Insurance is often portrayed as the bad guy. Or at best, it isn't talked about at all. Business owners want to get a quote, check the box, and move on with their lives. Insurance is background noise, something you deal with because you have to. You don't open a bakery to buy insurance; you do it because you love baking.

However, invisibility is exactly what makes insurance so easy to take for granted. While no one is thinking about it, insurance is quietly doing something remarkable: holding up the entire small business economy.

The United States is home to 35 million small businesses. They're the coffee shop where they know your name. The contractor who rebuilt your deck. The nail salon run by a first-generation immigrant who left everything behind for a shot at something better. They are the economic and social fabric of every community in this country, and they represent something fundamental about what America is — a place where anyone, regardless of where they come from, can build a rewarding life through their own effort and ingenuity. Behind every one of those businesses is someone who took an enormous personal risk. They put up their savings, left a comfortable job, took out a loan, or bet on themselves. What often goes unrecognized is the role insurance plays in making that bet possible.

That's why insurance is the oil that powers the engine of small businesses, the foundation of the U.S. economy. Put another way, insurance is the foundation on which American economic exceptionalism sits.

Consider how much of the small business ecosystem depends on insurance. A coffee shop can't sign a lease without liability coverage. A contractor can't bid on commercial jobs without workers' comp. A nail salon can't stock inventory without property insurance. The banks that approve loans, the landlords that sign leases, and the partners that sign contracts rely on the protection insurance provides to do business at scale.

At its core, insurance is an extraordinarily powerful risk transfer and aggregation system. It gives entrepreneurs the confidence to invest capital, hire employees, and expand. It gives their partners and lenders the confidence to bet on them. This is the kind of infrastructure that makes large-scale entrepreneurship possible, and America has built one of the most sophisticated versions of it in the world.

The downstream effects are profound. I've personally seen small businesses earn enough to send the first member of their family to college. Entrepreneurs across the country have turned a modest storefront into a multi-location operation, creating jobs and employing dozens of people.

It also helps create the next generation of doctors, lawyers, founders, and the next generation of small business owners. Insurance is the safety net that keeps that cycle going.

And despite this, the insurance industry has been slow to modernize. Too many business owners still associate the process with reams of paperwork, phone calls, and fax machines. Too often it takes weeks to get a quote, premiums are priced with a one-size-fits-all model, and the process feels opaque and frustrating.

Making insurance faster to obtain, easier to understand, and more precisely priced has real economic consequences. Every friction point we remove is a barrier lifted for the next entrepreneur. Every small business we protect is a job creator we keep in the game. Every risk we underwrite well is capital freed up to flow toward the next great idea.

Innovating in insurance is exciting because it involves genuinely complex, interesting problems, especially now, as advances in AI and technology give us the tools to finally revolutionize a legacy and yet vital industry.

But what gets me up in the morning is simpler than that. Any time I step into a restaurant or a small shop, I know that while the owners' hard work is what makes their business go, insurance helps give them the confidence to start.

Thirty-five million businesses depend on this industry today. Millions more that haven't started yet will depend on us to make it better. That's a purpose worth celebrating.

Independent agents have never lacked for hustle. They compete on relationships, local knowledge, and service in ways that national carriers simply can't replicate at scale. What they've consistently lost ground on is visibility. Specifically, the kind that comes from showing up on television week after week in front of prospective customers who haven't started shopping yet.

That gap wasn't a strategic failure. It was an economic one. The channel belonged to those who could afford it.

Two converging shifts are now changing that – the rise of ad-supported streaming and AI-driven creative automation.

Why TV Is Now a Realistic Option

For most of the past few decades, television advertising was structured in a way that excluded smaller operators by design. Broadcasters sold fixed time slots in bulk, minimum commitments ran into thousands of dollars, and production costs for a single 30-second spot could reach $50,000 before a single viewer saw it. Independent agents weren't the intended customer.

Two shifts have changed that. First, the audience has moved. Streaming, via connected TV (CTV), now accounts for 48% of total television usage, according to Nielsen's The Gauge report, a figure it reached in December 2025, up from 39% just five months earlier.

That migration has expanded ad inventory and significantly lowered prices. It has also changed how targeting works. Where linear television delivered ads to whomever happened to be watching, CTV allows advertisers to specify the audience: new homeowners, households within certain income brackets, or consumers who have recently shown interest in insurance products.

Second, production has become significantly more accessible. AI-driven tools can now generate broadcast-ready video from basic business inputs without the need for a crew, an agency, or a months-long timeline. What once required a substantial budget and outside expertise can now be handled in-house, quickly, and at a fraction of the former cost.

Neither shift alone would have been sufficient. Together, they make the channel viable for operators who were never able to consider it before.

Building a Local Presence That Actually Sticks

Understanding how CTV advertising works requires setting aside some assumptions carried over from other digital channels. This isn't search advertising, where a click signals intent and a conversion closes the loop cleanly. The mechanism is different, and so is the way you measure it.

The core function of TV advertising for an independent agent is familiarity. A prospective customer who sees a local agent's ad three or four times over the course of a week begins to register that agent as an established presence. Repetition signals credibility. Later, when that same person encounters the agent's Google ad or gets a referral, the prior exposure has already done the heavy lifting. The response rate goes up. The name is already familiar.

On Attribution

TV attribution has always been difficult to measure precisely, and it's worth being honest about that. Viewers aren't clicking anything. The signal is indirect. But that doesn't mean it's unmeasurable.

The most accessible starting point is before-and-after analysis – tracking website traffic, inbound inquiries, and quote requests in the weeks and months following a campaign launch. The baseline isn't perfect, but patterns tend to emerge over 60 to 90 days.

More granular options exist for agents who want them. Pixel-based website tracking can identify which visitors were previously exposed to a CTV ad, drawing a direct line between viewing and site activity. For agents with a CRM or quoting platform, integrating that data can surface whether exposed households are converting at higher rates than unexposed ones.

The framing that tends to serve agents best is to treat TV as a multiplier rather than a standalone lead source. It raises the performance ceiling of everything else in the marketing mix. An agent running search ads, maintaining a referral network, and doing periodic direct mail will often see each of those channels perform better with consistent TV exposure behind them.

On Optimization

A few levers are worth knowing. Audience targeting should be revisited periodically: new homeowners, households with recent life events, and consumers who have shown insurance shopping intent are generally strong starting segments, but what performs well varies by market and coverage mix. Most CTV platforms adjust delivery automatically based on engagement data, but agents should pay attention to which audience segments are driving site visits and leads and weight spend accordingly.

Creative also matters a great deal. A strong, specific call to action, like a free policy review or a named discount, will outperform vague brand messaging in driving near-term response. It also helps to refresh ads periodically with updated visuals or messaging. Regular updates signal that the business is active, which reinforces credibility with potential customers.

Finally, don't set an end date. The compounding effect of TV exposure builds over time. Campaigns that run continuously, even at low spend levels, tend to outperform those that run harder for shorter windows. Treat it the way you'd treat any long-term marketing investment – something that gets more efficient the longer it runs.

The Playing Field Is Shifting

Independent agents have always had the harder job on brand. The carriers had the budgets, the agencies, and the airtime. That asymmetry shaped consumer awareness for decades, not because national brands told a better story, but because they were simply more visible.

The conditions that allowed that gap have changed. Streaming has opened the inventory. Production costs have dropped to the point where they're no longer a deciding factor. And targeting has made reach efficient enough that a modest budget can find the right audience in a defined market.

None of this requires an agent to outspend a national carrier. It requires showing up consistently in front of the right households, often enough to become a familiar name before the shopping starts. That was never possible before. It is now.

Policyholders are dying with dozens of open digital accounts, no record of what they own, and no plan for what happens to any of it. When that happens, a fraud window opens. That gap has a cost, and insurers are absorbing it. Life insurance is where the stakes concentrate and the exposure is most acute.

Sandra filed the life insurance claim four days after her husband's death. She had everything she was supposed to have: the policy number, the death certificate, executor authority. Her insurer had 17 unverifiable digital accounts, a death record that hadn't reached the broker databases yet, and a fraud window that had been open since the obituary ran.

That's the default condition for life insurance claims today.

The scale of the problem

Policyholders maintain dozens of active digital accounts - financial, medical, cloud storage, subscriptions, social media. Many hold documentation directly relevant to estate and insurance administration. Death doesn't close those accounts; it severs access to them.

Only 36% of Americans use password managers, meaning most policyholders leave no systematic record of what they own digitally or how to reach it. Most major platforms offer some form of legacy contact or digital will feature, but adoption remains low. Death leaves a scattered, largely inaccessible digital estate, one that intersects directly with claims management processes.

Where the cost lands

This is where the exposure becomes the insurer's problem, and that immediate exposure is fraud. After a death, a gap opens between when the death certificate is issued and when that record propagates to the commercial databases that underpin identity verification. During that window, the deceased's digital accounts remain accessible to anyone who can answer a few security questions, questions drawn from the same broker records that haven't been updated yet.

Thieves target recently deceased identities, while life insurers absorb the cost - fraudulent claims, delayed payouts to legitimate beneficiaries, reputational harm when carriers pay bad actors.

There's a legal dimension too. Most platform terms of service were not written with estate law in mind. Even where the Revised Uniform Fiduciary Access to Digital Assets Act (RUFADAA) gives executors legal access to digital accounts, platforms often don't honor it in practice. The beneficiary has a legal right that the platform won't act on. The adjuster has no clean path forward.

Health insurance and workers' compensation face the same fragmentation - medical records, employer portals, and benefit accounts scattered across systems that don't communicate. But life insurance sits at the sharp end of the problem, where the industry's exposure is most acute.

The verification gap

The infrastructure for verifying identity after death has a gap built into it. Deceased individuals' records persist in commercial data broker databases indefinitely, with no real-time connection to official death records. Verification systems that rely on those databases can't distinguish between a living person and a recently deceased one. The fraud window is a consequence of infrastructure that was never designed to handle life transitions.

Sandra's experience perfectly illustrates both sides of that gap. Sandra couldn't get to her husband's financial accounts. Platforms that held documentation she needed for the claim locked her out despite her legal authority as executor. While she was fighting for access, the fraud window that had opened at his death was available to anyone with enough of his personal history to answer a few questions. The accounts she couldn't reach to support her claim were simultaneously drainable by strangers.

AI as accelerant

Voice cloning and deepfake technology now allow a bad actor to reconstruct a deceased person's voice or likeness from publicly available material, and use it to defeat authentication systems that were never designed with post-death scenarios in mind. As a result, the cost of perpetrating this type of fraud is falling and the risk is rising.

No standard consent or identity framework currently governs the use of a deceased person's biometric data. No enforceable mechanism exists for people to specify how their likeness can be used after death, and insurers have no protection against the claims that follow.

The limits of individual planning

Those who use password managers are ahead of their peers, but individual preparation has a ceiling. Even the most organized policyholder can't force their bank, their cloud provider, and their insurer to exchange data in a standardized way after their death. That requires infrastructure that doesn't yet exist.

The question is: Who shapes that infrastructure? And will the sectors with the most to lose have a seat at the table when the standards are written?

A call for industry engagement

The Death and the Digital Estate (DADE) Community Group at the OpenID Foundation, which I co-chair, recently published a white paper and a planning guide laying out the problem and recommendations for addressing it. Developing interoperable standards for the full lifecycle of digital estate management will require expertise from every affected sector; the insurance industry's knowledge of fraud vectors, claims complexity, and regulatory exposure is specifically what's missing from this conversation.

The groundwork for those standards is being laid now. The sectors that engage early will shape the agenda before the formal process begins. If your organization has a stake in how they get built - and insurers clearly do - the DADE Community Group welcomes participation.

Automobiles are becoming increasingly sophisticated and complex, as are the related risks for auto dealers.

For example, in February, 12.5 million accounts with CarGurus were compromised in a cyberbreach that involved the names, email and physical addresses, phone numbers and more of countless customers. CarGurus, a multinational online marketplace that connects car buyers with thousands of dealerships, is facing a bevy of lawsuits over the incident.

Cybercriminals target the auto dealer space because it is fertile ground to harvest the sensitive data collected during the car-buying process, including employment data, bank account information and Social Security numbers. Cyberattacks are now among the top five risks facing dealerships as well as a factor insurance agents and brokers must now consider when advising their dealership clients.

And while all things cyber-related continue to grab both headlines and the attention of consumers, these digital crimes are hardly the only threats driving dealership and car lot-related losses. From old-school auto theft—a risk since Ford's first Model T rolled off the assembly line—to parking lot mishaps, some of these risks are as old as the auto industry itself. However, with the rise of inflation, supply chain challenges and rising costs of litigation, the frequency and severity of these risks, and others, can affect showrooms everywhere that have not prioritized appropriate risk mitigation. As the safety counsel to many business owners, insurance agents and brokers are uniquely positioned to prepare insureds and help manage these threats. This process starts by understanding the complex risks that threaten car lot dealers.

Examining the leading car lot risks

Cyberattacks are far from the only area of concern for auto dealers. Working with an experienced agent or broker, business leaders can not only factor in a range of real-world risks for the industry, but also the severity and frequency of those risks based on their location, inventory types and other factors. Some of these categories might include:

• Weather-related claims. Most car lots are exposed to the elements, including hail, storms, winds, heavy rains, snow, ice and flooding and, depending on their location, wildfires. With the increasing frequency of extreme weather and the catastrophic impact of a single-but-severe event, ensuring a dealership's policy factors in weather risks is a critical tool to mitigate high-value inventory losses and lost income from business interruption.

  For example, a dealership in Texas suffered a huge financial loss because of a severe hailstorm. To prevent this loss, the business owner could have taken steps including regular monitoring of weather news, installation of a hail netting system and an emergency vehicle relocation plan, all of which would be within the consideration set of an experienced insurance agent specializing in the auto sector.

• Premises liability risks. Every parking lot, sidewalk or public entryway will eventually develop or produce uneven surfaces, standing water or oil spillage that can cause slips and falls and other customer and staff injuries. Car dealers are not immune. Agents and brokers should consider a range of factors specific to each property to advise their clients on establishing sufficient premises liability coverage to protect against claims related to these issues. In addition to coverage, agents and brokers can recommend clients conduct frequent lot inspections, surface repairs and cleaning.

• Organized theft. There has been a rise in vehicle theft, often by organized rings using sophisticated tools like key fob cloning. Installation of a key management system and implementing internal key audit log controls can formalize the process of knowing who takes a key and when, and when it is placed back in the cabinet.

  Dealership employees should be educated about the importance of the formalized key check-in/check-out process. We know of a dealership that recently experienced this type of loss when a group of criminals acted as buyers and distracted staff to access vehicles' on-board diagnostics (OBD) ports to program duplicate keys. They returned later and drove off with multiple high-end sport utility vehicles. These risks could have been mitigated through proper employee training where strict supervision is required during test drives, installing OBD port locks and disabling on-boarding capabilities after hours.

• Test drive and lot movement incidents. Accidents can happen during test drives, which can damage inventory and even lead to fatalities, such as the death of two people in Madison, Wis., last year. Meanwhile, car dealership inventory can also be damaged by improper lot movements that can lead to dents and scratches. Training staff on best practices for safe test drives, including verifying the age and identification of each driver, and vehicle movement protocols, can mitigate these risks. Agents and brokers should also recommend their clients implement a formal incident reporting system to emphasize the importance of maintaining consistent documentation as well as general safety awareness.
• EV theft and poor security management. Criminals don't just steal cars, they are also targeting electric vehicle (EV) charging stations to steal cables that contain copper wiring. Many dealers have these stations to service their EV inventory and protecting them from theft should be included in the property's security posture and lot management program. This includes installing motion sensor lighting systems, high-definition cameras and other nighttime surveillance protocols. Other tips include parking inventory in defensive patterns where valuable vehicles are blocked in by others, locking vehicles and steering wheels or immobilizing high-theft models.

Important coverage recommendations

As dealers look to better protect themselves, there are many types of coverage that are vital to protecting their businesses. Agents and brokers should offer a comprehensive auto dealer's insurance policy, often including property damage and bodily injury coverage, garage keepers, damage to garage-owned autos and more.

Optional, but often beneficial coverage could also include defective product and faulty work, also known as broadened garage liability coverage, to protect the auto repair side of many auto dealership businesses that might be involved in damage to a customer's vehicle. Dealership owners can also layer in an additional policy to cover the amount of the actual loss of a customer's vehicle, regardless of the dollar limit.

Ensuring the business has the appropriate cyber liability coverage to protect against ransomware and other cyber breaches is now an imperative. In one instance, a phishing email led to a ransomware attack on a dealership that threatened the exposure of confidential dealership customer data. The public release of these sensitive data types can result in extraordinary legal liability as well as bet-the-business-type reputational risks for dealerships. We cannot emphasize enough the need for regular employee education and training on phishing scheme techniques to ensure dealership employees are alert and more likely to spot a threat before the business is compromised.

Finally, agents and brokers have a responsibility to explore and be aware of where and how insureds are using multi-factor authentication on all computer systems and devices to provide increased security of critical data. Understanding where customer data is stored, who has access as well as limiting that access, and the nature of the business' security procedures to protect that data from bad actors must be the new normal of insurance agents to be able to advise clients appropriately.

Implementing technology

As the auto industry and technology continue to evolve in their complexity and sophistication, so too will the threats facing auto dealerships. With Bluetooth key-tracking, geofencing devices and real-time telematics, technology tools are continually entering the market to address rising threats. Other resources include AI-powered lot surveillance systems, digital lot movement apps, mobile check-in service for vehicles as well as deploying drones to assist in real-time monitoring of large lot inventories. As threats for auto dealerships grow and evolve, the technology resources to combat them promise to help business owners keep pace.

Insurance agents specializing in fleet insurance owe it to their insureds to keep pace with both the growing risk categories as well as the emerging technologies being employed to mitigate them. That awareness will allow agents to best advise their auto dealership clients as well as help reduce the frequency and severity of related claims across a range of risk categories.

With experienced and knowledgeable advisors, along with a suite of appropriate technology tools available to them, auto dealers can feel confident they remain in the driver's seat of protecting their businesses and mitigating their risks.

Conclusion

Agents should encourage dealerships to think beyond just buying insurance and focus on building good habits into their daily operations. Simple steps like verifying a driver's license before every test drive, keeping strict control over keys, and reconciling inventory at the end of each day can prevent major losses. Service teams should be trained not only on repairs but also on documenting their work and double-checking vehicles before they're returned to customers, especially with today's advanced technology and EV systems.

From a security standpoint, strong lighting, quality camera systems, secure key storage, and clear after-hours procedures go a long way. And because dealerships handle sensitive financial information, regular cyber training and basic safeguards like multi-factor authentication are just as important as physical security. In many cases, it is consistent processes and not expensive upgrades that make the biggest difference in preventing claims.

In 2026, the insurance landscape feels both challenging and full of promise. As someone whose vantage point is in agentic AI for insurance, I've seen firsthand how the landscape is changing. Rising catastrophe severity, cyber threats, and customer expectations for instant service are pushing claims operations to the breaking point. Recent data shows property claims now averaging over 32 days from filing to completion, up significantly from just a couple of years ago due to more frequent severe events. That's weeks of added stress for policyholders already dealing with loss.

But this is where I'm genuinely excited: Agentic AI is emerging as the breakthrough that's going to change all that.

Understanding the Agentic AI Difference

Before diving into integration strategies, it's good to understand what makes agentic AI fundamentally different from what came before, and why it works so well for claims. Generative AI gave us powerful tools for handling documents and communications at scale. Agentic AI builds on that foundation but goes much further: These systems can autonomously plan, reason, and execute complete multi-step workflows, while staying firmly within governance guardrails and human oversight.

In claims handling, this translates to transformation. Imagine a First Notice of Loss coming in: An agentic system immediately ingests it, assembles the full file from disparate sources, integrates real-time external data like weather or telematics, evaluates liability, flags potential fraud, and, for low-complexity cases, approves payment in hours instead of weeks.

Start with Strategic Line Selection

The carriers winning in 2026 will be those who integrate agentic AI deeply into their strategic choices, focusing on specific lines and segments where speed and consistency create real differentiation. Understand that not every claim process requires the same level of AI sophistication, and trying to automate everything at once can give you results you don't want to see.

So where do you start? Begin by identifying lines of business where volume is high, processes are relatively standardized, and speed creates genuine competitive advantage. Auto physical damage, property first-party claims, and workers' compensation medical-only cases often present ideal starting points. These segments typically have clear decision trees, well-documented workflows, and measurable success metrics.

Equally important is understanding where human expertise remains irreplaceable. Complex liability determinations, claims involving serious injuries, and cases requiring nuanced coverage interpretation will continue to demand experienced adjusters. The goal isn't to eliminate human judgment; it's to free adjusters to apply their human expertise where it matters most.

Build with Governance and Transparency from Day One

With regulations like the EU AI Act and NAIC guidelines emphasizing transparency and fairness, the most effective approaches ground these agents in carriers' own data, with full provenance, explainability, and human-in-the-loop controls built in from day one.

This isn't just regulatory compliance; it's operational necessity. When an agentic system makes a recommendation or takes an action, adjusters and managers need to understand the reasoning behind it. This requires building audit trails that capture not just what decision was made, but what data informed it, what rules or models were applied, and what alternatives were considered.

Governance frameworks should include clear escalation protocols. Define precisely which decisions can be fully automated, which require human review before execution, and which should only receive AI recommendations with humans making final determinations. These boundaries will evolve as systems prove themselves, but starting with conservative guardrails builds confidence and reduces risk.

Empower People, Don't Replace Them

We're already seeing forward-thinking carriers achieve 70-80% reductions in processing time for routine claims, with straight-through processing rates soaring and accuracy on par with top adjusters. Critically, this doesn't mean sidelining people; instead, it empowers them.

Adjusters shift from repetitive data chasing to high-value work: complex investigations, empathetic customer interactions, and strategic decisions where human judgment shines. When systems handle routine file assembly, coverage verification, and standard calculations, adjusters can focus on the elements of claims handling that genuinely require human expertise. This often entails understanding unique circumstances, exercising discretion in ambiguous situations, and providing the empathetic support that policyholders need during difficult times.

This reframing is essential for successful adoption. Position AI integration not as workforce reduction but as workforce enhancement. Involve adjusters in defining where automation adds value and where human expertise remains essential. Their insights will make implementation more effective while building buy-in for the change.

Measure What Matters

Successful integration requires clear metrics that go beyond simple efficiency gains. Yes, cycle time reduction matters but so does customer satisfaction, adjuster job satisfaction, and claim quality metrics like accuracy of reserves and appropriateness of settlements.

Track adoption rates alongside performance metrics. If adjusters are actively using AI recommendations and tools, that's a leading indicator of sustainable success. If they're finding workarounds to avoid the system, that's an early warning that requires attention regardless of what performance metrics show.

Establish feedback mechanisms that capture edge cases and unexpected results. These real-world lessons should directly inform system refinement, creating continuous improvement loops that make AI assistance progressively more valuable.

From Pilot to Production Impact

It's not about technology for its own sake; it's about delivering faster resolutions that rebuild trust and turn claims moments into loyalty builders. From where I sit, this isn't just about automating processes—it's about rehumanizing insurance, making it more responsive and reliable when people need it most.

2026 is the year these shifts from pilot to mainstream impact. The carriers that will thrive are those moving beyond proof-of-concept demonstrations to systematic integration of agentic AI across their claims operations thoughtfully, strategically, and always with policyholder outcomes at the center.

The technology is ready. The regulatory frameworks are emerging. The business case is proven. What remains is disciplined execution: choosing the right starting points, building with governance and transparency, empowering people rather than displacing them, and continuously learning from results.

I'm optimistic about what's ahead.

From 2022 to 2024, the U.S. life and annuity industry delivered extraordinary results, with record sales, expanding margins, and strong capital inflows. That momentum began to soften in 2025, with early indicators pointing to a more challenging environment ahead.

It is tempting to assume that 2026 will restore the conditions of 2024. I believe that is a risky bet. The market has moved on, the environment has changed, and the assumptions that supported recent growth no longer hold in the same way.

As we move deeper into 2026, life and annuity executives must adjust their strategies accordingly. The leaders who succeed will be those who focus on a small number of critical choices that shape long-term competitiveness. Below are eight strategic imperatives I believe matter most now.

1. Rethink Product Architecture

In 2025, rate cuts by the Federal Reserve compressed yields across the industry, making it harder for products to deliver competitive crediting rates. I believe the challenge goes beyond pricing; it's about product architecture. The forgiving rate environment of 2022-2024 allowed simple products to thrive, but that era seems to be over. I think the focus should shift toward comprehensive retirement income solutions that offer stability, flexibility, and confidence. Executives should be asking whether their products are designed only for favorable conditions, or for the full retirement journey customers actually face.

2. Move From Individual Products to Integrated Retirement Solutions

The next step is to stop treating each product as a silo and start designing a connected ecosystem that meets needs across life stages. For instance, combining a registered index-linked annuity (RILA) for growth, a deferred income annuity (DIA) for guaranteed income, and a fixed product for liquidity could meet diverse client needs. This approach, however, requires product integration, unified customer experiences, and tools that enable advisors to construct solutions rather than simply sell products.

3. Treat AI As a Necessity, Not an Experiment

Most carriers have moved beyond asking "should we use AI?" and AI is now a critical enabler for the industry and a baseline expectation. Accenture's research shows that 93% of life insurers have increased AI investments by at least 5% over the last three years, and 43% plan to increase investments by over 25% in the next three years.

Generative AI is already reshaping operations, from underwriting to claims processing, while agentic AI is poised to make autonomous decisions and actions. I believe the economic impact of AI, such as reducing operating costs and enabling scalable solutions, will be transformative. However, success requires process redesign, unified data infrastructure, decentralized governance, and workforce training.

4. Look Beyond "Investment Alpha"

While private equity has driven sophistication in asset management, I think sustainable advantage now requires combining investment expertise with actuarial innovation, distribution strength, and operational excellence. AI has a role here, too, not as a buzzword, but as a lever to reset the cost curve and improve decision quality across the enterprise.

5. Treat Regulation as a Partnership Opportunity

I believe the next wave of regulation will be more consequential, driven by private equity ownership and recent failures. The most resilient carriers will proactively invest in risk infrastructure, from stress testing and governance to controls and AI-enabled compliance monitoring, and they will use technology to make compliance faster and more reliable. Done well, that turns regulation into a trust advantage with customers, distributors and capital markets, rather than a reactive drain on resources.

6. Take a Renewed Distribution Focus

Distribution is becoming increasingly segmented, advisor models are evolving, and I think carriers should focus on excelling in specific areas rather than trying to serve all segments equally. For example, dominating Registered Investment Advisors (RIAs) might involve AI tools that analyze advisor client books and generate customized proposals, while engaging carrier agents may require entirely different strategies.

7. Become an Orchestrator, Not a Builder of Everything

I believe competitive advantage will come from orchestrating best-in-class capabilities rather than building everything internally. Strategic partnerships can accelerate transformation and innovation, especially as AI evolves.

8. Unlock the Mass-Market Retirement Opportunity

According to the Alliance for Lifetime Income (ALI), two-thirds of Boomers are not financially prepared for retirement, and I think this represents an opportunity for product design innovation. AI-powered tools could make sophisticated financial advice accessible at scale, enabling carriers to profitably serve customers with modest assets.

Final Thoughts

A few months in, it is already clear that this year is not simply a continuation of the conditions that defined the last cycle. The question for life and annuity leaders now is not theoretical; it is practical and immediate: if interest rates remain flat for three years, how can we gain market share? Investing in better products, superior distribution, AI-powered operations, and customer experience transformation will likely be key. The demographic wave and retirement crisis are permanent, and the AI revolution is accelerating. Preparing for these realities will be essential for long-term success. 

The boom is over. The opportunity is not.

Healthcare affordability is often treated as a pricing problem. Let us reexamine affordable healthcare as a system design problem - with measurement methods/metrics, shared infrastructure and practical adoption pathways.

I am borrowing a "grounded futurism" mindset similar to Dario Amodei's Machines of Loving Grace to make the vision concrete, identify leverage points, acknowledge adoption frictions and build pathways that can learn and adapt to societal needs.

In healthcare, the leverage points are clear and practical: a) protect households from financial shocks, b) control system costs through purchasing and delivery design, and c) build shared digital and data infrastructure so improvements can scale beyond pilots and be extensible.

What is affordable healthcare?

"Affordable" doesn't mean cheap. It means access to needed care without financial hardship. The most useful global yardstick is SDG indicator 3.8.2, revised in 2025 to better capture hardship among poorer households. It tracks the proportion of population with positive out-of-pocket (OOP) health spending exceeding 40% of household discretionary budget (relative to societal poverty line).

Why does affordability look different across countries?

The challenges vary by fiscal capacity, health system maturity, and implementation capability — i.e., ability to coordinate providers, payers, and supply chains. This is why WHO's global digital health strategy emphasizes institutionalizing digital health through an integrated approach of financial, organizational, human and technological resources. This is where affordability can be operationalized via shared infrastructure (identity, registries, exchange standards, claims rails, supply chain visibility, etc.)

What works (transferable design patterns), and why is data the key denominator?

Countries that sustain affordability tend to combine financial protection, cost discipline and organized delivery. Thailand's Universal Coverage Scheme (UCS) pairs coverage with explicit cost controls, including capitation for outpatient care and diagnosis-related groups (DRGs) under the country's budget for inpatient care, and positions its purchaser (NHSO) as an "active" manager of budgets and payments. NHSO's responsibilities include registration of beneficiaries and providers, establishing a claims and reimbursement process and using a standard dataset and APIs for claims flows — i.e., affordability reinforced through systems and not only policy.

India's ABDM (National Health Stack) reflects the same principle via a modern digital public infrastructure (DPI). It is built from Health IDs (ABHA), provider and facility registries (HPR/HFR), and a consent manager enabling consented exchange in a federated architecture, designed to support continuity of care and interoperability across a diverse ecosystem.

These examples imply that you cannot scale affordability without building country/state/region-specific datasets as public utilities, as targeting, purchasing, and delivery of health services (including AI) all depend on them.

The Affordable Healthcare Replication Stack: Systems View (three pillars)

The learnings from those transferable design patterns lend themselves to the systems view below for affordability.

1. Financial protection (prepayment + pooling + subsidies + safety nets) Goal: Reduce household hardship, measured using revised SDG 3.8.2 (2025) and complementary impoverishment measures. Required datasets: Household financial protection dataset (OOP spending and consumption/income) captured via household surveys, Beneficiary & entitlement dataset: Eligibility, enrollment and benefit rules captured as part of beneficiary registration and entitlement management by Thailand's NHSO. AI acceleration: AI can improve eligibility verification, detect anomalous enrollment patterns, and optimize outreach (renewals, maternal/NCD reminders), but only once entitlement datasets are reliable and governance is in place.

2. Cost Discipline + Access (strategic purchasing + primary care-first delivery) Goal: Keep care affordable for the system and accessible for patients by shaping incentives and shifting care upstream. Thailand illustrates how provider payment design (capitation + DRG/budget) can contain costs while scaling coverage. Required datasets: Provider and facility registry - who is licensed, where they operate and what services they offer. ABDM's HPR/HFR are direct analogs of this "registry layer", Utilization and case-mix dataset - outpatient visits, inpatient episodes, DRG groupers, Referral pathway and primary care dataset - catchment areas, referral rules, appointment and follow-up flows. AI acceleration: AI copilots can reduce clinical burden and expand capacity - especially documentation and decision support.

3. Digital Rails for Scale (Health DPI + Claims rails) Goal: Make affordability scalable and auditable by reducing fragmentation, duplication and payment friction. ABDM is a working reference to provide a federated, consent-based exchange with registries and gateway model for interoperable services. Required datasets: Longitudinal health record pointers and metadata that are discoverable and consented references to clinical history, Claims and payment status dataset: Standardized, machine-readable claims for adjudication and auditing enabled by National Health Claims Exchange (NHCX). AI acceleration: AI reduces leakage and delay when claims and registries are machine-readable.

An example/'living lab' archetype in creating datasets - A powerful way to build datasets from the ground up is to start in a region with real operational constraints and build end-to-end connectivity. This is demonstrated in Kuppam, Andhra Pradesh (India) via Tata's Digital Nerve Centre (DiNC) - by digitizing personal medical records, connecting an area hospital with 13 primary health centers (PHC) and 92 village health centers, enabling continuous monitoring, timely diagnosis and virtual consultations. DiNC integrates public health facilities through digital tools and protocols to improve coordination and patient convenience.

The supply chain resiliency on affordability - Affordability is not only financing and care delivery, but also the reliability and cost of diagnostics and supply chains, especially during shocks. C-CAMP's Indigenisation of Diagnostics (InDx) program that was launched to build molecular diagnostics capacity and supply chain networks during COVID, connects indigenous manufacturers, suppliers, service providers and health agencies to improve supply chain visibility and accountability. This can be leveraged as a "Diagnostics & Supply Chain Data rail" when connected to public procurements and primary care diagnostic needs.

A pragmatic roadmap of affordable healthcare for developing economies

Here's a practical sequence that acknowledges adoption frictions and delivers services:

1. Adopt revised SDG 3.8.2 (2025) metric and publish baselines/targets for financial protection.
2. Establish or strengthen an active purchaser function and implement payment discipline
3. Build health DPI early - India's ABDM provides a working reference architecture
4. Digitize claims via claims rails (similar to National Health Claims Exchange) to reduce friction
5. Use district "living labs" for social datasets, connected PHCs to harden workflows and enable scaling and outreach
6. Strengthen diagnostics and supply resiliency with InDx-like marketplaces
7. Deploy AI where it delivers value in the safest and most responsible way - tele-triage, imaging, clinician co-pilots, claims, etc.

Affordable healthcare is not achieved by one reform or one model, but a continuous journey when financial protection, cost discipline and digital rails evolve together - and when AI is used to reduce burden and extend scarce expertise, reinforcing responsible policies, controls and effective governance for social good.

Time for action is NOW

If you had to start tomorrow, what would you build first in your state/country and why?

1. Entitlement + benefit registry
2. Provider/facility registry + service directory
3. Digital public infrastructure
4. Claims rails
5. Diagnostics supply chain visibility

On Feb. 9, 2026, two AI-powered insurance apps went live inside ChatGPT.

The market's reaction was immediate. WTW dropped 12%. Aon fell 9.3%. Arthur J. Gallagher lost 9.9%. The MarshBerry Broker Composite Index was down 8.9% in a single session. Billions wiped from broker valuations before lunch.

Then BofA put a number on the fear: $15 billion in low-complexity insurance commissions at risk from AI disintermediation.

The consensus response was swift: overreaction. Too early. Brokers aren't going anywhere. Goldman Sachs called it "overdone." TD Cowen said near-term commercial broker disintermediation from AI was unlikely. McKinsey concluded AI would "reshape existing models rather than disintermediate them."

They may be right about the timeline.

They are wrong about the direction.

Because this is not a debate about whether AI replaces brokers. It is a debate about who owns the distribution infrastructure when AI becomes the front end — and whether incumbents move fast enough to be part of it.

What Actually Changed on Feb. 9?

The apps that triggered the sell-off were not sophisticated. Insurify launched a car insurance comparison tool inside ChatGPT. Tuio, a Spanish digital insurer, launched a home insurance quoting app. Neither was going to put Marsh McLennan out of business by Thursday.

But what changed was not the products. It was the mechanics.

For the first time, an insurance provider could distribute its products and offer quotes directly inside an AI platform where hundreds of millions of buyers already perform their research. Until that day, AI could only provide generic answers drawn from static Web content. It could not quote a real price for a real person or business.

That changed on Feb. 9. And it has not changed back.

Through OpenAI's App Directory — effectively an app store inside ChatGPT — third parties can embed real products and workflows directly into the conversation. Taken together, these are clear signals that conversational AI is shifting from an information layer to an action layer.

Distribution economics are shaped by whoever controls the customer's starting point. AI assistants are now delivering the first explanation of value, replacing the carrier, its agents, and distribution partners as the initial voice that shapes consumer perception.

When the first interaction happens inside an AI interface, the traditional pathway — website, form, comparison journey, broker call — becomes less central. Value migrates toward the firms that control, integrate with, or are discoverable within that new front door.

And the front door is moving fast. The technology is not limited to OpenAI. AI apps built on the same infrastructure and standards have also been adopted by Anthropic's Claude, and Google's Gemini is expected to publish its own standards for third-party apps in the coming months. The shift toward agent-to-agent distribution is becoming an industry-wide reality.

The list of insurance apps in ChatGPT has grown to include Neptune Flood, Steadily (landlord insurance), and Jerry.ai (auto), joining Insurify and Tuio. Neptune's chief engineer explained that they "architected our proprietary underwriting system as a modular, API-first underwriting system specifically so it could integrate into new digital environments like ChatGPT."

This is not a wave coming. It is already here.

Two Distribution Fronts — Not One

Here is what most of the coverage has missed.

The AI distribution shift is happening on two fronts simultaneously, and most incumbents are only watching one of them.

Front One: AI Chat Platforms. ChatGPT, Claude, Gemini. Consumer and SMB buyers asking insurance questions in natural language and getting real-time quotes from carriers that have built the API connectivity to respond. Tuio's co-founder said: "For the first time, AI can access real offers, quote on behalf of the buyer, and compare coverage in real time. Every insurer will be affected, whether they've built an AI app or not."

Consumers and businesses are already uploading commercial offers and policy documents into ChatGPT to get independent analysis and advice. AI voice agents are calling call centers on behalf of buyers to collect and compare quotes. Procurement teams are using AI to evaluate coverage terms, exclusions, and pricing across multiple carriers simultaneously.

Front Two: Vertical SaaS Platforms. The operational software where SMBs run their businesses every day — ServiceTitan for field services, Toast for restaurants, Procore for construction, franchise management platforms for franchise operators. These platforms are now embedding AI agents that handle procurement on behalf of their users. When that AI agent surfaces an insurance need — a contractor scaling their crew, a restaurant adding a location, a franchisee coming up for renewal — it will fulfill that need through whatever insurance infrastructure is connected to its platform.

These two fronts are converging. The AI agent in a business's operational software will query insurance products through the same API-first, MCP-compatible infrastructure that ChatGPT apps use. The question for every broker and carrier is whether their products are accessible via that infrastructure — or invisible to it.

The Protocol That Changes Everything

To understand why this is moving so fast, you need to understand MCP.

Model Context Protocol is an open standard, originally developed by Anthropic and now governed by the Linux Foundation, that defines how AI agents connect to external tools and data sources in real time. Think of it as USB-C for AI — a standardized interface that lets any AI model query any compatible system, regardless of who built either one.

With MCP in place, AI assistants can respond to a prompt like "How much would it cost to insure my business?" by understanding the user's intent, gathering necessary context from connected systems, and returning an accurate, personalized quote — all in the flow of a natural-language conversation.

Neptune Flood's ChatGPT app is built on MCP. Their chief engineer explained: "Using the Model Context Protocol, a lightweight API layer securely orchestrates data retrieval, risk modeling, and rating in real time on top of our existing underwriting infrastructure. Because our underwriting stack is fully automated and cloud-native, we can extend instant quoting into conversational AI without changing our core workflow."

That is the key phrase: without changing our core workflow. The carriers that move fast in this channel are not rebuilding their systems. They are exposing them through a standardized API layer that AI agents can query.

For a broker or carrier that is API-ready, connecting to the AI distribution layer is not a multi-year technology program. It is a configuration exercise. For one that is not API-ready, it is a multi-year technology program — and the market will not wait.

What the Incumbent Advantage Actually Is

Here is where the narrative gets more nuanced — and more useful.

The carriers and brokers that are panicking about AI disintermediation are asking the wrong question. The right question is not "will AI replace us?" It is "what assets do we have that AI distribution actually needs?"

The answer is substantial.

Capacity and compliance. An AI agent can surface a quote. It cannot underwrite the risk, hold the regulatory authorization, or carry the balance sheet. Every AI distribution channel, whether it is a ChatGPT app or an embedded insurance offer in a SaaS platform, needs a licensed, regulated, capitalized carrier behind it. Berenberg analysts pointed out that the regulatory burden and liability exposure of selling insurance directly are significant hurdles that OpenAI and others may not want to manage independently. The incumbent's regulatory infrastructure is a moat, not a liability.

Product breadth and market relationships. The AI agents quoting inside ChatGPT today are doing simple, single-product personal lines. The SMB that needs a BOP, commercial auto, workers' comp, and an umbrella needs a broker with multi-carrier access and placement expertise. AI accelerates the front of the journey. It does not replace the depth of what a well-positioned broker or MGA brings to complex commercial placement.

Customer data and relationship history. The broker that has a five-year relationship with a growing contractor business has renewal data, claims history, and risk context that no AI agent querying a cold lead can match. The retention economics in an embedded, data-rich context — where the broker is present in the platform the customer uses every day — are structurally superior to cold acquisition.

The distribution network. The brokers and MGAs that thrive will not be those who panic. They will be the ones whose infrastructure lets them plug into every new distribution channel. The broker with 50 carrier relationships and a well-managed delegated authority framework can deploy across AI channels, SaaS platforms, and traditional routes simultaneously. The challenger with one carrier and a ChatGPT app cannot.

The incumbent's problem is not a lack of assets. It is a lack of the connectivity layer that makes those assets accessible to the channels where SMBs are moving.

The Infrastructure Gap — And How to Close It Fast

The gap between where incumbents are today and where the AI distribution channel requires them to be is primarily a technology infrastructure gap. It has three components.

API-first rating and binding. For an AI agent to quote and bind your product, whether inside ChatGPT, Claude, Gemini, or a vertical SaaS platform's embedded AI — your rating engine must respond to real-time API calls. Get API-ready. Can your systems deliver a real-time quote to a third-party platform today? If not, that is the first priority. Not because ChatGPT is coming for your book tomorrow, but because every emerging distribution channel requires this capability. Embedded insurance, partner integrations, comparison platforms, and AI agents all depend on the same thing: open, real-time API access to your rating and binding engines.

Data orchestration across channels. The SMB vertical SaaS channel holds operational data — revenue, headcount, transaction volume, job types — that makes for dramatically better underwriting than a static ACORD form. Real-time underwriting driven by live platform data improves policy accuracy by up to 40%. A broker or carrier that can ingest that data through an API layer and price against it has a structural advantage over one quoting blind. The challenge is connecting those data flows compliantly across multiple platforms without building bespoke integrations for each one.

Compliance architecture at scale. Distributing insurance through third-party AI platforms or SaaS channels is a regulated activity. In the U.S., that means surplus lines compliance, state-by-state authorization, and varying requirements for affinity-style distribution across 50 states. In the U.K., it means FCA authorization, ICOBS, and Consumer Duty obligations. The AI chat platforms are not going to carry this. The SaaS platforms are not going to carry this. The broker or carrier must — and the ones who have pre-built this infrastructure will move in weeks where others move in years.

The purpose-built embedded insurance infrastructure layer — connecting rating engines, data orchestration, and compliance across both AI chat channels and vertical SaaS platforms — is the asset that allows an incumbent to move at the speed the market now requires.

What the market needs is not another distribution channel or a competing product. It is the API infrastructure and compliance architecture that allows a broker or carrier to plug into ChatGPT, Claude, ServiceTitan, Toast, and the next 10 platforms that emerge — through a single integration, with a single compliance framework, without rebuilding their core systems.

The Playbook for Incumbents

The market has already moved past the point where watching is a strategy. The ChatGPT app store is live and growing. Vertical SaaS platforms are embedding AI agents. MCP is becoming the standard interface through which AI accesses everything.

The playbook for incumbents who want to win has three moves, executed in parallel.

Move 1: Get your rating engine API-ready. This is the table-stakes requirement for any channel that matters in the next five years — AI chat, SaaS-embedded, or otherwise. If your products cannot be quoted in real time through an API call, they will not be quoted at all in the channels where SMBs are moving.

Move 2: Partner with the infrastructure layer, not the distribution channels directly. The mistake that slow-moving carriers and brokers will make is to try to build point-to-point integrations with individual AI platforms or SaaS tools. That does not scale. Each integration becomes its own project. The right move is to connect once to an infrastructure layer that handles the distribution mechanics across all channels simultaneously — and focus your resources on product, capacity, and the customer relationships that AI cannot replicate.

Move 3: Reframe your value proposition for the AI-front-end world. Your margin is not in quoting. It never was, really — quoting is about to be free. Your margin is in the depth of coverage, the accuracy of risk assessment, the quality of claims handling, and the retention economics of a customer who never leaves the platform where you are embedded. Position there.

Tuio's co-founder said this about the Feb. 9 launch: "Today is day zero of that transformation."

Day zero was two months ago. The incumbents who move in the next quarter will have a structural head start. The ones who wait for more evidence will be building against competitors who already own the channel.

The Bottom Line

$15 billion in low-complexity SMB insurance commissions at risk from AI disintermediation. The AI chat platforms are live and growing. The vertical SaaS AI agents are being deployed. MCP is standardizing the protocol through which AI accesses insurance products.

The distribution infrastructure is being rebuilt. The question is not whether incumbents are part of that rebuild. The question is whether they are part of it on their terms — or someone else's.

The brokers and carriers that connect to the AI distribution layer now, through infrastructure built for the purpose, will not just defend their SMB book. They will grow it — into channels with better data quality, lower acquisition costs, and retention economics that traditional distribution has never been able to match.

The new front door is open. The incumbents who walk through it first will own what is behind it.

For most agencies, payments are plumbing. Money comes in, policies stay active, and nobody thinks much about the experience in between. But that's starting to change—and the agencies paying attention are seeing real results.

As policyholders grow accustomed to one-click purchases, real-time payment confirmations, and flexible billing everywhere else in their lives, the gap between what they expect and what most agencies deliver is widening. That gap is quietly becoming a retention problem. Not because customers wake up angry about a clunky payment portal, but because friction accumulates and colors how people feel about doing business with you—even when everything else is going well.

The agencies that are getting ahead of this aren't just upgrading their payment technology. They're rethinking what the payment moment actually means for the customer relationship.

Payments Are a Loyalty Signal—Whether You Realize It or Not

Your clients are already evaluating you by the same standards they use for their bank, their favorite retailer, and their streaming service. They want options, clarity, and speed. When the payment experience falls short of that, it doesn't just create a minor annoyance—it raises questions about how the rest of your operation runs.

Think about it from the customer's perspective. A smooth payment process signals competence: this agency has it together. Friction—unclear instructions, limited payment options, manual steps that feel like they belong in 2009—signals the opposite. It may not be fair, but it's how people think.

Transparency matters just as much. Insurance already feels complicated to most people, and billing is where that complexity tends to surface. Clear confirmation messages, real-time updates, and straightforward invoices go a long way toward reducing the low-grade anxiety that comes with financial transactions—especially for small business owners watching cash flow or clients managing high-premium policies.

And then there's choice. Offering ACH, debit, credit, and digital wallets is table stakes at this point. But the agencies that stand out are also giving customers control over scheduling, installment options, and autopay—making it easy for people to manage the relationship on their own terms. That sense of control directly affects how satisfied they feel, and satisfied customers don't shop around at renewal.

Why This Matters More Than Most Agents Think

Renewals rarely come down to one big moment. They're shaped by a series of small interactions that either build confidence or chip away at it. Payments stand out because they happen more frequently than almost any other touchpoint you have with a client. Every invoice, every autopay confirmation, every billing notification is a data point in how that customer feels about your agency.

There's a well-documented pattern in how people evaluate experiences: they tend to remember the most intense moment and the final moment most vividly. For a lot of policyholders, the last interaction of the policy year is the renewal payment. If that moment is frustrating because of a confusing portal, unexpected charge, unclear due date, or something else, it can overshadow 12 months of solid service.

On the flip side, agencies that deliver consistently smooth payment experiences are building trust in ways they might not even realize. Clients who trust the billing process are more likely to renew without shopping the market, enroll in autopay, manage their policies digitally, and say yes when you bring up additional coverages. The easier you make it for your customers to stay with your agency, the more likely they are to do so.

And don't overlook the data. Digital payments generate signals—late payments, partial payments, failed autopay attempts—that often surface well before a customer reaches a true retention tipping point. Agencies that pay attention to these patterns can reach out proactively, addressing billing friction before it turns into a lost client.

The Revenue Angle Most Agencies Are Missing

When payments feel easy, they start working for you in ways that go beyond cost savings. Customers who enroll in autopay, for example, tend to renew at meaningfully higher rates. The renewal shifts from an active decision—do I want to stay with this agency?—to a routine financial transaction that happens in the background. Automated billing also cuts down on missed invoices and payment lapses that can disrupt coverage and create unnecessary back-and-forth. Over time, your agency becomes part of the customer's default financial routine, which is exactly where you want to be.

There's a cross-sell dimension here, too. Customers who've just had a smooth payment experience are more receptive to follow-up conversations. It's a natural opening for coverage reviews, umbrella policy discussions, endorsement additions, or bundling opportunities. When the last interaction someone had with your agency felt professional and painless, they're more inclined to listen to what else you might recommend.

What the Best Agencies Are Doing Differently

The agencies treating payments as a strategic capability—not just a back-office function—tend to share a few common habits.

They design payment journeys on purpose. Rather than letting billing flows, renewal paths, and mobile experiences develop by default, they map them out intentionally and look for friction points before customers find them. They also connect payment data with broader customer data, using those integrated signals to spot behavioral patterns and trigger meaningful follow-up automatically.

Flexibility runs through everything they do: multiple payment methods, recurring billing, automated reminders, one-click options. The goal is to reduce customer effort at every step. They're also deliberate about eliminating ambiguity—clear invoices, visible due dates, accessible payment history—which cuts down on support calls and the frustration that drives them.

Maybe most importantly, these agencies treat every payment notification, confirmation, and receipt as a communication opportunity. Not just a transaction closing, but a chance to reinforce value, surface a coverage reminder, or start a conversation that deepens the relationship.

The Window Is Open—But It Won't Stay That Way

Digital payments have moved well past the back-office upgrade stage. They're now a strategic lever for customer experience—and as more agencies adopt embedded payments, mobile wallets, instant verification, and intelligent billing, customer expectations will keep rising.

Agencies that move early will see it show up in higher renewal rates, stronger satisfaction, clearer operational insights, and more revenue from deeper client engagement. Those that wait risk being defined by friction at exactly the moments their customers interact most.

The agents who win the next phase of customer loyalty won't just process payments efficiently. They'll build payment experiences that feel effortless, transparent, and trustworthy, turning what used to be a routine transaction into a genuine competitive edge.

Insurance companies are experts at managing financial risk. They model catastrophe exposure, monitor capital ratios, and carefully manage reserves. Yet in conversations I've had with insurers across Europe and North America, a quieter challenge continues to surface within finance and treasury teams: many carriers still lack a clear, real-time view of where their cash sits across the business.

This is not because insurers lack discipline or oversight. In most cases, it reflects the operational complexity that has built up over decades. Funds move across billing platforms, claims systems, banking relationships, and external partners throughout the insurance lifecycle. Because each stage manages financial flows differently, these systems were rarely designed to support a single, unified view of liquidity.

In many organizations, each new payment method, banking relationship, or vendor integration has historically been added as a separate connection point. Over time, this creates a patchwork of financial pathways that are difficult to monitor from a single operational view.

As a result, finance teams often rely on reconciliation after transactions occur rather than seeing liquidity positions as funds move through the organization. The problem is rarely missing funds, but rather delays in assembling a complete financial picture.

A treasury challenge hiding in plain sight

For treasury leaders, this lack of visibility creates a difficult balancing act. Insurance companies depend on premiums not only to fund claims but also to support investment portfolios that generate returns. Managing that capital effectively requires confidence in liquidity positions at any given moment.

When financial visibility is fragmented, treasury teams naturally err on the side of caution. They maintain additional reserves to ensure obligations can always be met, even if underlying financial data is incomplete or delayed. That approach is prudent, but it can also introduce inefficiencies.

Capital that could otherwise be invested or deployed strategically may remain idle simply because finance leaders cannot easily track how funds move across operational systems. Over time, this can limit financial flexibility and reduce the returns insurers generate on the assets they hold. In an environment where margins are under pressure, capital efficiency matters more than ever.

What treasury leaders increasingly need is not simply faster reconciliation but a clearer operating layer that normalizes and tracks financial activity across billing, claims, and partner payments as those transactions occur.

The cost of operating without clarity

Limited visibility into financial flows also affects how insurers plan and manage their operations. When liquidity positions are difficult to assess in real time, financial planning may become reactive rather than strategic.

Finance teams can spend significant time reconciling transactions across multiple systems rather than focusing on forward-looking analysis such as liquidity forecasting, capital deployment, or risk planning. This dynamic makes it harder to respond quickly to shifting conditions, whether those involve rising claims costs, economic volatility, or evolving regulatory expectations.

In some cases, the challenge is not the volume of payments that need to be reconciled but the number of disconnected pathways through which those payments travel. Different payment rails, banking partners, and vendor channels often operate independently, limiting the ability to see and manage financial flows holistically.

These operational challenges are becoming more significant as the industry faces growing economic pressure. Person Holding Banknotes says insurers are operating in an environment marked by economic uncertainty, inflation-driven claims costs, and increasing pressure on profitability. In that context, operational efficiency and disciplined capital management are becoming even more important. Understanding how funds move through the organization plays a larger role in that equation than many insurers previously realized.

A shift in how payments are viewed

Historically, payments infrastructure has been treated as a supporting function within insurance operations. If transactions were processed accurately, the infrastructure rarely received the same strategic attention as underwriting systems or distribution platforms.

Finance leaders are increasingly recognizing that payments sit at the center of the industry's financial activity. Every premium collected, every vendor payment issued, and every claim settlement ultimately affects the balance sheet.

This realization is prompting insurers to rethink how payments infrastructure should operate within the enterprise. Rather than treating payments as isolated transactions, many organizations are beginning to view them as a connected network of financial flows that require orchestration and visibility across the entire insurance lifecycle. Seeing those financial flows clearly, rather than reconstructing them after the fact, gives treasury teams stronger control over liquidity, capital deployment, and financial risk.

Why payments visibility must become a strategic capability

The insurance industry has made enormous progress in underwriting analytics, pricing models, and digital customer engagement; however, financial visibility across operational payments has not always advanced at the same pace.

Yet as economic pressures increase and capital efficiency becomes more critical, the ability to see where money sits, and how it moves, may prove just as important as any underwriting insight.

For many insurers, the next phase of operational modernization will not only involve better systems for underwriting or claims, but clearer financial infrastructure that connects payment providers, financial institutions, and internal systems into a transparent operating layer. When financial flows can be understood in real time rather than reconstructed afterward, finance leaders gain a stronger foundation for capital management, liquidity planning, and risk oversight.

Because at its core, insurance remains a financial business. And the insurers that can clearly see how money moves through their organizations will be better positioned to manage both risk and opportunity in an increasingly complex market.