Group health insurance executives: If your AI is constricted, so are your policies.

In recent years, underwriters and other group health insurance policy designers have leaned in to artificial intelligence tools. In fact, a recent NAIC survey of U.S. health insurers showed that 84% currently use some form of AI and machine learning.

This substantial shift toward AI policy design and management tools is completely understandable – and completely necessary to stay competitive. In a sector where rising costs, unpredictable claim patterns and shifting risk profiles continue to hinder forecasting precision, business-as-usual methods often fall short as teams responsible for assessing and managing risk are charged with making faster, more accurate decisions with fragmented data.

In this increasingly demanding landscape, AI solutions can analyze large datasets quickly, apply consistent methodologies and uncover insights that otherwise would have gone overlooked. In addition to expediency, such tools can yield cost containment, pricing transparency and deeper customization. Unsurprisingly, then, 75% of executives polled in a recent Roots Automation survey deemed AI tools key to premium growth; more than half reported that AI accelerates the quoting process, and nearly half are using AI solutions to help reduce loss ratios.

However, fully realizing the risk management benefits of AI solutions means bringing them out of their single-use silos. The underlying principle is simple: since AI excels at mining and measuring multiple factors with exceptional speed… why limit the data it analyzes? After all, the more factors group health policies can consider, the more accurate, resilient and cost-effective they will inevitably be.

This article explores how group health insurers can optimize AI usage and maximize its game-changing effect. This can only be achieved when AI-powered solutions are integrated into enterprise-level workflows to facilitate consistent, data-driven insights throughout policy lifecycles.

The AI Silo Trap

Anyone of a certain age will remember the big, boxy desktop computers of the 1980s and early 1990s. As the PC revolution took off, so did the ease and speed at which once-onerous tasks could be performed. Everything from word processing to number crunching became a lot easier in a hurry. It was useful, impressive and altogether helpful.

And it was nothing compared with what came next: networking.

Once computers were linked to each other via the World Wide Web, their applications and usefulness were exponentially amplified. Knowledge could be collected more broadly; trends and the opportunities they uncovered could be noticed and acted upon more quickly. The tagline of the day may have been "You've Got Mail," but the force that drove the internet's rapid proliferation was that, suddenly and forevermore, our newly connected computers provided access to far more knowledge than any one PC could offer. Knowledge shared was knowledge gained.

Fast forward to today, and artificial intelligence is emerging from its nascent, newfangled days into the biggest buzzword on the planet, let alone the insurance industry. And like the pre-internet days of PCs, the potential benefits of today's early-stage AI solutions – let's call them AI 1.0 – are being underused largely for lack of connectivity.

In this still-siloed landscape, many organizations that design and manage group health policy lifecycles rely on one set of AI tools and methodologies for assessing risk in new business, another for existing client renewals, and yet another for managing member health risk. Still, the results have been undeniably encouraging: with growing volumes of consumer data available from medical, prescription, and lab sources, even rudimentary AI solutions are making crisper, more confident decisions that go beyond the limits of personal judgment and historical patterns.

Unfortunately, these benefits have led to blind spots. AI solutions have proven so promising that most organizations have overlooked the logical next step: connection.

While AI solutions in and of themselves are exceptional inventions, their effect is limited when constrained to single-set columns. Among other pitfalls, this approach may lead to disconnected data and a potential inability to account for shifts in group risk profiles.

In an environment as multifactored and ever shifting as group health insurance policy lifecycle management, the time has come for AI solutions to take the natural next step in their evolution. Insurance players are well-advised to move from standalone AI processes to enterprise-level, full-cycle workflows that align risk management across new business acquisition, population health management, and existing group renewals.

Better Together: Connected AI Solutions

Much like the dawn of high-speed internet in the late 1990s, group health's fledgling "AI 2.0" era promises unprecedented advantages. Opportunities now exist to transcend segmented AI tools by implementing sweeping AI solutions that provide truly integrated lifecycle risk management. Simply put, such solutions replace several stagnant tools that each examine one aspect of policymaking with one versatile solution that monitors all aspects.

When AI solutions are properly integrated across the myriad datasets inherent in group health, they can maintain tightly controlled continuity across policy lifecycles. First and foremost, connectivity breeds data consistency, which in turn supports enhanced decision-making while providing actionable, member-level insights to power care management solutions.

By rooting decisions in the same risk logic across initial quoting, renewal pricing and continuing population management, this un-siloed, unshackled approach enables end-to-end application of shared data signals and risk methodologies. The result is reduced variability and improved portfolio performance.

Like the internet before it, such solutions thrive on one overarching principle: knowledge shared is knowledge gained. Faster quote turnaround times reduce underwriting lifecycle friction, and unified historical and real-time data inform seamless renewal transitions. With group health's countless footnotes and fine print suddenly on the same, succinct page, the resulting reliable benchmarks optimize underwriting strategies through the newfound ability to measure performance and identify improvement opportunities at each stage of a group's lifecycle.

Of course, any transition can bring challenges – including, for starters, determining precisely how to begin. At the inception of the enterprise-level AI integration journey, insurance organizations should carefully consider their key priorities. At the heart of this introspection is one question: what does interconnectivity-driven success look like?

What challenges are the organization's policy lifecycle management experiencing because, for example, its new business and renewal underwriting tools are separate? Which workflows or decisions would benefit most from shared data and consistent risk scoring? What internal systems or processes will need to connect with the new platform? And of course, how will we train and properly prepare our workforce for this next-generation solution?

In many cases, these considerations mirror patterns seen across the broader market. Let's close with a few examples showcasing the value of AI solution synchronization.

Use Case #1: Detecting New and Emerging Health Risks During a Policy Term

Based on the original census, an employer group appears healthy during initial policy quoting. Of course, several factors can affect this risk assessment, including final member enrollment and the entrance of additional members during the policy's lifecycle. To better account for these factors, an integrated risk scores solution can provide supplemental data that informs pricing and cost containment strategies at renewal.

Integrated risk score solutions can be especially valuable to companies with high member turnover, or that have newer groups with limited experience. The goal is to supplement a group's limited experience-based risk scores with models that mine third-party datasets.

Use Case #2: Consistent Risk Scoring for Refined Pricing Accuracy

Using different tools for new and renewal underwriting can result in inconsistent risk assessments and pricing. An integrated approach uses the same underlying data signals and modeling logic across both business phases.

Such consistency supports fairer and more accurate pricing, reduces volatility in rate changes, and strengthens relationships with employer groups that expect predictability. When the same factors drive decisions from quoting to renewal, underwriting teams can take different actions based on risk scores, explain pricing shifts more clearly, and maintain trust.

Use Case #3: Improving Underwriter Efficiency and Speed

When new and renewal underwriting data resides in separate systems, underwriters may spend extra time reconciling information or duplicating analysis. With an integrated workflow, teams gain access to a consolidated view of group data, historical insights, and predictive signals in one place. This eliminates repetitive work, speeding up both the quoting and renewal processes. As a bonus, faster decisions mean insurers can respond more expediently to broker and employer requests.

Artificial intelligence promises speed, analytics, and cost savings. Many in the insurance industry see it as the "magic pill" that can fix everything.

But that is an illusion. Algorithms do not build trust. In critical moments, clients remember not the dashboards but how they were treated. Our Ukrainian wartime experience has proved it: technology helps companies function, but humanity is what creates loyalty.

The Illusion of Sufficiency

Chatbots. Automated underwriting. Predictive analytics.

They work — until they don't. The first unexplained denial. The first claims glitch. The first case where the system is "technically correct," yet the customer feels betrayed.

Efficiency is about numbers. Trust is about people.

What AI Cannot Replace

AI can optimize processes. But some things remain deeply human:

• Empathy. On the day someone loses a home or a car, they don't need a bot. They need support.
• Ethics. Statistics do not capture humanitarian exceptions. Human judgment does.
• Leadership. In a crisis, employees and clients listen for an honest voice from the top, not another push notification.

AI can assist. But it cannot show humanity.

Lessons from Practice

We have already seen algorithms cause reputational risks. In mature markets, claims denials without transparent explanations triggered public backlash — and losses far greater than the savings.

In Ukraine, the war became a true stress test. Systems operated under constant disruption. Yet loyalty was built by people — managers answering calls in dark days when power was out.

Clients don't remember the speed of a payout. They remember that someone cared.

The Future: Not AI vs. Humans, but AI With Humans

The winning model is not replacement but partnership.

AI should take over routine: risk analysis, fraud detection, data crunching.

Humans should remain where trust is built.

The companies of the future will not be defined by full automation. They will stand out by combining algorithms with a human face.

Conclusion

AI can count.

But only people can build trust.

Four years ago, days after Mark Zuckerberg debuted the Metaverse, I wrote a Six Things commentary that began: "The vision of a metaverse laid out by Mark Zuckerberg last week is bonkers. Nutso on steroids. It won't be realized in my lifetime, yours or his, even if some of the wildest claims about longevity come true and we all live to be 150."

Since then, the Metaverse group within the company Zuckerberg renamed after what I referred to in that commentary as "a fever dream for gamers" has racked up $70 billion in losses, and Bloomberg and the New York Times reported last week that he is planning to cut staff by between 10% and 30%, possibly in January.

So, in retrospect, I'm just sorry I pulled my punches. :)

Trashing the Metaverse on Day One was not a remotely hard call, because it violated one of the cardinal rules of innovation: As much as possible, an innovation has to fit within the existing work environment or lifestyle of the prospective user. Yet the Metaverse required radical changes in how individuals interact — with, as far as I could discern, no appreciable benefits.

It's worth taking a minute to look at where Meta went wrong, because the mistake is awfully tempting for all of us. 

The Metaverse assumes that people want to live online a huge percentage of the time. You have to produce an avatar to act as you and learn all sorts of new behaviors to interact with other avatars and with everything else that populates the online world. (I tried this a couple of years into the Metaverse experiment, courtesy of a consulting firm that was enthusiastic about its prospects, and it was still quite hard just to maneuver, let alone to talk with others' avatars or to conduct a transaction.) 

The rule of thumb in Silicon Valley is that an innovation has to be 10 times better than anything it is intended to replace, yet the Metaverse was far less useful than the Zoom calls and other technologies we already used, while requiring huge changes in people's routines. 

Apple made a similar mistake with its Vision Pro virtual reality device — and yes, I trashed that, too, right after it was announced at the beginning of last year. I wrote: "There's simply no reason to strap a 1 1/2-pound device to your face (nearly the weight of a quart of milk) and put a three-quarter-pound battery in your back pocket so you can type with your two index fingers in mid-air while strangers or officemates gawk at you. Not when some combination of today's laptops, tablets and phones will do just fine."

The Vision Pro has been a dud for precisely the same reasons the Metaverse has flopped. 

By contrast, Metaverse has a budding hit with the AI it has built into Ray-Ban "smart display" sunglasses. The capabilities are still pretty limited but are enough to get started: You can use voice commands to snap photos, record videos, send messages, make calls, and ask questions of Meta's AI. And Meta isn't asking customers to do anything out of the ordinary. Just about everybody wears sunglasses. Besides, Ray-Bans look cool.

When you look at the history of major technology innovations, they almost all replace something similar. Smartphones replaced iPods, which replaced the Walkman, which replaced transistor radios. Smartphones also replaced early cellular phones, which replaced hardwired phones in homes. There was almost no need for changes in behavior; everything just became easier and better.

Note that once you get a new device into people's lives, like a smartphone, you can start to get them to change behaviors that have nothing to do with the original purpose — when I first saw a smartphone demo, some 25 years ago, I had no idea I'd be doing my banking and shopping on a phone, or listening to podcasts on it and having it monitor my driving.

The insurance industry seems to mostly get this principle, that innovation has to fit into existing behaviors. That's why we're seeing so many dashboards that incorporate the advances in generative AI, gathering information and making evaluations in the background and presenting them to underwriters, claims professionals or agents and brokers as part of their normal workflow. I think chatbots were initially seen too much as a standalone technology but are now being integrated much better into the customer experience.  Whisker Labs' Ting device has taken off because a customer simply has to plug it into a wall socket to have it monitor for electrical issues and prevent home fires. Roost built another Predict & Prevent business by offering batteries that can be plugged into existing smoke detectors and ping a customer's cellphone when an alarm sounds, in case they aren't at home to hear it.

Still, the principle is worth keeping in mind, because the temptation — which I've witnessed across industries in my decades of writing about innovation — is to think that what you're doing is so useful that people will adapt to you, freeing you from worrying about how to adapt to them. 

If Meta and Apple can make that mistake, you can, too.

Cheers, 

Paul

P.S. While I've patted myself on the back for dumping on the Metaverse and Vision Pro right out of the gate, I need to acknowledge that I've made mistakes, too. While I can't recall a time when I savaged an idea or product and been wrong, I've certainly been too optimistic about how quickly change would happen. I try to live by the Silicon Valley dictum that "you should never confuse a clear view for a short distance," yet, well, I sometimes do.

For instance, I wrote an article in 1991 or 1992 that said paper forms no longer had a reason to live, given that we could all input information into personal computers connected to whomever or whatever needed the data. That was more than three decades ago, and, hmmmm....

But at least the article only ran on the front page of the second section of the Wall Street Journal, so only a few people read it, right? 

The insurance industry is entering 2026 with pressure mounting from every direction. Regulations are tightening, consumers are walking in more informed than ever, and agencies are still battling the same silent threat that slows them every day. Their systems can't talk to one another. Data shows up late or contradicts itself. And the very people tasked with guiding buyers through complex decisions often lack the real-time information they need to do it.

This is why 2026 is shaping up to be the year of the connected agency. Agencies that reduce fragmentation, unify their data, and adopt technology with intention will be positioned to capture the full value of the industry's next wave of digital investment. Those who cling to disconnected processes will fall behind consumers who now validate information as fast as it is shared.

Clarity Drives Connection

A connected agency is not defined by how many tools it owns. It is defined by how seamlessly those tools work together. The goal is a low-touch, high-accuracy workflow where agents have immediate access to reliable carrier data, leaders see what drives performance, and consumers receive information they can trust without hesitation.

This shift is happening fast. By 2026, digital investment in insurance is expected to climb more than 25%, driven by demand for automated workflows, integrated data systems, and more personalized consumer experiences. Technology is no longer a support function. It is shaping marketing, underwriting, distribution, and customer engagement.

Still, investment alone does not guarantee improvement. Agencies cannot unlock the value of new tools if their data foundation is fragmented.

Fragmentation Holds Agencies Back

Ask agency leaders about their greatest operational challenges and you hear the same themes. Carrier data arrives in inconsistent formats. Product details get updated in one system but not the others. Teams rely on manual workarounds to resolve discrepancies. Compliance reviews become more difficult as regulations change and competitors accelerate their digital capabilities.

This fragmentation is more than a workflow issue. It affects credibility. Consumers have changed the buying dynamic in a way the industry has never seen before. ACA marketplace enrollment reached more than 21 million people, and combined marketplace and Medicaid expansion enrollment climbed past 44 million last year. That scale reflects a buyer base that is more informed, more comfortable with independent research, and more likely to challenge any detail that feels out of sync.

It is now common for a consumer to verify a quote during a live sales call. When the information they find online contradicts what an agent is sharing, trust erodes instantly. The agency that loses trust loses the sale.

The First Step Toward Modernization

Most leaders understand they need to modernize. What they do not always know is where to start. The instinct is to focus on new platforms, upgraded CRMs, or advanced analytics dashboards. But the most effective starting point is much simpler. Agencies need a clear understanding of their historical data.

Knowing who you serve, which products perform, where gaps exist, and how your book of business has evolved gives you the context to make smarter decisions about every step that follows. It also gives you a realistic baseline to measure improvement.

This approach aligns with broader industry research. Deloitte's digital insurance analysis shows that organizations with unified, trusted data foundations see higher returns on modernization efforts because they can automate more workflows and personalize the consumer experience more effectively.

Once the data foundation is clear, the next decision is choosing the right partners. Smaller and mid-sized agencies do not need to build their own technology. They do not need to hire internal engineering teams or maintain complex systems. They need partners that can bring accurate data, integrated workflows, and automated decision support to the table. That is how they modernize without stretching budgets or shifting attention away from their core work.

The Truth About AI's Role

AI is dominating insurance headlines, and there is real potential in the technology. But agencies should approach it with clear eyes. AI can guide consumer decision-making, support call center staff, and help identify needs based on anonymized data inputs. It can also expand access to personalized insights that would be impossible to generate manually.

But AI only works when the underlying data is clean and accurate. If an agency's data is fragmented or outdated, AI amplifies those problems instead of fixing them. Advanced analytics and AI can drive meaningful value, but only with strong governance and reliable data inputs. Without those elements, AI introduces more oversight responsibility and more operational risk.

For most agencies, the path forward is not to become AI experts. It is to build the foundation that allows AI and automation to work safely and effectively when they are ready to adopt them.

Why Connected Agencies Win

Agencies that embrace a connected model see improvements across several dimensions. Efficiency rises because teams spend less time reconciling data. Institutional knowledge becomes easier to preserve as workflows are documented and digitized. Compliance risk decreases because processes are structured and repeatable. And growth accelerates because agents can spend more time selling and less time troubleshooting systems.

Connected agencies also protect themselves from external volatility. Whether regulations change or carriers update products, a unified system makes it easier to adjust without disruption.

How to Evaluate Technology Partners

As agencies evaluate their options heading into 2026, selecting the right partners matters as much as choosing the right tools. Reputation still carries weight, but agencies should look deeper. The strongest partners offer capabilities that feel like table stakes for a modern agency, not add-ons that require extensive customization or additional cost.

The right partner should reduce friction, expand visibility, and help an agency operate with confidence even when the market changes. They should support a clean data foundation, integrated workflows, and the level of accuracy required to serve a consumer base that double-checks details in real time.

What It Takes to Win in 2026

Success in 2026 is not about size or budget. It is about clarity. Agencies that know their data, streamline their workflows, and align with partners that fill technical gaps will thrive. Those who continue working in fragmented systems will find it harder to grow, harder to stay compliant, and harder to keep pace with a consumer base that expects precision.

The industry is evolving quickly, but the path forward is clear. The connected agency is not a trend or a buzzword. It is an achievable and necessary model for every organization that wants to stay competitive in the years ahead.

In an era when cyberattacks are escalating in frequency, sophistication, and financial impact, the insurance industry finds itself in a peculiar moment—not only underwriting cyber risks for clients but also struggling to protect itself from those very threats. The rising tide of digitalization has created an urgent need for cyber resilience within insurers' own operations. However, the industry is facing a growing challenge: a critical shortage of cyber talent.

As we move into 2026, insurers are increasingly asking a difficult question—can they insure themselves against cyber threats when they are struggling to hire and retain the very talent needed to defend their own systems?

The Double-Edged Sword of Cyber Risk

The insurance industry is uniquely positioned in the cybersecurity conversation. On one hand, insurers are developing and pricing cyber risk products for clients—especially businesses vulnerable to ransomware, data breaches, and phishing scams. On the other, their own systems hold vast amounts of sensitive personal and commercial data, making them lucrative targets for hackers.

Cyber insurance is one of the fastest-growing lines of business in property & casualty (P&C). According to industry projections, the global cyber insurance market is expected to exceed $30 billion in premium volume by 2027. Yet this growth is tempered by mounting internal threats and limited in-house cybersecurity capacity.

Internal Vulnerabilities on the Rise

Recent incidents have made clear that insurance firms are not immune to breaches. In fact, attackers see insurers as high-value targets due to their access to confidential policyholder data, claims histories, and financial information.

Despite increased investments in firewalls, intrusion detection systems, and endpoint protection, many insurers lack the personnel to monitor and respond to cyber incidents around the clock. The result? Gaps in defense, delayed response times, and higher exposure to reputational damage and regulatory fines.

The Cybersecurity Talent Shortage

At the heart of this vulnerability lies a growing talent crisis. Cybersecurity roles—such as threat analysts, security architects, and SOC (security operations center) analysts—are among the hardest to fill in the insurance sector. According to (ISC)², the global shortage of cybersecurity professionals stood at over 3 million in 2024, and the demand has only surged since.

Insurance firms are particularly affected because they must compete with tech giants, fintech startups, and government agencies that often offer more dynamic roles, faster career progression, and higher compensation. Many young professionals perceive the insurance industry as slow-moving or less innovative, further compounding hiring difficulties.

Legacy Systems and Innovation Drag

One of the key barriers to attracting cyber talent is the industry's continued reliance on legacy systems and outdated IT infrastructure. For cybersecurity professionals trained in modern cloud architectures, DevSecOps, and zero-trust frameworks, legacy environments are often perceived as stagnant or restrictive.

While some carriers have accelerated their digital transformation journeys, many are still in transition, which creates both technical and cultural obstacles for cybersecurity hires. This gap between modern cybersecurity demands and legacy environments makes onboarding and retention all the more difficult.

What Can Insurers Do?

To address the cyber talent crunch, insurers must rethink both their talent strategy and their organizational culture. Here are a few steps leading carriers are taking:

1. Rebrand Insurance as a Tech-Forward Industry

Firms need to reposition themselves as digital leaders. Highlighting innovation in AI-driven underwriting, blockchain-based claims processing, and cloud-native architectures can attract a new generation of tech talent who want purpose-driven and cutting-edge roles.

2. Invest in Internal Talent Pipelines

Rather than exclusively hunting externally, insurers can build internal training programs to upskill existing IT staff in cybersecurity. Partnerships with universities, bootcamps, and certification bodies like CompTIA, (ISC)², and SANS can help develop talent in-house.

3. Strengthen CISO Leadership

Chief information security officers (CISOs) must be empowered with a direct line to the board, strategic autonomy, and a clear mandate to drive transformation. Elevating the visibility and authority of cybersecurity leadership can improve team morale and signal seriousness to prospective hires.

4. Leverage Managed Services and AI Tools

Until talent gaps are fully addressed, insurers can turn to managed security services providers (MSSPs) and AI-based threat detection tools to bolster their defenses. Automation can't replace humans, but it can reduce the burden on limited teams.

5. Create Mission-Oriented Cyber Roles

Younger professionals are increasingly motivated by purpose and impact. Insurers can emphasize the role their cybersecurity staff play in protecting policyholders, critical financial infrastructure, and even disaster response systems.

A Call to Action for 2026 and Beyond

The cyber threat landscape isn't going to ease anytime soon. As quantum computing, generative AI, and decentralized finance (DeFi) introduce new vectors of attack, insurance firms must urgently fortify their digital perimeters.

But technology alone isn't the answer. The human layer—those who configure, monitor, and manage these systems—remains the most vital and vulnerable link. Closing the cyber talent gap is no longer a back-office IT issue; it is a business-critical challenge that could determine the long-term viability of an insurer.

The industry must act boldly. This includes building more inclusive pipelines, embracing flexible work models, offering competitive compensation, and nurturing a mission-driven, security-first culture.

Because in 2026, it's no longer just about insuring others. It's about ensuring the insurer can protect itself.

Workers’ compensation insurers are turning to generative AI to improve injured worker outcomes, strengthen performance, and build safer workplaces—here’s how:

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Transparent health reinsurance may now emerge as one crucial and in all probability uncontemplated reform of the government shutdown crisis. The shutdown drew attention to a perfect storm: legislators, impotent to distribute patronage to donors and sponsors dependent on a malfunctioning health care system animated by the best of intentions as Congresswoman Nancy Pelosi (D-Calif.) pointed out in the House recently yet now so overburdened with special interest profit taking that it fails too many participants.

Transparent health reinsurance fills a missing link achieving health freedom and presents nonpartisan solutions addressing Democratic leadership challenges to Republican Senate and House majorities to bring forward workable healthcare policy.

Traditional reinsurance affords risk transfer, often with defined risk exposure parameters, percentages, and ratios for certain lines of risk (treaty reinsurance), facilitates specified claim reimbursements, and frees insurer capital, otherwise set aside for catastrophic claims, for investment. And, it accommodates negotiated deal making for specific policies an insurer may seek to reinsure (facultative reinsurance). It typically mitigates insurer exposure for large scale natural disasters and sustains reinsurer solvency during incidents impelling insurer reimbursements for exceptionally catastrophic claims.

Transparent health reinsurance, by contrast, assures, constitutes, and advances marketplace solutions to "managing risk in connection with healthcare costs," absorbing applicants, and legislating and implementing public policy in synch with technology and the times. The approach is equally ideal for health freedom as it is for remediating current, partisan, patronage system shortfalls.

Timing could not be more propitious. Health and Reinsurance Market Report 2025, a Research and Markets expert report, forecasts that "health and medical reinsurance market size… will grow to $103.25 billion in 2029 at a compound annual growth rate (CAGR) of 7.8%. In the forecast period, growth is expected to be supported by the expanding adoption of digital health platforms, increasing demand for customized reinsurance models, greater participation by self-insured employers, a stronger focus on financial risk mitigation, and rising awareness of reinsurance benefits among smaller insurers. Key trends anticipated include innovations in underwriting models, the development of AI-powered risk assessment tools, increased investment in data analytics and automation, and advancements in health claim management systems."

Transparent health reinsurance, in consequence, now embodies an instance in which corporate and public interests align for consumer welfare.

Health reinsurance innovators enjoy appreciable investment opportunities. "At Qatar Investment Authority [Qatar's sovereign wealth fund], we believe healthcare investments should aim to solve big problems for societies – and the businesses that are doing this are the ones that should thrive and survive…. QIA…can provide patient capital…This long-term approach to growth works best for our partners and aligns with our mandate," Dr. Mohamad Ghanem, QIA healthcare head, observes.

Market liberalization through transparent health reinsurance would also achieve President Trump's vision of empowering citizens to become entrepreneurs with their health care. Transparency animates voluminous data markets, which would enable all market participants to monetize the value of their information. These liberalizations should generate new revenues for all participants.

"Affordability" characterizes contemporaneous sensibilities, and transparent health reinsurance expedites and decongests easy passes to reasonable costs, rational profits, competitive prices, and high quality.

For instance, transparent health reinsurance could compensate outcomes. So doing would provide incentives for physicians to treat and cure patients.

Instead, contemporaneous health insurance compensates services. Just about everything palliates symptoms and protracts illness while patients struggle to get and be well.

As importantly, transparent health reinsurance can initiate systemic reforms by creating and rationalizing markets to address cost and price, key Trump Administration goals and concerns. So doing would liberate and empower all participants to loosen and break the shackles of current health insurance by vastly increasing market reach, size, and variety.

Like the old saw that doing good is doing well, transparent health reinsurance creates wide varieties of new products for the insurance and reinsurance industries 1) rewarding physician, health care provider, institution and hospital supply, and 2) responding to health care consumer demand.

For instance, transparent health reinsurance could broaden insurance coverage for homeopath physicians, whose remedies are often as, if not more, effective than allopathic pharmaceuticals at a fraction of those costs achieving timely cures rather than dependencies on recurrent symptomatic treatments.

There would, of course, be some heavy lifting. Homeopathy is all constitutional while allopathy focuses on addressing and, ideally, curing symptoms afflicting specific systems in one's body. So, pricing and coding integrating homeopathy would have to be conceptualized and tested, and it would have to achieve practitioner, insurance industry and patient consensus and adoption.

National Institutes of Health Director Jay Bhattacharya is especially insightful on systemic health reform in a wide ranging conversation.

The current state of health insurance is vastly more expensive than the original 2010 legislation. Key Obamacare risk coverage, notably risk corridors, went by the wayside. Premiums, point of service costs, and taxes are all higher. Legislator and regulator are each distinctly prey to industry capture. And, going forward, ever ballooning taxpayer contributions and higher health care insurance prices loom.

Unless we do something. 

The insurance industry is facing a critical challenge driven by significant staffing shortages and rising turnover rates. Data from the U.S. Bureau of Labor Statistics suggests the industry is expected to lose nearly 400,000 workers through attrition. This trend highlights the urgent need to backfill an aging workforce and bridge the worker gap, especially as retaining employees for tedious back office work becomes increasingly difficult amid shifting regulatory and customer requirements.

While there has been plenty of hype surrounding artificial intelligence (AI), the real opportunity today lies in using AI agents to strategically fill this impending claims management workforce shortage. By focusing on practical, proven use cases, carriers can determine what tasks can be automated, what will remain a human function, and how AI agents can interact to maximize the benefits for the workforce and overall back-office throughput. The goal is to incorporate the human-in-the-loop so that AI is safe and actually used. Let AI agents do the boring, repetitive tasks so adjusters can focus on judgment, negotiation, and empathy. Humans will be kept in command via review queues and escalation rules.

Here are three ways AI is actually changing claims management and where humans still matter most:

1. AI Handles the Clerical, So People Handle the Critical

The biggest gains in efficiency come from removing friction so that claims professionals can spend more time on strategy, empathy, and problem solving. AI is currently adding real value in focused, repetitive areas and big data applications. Success can be measured by metrics such as intake resolution rate (% calls/emails fully handled by agent), AHT (average handle time) delta (minutes saved per claim) and error rate on field extraction (when extracting knowledge from data).

Key practical and proven use cases where AI is delivering value today include:

• Omnichannel claim intake across email, SMS, and telephony, with entity capture (name, policy, plate/ID) and automatic case creation.
• Knowledge-mining and data processing over large document sets per claim/patient; agents extract tasks and schedule nudges for upcoming visits or missing paperwork.
• Risk signals and fraud triage by comparing millions of claims to spot outliers for SIU review.
• Subrogation and recovery automation: detect subrogation opportunities from facts, generate demand letters, track recoveries.

These applications highlight the concrete ways AI can address the rising difficulty of retaining employees for back-office work.

2. Keeping AI Safe and Trusted Through Human-in-the-Loop Design

As AI systems handle more aspects of the claims process, it is paramount that organizations design systems where humans stay in control, ensuring both safety and trust. This approach is known as human-in-the-loop design, where the AI assists but the human remains in control.

To keep AI safe and trusted, organizations must prioritize the following design principles:

• Confidence Thresholds and Guardrails: These are necessary to decide when AI acts independently versus when it escalates the task to a human. (For example: LLM as a judge "license-plate number ≥0.95, name ≥0.90"→ auto-apply vs queue)
• Designing the Handoff: Claims leaders must focus on designing the precise interaction and transition between the human and the AI, rather than just the underlying model. An incremental adoption example of this is seen in IVR systems with forwarded calls serving as human escalations.
• Trust as a Feature: Transparency, explainability, and auditability must be prioritized at every step. This means showing the sources for information, not just providing answers.

3. Driving Adoption – Because Tools Only Matter If They're Used

AI tools can only deliver strategic advantage and address the workforce gap if they are actually incorporated into daily workflows. Focusing on adoption over mere availability is crucial. Successful incorporation depends on leveraging behavioral and cultural levers.

Agents should join like a new teammate: they sit in channels, see only the data they're allowed to see, and can @mention humans when confidence is low. Companies that route people to a separate 'AI dashboard' will lose adoption; companies that embed agents into existing flows win.

The drivers of real adoption can be broken down into three areas:

• Ability: AI solutions must meet users in their existing workflows; employees should not be asked to change tools. For example, AI functionality should be integrated within claims management systems or email platforms like Outlook.
• Motivation: Organizations must identify champions within the workforce and highlight peer success stories to drive internal motivation.
• Prompts: Adoption can be encouraged through in-workflow nudges, such as prompting a claims adjuster when creating a plan of action note. Other effective reminders include in-system messages, like "You saved 2.5 hours using AI drafting this week," or social/peer prompts sharing success stories.

By focusing on these three foundational approaches, the insurance industry can strategically leverage AI to address its critical staffing shortage and elevate the remaining workforce to focus on high-value, strategic functions.

A Note about Privacy, Security, and Governance

AI in claims is cultural, not just technical: every claimant is a human with an inviolable right to privacy. Agents should be designed to honor that first, then apply industry controls.

• Privacy principles: Data minimization by default; purpose-bound processing; least-privilege access; explicit consent for recordings; subject access and deletion flows.
• Security controls: Encryption in transit and at rest; envelope key management with regular rotation; short, business-justified retention windows; immutable audit trails; per-tenant isolation and row-level security; tamper-evident logs for model/tool outputs.
• Governance: Data Processing Agreements and BAAs where required; vendor due diligence; model/version change logs; approved "never-autonomy" actions; periodic access reviews.
• Regulatory alignment: Designed to align with HIPAA principles for PHI, GDPR for EU data rights, and SOC 2 control families for security and availability.
• Human accountability: High-impact actions require human approval; overrides and escalations are attributed to specific users; exceptions are reviewed in weekly ops.

For insurers, 2025 marked a reset in core systems strategy. After a decade spent patching legacy and modern-legacy platforms, layering point solutions, and stitching together data across disconnected architectures, insurers are now shifting toward rebuilding the core operational backbone required for resilience, agility, and sustainable growth. This is not a cosmetic upgrade; it's a structural re-architecture. The industry signal is now evident: competitive advantage will hinge on the agility, intelligence, and adaptability of an insurer's core platform.

This shift reflects a clear market reality: after years of incremental fixes and deferred modernization, insurers are being forced back to fundamentals. Achieving long-term, risk-adjusted profitability in a volatile environment while meeting rising customer expectations now depends on strengthening the foundations — systems, data, and operating structures — before meaningful innovation can take hold.

Insurers are entering 2026 with one clear mandate: Strengthen the core to unlock scalable, AI-enabled growth. GenAI and agentic AI have transformed expectations across underwriting, claims, and service, but legacy architectures cannot support the data fluidity, governance, and orchestration required. Modernization is no longer a technology upgrade; it is a business model reset.

Below are the key trends and imperatives that I believe will define the insurance sector in 2026:

1. Legacy Systems

Across all lines of business, insurers recognized that 'legacy' and 'modern-legacy' systems — platforms and systems built in the last 10–15 years, but architected on monolithic design principles — are already outdated and have become a structural barrier to progress.

As volatility increased across climate, capital, and customer expectations, the constraints of modern legacy systems became harder to work around and impossible to ignore. These platforms were never designed for API-first distribution, dynamic product configuration, governed AI, continuous delivery, cloud elasticity, or complex ecosystem integration. The result is the same across markets: slow product development, limited data flow, high integration cost, and constrained customer experience innovation.

Imperative for insurers: Seeking efficiency improvement alone is no longer enough, nor should it be the goal. Insurers must shift from a traditional, technology-centric ('inside-out') process automation approach to a more customer-centric ('outside-in') operating model redesign that is squarely anchored on customer journeys, contextual intelligence, and connected data. This requires replacing rigid, policy-centric architectures with data-fluid, AI-ready core platforms that evolve at market speed.

2. The Legacy–AI barrier 

It is real, and insurers are prioritizing platforms that remove it. AI advanced at extraordinary pace in 2025, but insurers discovered a hard truth: AI cannot deliver value if the core platform cannot provide clean, contextual, real-time data. Siloed, batch-based architectures choke AI's ability to reason across the customer lifecycle, perform real-time risk adjustment, orchestrate multi-step workflows, and meet regulatory explainability standards.

Imperative for insurers: The market trend is clear: 2026 is the year AI becomes operational, moving out of innovation labs and into the core workflows of underwriting, claims, billing, service, and distribution. However, AI is only as powerful as the governance and data lineage behind it. To ensure confidence in execution and enable the system to operate at its full intelligent potential, the next generation of cores must embed intelligence at the platform layer, ensure end-to-end traceability, support natural-language workflows, orchestrate AI agents safely, and provide explainable models by design. Systems that can't meet these requirements will be replaced, not augmented.

3. Continuous underwriting

Prioritizing continuous underwriting and real-time risk intelligence will be key in 2026. Static pricing models tied to annual cycles can no longer keep pace with market volatility. 2025 exposed a fundamental shift in how data works inside an insurer: the archive-and-retrieval model of the past is no longer viable in a market that demands real-time intelligence. Data has moved beyond being a historical record that's stored and is now a live, strategic asset that powers real-time decisions, customer engagement, and regulated AI at scale.

Imperative for insurers: Insurers need platforms that treat data as live, structured intelligence rather than historical policy snapshots. That requires event-driven cores capable of ingesting continuous signals, recalculating exposure dynamically, and orchestrating rules and AI-driven decisioning in real time. Only with this architecture can insurers shift from reactive loss absorption to proactive risk mitigation, precision pricing, and real-time portfolio steering embedded directly into operational workflows.

4. Trust, transparency, and governance 

They are now hard requirements, not optional values. In 2025, trust became a measurable business KPI. Regulators, and increasingly customers, are demanding clarity on how insurers handle data, automate decisions, and deploy AI. From the EU AI Act to new North American standards, scrutiny now requires model accountability, explainability, auditability, bias monitoring, and transparent decision trails.

Imperative for insurers: Trust will become the defining currency of the insurance industry and it will hinge on core system governance, not just goodwill. Trust must be engineered, not declared. Core systems will increasingly differentiate on their ability to provide full data lineage, governed workflows, explainable AI, audit-ready logic. This governance layer is becoming a top-three buying criterion for CIOs, CTOs, and CDOs.

5. Climate volatility 

It is forcing a radical re-engineering of property risk assessment. The convergence of climate shocks, fraud complexity, and operational pressure is accelerating demand for unified data orchestration, embedded fraud detection, advanced claims automation, and predictive resilience modeling.

Imperative for insurers: The static, inflexible policy-centric architectures of legacy systems must give way to intelligent, adaptive platforms. Insurers need cores that can merge climate data, IoT telemetry, satellite imagery, fraud analytics, operational AI, and human oversight into governed, auditable workflows. Modern platforms must support real-time risk signals, predictive resilience modeling and event-driven orchestration, enabling insurers to manage volatility, prevent loss and maintain trust at scale. Efficiency, accuracy and resilience must now co-exist in the same system.

6. Group benefits

They are evolving into intelligent, portable well-being ecosystems. Employers and employees alike are demanding personalized, adaptive propositions. Traditional product-centric systems cannot support the flexibility needed for next-generation benefits design.

Imperative for insurers: Success depends on cores that can orchestrate outcomes, not just administer products. This means platforms that support continuous personalization, governed data flows, multi-party ecosystems, and contextual well-being journeys. These platforms must enable outcomes such as benefits that adjust automatically to life events, proactive well-being support, and seamless portability as people move between roles or working patterns. In this model, value comes not from the product but from the continuous, connected experiences that surround it.

7. Pet insurance 

It is rapidly becoming a proving ground for ecosystem-driven, customer-centric transformation. Rising vet costs, increased pet humanization, and demand for digitally enabled care are accelerating the shift from transactional reimbursement models to proactive well-being ecosystems. As pet insurance evolves, insurers are beginning to confront foundational complexities, especially the lack of standardized coding and wide cost variability across veterinary services, which makes product design, underwriting, and pricing more difficult than in human health. These challenges are precisely what next-generation platforms must address.

Imperative for insurers: Pet insurers must operate on platforms capable of turning real-time data into proactive care. This means ingesting real-time pet health telemetry (e.g., wearables, vet diagnostics), integrating across vet, nutrition, and behavioral care networks, orchestrating digital ecosystem partners, and dynamically adjusting coverage and pricing based on evolving health and lifestyle data.

Competing in this new model requires a modern core platform that turns real-time health and lifestyle data into personalized care at scale. Leaders will use intelligent, AI-ready core systems to bring order to this fragmented landscape, enabling data ingestion from wearables and vet systems, dynamic pricing based on real behavior, personalized wellness plans, and preventive care journeys tailored across a pet's life.

The future of pet insurance isn't in replicating traditional health coverage, but in creating connected well-being ecosystems that anticipate needs, deliver real-time value, and position insurers as trusted care partners, not just payers of last resort.