know which producers have added them.

7. State disclosures aren't a marketing problem. They apply to every email.

   California, New York, Florida, Texas, and Massachusetts each maintain their own disclosure expectations for licensed producers and agencies. Compliance teams typically catch this on website filings, brochures, and renewal notices.

   Almost no one catches it on the hundreds of thousands of one-to-one emails the workforce sends every week.

8. Inconsistency reads as a weak control

   Auditors, regulators, and plaintiffs' counsel read inconsistent email signatures the same way: the company does not have a system. When two employees in the same office send several different versions of the same required disclaimer, the control and ownership becomes very hard to tell.

   This is the moment "small problem" becomes "finding."

9. Self-managed signatures fail compliance review essentially every time

   I have never seen a "set it up yourself, here's the template" rollout survive a serious audit. People copy from old emails. Mobile devices revert to the carrier default. New hires inherit the wrong version from the colleague sitting next to them.

   Voluntary compliance is not compliance. It's a hope.

10. Off-boarding is the largest signature risk most carriers ignore

    When an agent or employee leaves, their signature does not leave with them. It lives on in forwarded threads, archived chains, and the autoresponders nobody updated. It travels in attachments and quotes for months.

    Until that signature is centrally retired, the company is still implicitly representing a person who no longer holds an appointment with that carrier.

Email signature issues are a matter of governance, not formatting

The reason these 10 gaps persist is structural, not technical. Email signatures sit in an organizational gray zone. They are too small for legal to own, too regulated for marketing to own, and too operational for compliance to own. So, far too often, no one does. And risk tends to happen in the place no one is looking.

The fix is unglamorous but straightforward. Centralize signature management on a single platform. Lock down the fields producers can edit. Version-control disclosures the same way you version-control a license file or a policy form. Tie sign-on and off-boarding to the email signature itself, not just the mailbox.

Disclosure: this is the workflow my company, WiseStamp, builds for regulated industries, but the principle holds with any vendor you choose, and with any in-house system that meets the bar.

Most insurance boards are not yet asking about email signatures. They will be. The first time email signature inconsistency surfaces in an enforcement action, a department of insurance market conduct exam, an OCR audit, or a discovery request, it stops being an IT ticket and becomes a governance question.

It is cheaper, calmer, and more defensible to answer that question before it is asked.

This is part two of a two-part series on talent and organizational capability in insurance. Part one is here. 

Imagine you read the first piece in this series and made a decision. You're going to hire differently. You're going to look beyond the familiar profile, find someone with genuine cross-boundary judgment, and put them in a role where that capability can matter.

What happens next is the subject of this piece.

In many insurance organizations, what happens next is this: The capability arrives and the organization fails to absorb it. Not intentionally. Not because anyone decided the new thinking wasn't valuable. But because the systems, processes, and approval structures that govern how work gets evaluated and decisions get made were built for something else entirely. The new hire learns, faster than anyone expected, how things work around here. And how things work around here tends to win.

This is not a hiring problem. It is an organizational conditions problem. And it is the reason that strategic ambitions stall in execution even in organizations with genuine commitment, real investment, and talented people who want to make change happen.

The operating infrastructure problem

Insurance organizations didn't build their operating infrastructure carelessly. They built it deliberately, over decades, in response to real requirements: regulatory compliance across multiple jurisdictions, shareholder and efficiency pressures, consistent underwriting standards, claims processes that could scale without introducing unacceptable variability. In the United States alone, an insurer of any significant size is navigating 50 separate regulatory environments, each with its own requirements, relationships, and oversight expectations. For global insurers, that complexity multiplies across every country in which they operate.

The operating infrastructure that manages that complexity is genuinely impressive. It is also, by design, optimized for consistency. Approval processes exist to catch exceptions. Review structures exist to ensure conformity with established standards. Escalation paths exist to bring unusual situations to people with the authority to make binding decisions.

All of that makes sense for the environment it was built to serve. The problem emerges when that same operating infrastructure is applied to the work of strategic change — which by its nature produces results that don't conform to established patterns, requires evaluation frameworks that don't yet exist, and depends on the willingness to learn from early signals rather than judge them against historical benchmarks.

The operating infrastructure isn't broken. It's optimized for the wrong conditions.

What this looks like in practice

I watched this happen firsthand at a large financial services organization. A capable digital team ran a test. The results were entirely normal for a first attempt in a new channel, with response rates that any experienced digital marketer would recognize as a reasonable baseline from which to learn and iterate. In the weekly meeting of the full senior executive team, a leader with significant authority over the program declared the results a failure — speaking with enough confidence that his view became the prevailing one in the room. With the CEO present and a culture that discouraged challenge, no one pushed back. A single comment, from someone with no digital or marketing background, applied with authority to work he lacked the context to evaluate, ended the conversation. The capability had arrived. The organizational conditions to use it correctly hadn't.

This pattern has three common expressions in insurance organizations today.

The first is evaluative mismatch: new kinds of work being assessed against criteria designed for old kinds of work. A digital initiative judged by the same ROI timeline as a mature product line. An AI pilot evaluated for consistency when the relevant question is what it's learning.

The second is authority distance: decision-making power sitting structurally removed from the market or operational reality being addressed. The people with approval authority don't have direct experience of the conditions the work is designed to respond to. Their judgment isn't wrong in the abstract. It's not calibrated for the specific context.

The third is what I'd call capability capture: the organization is so effective at socializing people into how things are done that genuinely different thinkers gradually stop thinking differently. It happens without anyone noticing. The cross-boundary judgment you hired for gets sanded down by the daily reality of operating inside a system that rewards conformity to established patterns.

None of these are personnel failures. They are system behaviors.

The data behind the pattern

BCG's research on insurance and AI tells a story that is easy to misread. Insurance, it turns out, is ahead of nearly every other sector in AI adoption. Insurers are experimenting, piloting, and investing. By the adoption measure, the sector looks like a leader.

The same research shows that only 7% of insurers have brought AI to scale across their organizations. Two-thirds remain in pilot mode.

That gap — high adoption, minimal scaling — is the organizational conditions problem made visible in numbers. It is not primarily a technology problem or a talent problem, though both matter. It is a problem of operating infrastructure. Pilots succeed in controlled conditions precisely because they operate outside the normal approval and evaluation processes. Scaling requires bringing the work inside those processes. And the processes weren't designed for it.

The question worth asking is not why so many initiatives fail to scale. The more useful question is what would have to be true about how the organization operates for scaling to be possible.

The capability gap underneath the execution gap

Closing the distance between ambition and execution requires building organizational capabilities that most insurance companies have not yet systematically developed.

The first is decision quality under uncertainty: the ability to make sound judgments when the data is early, the patterns are unfamiliar, and the right evaluative framework hasn't been established yet. This is different from risk management. Risk management is about known categories of uncertainty. This is about navigating genuinely novel conditions.

The second is coalition building across boundaries that don't naturally connect. Strategic change in insurance requires people in underwriting, technology, compliance, distribution, and customer experience to develop shared frameworks for evaluating work in progress. That doesn't happen through org chart alignment. It happens through deliberate capability building across functions that have historically operated in parallel.

The third is governance embedded in execution, as distinct from governance applied to execution. The difference matters. Governance applied to execution is a checkpoint — often a late one — where work is reviewed against compliance and consistency standards. Governance embedded in execution means the oversight function is integrated into how the work is designed and evaluated from the beginning, which allows for faster iteration, earlier identification of genuine risks, and fewer expensive course corrections.

These are not soft organizational development topics. They are the operating conditions that determine whether the talent you hire — whether traditionally credentialed or cross-boundary — can actually do the work the organization says it needs done.

The real question for insurance leaders

Insurance organizations are serious about strategic change. The investment is real. The intent is genuine. In many cases, the talent decisions are improving.

The harder question is whether the organizational conditions are keeping pace.

It is possible to hire for the AI era and still run a review process calibrated for a different one. It is possible to recruit cross-boundary judgment and then route every significant decision through an approval structure designed to enforce conformity. It is possible to announce a bold agenda and have it arrive at the middle of the organization as a set of directives that don't connect to how work actually gets done. A vision that generates genuine executive commitment can quietly lose momentum six months in — not because the commitment faded but because the organization lacked the operating conditions to carry it.

Nobody canceled it. It just stopped being anyone's job.

The gap between ambition and execution in insurance is not primarily a strategy problem or a technology problem. It is an organizational capability problem. And it will not be closed by the next hiring class, however strong they are, if the operating conditions and operating infrastructure they walk into were built for a different era.

The talent question and the organizational conditions question are not sequential. They have to be answered together.

There is something that happens inside large insurance organizations that is easy to observe and hard to argue with.

Technical expertise accumulates over years, sometimes decades. The people who have it know things that cannot be quickly learned — the product complexity, the regulatory relationships, the underwriting logic, the claims nuances that separate a defensible decision from an expensive one. That knowledge is real. It was hard-won. And organizations that prize it aren't wrong to do so.

But at some point, pride in expertise stops being a competitive advantage and becomes a closed door. "We know how it's done" is a statement that can mean two very different things. It can mean: we have deep capability that outsiders underestimate. Or it can mean: the way we've always done it is the way it will be done.

Those two meanings lived comfortably together for a long time. In a stable, regulated environment where the job was consistent execution at scale, they were essentially the same thing. They are not the same thing any more.

The role has changed. The hiring criteria haven't.

My direct experience is in life insurance, which gave me a specific window into one part of a much larger and more varied industry. But the structural pattern I'm describing shows up across the sector in the data.

Insurance has one of the highest employee tenure rates in the U.S. economy. According to the Bureau of Labor Statistics, median tenure in insurance was 4.9 years as of January 2024, compared with 3.5 years across the private sector overall. That gap reflects something real: Insurance is technically complex enough that the learning curve is steep, the career pathways are well-defined, and once someone has built genuine expertise, there are good reasons to stay.

The result is an industry with deep organizational memory, strong internal culture, and — this is the part worth sitting with — a hiring logic built around selecting for people who already fit that culture. Sector experience as the primary filter isn't laziness. In a domain this technical, it looks like prudence.

The problem is that the middle manager role in a transforming insurance organization now requires something sector experience doesn't reliably build. It requires what I'd call cross-boundary judgment: the ability to synthesize signals across domains that didn't used to talk to each other, to make decisions without a clear precedent in the playbook, to manage a workforce whose skills and expectations are shifting while simultaneously absorbing a strategic pivot and maintaining execution velocity. All at once. Often with the same or reduced resources.

That is not a job description. That is a description of what transformation actually asks of the people accountable for making it happen. And it is a set of demands that years of deep sector experience — on its own — does not prepare you for. In some cases, it prepares you against it. The longer you've succeeded by applying known patterns, the harder it becomes to recognize when the pattern no longer fits.

What the wrong filter produces

Steve Jobs made a version of this argument decades ago, about the need for people who could move fluently between technical depth and human experience. George Anders developed it further in his work on what he called "jagged resumes" — candidates whose career paths crossed domains in ways that looked unconventional on paper and proved, in practice, to be exactly the flexibility that complex environments require.

Insurance has its own version of this problem, and it is structural. The sector-experience filter isn't applied by accident. It's applied because the technical complexity is real, because the regulatory environment — state-by-state in the U.S., country-by-country for global insurers — demands people who understand the stakes, and because the consequences of a bad judgment call in a regulated environment are not abstract. These are legitimate reasons to prize expertise.

But the filter is being used to solve a different problem than the one that now exists. The technical complexity of insurance hasn't disappeared. What's changed is that operating in that complexity now requires people who can also navigate conditions that have no established pattern — AI-driven workflows that are being invented in real time, workforce dynamics that have no precedent, competitive pressure from insurtechs that are unburdened by the infrastructure that makes large insurers what they are.

The sector is not short of people who know how insurance works. It is short of people who know how insurance works and can operate effectively when the rules of how it works are being rewritten around them.

Vertafore's 2023 survey found that roughly one-third of insurance professionals entered the industry from another sector. That means cross-sector pathways are already significant — the question is whether those entrants are being placed in roles where their cross-boundary capability actually gets used, or filtered past hiring managers who default to the most familiar profile.

What this costs

The talent shortage pressure is real and accelerating. Industry projections suggest approximately 400,000 workers will leave the insurance industry through attrition and retirement in the near term. That is not a diversity initiative argument. It is a pipeline arithmetic argument. The experienced cohort is aging out faster than it is being replenished, and the incoming generation has different expectations.

A 2025 survey by Young Risk Professionals found that 69% of insurance workers ages 21 to 35 believe AI will improve their workflow — but only 8.5% report being strongly encouraged to use it at work. That is not a technology gap. That is a judgment gap. The people who could help the sector absorb what is coming are already inside the building. The question is whether the organization is structured to hear them.

The hiring filter problem compounds this. If the primary selection criterion remains sector experience, the incoming talent pool shrinks precisely when the need for new capability is at its highest. And if the organizational culture treats unfamiliarity with established patterns as a disqualification, it will systematically exclude the cross-boundary judgment that transformation now requires.

The question worth asking

Insurance organizations know they need to transform. The evidence is visible everywhere: AI pilots underway, digital initiatives announced, transformation programs staffed and funded. The commitment is real.

What is less clear is whether the talent strategy is keeping pace with the transformation ambition. The sector's technical and regulatory complexity hasn't diminished — it has grown. The expertise required to navigate a multi-state regulatory environment, to underwrite complex risk, to manage claims with precision — none of that is going away. Those capabilities still matter enormously.

The question is whether organizations are also building muscle in what transformation now additionally requires: the ability to synthesize across boundaries, to act under genuine uncertainty, to lead people through conditions that have no established pattern. These are not soft skills. They are the core operating requirements of change leadership in this era — capabilities like cross-functional coalition building, decision quality under ambiguity, and the organizational readiness to absorb what AI and digitization are actually asking of the people responsible for making them work.

Are insurance organizations finding the right balance between deep sector expertise and these newer demands? Are they developing change leaders built for this era — or are they still relying on the 20th-century model of change management, which assumed that expertise plus a clear playbook was enough?

Those are the talent questions that will determine whether transformation investments produce results — or produce another round of pilots that never quite scale.

This is the first in a two-part series. Part Two examines why organizations that hire differently still struggle to deploy better judgment when they find it.

One in five. That's roughly how many drivers in states like Florida get behind the wheel without insurance, according to the Insurance Research Council's most recent data. The standard explanation is economic. Coverage costs are often too much, so some people go without. The policy response follows: steeper penalties, higher surcharges for lapsed drivers trying to come back. The diagnosis is not wrong, exactly. But it is incomplete in one critical respect: it treats the uninsured rate as something that happens to the insurance industry, rather than something the insurance industry has, in meaningful part, produced. I'd argue that a clear look at how non-standard auto products are designed in Florida suggests the latter and that the implication, for those of us who build these products, is more uncomfortable than the industry has typically been willing to acknowledge.

The Fee Cascade

Picture a driver who has been paying premiums faithfully for months. Then one paycheck comes up short. One missed installment. What happens next isn't bad luck; it's a sequence that was designed. Many non-standard carriers respond to a missed payment by assessing a Late Payment Fee. That fee gets added to the arrears, inflating what's already owed. If the swollen balance tips the driver over the edge, the policy cancels. Then comes the Reinstatement Fee. Now the driver is staring down up to four compounding obligations at once: the original missed amount, the late fee, the reinstatement fee, and potentially a catch-up payment to get back in good standing.

For a household running on variable income, that cascade is often the breaking point. Not a choice. Not a misunderstanding of consequences. The product made recovery too expensive at exactly the moment financial strain was most acute. This isn't an edge case. It's the mechanism by which the non-standard market, in aggregate, produces and sustains a meaningful share of the uninsured population.

The Price of Re-Entry

The compounding doesn't end at cancellation. When a lapsed driver's financial position stabilizes and they try to get back on the road legally, the industry often greets them with a surcharge. The lapse, the very outcome the fee structure helped produce, is now a rating factor. Re-entry premiums are higher than they were before cancellation. Down payments may be steeper. Carriers often treat the interrupted tenure as a non-payment risk signal, so the customer who couldn't clear a compounded reinstatement balance may now face a bigger first-payment obligation than they would have had they never lapsed at all.

The cycle sustains itself. Fee structures, reinstatement terms, and rating factors are deliberate product choices, not features that emerged without anyone's involvement. The uninsured rate is, among other things, a record of their cumulative effects.

A Different Product Design

When we built Clearcover's non-standard product in Florida, we started from a different premise: The fee cascade isn't an inevitable cost of serving a financially volatile segment. It's a design choice, and design choices can be remade.

We replaced the typical compounding structure with a single, knowable charge that doesn't grow during periods of financial strain. Paired with payment flexibility built around the income variability that defines much of the non-standard segment, the goal is straightforward: design products for the reality of how customers in this market actually manage money, and price the risk accordingly.

We're not arguing this is the only way to design a non-standard product. We're just saying it's a way worth trying, and that the early signal is promising enough to invite the broader segment to keep experimenting too.

The Honest Reckoning

Product design isn't the only reason drivers go uninsured. But honest reckoning requires acknowledging that the industry's fee structures and rating rules have not been neutral. They have worked, systematically, to make re-entry harder for the drivers most likely to lapse, compounding financial strain in a population that had already demonstrated it was operating at the margin. That's not an accident. It's a policy choice, and it has consequences that show up in uninsured rate data every year.

The philosophical shift the moment calls for isn't complicated, even if the execution is. As an industry, we all need to stop designing products that treat a missed payment as an opportunity. We need to build them for the reality of how customers in this segment manage money. The uninsured driver problem isn't a compliance problem to be resolved through enforcement. It is the predictable output of product decisions often made by this industry and we have the capacity to rebuild those decisions intentionally.

Consider agriculture. It is one of the oldest industries in human history, and among the last you might expect artificial intelligence (AI) to meaningfully reshape. Yet precision agriculture is doing exactly that. Satellite imagery, soil sensors, weather models and other tools are being integrated and synthesized by a new generation of AI models to guide planting decisions, predict yield variability, and optimize irrigation at the individual acre level. Crop insurance underwriting, for an example closer to home, once driven almost entirely by historical loss tables and weather averages, is being rewritten around real-time field data that only machine learning models can interpret at scale. An industry defined by tradition and seasonality is being transformed by technology faster than some financial services firms have updated their customer portals.

The insurance industry is at a similar turning point. For years, insurers have orbited platform modernization, making small improvements and then pulling back due to operational risks. Legacy systems have kept organizations in a holding pattern: stable enough to operate, but less agile in adapting to the pace the market now demands.

That dynamic is shifting. AI is fundamentally transforming the way insurance platforms are built and run, turning modernization from a long-term goal into an immediate strategic priority. This is no longer only about small efficiency gains. Platform modernization now takes center stage in competitiveness, partnerships and making better decisions at scale.

Why legacy platforms keep insurers grounded

Many insurers operate within monolithic core systems that integrate policy administration, billing, claims, underwriting and reporting within a tightly coupled environment. Often customized over decades, these systems are deeply embedded in daily operations. As a result, modernization can feel less like a technology upgrade and more like open-heart surgery.

The limitation is not age but adaptability, and at a more fundamental level, the design philosophy of what a core transaction system should be. Legacy platforms were not architected to be open. They are walled gardens with narrow access, mostly through user interfaces, built to control entire workflows and departments within a single environment. This philosophy benefits software vendors but limits an insurer’s ability to customize, adapt and integrate AI capabilities. The issues go deeper than closed systems: Many use data models that evolved haphazardly over time, which hinders external integration, limits automation, and makes large-scale changes slower and more costly than organizations would like.

This creates a frustrating paradox. To leverage AI-assisted development or intelligent automation, insurers must first invest in foundational data cleanup and restructuring. These efforts are costly, time-consuming and out of sync with the pace of innovation today. For technology leaders, the question is no longer whether to modernize, but how to sequence it without destabilizing the business.

The data mindset that determines success

Modern, open systems help deliver faster underwriting, improved claims outcomes, sharper risk selection and scalable automation. However, these outcomes depend heavily on the quality of the underlying data, which, for many insurers, is the main limitation.

Specialty insurers working with diverse distribution networks across many lines of business encounter partners spanning a wide spectrum of technical maturity. From small, focused underwriters with spreadsheet-based toolsets to large organizations with dedicated engineering teams, each engagement brings its own data structures, conventions and integration requirements. The challenge is not only ingesting that data, but normalizing and validating it to support actuarial analysis, financial reporting and program oversight across a complex book of business.

When data foundations are weak, the consequences appear across everyday operations:

• Program onboarding processes stall because agents and brokers cannot quickly answer questions that existing data should already resolve.
• Claims adjudication is fragmented, with processes and details scattered across systems and inaccessible to all stakeholders in real time.
• Bordereau files remain the standard, with limited adoption of modern data integration methods such as APIs, leaving validation manual and error prone.
• Reporting remains rigid, depending on static PDFs and IT assistance for even minor updates.

These are not merely edge cases; they are the natural result of platforms built before today’s data and integration requirements fully took shape.

Forward-thinking insurers are already addressing these issues by validating data earlier in the submission flow, streamlining ingestion pipelines, and offering program-level analytics that improve transparency for distribution partners. The ability to exchange accurate, timely data is becoming a meaningful competitive differentiator.

Knowing where, and where not, to apply AI

One of the most consequential decisions technology leaders face during modernization is not which AI tools to adopt, but where to deploy them. AI delivers outsized returns in specific contexts and introduces risk when applied in the wrong ones.

The highest-value, lowest-risk applications tend to cluster around workflows and customer interactions: automating bordereau validation, surfacing claims anomalies, generating underwriting summaries, accelerating document review, or guiding agents through submission requirements. These are areas where AI augments human judgment, reduces friction, and operates alongside existing systems without requiring those systems to change.

Replacing core transaction systems is a different conversation. Policy administration, billing, and claims settlement involve regulatory compliance, audit trails and financial integrity requirements that demand extreme care. Applying AI directly to these systems, without strong data governance and testing frameworks, introduces risk that often outweighs the short-term gain. The better path is typically to modernize the underlying architecture first, then build AI capabilities on a stable foundation.

Organizations that conflate “apply AI everywhere” with a modernization strategy often find themselves with sophisticated models sitting on unreliable data, or automated workflows breaking at the points where legacy systems assert themselves. Discipline about where AI creates value, and where foundational work must come first, is what separates effective transformation from expensive experimentation.

How AI changes the modernization equation

AI is not only speeding up platform modernization in insurance; it is transforming how it occurs. In the past, transformation has often been seen as a large-scale, multi-year project to replace core systems. For platforms handling high transaction volumes, the cost, complexity and operational risk of this “big bang” method often outweighed the advantages.

AI shifts that calculation in two distinct but complementary ways: how new applications and tools are built and deployed and how AI is embedded directly into workflows to support and automate decisions. These are not the same thing, and conflating them leads to poorly sequenced investments.

AI development tools: Building and deploying faster

The first wave of AI impact for most technology organizations is on the build side: using AI-assisted development tools to compress the time it takes to design, build, test and ship new internal applications. Tools that generate code, write tests, scaffold architectures and accelerate documentation review are not marginal productivity improvements. They are changing what a small team of engineers can deliver in a quarter.

For insurers, this means that internal tools, which previously required months or years of development, in addition to a vendor and system integrator relationship, can now be prototyped in weeks by a small internal team: a partner portal that consolidates program reporting, a claims intake tool that pre-populates fields from submitted documents, and a bordereau ingestion utility that catches data errors at intake rather than surfacing them days into the processing cycle. These applications do not require replacing the core system; they sit alongside it, connect via APIs, and deliver immediate operational value, if the core system supports it.

Technology teams that embrace AI development tooling can reclaim capabilities that have historically required large vendor programs or costly system integrators. They can move faster, iterate based on user feedback, and build institutional knowledge rather than external dependency. The organizations deploying these tools today are already compressing timelines that once seemed fixed.

Embedding AI in workflows: decisions at scale

The second wave is more fundamental: embedding AI directly into operational workflows to improve and automate the decisions that drive the business. This is where the economic case for modernization becomes clearest, and where the data foundation matters most.

Workflow-embedded AI is not a tool a user opens and closes. It is:

• Judgment built into the process itself;
• An underwriting workflow that scores submission quality before a human reviews it;
• A claims triage model that routes cases by complexity and coverage signals in real time; and
• A renewal pricing engine that incorporates loss history, external data, and portfolio exposure without requiring manual assembly. 

These are structural changes to how decisions get made, not incremental improvements to existing processes.

 The distinction between these two modes matters for sequencing. AI development tools can deliver value relatively quickly, even in environments with imperfect data, because they accelerate human work rather than depend on it. Workflow-embedded AI, by contrast, is only as reliable as the data it operates on. A claims-routing model built on incomplete or inconsistently coded data will produce inconsistent decisions. Getting the data foundation right is a prerequisite for this second wave, not a parallel workstream.

Together, these shifts fundamentally change the economics of modernization, lowering barriers to entry and expanding what is possible for more organizations.

Choosing the right retirement strategy for legacy systems

How an organization exits its legacy systems matters as much as what it builds next. The right strategy depends on transaction volume, regulatory complexity, partner dependencies and appetite for operational risk. Three patterns emerge repeatedly in practice.

The strangler pattern

Rather than replacing a legacy system wholesale, new functionality is built alongside it. The modern system gradually takes over individual capabilities — a microservice here, an API layer there — until the legacy platform is functionally surrounded and can be decommissioned without a disruptive cutover. This approach minimizes operational risk and is particularly effective for large, tightly coupled systems where a full replacement is not feasible.

Microservicing and modular decomposition

Some organizations carve specific domains out of a monolithic system and rebuild them as independent, API-driven services, such as claims intake, document generation, or rating, while leaving the core transaction engine intact for now. This creates optionality: Each domain can evolve independently, integrations become cleaner, and the organization builds modern engineering capability without betting the business on a single transformation program.

Sunsetting and runoff

For legacy systems supporting books of business with short or reasonably short policy periods, managed wind-down is often the most pragmatic answer. New business moves to the modern platform immediately; the legacy system is maintained, but not invested in, for the life of the in-force policies. This approach is less visible than transformation but is frequently the most cost-effective and operationally sound path for systems that are not worth rebuilding around.

A mature modernization strategy typically combines elements of all three: strangling core transaction systems, decomposing specific domains into services, and sunsetting legacy platforms that no longer justify investment. Recognizing which pattern applies where is itself a strategic discipline.

The right conditions for change

Since the insurance ecosystem will never be entirely uniform, achieving complete alignment across platforms or data models is neither practical nor essential.

What is achievable is better data exchange. More interactive, near-real-time data integration can deliver measurable value without requiring a complete system overhaul. Progress depends as much on collaboration as on technology, emphasizing the need for open, practical discussions about current data flows and how they can be enhanced for the future.

Ultimately, success will not be measured by who creates the most advanced platform, but by who develops the most adaptable one. Open integration, flexible data structures, and the ability to meet partners where they are will define the next wave of market leaders. The industry has spent years addressing this challenge. With the right tools, patterns, and organizational discipline now in place, the conditions for meaningful change are finally within reach.

About the author

Joe Lettween is Chief Innovation, Data Science, and Technology Officer for global specialty insurer Fortegra. 

Sponsored by: Fortegra