Brute force has been the corporate response to the normalization of nine-figure payouts—build taller insurance towers. But by 2026, we've reached the breaking point of that strategy. Adding more capacity is no longer a hedge; it's a target. Leaders who continue adhering to a "wait-and-see" strategy will likely hand over their negotiating power to plaintiffs' counsel. It's time to stop reacting and shift to a more aggressive tactic of data-driven litigation counter-anchoring, a tactical maneuver that uses historical benchmarks and hard modeling to ground a case's valuation.

The Psychology of the First Number

Refusing to name a number isn't a denial of liability; it's a tactical surrender. When we stay silent and treat it as a problem for later, we leave a vacuum that the plaintiff is only too happy to fill. This is the psychology of anchoring: the first number heard becomes the mental hook upon which all subsequent negotiations hang. If the opening bid is a $100 million "lottery ticket," even a successful defense that cuts it in half results in a $50 million disaster.

Counter-anchoring disrupts this by providing a grounded alternative before the plaintiff's number can take root. This isn't a guess; it is a calculated figure backed by historical industry benchmarks and internal safety data. By presenting a credible, data-backed valuation early, we offer juries a "safe harbor."

Most jurors are actually overwhelmed by the emotional volatility of nuclear-risk cases; they want to be fair, but they lack a yardstick. When the defense provides that yardstick—derived from logic rather than emotion—it grants the jury the permission they need to reject an inflated demand without feeling they are dismissing the injury itself.

Deployment: When to Anchor (and When to Pivot)

Counter-anchoring is most effective in "gray area" liability cases—scenarios where the question isn't if the company is responsible, but for how much. In these high-value moments, the goal is to cap the ceiling before it vanishes. By introducing a data-backed valuation early in mediation, you effectively narrow the range between "reasonable" and "astronomical."

However, data is a double-edged sword. The greatest risk in this strategy is the "Cold Corporation" trap. If your counter-anchor looks like a sterile spreadsheet in the face of a human tragedy, you don't just lose the argument; you lose the jury.

There is a razor-thin line between being "grounded in reality" and being "callous to suffering." The math must be the foundation, but the delivery must be human. If the jury perceives your data as a tool to devalue a life rather than a method to find a fair resolution, the anchor will drag your defense to the bottom.

When executed with empathy, speed becomes your primary weapon. By removing the "valuation fog" early in the process, counter-anchoring forces both sides to deal with reality. It strips away the performative inflation of the discovery phase and gets to the heart of the settlement, often shaving months—and millions—off the litigation lifecycle.

The 2026 Toolkit: Credibility Over Calculation

In 2026, a spreadsheet is not a strategy. While internal loss runs are necessary, they are rarely sufficient to move a jury. To make an anchor stick, you must look beyond internal data. A jury will instinctively view a company's own historical figures as self-serving; to achieve true "safe harbor" status, your numbers must be validated against industry cohorts. Credibility is built on external benchmarks—proving that your valuation isn't just what you want to pay but what the broader market defines as objective reality.

The most critical hurdle, however, is the communication gap. Raw modeling is the foundation, but the courtroom narrative trumps all. If you cannot translate a complex actuarial model into a story about fairness and community standards, the data will be dismissed as "corporate math." The numbers provide the boundaries, but the narrative provides the "why."

Finally, this strategy demands a collapse of the traditional corporate silo. We are seeing the rise of the general counsel/risk manager nexus. In the past, Risk bought the insurance, and Legal fought the claims. Today, these two must merge their datasets well before a summons is served. By aligning on valuation models during the underwriting phase, the defense is armed and ready on Day 1 to set the anchor before the ink on the complaint is even dry.

The Underwriting Reality: From Defense to Differentiation

Adopting a counter-anchoring strategy does more than win cases; it fundamentally shifts the power dynamic at the renewal table. In the 2026 market, excess underwriters are no longer just looking at loss history—they are scrutinizing a firm's "litigation maturity." When you can demonstrate a repeatable, data-backed method for suppressing social inflation, you move from being a commodity risk to a "preferred risk."

The conversation with underwriters changes the moment you move beyond passive risk transfer. Instead of simply presenting a tower of limits, you are presenting a proactive defense framework. Underwriters are tired of "blank check" litigation; showing them that you have the tools to anchor damages early provides them with something they value more than anything: predictability. By proving you can cap the ceiling of a potential nuclear verdict, you provide the actuarial certainty that justifies lower attachments or more competitive pricing.

The ultimate result is a stronger strategic partnership with your carrier. You aren't just buying paper to cover a potential disaster; you are demonstrating a sophisticated operational control that protects the carrier's capital as much as your own balance sheet. In an era of escalating awards, the companies that thrive will be those that prove they aren't just insured against the storm—they have the data to ground the lightning.

A Grounded Future

The era of "buying our way out" of litigation risk is over. In a 2026 landscape where $100 million is the new baseline for a nuclear verdict, silence on damages is a luxury no risk team can afford. By embracing data-driven counter-anchoring, general counsels and risk managers can reclaim the narrative, providing juries and mediators with a logical "safe harbor" before the emotional tide takes over.

Success now requires a fusion of math and empathy—a strategy where the data is the foundation, but the story is the house. Ultimately, those who anchor early won't just lower their payouts; they will redefine what it means to be a resilient, data-forward organization in an age of outsized expectations.

In 1979, Gallup asked Americans how much confidence they had in banks. Sixty percent said a great deal or quite a lot. Banks ranked second out of nine institutions — behind only the church.

Today that number is 26%.

The collapse didn't happen because of one crisis or one bad actor. It happened over 40-plus years, one automated interaction at a time. ATMs that replaced tellers. Interactive voice response systems that replaced those ATMs. Digital channels that replaced the IVR. And now AI-driven decisions replacing the digital channel that replaced the thing that replaced the person who used to know your name.

Each wave came with a business case. And each wave, when it touched the moments that actually matter to customers — a confusing charge, a decision that needed explanation, the thing that went wrong at the worst possible time — quietly withdrew a small deposit from an account that doesn't show up on any balance sheet.

That account is trust. And trust, it turns out, is an organizational capability problem — not a sentiment problem.

The Moment That Reveals Everything

Here's what I observed working inside a global bank during those automation waves: the technology worked. The process was faster. The costs came down. And customers were fine — until they weren't.

When something went wrong, people didn't want a faster process. They wanted a person who understood the situation, had the authority to act on it, and demonstrated that the institution they'd trusted actually cared what happened to them. What they got, too often, was a system designed for the average case, handling something that wasn't average at all.

What struck me wasn't the technology failure. It was the organizational failure underneath it. The leaders driving automation were making efficiency decisions. Nobody was accountable for the capability question: Does this organization know how to rebuild trust when the automated system fails a real person? The answer, in most cases, was no — because that capability had never been built. It had been assumed.

That pattern — confusing an efficiency decision for a capability decision and discovering the difference too late — is what eroded four decades of public confidence in banking. And it's the pattern insurers are now repeating.

This Is Now Insurers' Problem

Insurers are making the same bet banks made, in the same places banks made it.

Claims. Denials. Coverage decisions. Underwriting. These are not commodity interactions. They are, almost by definition, the moments when a policyholder is most vulnerable — a damaged home, a health crisis, a business interruption, a death. They are the moments that test whether the relationship the insurer sold is real.

The industry is automating them anyway. With AI systems that make faster decisions, with chatbots that handle first contact, with models that assess claims before a human ever sees them. The business case is real. The efficiency gains are real. The risk is also real — and it is being systematically underestimated.

Here's what gets missed in most of these conversations: The risk isn't primarily in the technology. It's in the organizational capability gaps the technology exposes. Does this organization have the judgment infrastructure to know when a claim needs a human? Does it have the change leadership — not change management, but genuine leadership capability — to ensure that the people still in the room when it matters are empowered to act? Can it tell the difference between a process that's working and a relationship that's quietly eroding?

Most organizations can't answer yes to all three. Not yet.

What Happens to the Humans Left in the Room

Here is the part the business case doesn't model: what automation does to the agents and claims professionals who remain.

When an organization systematically automates the high-stakes moments, it doesn't just remove humans from those interactions. It degrades the humans who stay. Authority gets stripped. Judgment gets overridden. The agent or adjuster who once had the latitude to assess a situation and act on it becomes an escalation path for complaints the system couldn't handle — without the context, the tools, or the organizational backing to actually resolve them.

This matters because the agent is still the face of the insurer when the policyholder calls. The claims handler is still the voice on the other end when the denial needs explaining.

The data on this dynamic in financial services is stark. An Eagle Hill Consulting survey of more than 500 U.S. financial services employees found that 62% say their organizations have prioritized improving customer over employee experience — yet those same employees report that their own work experience directly affects their ability to serve clients. Dissatisfied employees are more than three times as likely to report that their negative work feelings reduce their willingness to help others.

Deloitte's research adds another dimension: When AI tools are introduced without careful design and change leadership, employees perceive their organizations as nearly two times less empathetic and human. That dynamic doesn't stay inside the organization. It travels. Policyholders feel it.

For insurers that rely on independent agents — professionals whose loyalty is earned, not owned — the stakes are even higher. Think of independent agents as the community bankers of insurance: For decades, they've translated corporate rules into human terms, sitting across the table from policyholders at the moments that matter most. J.D. Power's independent agent satisfaction research consistently finds that scores are dramatically higher — by hundreds of points — when carriers make agents easier to work with: faster quotes, transparent claims status, access to a human on complex cases. When AI becomes a black box agents can't explain to a policyholder, that advantage reverses. An agent who can't get a straight answer on a claim denial, or can't reach a human on an exception, doesn't complain to the carrier. They quietly shift their next piece of business elsewhere. The trust problem isn't just with policyholders. It runs through the entire distribution chain.

The Balance Sheet Doesn't Show the Problem — Until It Does

What makes this dynamic particularly dangerous is that trust erosion is invisible on a quarterly basis.

The banking sector learned this the hard way in early 2023. When Silicon Valley Bank failed, uninsured deposits left the broader banking system at the fastest rate recorded since the FDIC began tracking data in 1984 — an 8.2% quarterly decline, industry-wide, in a single quarter. The FDIC noted that SVB's deposits were "remarkably quick to run" precisely because they were concentrated among depositors whose trust, once shaken, had no friction to slow it.

Insurers don't face bank runs. But they face their own version: policy non-renewals, lapse rates, coverage migration, claims disputes that become regulatory attention, and the slow erosion of the trusted advisor position that has historically made insurance a relationship business.

The erosion rarely announces itself. It accumulates in policyholder satisfaction scores that drift, in agent feedback that doesn't make it up the chain, in claims handling data that gets read as operational variance rather than relationship signal. By the time it's visible on the balance sheet, the capability gap that caused it has been open for years.

This Is a Capability Problem. Capability Can Be Built.

The research on AI deployment in financial services confirms what the banking experience suggests. McKinsey finds that AI high performers are more than 1.5 times as likely to have changed their standard operating procedures and talent practices — not just deployed tools. MIT CISR shows that firms stuck in the pilot stage financially underperform their industries, while those that have embedded AI into their operating models significantly outperform.

What those numbers describe, underneath the data, is an organizational capability gap. The high performers aren't distinguished by better technology. They're distinguished by having built the mindsets, the skillsets, and the operating conditions — the governance, the decision rights, the human judgment infrastructure — that allow them to absorb what the technology makes possible without losing what made them trustworthy.

That's the real lesson from banking. The institutions that automated their way into a trust deficit weren't led by people who didn't care about customers. They were led by people who treated trust as a communications challenge rather than a capability one. They managed it. They didn't build it.

Insurers now face a choice that banks didn't get to make deliberately. Insurers can design AI deployments that preserve human judgment at the moments that matter most. They can build the change leadership and workforce capability that determines whether AI enhances the relationship or quietly erodes it. They can treat trust not as a sentiment to be managed after the fact but as an organizational capability to be built before the moment of truth arrives.

Or they can assume their situation is different from banking.

Banks assumed that, too.

As the Penn State wrestling team won yet another Division 1 title over the weekend--its 13th of the past 16 awarded--and did so in overwhelming fashion, I realized there is a deeper competitive advantage at play than exists even in other sports. 

College wrestling dominance requires a layer that goes beyond the normal advantages that come from having a great coach and a roster of superb college athletes. Penn State-level dominance in wrestling requires an additional, self-reinforcing factor--of the sort I think can come from early success with AI, as it builds and builds and builds on itself.

I'll explain. 

To understand that self-reinforcing factor, you need to look at the Penn State coach and at the coach whose record of 15 NCAA wrestling titles in 21 seasons Penn State is now approaching. 

The Penn State coach is Cael Sanderson, arguably the best college wrestler ever. He was undefeated in college, winning 159 matches, and won four NCAA individual titles. He also won a gold medal at the 2004 Olympics. 

The man he's chasing, Dan Gable, who coached the University of Iowa from 1976 through 1997, ranks even higher in the wrestling pantheon. He not only won two NCAA individual titles (in an era when freshmen weren't allowed in the tournament) but took the gold medal at the 1971 world championships and at the 1972 Olympics. In those tournaments, Gable won each of his six matches in those tournaments without giving up a point--a preposterous achievement given how scoring works in international wrestling.

Sanderson's and Gable's credentials are so impressive that they naturally attracted top recruits -- and started to build that self-reinforcing layer. 

Wrestling differs from most college sports because the very best tend to pursue international careers after graduating but don't have any affiliation akin to what other athletes take on in professional leagues. Post-college wrestlers need a home. They need a wrestling room. And the best go to the best room, making it even better... and on and on we go.

Penn State has easily the best roster of collegiate talent at the moment -- six wrestlers made it to the NCAA finals among the 10 weight classes last weekend, tying the record, and four won titles. And Penn State has even better talent among the international wrestlers, who bring with them scores of NCAA titles and medals from world championships and the Olympics. In the finals of the 190-pound weight class at the U.S. trials for the 2024 Olympics, two wrestlers from that room went up against each other and had an epic battle -- which qualified as just another day in the life of Penn State wrestling.

The insurance industry should, I think, draw a lesson because AI can create a flywheel effect similar to what's happening at Penn State and what happened under Dan Gable at Iowa in the '80s and '90s. 

Adopting AI won't happen overnight. Using it is an unnatural act for many people, especially older ones, so you need to find ways to get people to start to get comfortable with it. You need to produce successes that you can use to evangelize about AI. You need to create rock stars that, while not at the level of a Sanderson or Gable, can attract talented people who want to take on more ambitious projects. You need to keep testing and feeling your way toward more aspirational business models, going beyond efficiencies to, perhaps, embedding insurance in other companies' sales processes or developing services that predict and prevent losses before they can occur.

In fact, early successes with AI can generate savings that you can pump into more future projects, so you just keep accelerating. 

(I realize I made more or less this point about a flywheel in last week's commentary on Lemonade, but I think it's so important that it's worth reinforcing, and college wrestling turns out to be even a better example than Lemonade.) 

No competitive advantage lasts forever. Gable retired at age 48 -- coaches often mix it up with their wrestlers, and even an all-time great eventually wears down. The Iowa program, while still strong, has drifted in the decades since. Sanderson is now 46, and maybe he'll tire out one of these days, too. Meanwhile, David Taylor, a just-retired big name, has set up camp at Oklahoma State, which had four wrestlers make the NCAA finals. Three won. All four are freshman. So another cauldron of a wrestling room may be taking shape.

But I'll bet any insurer would be happy with an advantage on AI of the sort that Sanderson has produced at Penn State and that Gable developed at Iowa before him.

Cheers,

Paul

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