There's nothing like starting the year off with some good news. Conning's Year-End Life-Annuity forecast for 2025-2027 certainly has a lot of that. For life-annuity industry executives, statutory net operating results, after tax and dividends, are forecast to increase from $27 billion in 2025 to $30 billion in 2027.

Those strong results start with premiums, of course. Due to an increasing number of individuals saving for retirement and pension risk transfers, annuity premiums are projected to increase 16% from 2025 to 2027. Meanwhile, life insurance remains a key financial need for younger generations starting or building their families. As a result, life insurance premiums are forecast to increase 8% from 2025 to 2027.

While premium headlines grab attention, life-annuity insurance executives know profitability depends on successfully managing investment returns, reserves and capital. At the same time, both life and annuity insurers need to begin grappling with the potential impact of GLP-1 drugs on claims and pricing.

Investment performance drives sales and profitability

Life-annuity sales and profitability are sensitive to General Account investment performance. That performance depends on two broad factors: the external rate environment and asset allocation strategy. As we look out to 2027, we see the continuation of a favorable external interest rate environment. We also expect insurers to continue diversifying their asset strategies to achieve higher portfolio yields.

Portfolio yields forecast to increase through 2030

Even if the Federal Reserve cuts rates over the forecast period, we project life-annuity insurers will benefit from higher portfolio yield. Higher portfolio yields support fixed annuity and universal life interest-crediting rates, which are favorable for more sales over our forecast period.

Our portfolio projection uses the moving average ten-year Treasury rate and models three scenarios. The first is based on the third quarter of 2025 Philadelphia Federal Reserve's Survey of Professional Forecasters. The second assumes credit losses reduce the spread over Treasuries achieved in insurer portfolios. The third is an aggregate blend of one and two. By 2027, our aggregate projection is for the portfolio yield to reach 4.22%, up from 4.03% in 2024.

Continued asset diversification supports higher portfolio yields

During the longer-for-lower interest rate period of 2015 through 2021, life and annuity insurers began diversifying their assets to generate higher General Account portfolio yields. They decreased allocations to bonds and redistributed to mortgages and Schedule BA assets (alternative assets such as joint ventures, hedge funds, and private equity investments) to gain yield. In addition, there has been a marked shift within the bond portfolio towards private credit.

Even with the recovery of interest rates, we anticipate that diversification efforts will continue through our forecast period.

Reinsurance Continues to Support Growth

Whether onshore or offshore, reinsurance remains a key reserve and capital management tool for life annuity insurers. For example, in 2024, 21% of direct and assumed premiums were ceded. In 2025 and through 2027, we expect that key role will continue. What will be noticed, however, is the growing use of sidecars to support life-annuity reinsurance transactions.

Since 2019, over 15 new sidecars have formed. Looking ahead, we believe sidecars will continue to bring more capital to support life and annuity reinsurance growth. This is a strong positive for the life and annuity industry's forecast capital strength and profitability through 2027.

GLP-1s Affect Claims and Pricing

When we think about claims through 2027, the good news is that excess mortality has receded from the COVID-19 pandemic peak. At the same time, an increasing number of retirees use annuities to generate retirement income and increase annuity benefits. However, the impact of GLP-1 drugs on mortality, morbidity, and longevity is a new factor we and insurers need to consider. These drugs hold the potential to affect life insurance underwriting as well as life insurance and annuity pricing.

There is a concern that applicants may be using the drugs when underwritten for new policies, but then later stop using the drugs, leading to a return of weight and/or other health conditions the drugs treat. On the annuity side, longevity improvements due to GLP-1 change longevity expectations and strain original annuity pricing assumptions. The current impact of GLP-1 on life-annuity profitability may not be large. That said, these drugs may have a long-term effect on claims and pricing beyond 2027.

A Forecast for Strategic Adaptation

How should life-annuity executives respond to the good news in this forecast? The next three years give insurers a rare alignment of strong earnings, improving yields, new sources of third-party capital, and a return to more normal mortality. The companies that use this period to adapt and realign strategy, instead of simply enjoying favorable conditions, will be the ones that lead the industry in the decade ahead.

To position for continued growth and success, life-annuity insurers should accelerate product innovation. Developing flexible products that can adapt to shifting longevity and morbidity trends will help address emerging customer needs and market dynamics.

At the same time, enhancing the investment strategy remains essential. Continued diversification of the General Account portfolio, with a focus on private credit, mortgages, and alternative assets, will help optimize yields. Partnering with key asset management partners will be crucial to executing successful diversification strategies.

Leveraging reinsurance and third-party capital is another strategic priority. Expanding the use of reinsurance, including innovative structures like sidecars, can help manage capital efficiently and support growth objectives.

Driving operational excellence is crucial for sustainable growth. Investing in technology and analytics can improve underwriting, claims management, and customer engagement, while streamlining operations will enhance efficiency and scalability.

Finally, strengthening regulatory and capital management practices will help companies stay ahead of evolving requirements, maintain robust capital buffers, and support business expansion in a volatile market environment.

By executing on these strategic priorities, life-annuity insurers can capitalize on favorable market conditions, manage emerging risks, and position themselves for sustainable growth through 2027 and beyond.

Elite athletes don't wait for an injury to start strengthening their bodies. They invest in preventative rehab or 'prehab'—the stretching, conditioning, mobility work, and small adjustments that keep them performing at their peak. It's a disciplined commitment to staying strong and ready for what's next.

Independent agencies can approach the year ahead the same way.

Too often, agency leaders slip into rehab mode, discovering a producer's lagging numbers months too late, noticing revenue leakage only after renewals drop, or realizing that their commercial lines pipeline is weak just as growth goals loom. These issues could have been spotted much earlier if the right visibility had been in place.

The agencies that consistently grow and retain clients are the ones that operate with a continuous prehab mindset. They use real-time reporting and analytics to keep a close watch on their business, ensuring everything stays on track and healthy.

As the new year approaches, this kind of clarity becomes one of the most valuable assets an independent agency can have. Here's how analytics can help keep your agency in peak shape for the year ahead:

Daily Performance Visibility Prevents Costly Surprises

Small imbalances like tightness, fatigue, or weakness tend to appear long before a real injury occurs. Athletes rely on these early warning signs to adjust their training to prevent setbacks before they affect performance.

Tracking your agency's key metrics works the same way. Minor shifts like a slipping close ratio or an underperforming line of business can signal larger issues if left unnoticed. By catching them early, you can take corrective action before they seriously affect your agency.

The key is having an agency management system that not only collects and tracks your performance data but also presents it in an intuitive way. Tracking your agency's metrics provides access to dashboards and reports that give a centralized view of its performance, making it easy to act on insights.

Agency analytics tools can help agents track:

• Policies per term, customer, and transaction type
• Revenue per client
• Retention and renewal performance
• Close ratios per producer and line of business
• Book mix, risk concentration, and line diversification

When these numbers become part of your daily routine, presented through dashboards that highlight trends, risks, and opportunities, you gain a truly proactive view of your business. You can easily identify top-performing areas, pinpoint where improvements are needed and ensure team members stay aligned with your agency's goals.

Leverage Your Book of Business to Uncover Growth Opportunities

Just as monitoring agency metrics helps you identify where your agency is excelling and where improvement is needed, it also helps you uncover new opportunities for growth. With consistent metric tracking, your agency management system can help identify cross-sell and upsell opportunities within your existing book of business, such as clients with monoline policies who could benefit from bundled coverage or accounts with underinsured risks that may need additional protection or policy enhancements.

Instead of relying on producers to manually hunt for upsell opportunities, agency management systems can surface prioritized lists of prospects ready for outreach. This empowers your team to act quickly and efficiently, maximizing the value already sitting in your book.

By using the data you already have, the right agency management technology can turn everyday servicing into meaningful growth that strengthens client relationships and boosts revenue and retention.

Shared Insights Align Your Team Around What Matters Most

Athletes perform best when they know the game plan and understand how their role affects the team's success. Similarly, agencies thrive when insights are shared across the entire team.

The key is having a system that lets you build reports once and schedule them to run automatically. You can use these reports in meetings, share summaries with producers, or provide leadership with KPIs.

Agency management systems often allow customization, letting you filter, group, and visualize the metrics that matter most—whether it's top producers, policy types, or conversion rates—without extra steps or manual reformatting. Having this clarity gives your team the insight to adjust tactics in real time, keeping your agency in top shape.

Turning Insights into Everyday Action

Setting your agency up for success next year goes beyond quarterly check-ins. It's about building daily habits that keep your business strong. By taking advantage of real-time insights and analytics technology, your agency can spot opportunities early, avoid surprises, and stay ahead all year long. Because when you know more, you can do more.

KEY TAKEAWAYS:

• The top-down leadership model is losing relevance as future insurance leaders seek collaboration and dynamic career growth
• Emphasizing the industry's purpose-driven mission and community impact helps attract professionals seeking meaningful work
• Affinity insurance, mentorship, and emerging tech roles offer diverse career paths that foster growth and customer engagement in insurance

With the U.S. Bureau of Labor Statistics projecting the loss of nearly 400,000 workers by 2026, the insurance industry is entering a period of significant change. This potential workforce decline underscores the urgent need for insurers to invest in people, culture, and modern career paths that appeal to today's professionals. To attract and retain future insurance leaders, insurers must rethink how they engage talent and structure career development.

Though often seen as traditional and slow to adapt, the industry is actually changing fast. Technology, evolving consumer expectations, and a workforce that values purpose and flexibility are driving this transformation. To bridge the gap between perception and reality, insurance leaders must embrace modern approaches to leadership, career development, and company culture.

To help our industry overcome today's talent challenges, I believe there are four key areas leaders in the industry should focus on.

1. Evolve Leadership Models

The traditional top-down leadership model, where decisions flow from the top, is losing its effectiveness in today's fast-paced environment that emphasizes collaboration and adaptability. Once common, this hierarchical approach no longer aligns with the expectations of younger professionals. According to PwC's 2024 Workforce Radar, this group is seeking environments where their voices are heard, their ideas influence outcomes, and their career paths feel dynamic rather than rigid. They want to be contributors, not just implementers.

Similar research shows that employees who see a bright future at their company are 1.7 times more likely to stay, 2.3 times more engaged, and 2.4 times more likely to recommend their employer. Yet, more than half of Gen Z and Millennial workers hold a negative or neutral view of our industry, often perceiving it as complex and rigid. This growing desire for dynamic workplaces, combined with the industry's negative perception, creates a major challenge in attracting and keeping new talent.

By shifting to collaborative, team-based leadership, we can bring a wider range of perspectives into decision-making, which is crucial for understanding the diversity of today's consumers. Diverse perspectives foster environments where fresh voices enhance market understanding, strengthen decisions, and build greater customer engagement in insurance. This shift also serves as a powerful signal to younger talent that their contributions are not only valued but also essential to our success and future. Collaborative cultures will help forward-thinking insurance leaders appeal to the next generation of talent.

2. Play Up Our Strengths: The Purpose-Driven Core

At its heart, insurance is an industry built on trust and human connection. Our fundamental mission is to help people navigate some of the worst moments of their lives by providing support and financial security. This core mission is a powerful differentiator for attracting talent who seek meaningful, purpose-driven careers.

For many younger professionals, a job is an opportunity to make a tangible, positive impact. Findings from Deloitte suggest that this generation is motivated by a desire to contribute to something bigger than themselves. By highlighting the security and peace of mind we provide for individuals, families, and communities, we show that a career in insurance is deeply meaningful. When corporate culture and leadership embody the industry's thoughtful, service-oriented values, they resonate with employees who want their work to matter and inspire them to bring their best every day.

Emphasizing this core purpose helps attract emerging talent, demonstrating to them that a career in insurance offers meaningful, lasting impact.

3. Highlight Diverse and Emerging Career Paths

While traditional insurance roles like underwriting, claims, and sales remain vital, they're only part of the story. The industry has untapped potential to showcase exciting paths that attract talent seeking innovation, impact, and growth.

Advanced technology, analytics, and digital platforms are redefining the insurance experience from the ground up. New opportunities in AI, cybersecurity, data science, and user experience design open the door to talent that may have once overlooked the industry as a viable career path. By showcasing how these modern disciplines are transforming our industry, we can attract professionals who may have otherwise gravitated toward the tech sector.

Beyond technology, we can highlight unique career paths like those in affinity insurance programs, where professionals work with organizations that reflect their personal interests, such as a university or a professional association. These positions allow individuals to combine their professional skills with their passions, creating highly engaging and fulfilling careers.

Mentorship programs also play a critical role in both recruitment and retention. Mentees gain exposure to cross-functional experiences and broaden their understanding of career possibilities, while mentors gain fresh perspectives on the priorities and aspirations of the next generation. This mutual learning builds stronger connections and encourages long-term engagement.

When insurance leaders effectively highlight diverse career paths and foster mentorship and engagement, emerging talent is more likely to see the industry as a place where they can grow, innovate, and build meaningful careers.

4. Create Genuine Connections

Building and maintaining strong ties with young professionals is essential for long-term success because when employees feel part of a community, they stay engaged and motivated. In addition to nurturing internal relationships, cultivating external networks and partnerships helps new professionals feel welcome, included, and connected to the broader industry community.

Leveraging professional membership organizations is a powerful way to facilitate meaningful connections between young professionals and experienced leaders. These groups provide a vital link between young professionals and experienced leaders, creating opportunities for collaboration and learning. By participating in these networks, new employees can attend special interest groups, join committees, and form relationships that will shape their careers for years to come.

A strong sense of community helps ease the feeling of being an outsider in a new and complex industry. Membership organizations also provide valuable resources and support, especially as we navigate new technologies and evolving market dynamics. In an era defined by change, having a trusted network for shared knowledge and support is more important than ever.

These connections strengthen the industry as a whole. While young professionals gain guidance and insight, established leaders gain access to fresh perspectives and promising talent. Together, these relationships strengthen the industry's future and foster a culture of shared growth and innovation that will help attract, develop, and retain the next generation of insurance leaders.

Shaping the Future of Insurance Careers

The talent gap in the insurance industry is both a challenge and an opportunity shaped by technology, talent trends, and culture. Companies that invest in meaningful connections, diverse career paths, and inclusive environments will not only retain top performers but also build a more engaged workforce. Supporting mentorship and embracing new technologies helps organizations innovate while staying grounded in core industry values. By doing so, they create environments that draw emerging professionals and strengthen the industry for years to come.

How quickly have enterprises across industries embraced AI? The answer depends on your view of its origins. Some people believe AI dates back to Alan Turing's early theories of machine intelligence. Others see the Dartmouth Summer Research Project on AI in 1956 as its birth.

What is unarguable, however, is that AI has moved at breakneck speed since the launch of ChatGPT three years ago. A recent McKinsey survey revealed that 88% of organizations now use AI regularly. Yet there is a downside. Only one-third of respondents have scaled AI enterprise-wide, and fewer than half can tie any significant earnings before interest and taxes (EBIT) to their efforts.

What will it take for insurers to see real ROI from their AI investments? And how can carriers prepare for an agentic AI future? A look at past tech trends can show us quite a bit about what is to come.

Why Every Tech Revolution Stalls Before It Scales

All significant technological breakthroughs of the past 20 to 30 years have followed a distinct progression. First comes the hype, where inflated expectations generate massive excitement and speculation. Next comes normalization, where practical use cases emerge. Lastly, there is substantive change, where technology becomes embedded in core operations, driving genuine business value.

Within each cycle, however, key barriers exist that block progress once the initial hype fades. In the late 1990s, high-speed connectivity was going to reshape business overnight, but ROI did not come until broadband access expanded. Apple's first iPhone launched in 2007 but did not achieve widespread use until its app store and developer ecosystem matured. Cloud computing brought huge promise in the mid-2000s but did not mature fully until operating models shifted toward DevOps, APIs and microservices.

Even insurtech itself went through a metamorphosis. Early disruptors that set out to overturn insurance incumbents about a decade ago have either been acquired or failed to prosper. Those that focused on partnering with incumbents to modernize core parts of the insurance value chain, however, have grown stronger and manifested an industry that drives innovation in insurance forward.

What's Holding AI Back?

There clearly is no shortage of excitement or action when it comes to implementing AI in insurance. Seventy-six percent of insurers already use generative AI in at least one core business function, making insurance the second-fastest industry to AI adoption, trailing only tech, according to BCG research.

Yet true transformation value remains rather elusive. Despite the breakneck speed of adoption, the results of AI pilots are uneven, and progress is patchy. Only 6% of respondents to McKinsey's survey are classified as high performers. That means they set targets beyond efficiency, embedding AI in existing operating models and new products to capture more revenue and expand cross-selling opportunities.

These high performers have already moved beyond generative AI and toward agentic AI, which involves using AI-powered agents to plan and execute multi-step work. Sixty-two percent of McKinsey respondents are already experimenting with agentic AI, and 23% are scaling agents somewhere in the enterprise. Here again, however, penetration is narrow, with agentic deployments living in one or two functions.

So, what's holding insurers back from ROI with generative and agentic AI? One common thread is deployments that are saddled by the limitations of legacy and mainframe systems. Put simply, insurers cannot reach an AI-enabled future if their workflows are dependent upon decades-old technology that is inflexible and impossible to integrate.

Breaking Through the Barrier

To deliver meaningful returns from their AI bets, carriers must rethink the foundations supporting their technology ecosystems. Many of today's challenges stem from years of product-centric digital transformation that delivered incremental wins but left insurers with a patchwork of disconnected systems. These solutions were never designed for the real-time data or observability that AI and agentic AI require.

A platform-centric approach offers a practical path forward. Unlike point solutions that focus on single steps in the value chain, modern platforms provide the connective tissue that allows data, workflows, models and controls to operate together. Platforms succeed because they allow insurers to redesign workflows holistically; something point solutions were never built to support.

The most effective platforms in the coming three to five years will share several attributes.

• Flexible, API-driven architectures that allow carriers to integrate new technologies into existing systems without a costly rip-and-replace.
• A broad ecosystem of partners and integrations, giving carriers access to specialized tools, such as distribution and fraud detection, without adding further fragmentation.
• AI-enablement at the foundation, meaning agents and models can operate safely and consistently across underwriting, claims, servicing, finance and product development.
• Built-in governance and human-in-the-loop patterns that build transparency and trust as insurers ask AI to take on more complex work.
• Outcome dashboards that provide clear metrics, including cycle time, loss adjustment expense, bind ratio, Net Promoter Score, and revenue uplift per use case.

With a platform-centric mindset, insurers can begin building momentum with their existing AI investments and gradually move toward an agentic future. A smart approach is to embed AI agents into crucial workflows where it can consolidate data, reason about actions, and hand off smoothly to humans, such as quote triage in commercial lines or claims final notice of loss (FNOL) to settlement recommendations. Start small, measure the impact, and then implement AI agents into other projects to make adoption stick.

Navigating the Road Ahead

From the dot-com boom to the rise of AI, history shows us reinvention in insurance happens over time, not overnight. AI is moving faster than any wave before it, but the core principles for insurtech companies and carriers alike remain the same: build and implement platforms that solve real business problems and engender trust with customers along the way. This type of measured approach will propel the next generation of AI-powered insurtech platforms and help accelerate the AI adoption cycle.

The latest government shutdown was the longest in U.S. history. For the insurance industry, it was also a stark reminder of the many difficulties homeowners face when getting policies via the National Flood Insurance Program (NFIP).

During shutdowns, funding for this program can face delays that may prove devastating for those who need it most. That sense of unreliability — along with coverage caps, technological limitations and recent cost increases — helps explain why government-funded flood insurance declined by roughly 2% each year between 2020 and 2024, according to one report.

Meanwhile, private flood insurance has boomed, with the total number of policies expanding at a rate of 20% during that same period. By some estimates, private companies now hold as much as 12% of all flood insurance policies.

It's a trend that shows no signs of slowing, especially with the risk of dangerous floods increasing, bringing greater — or in some cases, newly realized — threats to homeowners across the country.

Below, we'll explore what this change means for insurers, including why it's happening, what makes the situation unique and how providers can prepare for the consequences.

Why is private flood insurance on the rise?

Many of the reasons for the private flood insurance boom can be found in the shortcomings of the NFIP, which has provided federally funded coverage for Americans since 1968.

Today, the program is in trouble, with over $22 billion in debt and an inextricable dependence on a Congress that is regularly in strife. While it's too soon to know the full consequences of the most recent government shutdown, it's estimated that over 120,000 home sale transactions were affected due to policy delays (flood insurance is sometimes required by mortgage lenders when properties are in flood-prone areas).

And, frankly, that's far from the worst-case scenario because it only involves delays for those seeking a new policy. What about those who need to file a claim during a shutdown? Those claims are often delayed and can go totally unpaid until the government reopens, if all previously allocated funds run out.

Meanwhile, accepting these risks and the unreliability of NFIP coverage offers policyholders no protection from skyrocketing costs. Thanks to changes in how rates are calculated, NFIP premiums have surged in some parts of the country. NPR highlighted one flood-prone area of Louisiana where costs increased by an average of over 500%.

This particularly hurts new customers, who may move to an area with a high flood risk — or realize their current home is now at an increased risk — and face much higher rates than expected. Plus, longstanding policyholders may have benefited from subsidies that lowered rates in the past, while newcomers get no such relief.

The good news is that the private flood insurance market has grown because it recognizes these issues and is able to bring reliability, flexibility, competitive rates and new features to the landscape. With an estimated 95% of new policyholders now eligible for coverage via a private provider, there are plenty of ways for homeowners to shop around for an option that suits them best.

What are the biggest benefits?

One of the biggest innovations private insurers have made is to create higher coverage limits, provided policyholders are willing to pay for it. Under NFIP, coverage is capped at $250,000 for residential buildings and $100,000 for their contents. These limits were last increased over a decade ago, and don't provide the level of coverage that some homeowners — especially those in high-risk areas — want.

And, perhaps most importantly, private coverage can still offer competitive rates. Research from Neptune Flood Insurance indicates that around 60% of policyholders would pay less in the private market than they would with NFIP.

While those may be the marquee benefits, they're not the only ones. Private insurers are also able to offer better, more efficient and — in the event of another government shutdown — more reliable claims processing than NFIP, bringing all the technology and know-how of the modern digital marketplace.

For many homeowners, this means not just a better customer service experience, but also improved coverage, with providers using advanced analytics and mapping to create bespoke, fairly priced policy plans.

Parametric insurance, which has become quite popular in disaster coverage, is perhaps the best example of this. By designing claims payouts around set trigger points, like water level or rainfall amount, insurers can guarantee that customers will be paid what they deserve quickly, conveniently and transparently.

What hurdles do providers face?

Still, this fledgling industry is not without threats. Price is one, as providers will need to ensure rates that consistently beat out NFIP in order to secure a true market share.

It's true that most people will save money by going the private route but, currently, not everyone will. Of course, as more people purchase private policies, providers will be able to offer lower rates — but this is where a bigger potential problem reveals itself.

Flood insurance, in general, has a visibility issue. Despite the fact that 98% of U.S. counties have been affected by flooding at some point, only 4% of homes are covered.

This has led to billions in uninsured destruction — for example, only 35% of all flood damage from 2010 to 2023 was covered by insurance — with homeowners often realizing too late that their area was at risk.

Here, private insurers will need to do the work of informing a population that is vastly underinsured and increasingly at risk. This will not be an easy process, but it should involve working with mortgage lenders, state governments and other bodies that help determine who needs flood insurance in the first place.

It will also involve educating the public about the risks. In many areas, the risk of flood damage is substantially higher than fire damage, and yet many homeowners are not prepared.

How can the industry adapt?

Flood insurance is a new frontier for private insurers, and as a result, there's a lot to learn from it.

And even in this short time, there are already takeaways for providers of all kinds. One of the most critical lessons is the way private insurers have dissected a preexisting product — in this case, NFIP — and determined what they can improve on. This philosophy will prove essential as the insurance industry faces worsening severe weather, changing customer expectations and rapid technological change.

If providers fail to keep up, they'll be left behind. And this is especially true when it comes to innovations like parametrics, which have a direct relationship to the customer's bottom line. It will become critical to apply these technologies in the right ways, especially as artificial intelligence quickly transforms the business.

Today, a typical enterprise security team comprises over a dozen specialized functions, such as alert investigation, incident response, threat hunting, vulnerability management, and penetration testing. Even within the application security team, the people doing threat modeling and security assurance are very different from the people doing static application security testing (SAST) and dynamic application security testing (DAST), for example.

As AI agents and assistants increasingly take on the nuts and bolts of many security functions, the role of the security professional transforms into guiding the AI, feeding it the right data and business context, and making more strategic decisions for the business.

In this new world, the most important skill is understanding the shared business context and priorities across the organization, not knowledge of a specific tool or specific alert details. The enterprising employees who do this effectively are the ones who will excel in 2026. They will be positioned to take on any role in the security team, and perhaps all roles at the same time—part-time SOC analyst, part-time AppSec engineer, part-time threat hunter and more. This is because they can simply delegate the domain-specific details to specialized AI agents and assistants.

This is great for CISOs, as they get a highly fungible team of security generalists who can take care of whatever is the top security issue of the day in any domain. Having a single person take care of issues across all domains creates fewer gaps. And it's also great for security team members as it gives them mobility in their careers. It's a win for security across the board. If you're a security generalist, 2026 is your year.

Deepfakes have been a common problem on the Internet pre-2025. In 2025, they entered the workplace, with many incidents of fraud involving adversaries posing as interview candidates or a business partner in a video call.

An orthogonal problem in workplaces has been rogue insiders, employees who hurt their organizations from inside. Sometimes they do this on behalf of an external adversary in return for money, whereas others are lone wolves.

In 2026, these two will converge, with rogue insiders leveraging AI and deepfakes. Employees who have the proclivity to cheat but were previously afraid will be encouraged to cheat, with AI making it easy and deepfakes providing plausible deniability. Any insider has all the business context to customize deepfake attacks to seem much more real than anything we've seen in 2025.

The research on how to detect and defend against deepfakes, as well as techniques to detect and defend against rogue insiders, needs to catch up and tackle this threat effectively. Until it does, there will be a period in 2026 when trust within organizations will be broken. You no longer trust that the email, or the voice call, or even the video call from your teammate is truly from your colleague. During this phase, regular employee training combined with fast adoption of better deployment tools will become critical.

Additionally, the offensive security landscape will change more in the next 24 months than it has in the last 10 years. Traditional pen-testing has remained largely manual and very expensive, while DAST tooling is great at surface-level scanning, weak at context and logic.

In 2026, we'll see new automated approaches to offensive security that understand context, state, and business logic, not just endpoints. Think tools that behave like a creative attacker—chaining vulnerabilities, exploiting misconfigurations, and validating impact the way a human red-teamer would.

That evolution will turn what used to be a quarterly or annual pentest into something continuous and integrated into engineering workflows. Security shifts left to match attacks that are doing the same, into CI/CD, pre-prod validation, and runtime guardrails. Once offensive testing becomes autonomous and contextual, organizations will stop treating pentests as compliance artifacts and start treating them as live safety nets for every software change. 2026 will be the year offensive security becomes just another part of the delivery pipeline.

As AI transforms both security attacks and security tools, this fragmentation hurts agility, restricts scalability, and most importantly creates silos where adversaries hide. We predict this will change in 2026.

The esteemed science-fiction author Arthur C. Clarke believed that "any sufficiently advanced technology is indistinguishable from magic." When it comes to artificial intelligence and its widening effect on the insurance sector, 2026 promises to be a magical year.

In recent years, AI has been tested, examined, admired, and even feared, but not often deployed at scale in the P&C market. That paradigm, I am convinced, will change in the coming months. Look to 2026 as the year when AI progresses from pilot to production across insurance sectors. That's when I expect to see more real-time underwriting, conversational experiences, and dynamic pricing, with many players shifting from the "dipping your toe in the water" phase to the "taking the plunge and swimming confidently" stage. Already, we're seeing underwriting models that can continuously learn, customer interactions that feel like natural conversation, and pricing that adapts to different behavior, context, and risk signals. When I observe how swiftly we've gotten here – I can't help but be bullish on the near future.

The latest numbers buoy my confidence. S&S Insider reports that the worldwide market for artificial intelligence in insurance is set to jump from about $8.6 billion in 2025 to nearly $59.5 billion by 2033. Annual growth rates of approximately 27% suggest an industry quickly implementing AI for claim streamlining, fraud detection, customer service enhancement, and stronger risk management. What's more, early AI adopters are benefiting from cost reductions of 20% to 40% across claims, onboarding, and back office operations, as well as premium growth of up to 15% thanks to improved customer segmentation and more personalized offerings, according to McKinsey data.

AI moves from experimentation to everyday operations

I foresee several transformative trends in 2026 based on advancing technologies and data innovations. One particular breakthrough will come from unifying fragmented data. One of insurance's oldest and stickiest problems has been that policies reside in one system, claims in another, and interactions in a dozen more. But the accelerating push to combine every piece of data into a single intelligent layer that connects all policies, claims, and consumer interactions will pay off this year. Innovations in entity resolution, retrieval-augmented generation, and privacy-safe synthetic data will unlock personalization at scale while safeguarding customers. (It's encouraging that, according to SAS, 79% of carriers are open to using – or are already employing – synthetic data to resolve privacy and data-quality challenges.) Those insurers who check these boxes will win both efficiency and trust from consumers.

I'm also excited about the increasing acceptance of generative AI. Consider that 82% of insurance companies adopting AI are also incorporating GenAI, which demonstrates a rapid progression toward more naturally conversational and content-driven experiences. Additionally, 2026 is poised to be another year of innovation and progress in advanced driver assistance systems (ADAS), which won't necessarily make fully autonomous driving mainstream but could lead to greater clarity involving liability assignments and claims patterns. S&P Global expects that, by 2035, around 40% of new vehicles sold around the world will include advanced driver-assistance features from Level 2-plus to Level 4.

The future belongs to trust builders

What's more, AI is positioned to remove friction from interactions in the coming year, converting what used to be a transaction into a relationship. By analyzing intent, preferences, and behavior in real time, AI-equipped carriers will truly understand – not just price quote – their clients. The companies that stand to benefit most are those that deepen trustworthiness via personalizations that feel effortless and provide real value to the consumer, leading to partnerships that foster long-term loyalty.

Looking ahead, we can also expect insurance technology teams to further evolve. I envision a shift from teams that build isolated services to those that orchestrate intelligent ecosystems. AI systems engineers, data governance leads, and machine learning operations specialists will be among the most indispensable roles. And the best teams won't just be deep technically: they'll blend domain fluency with adaptability, curiosity, and cross-functional collaboration as AI becomes further embedded into every layer of the business.

Obstacles and opportunities

Of course, significant challenges remain. One of the biggest is balancing AI-driven automation with the need for transparency, fairness, responsible governance, and human oversight. After all, automation without transparency erodes trust, and in insurance, trust is the whole ballgame. Although only around 5% of insurers have a fully mature AI governance framework in place right now, Market.US reports that nearly 70% of large enterprises are currently investing in fairness controls, audit trails, and model monitoring. I anticipate that 2026 will be the year when carriers embed explainability into every decision-making model, making "why" as visible as "what." That doesn't mean human oversight will disappear; instead, it will evolve into governance frameworks that ensure fairness, auditability, and consumer confidence.

The new year gives us a lot to look forward to. But it also reminds us that the AI timeline in insurance is unfolding at record speed and is sure to present fresh obstacles and unforeseen X factors that could upset even the most reliable predictions. Still, I've never been as excited about what's beginning to emerge just beyond the horizon.

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?