Today's healthcare and insurance organizations manage vast amounts of highly sensitive data across an increasingly complex ecosystem. Clinical providers, insurers, and partners rely on shared data from diverse sources—including electronic medical records, imaging systems, and billing platforms.

However, many lack a clear understanding of where this data resides, who owns it, and how it's accessed. Without this foundation, they cannot confidently classify or protect sensitive information—leaving them vulnerable to compliance violations, regulatory fines, and legal risks. This lack of visibility also affects critical operations. In insurance workflows such as underwriting, decisions may be based on incomplete or inaccurate data, increasing risk—especially in high-stakes scenarios like mergers and acquisitions, where evaluating the data security posture is crucial.

These challenges are intensified by outdated, fragmented environments that are difficult to integrate and modernize. Sensitive data is scattered across disconnected systems and formats, leading to duplication, inconsistency, and reduced visibility. Meanwhile, excessive permissions remain a constant risk, increasing the likelihood of misuse, insider threats, and accidental exposure.

As organizations accelerate AI adoption, including generative AI, to enhance clinical and operational efficiency, they introduce a powerful new capability alongside a significant increase in risk. When sensitive data is used without proper governance and controls, exposure can grow quickly and unpredictably.

Operationalizing data security has long been a challenge. Despite significant investments, many organizations still lack complete visibility. Traditional tools that rely on regex, trainable classifiers, and other pattern-based methods identify only a small portion of sensitive data and often overwhelm teams with false positives.

The good news is that modern data security governance platforms have moved beyond these limitations. Healthcare and insurance organizations should seek solutions that leverage context-aware AI for discovery, risk monitoring, and remediation—delivering outcomes such as:

Gain better visibility into data: To effectively protect sensitive information, providers first need to understand exactly what data they possess, where it is stored, who accesses it, and how it is shared.

Context-aware AI scans each data record thoroughly and can identify not only personally identifiable information (PII), protected health information (PHI), and payment card information (PCI), but also detect other important business records that other tools might overlook. It also recognizes duplicate or near-identical data and determines the category and subcategory for each record. For instance, it differentiates between a HIPAA authorization and a workers' compensation document. This detailed level of information helps security teams make smarter decisions when assigning classification labels, choosing where data should be stored, or setting access and retention rules.

Prevent sensitive data leaks: Security teams must ensure that employees and third-party contractors do not access data they shouldn't and verify that authorized users do not share it. They need a solution that enables them to contextually discover, monitor, and protect their sensitive data—not only at rest but also in transit—to prevent it from being shared with unauthorized users, personal email addresses, file-sharing applications, social media, or GenAI tools.

Enable GenAI without expanding the attack surface: Generative artificial intelligence (GenAI) is reshaping our world in real time. Tools like Microsoft Copilot, ChatGPT, Perplexity, and Claude are changing the way we make decisions, solve problems, create content, and interact both at work and at home. While they offer greater operational efficiency, better decision-making, and lower costs, they also introduce significant data security risks for insurers.

Providers need a solution that helps them detect when employees use unsanctioned or "shadow AI," so they can maintain control and protect their data. They also need to ensure that, no matter where data is stored, it is accessed by the correct identities, at the appropriate times, and for the intended purposes. A comprehensive data security and governance solution will allow them to set guardrails on which data should be blocked or redacted by groups and for each GenAI application, and help them curate data when training their own proprietary GenAI models.

Excel in regulatory compliance audits: Regulatory frameworks help healthcare and insurance companies reduce risks, implement processes, and maintain customer trust. However, mapping security controls to these frameworks can quickly become overwhelming. An additional challenge is that different regions may have vastly different data handling and classification requirements.

Organizations need a clear overview of their compliance status, tools to resolve issues, and peace of mind that they aren't one audit away from disaster. They should seek a solution that offers a dashboard displaying their current compliance status across all relevant regulations and security controls, as well as support for custom frameworks. Additionally, they require granular visibility into all data records that violate compliance, with the ability to remediate them directly within the platform.

Improve the effectiveness of current security tools: Tools like zero trust network access (ZTNA) and cloud access security broker (CASB) don't analyze data to determine whether to allow or block access. Instead, they enforce policies based on labels, so if those labels are wrong or missing, they could either expose sensitive information to unauthorized users or prevent access necessary for productivity. Context-aware AI and autonomous classification help ensure that sensitive data is labeled correctly and remains accessible only to authorized individuals.

Experience faster ROI, smarter policies, and less stress: Context-aware AI significantly accelerates the data discovery process and saves countless hours that administrators used to spend on tuning algorithms and chasing false positives. However, since new data is constantly generated and continually changing, capturing only a snapshot at a single point in time is insufficient.

Security teams can save time and enhance data protection by implementing a solution that continuously monitors data, flags risks, and automates remediation steps. Picking a provider that offers managed services can also reduce the workload on overstretched security teams by providing data security experts to assist with tasks such as deploying the platform and training their teams on it, building a data governance roadmap, mapping classification labels, and reporting on and tracking progress toward their goals.

For years, the insurance industry has built systems to reduce dependence on the human factor.  Processes, rules, controls, automation — all aimed at making decisions more stable and predictable.

But something changed.

We began building systems where people increasingly stop thinking — and start executing.

How We Got Here

Insurance has always sought to reduce uncertainty.

The natural response was standardization: more rules, more control, more structured processes.

Technology accelerated this:

• more complex workflows
• additional validation layers
• stronger enforcement of consistency

The logic was sound.

But each new layer reduced the space for individual judgment.

The Subtle Shift

No one decided to "stop thinking."

It happened gradually.

Decisions became checklist validation.

Analysis turned into rule compliance.

Accountability shifted to "this is how the process works."

The system looks more controlled — but becomes less adaptable.

What This Looks Like

In underwriting, decisions are technically correct but lack context.

In claims, cases are handled "by the book" even when they don't fit it.

In distribution, process often outweighs common sense.

Nothing looks broken.

Everything appears to work.

That's the problem.

Why It Matters

This is not a crisis.

It's a slow degradation of decision quality.

Complex situations are reduced to templates.

Accountability becomes blurred.

Thinking is replaced by process adherence.

And Then Comes AI

AI performs best in structured environments.

It accelerates processes, reinforces standardization, and reduces variability.

But if thinking is already weakened,

AI doesn't fix it — it amplifies it.

And scales it.

The Hardest Part: Accountability

When decisions are made "by the process," they appear correct.

But the real question becomes harder to answer: Who is responsible for the outcome?

This may be one of the most critical shifts in the industry.

What Needs to Change

The problem is not processes.

It's when they start replacing thinking.

What matters:

• separating where standardization is needed — and where judgment is essential
• preserving space for thinking in critical decisions
• not hiding complexity behind procedures

And accepting that not all decisions can be reduced to an algorithm.

A Practical Observation

Even in a highly standardized environment, a different approach is possible.

Keeping key decisions with people rather than fully transferring them to processes is not always easier — or faster in the short term.

But it allows us to stay closer to clients and respond more flexibly.

Conclusion

Insurance companies don't fail because they lack processes.

They fail when processes replace thinking.

And this happens far more quietly than any technological disruption.

While forklift accidents have always carried serious consequences, the financial stakes have never been higher. A single incident involving a vendor or outside visitor can quickly escalate from a routine claim into a multimillion-dollar lawsuit, especially when the defendant cannot produce training records or maintenance logs.

As social inflation and nuclear verdicts push claim severity to new levels, it's critical that companies review their training, maintenance and safety protocols. Producers can help their clients understand their exposures and put practices in place before an accident forces the conversation.

The Rising Costs of Forklift Accidents

Forklift accidents represent a growing risk in many industries. In 2024, 84 workers died and over 25,000 were injured in accidents in the United States involving forklifts, order pickers or platform trucks, according to the National Safety Council.

While forklift-related claims have remained steady at Pennsylvania Lumbermens Mutual Insurance over the past five years, claims severity is rising, with more serious injuries, rising litigation costs and nuclear verdicts. Workers' compensation insurance typically covers the claim when the injured party is an employee. However, when the injured party is a vendor, delivery driver or outside visitor, the claim falls under a business's general liability policy. And in today's environment, the costs associated can quickly accrue.

In one claim example, a forklift operator did not see a delivery driver working with his tarp and struck him, resulting in serious injury. In two other cases, an operator ran over a bystander's ankle, while another operator hit an independent truck driver. Not only do these incidents alter the victims' lives, but the claims can be particularly costly, ranging from a couple of hundred thousand to potentially millions of dollars.

There are two broader trends exacerbating the severity of forklift-related claims. Social inflation is driving up claims' costs and nuclear verdicts, where courts are awarding increasingly large judgments, which are far more common than ever before. Investigation costs, litigation expenses, and large court awards all compound the cost of a claim. Such costs can grow much larger when the plaintiff's attorney gets involved and requests training records and maintenance logs that do not exist.

Paper Trails Can Save or Sink Clients

Human error remains the number one cause of forklift accidents. However, these errors rarely happen in a vacuum and are usually a symptom of something missing upstream, typically a lack of formal, documented training or thorough maintenance practices.

OSHA notes that proper training can reduce forklift accidents by 70% and is most effective when it is consistently reinforced. OSHA Standard 1910.178, Powered Industrial Trucks, identifies specific, detailed requirements for industrial truck training. It includes classroom and hands-on training, during which operators must demonstrate a pre-operational check of the unit and competency in the tasks they would perform on the job.

Adequate training must also address the specific hazards present on the premises where an operator works. This might include establishing clearly defined loading and unloading zones and setting protocols for where vendor drivers and outside personnel should stand. In the case of the delivery driver struck while rolling his tarp, a documented standard that kept the driver clear of the unloading area may have prevented the accident.

A common problem in smaller businesses is assuming a forklift operator is competent. A business owner with one or two forklifts may believe their employees know how to operate the machines because they have been doing it for years. However, experience alone doesn't hold up in court. With no training records to show, the legal exposure and costs compound.

Another major cause of forklift accidents is inadequate or inconsistent maintenance. Informal arrangements with a mechanic down the street will not hold water in the event of a claim. As with operator training, documentation is critical. If maintenance is not documented as being performed by a certified mechanic and in accordance with manufacturer standards, from a liability standpoint, it may as well not have happened.

The Conversations Producers Should Have About Coverage and Risk Gaps

One of the most important steps an insurance professional can take is to identify coverage gaps their clients may not know exist. Many business owners list forklifts under general contents and business property when they should actually be scheduled under an inland marine policy. Inland marine insurance offers better protection for mobile equipment, such as forklifts and piggybacks. Rather than lumping it into a blanket coverage amount, the policy assigns a specific schedule value to each piece of equipment. It is especially important for operations with multiple locations, as an inland marine policy will cover the forklift even if it is moved to another location.

Beyond coverage, producers should equip clients with key risk-mitigation recommendations such as:

• Implement a formal, documented training program that meets OSHA 1910.178 Powered Industrial Truck standards, covering both classroom instruction and hands-on training.
• Establish defined loading and unloading zones with protocols for where forklifts operate and where outside personnel are not permitted to be.
• Adopt a formalized maintenance program where maintenance is documented and performed by a certified technician according to manufacturer standards.
• Establish protocols on how to respond in the event of an accident. This includes caring for the injured person, documenting the incident and calling the broker as soon as possible.
• Consider technology as a secondary layer of protection. There are now telematics and collision-avoidance systems available for forklifts that can sense people and objects and alert the operator.

Forklift accidents are often preventable, and insurance professionals can help reduce operational risk and financial exposure. Across all areas, documentation is critical because, in litigation, if you did not document it, you did not do it.

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.

There is a moment that plays out hundreds of times a day across American and British cities: a delivery rider logs into an app, pulls into traffic, and gets into an accident. What happens next has very little to do with the severity of the crash. It has everything to do with a clock — specifically, whether there was an active order on the screen at the moment of impact.

If there was, platform coverage may kick in. If there wasn't, the rider is almost certainly on their own. And the personal auto or motorcycle policy they are still paying premiums on will, in all likelihood, deny the claim.

This is not a fringe scenario. It is the default state of insurance for one of the fastest-growing categories of workers in the developed world — and the industry has been aware of the problem for years without resolving it.

Consider the scale of what we're talking about. According to McKinsey's American Opportunity Survey, roughly 36% of the U.S. workforce — approximately 59 million Americans — now identify as independent workers in some capacity (McKinsey, 2024). A significant proportion of that population drives or rides for delivery platforms as either a primary or supplementary income source. And yet the workers most physically exposed to risk — those navigating urban traffic on motorcycles and bicycles for platforms like DoorDash, Uber Eats, Amazon Flex, Instacart, and Grubhub — occupy an insurance gray zone that no single policy cleanly covers.

The Bureau of Labor Statistics' most recent fatality data makes the stakes concrete: transportation and material moving workers suffered 1,391 fatal occupational injuries in 2024, representing 28% of all worker fatalities that year — more than any other occupational category (BLS Census of Fatal Occupational Injuries, 2024). The fatality rate for these workers stood at 12.5 per 100,000 full-time equivalent workers. For delivery riders operating in dense urban environments without the protection of a vehicle cabin, the exposure is even more acute.

The question this should raise for insurance executives — particularly those running commercial lines, personal auto, and product development — is not whether the gap exists. It does, demonstrably. The more interesting question is why a market worth tens of millions of potential policyholders remains so poorly served, and whether that represents a product failure, a distribution failure, or something else entirely.

Understanding the Coverage Gap: Three Periods, One Dead Zone

The mechanics of gig delivery coverage have been reduced, in industry shorthand, to a three-period model. Most drivers have never heard of it. Most platform terms and conditions don't explain it clearly. But for anyone who works through it methodically, the problem becomes immediately apparent.

Period 1 covers the window when a driver has logged into a platform app but has not yet accepted a delivery order. This is the dead zone. Personal auto insurers have increasingly added exclusions for commercial use, and being logged into a delivery platform is sufficient grounds for many carriers to classify the activity as commercial and deny a claim. Platform liability coverage, meanwhile, has not yet activated because no transaction is underway. The driver is moving through traffic, bearing the full risk personally.

Period 2 begins the moment an order is accepted and the driver is heading to the pickup location. Here, most major platforms — DoorDash, Uber Eats, Lyft — do provide some liability coverage, often up to $1 million in third-party liability. The coverage for the driver's own vehicle damage, however, typically requires the driver to already carry comprehensive and collision coverage on their personal policy, which the platform then layers on top of as secondary.

Period 3 covers the active delivery window, from pickup to dropoff. This is where platform protection is most robust. But even here, the driver's personal policy remains primary for vehicle damage, and that policy may have a commercial exclusion that voids the coverage entirely.

Grubhub provides no auto insurance coverage for its drivers at any point. Instacart offers no company liability policy for shoppers. For both platforms, claims route directly to the driver's personal insurer — the same insurer that likely has a commercial use exclusion buried in the policy language. The patchwork is not accidental. It reflects a deliberate liability architecture in which platforms minimize their insurance obligations to the narrowest defensible window and classify workers as independent contractors to justify that approach.

For motorcycle couriers in the U.K., the situation is more straightforward and arguably worse. Standard personal motorcycle policies explicitly exclude hire-and-reward use. The moment a rider carries goods for payment — regardless of the amount, regardless of whether the insurer knows — coverage under a personal policy is voided. There is no gray zone in U.K. law on this point. The rider is simply uninsured.

The Scale of Underinsurance and Who Bears the Consequences

A 2022 survey of more than 4,000 gig and independent workers by Stride Health found that 24% of gig workers had no health insurance whatsoever, with Hispanic and Latino workers disproportionately represented at 31% uninsured (Stride Health / PR Newswire, November 2022). By 2023, Stride's data showed that figure had improved to around 19%, partly due to expanded ACA subsidies. But health insurance and vehicle insurance are not equivalent risks, and the vehicle coverage picture is darker because personal auto policies carry explicit commercial exclusions that health policies do not.

The consequences of this underinsurance extend well beyond the individual worker. When an uninsured or underinsured delivery rider causes an injury to a third party, the subrogation chain becomes contested and protracted. Injured parties face diminished recovery prospects. Carriers handling third-party claims absorb costs they cannot recover. And platforms structured to distance themselves from liability face growing regulatory and litigation pressure in markets where legislators are beginning to pay attention.

California's AB5 and subsequent gig-worker legislation disrupted the independent contractor classification that underpins the entire platform coverage structure. U.K. employment tribunals have pushed Uber, Deliveroo, and others toward worker status reclassifications that carry associated benefit obligations. The regulatory direction is not ambiguous. The legal scaffolding that allows platforms to restrict their insurance obligations to narrow delivery windows is under sustained pressure on both sides of the Atlantic.

Why the Market Hasn't Fixed Itself

Coverage endorsements exist. Several U.S. carriers offer rideshare or delivery endorsements that extend personal policy coverage into Period 1 and fill gaps in Periods 2 and 3. These typically cost between $15 and $50 per month—meaningful but not prohibitive for a full-time driver earning through deliveries. In the U.K., hire-and-reward specialist policies are available through brokers and comparison platforms including Quotezone and Zego, with some policies starting at under £1 per hour of active delivery work.

The take-up rate for these products remains low, and it is worth being precise about why. It is not primarily a cost problem. It is a distribution problem—one with a specific structural cause.

Delivery platforms onboard drivers quickly and with minimal friction. Insurance disclosure is not part of that process. Personal auto insurers, who hold the most information about vehicle usage, historically treated non-disclosure of commercial activity as grounds for post-claim denial rather than a trigger for proactive coverage conversations. Neither party in the existing system had a financial incentive to identify the gap before a loss occurred. The incentive only materialized at claim time, when denial was the cheapest available response.

The result is a market where the product solution is known, priced, and available, but reaches a fraction of the people who need it. That is not a consumer awareness problem. That is a structural distribution failure.

The Embedded Insurance Opportunity

The most direct path to closing this gap at scale is embedded insurance coverage integrated into the platform onboarding process itself. The mechanics are available. Insurtechs have built platform-integrated models that allow drivers to quote, bind, and manage coverage without leaving the app environment. The carrier appetite and platform partnership structures required for this to become standard, rather than exceptional, have not yet materialized across the market.

There is also a product design opportunity that the market has not fully claimed. Full-time delivery workers do not want to manage two separate policies or mentally track which coverage mode applies at any given moment. Driver research points consistently toward demand for a single policy that covers all phases of vehicle use personal and commercial without requiring the driver to distinguish between them. A unified policy that prices the commercial mileage component appropriately and eliminates the Period 1 dead zone would address the core structural problem. Some carriers offer versions of this. None have made it a dominant market position in the delivery worker segment specifically.

The delivery worker population is large, growing, and concentrated with a small number of major platforms that have direct digital relationships with every worker they onboard. DoorDash, Uber Eats, Amazon Flex, Deliveroo, and Just Eat collectively onboard hundreds of thousands of new drivers annually across the U.S. and U.K. Each onboarding is a moment of maximum insurance relevance—a point when the worker is actively thinking about their vehicle and the terms of their new engagement. That moment is currently wasted from an insurance distribution standpoint.

What the Carriers Who Move First Will Own

The regulatory clock is running. Several U.S. states have mandated minimum platform liability coverage during Period 1 for rideshare drivers. Delivery-specific mandates are less advanced but tracking the same trajectory. In the U.K., worker reclassification rulings create the conditions for coverage obligations to follow. The floor is rising.

Carriers that build delivery worker products now while the market is fragmented and underserved will have three compounding advantages when regulation eventually sets minimum standards: pricing data from a meaningful claims base, established distribution relationships with platforms, and brand recognition among a workforce that currently has no particular loyalty to any insurer.

The carriers that wait will inherit a regulated market with thinner margins, established competitors, and no data advantage to speak of.

The Product Design Problem, Stated Simply

The delivery worker coverage gap persists because the existing system was not designed for workers who inhabit two insurance categories simultaneously. Personal auto policies were written for personal use. Platform coverage was written to be a minimum-liability instrument. The gap between them is not a regulatory failure or a consumer education failure. It is a product design failure—one that leaves a growing segment of workers effectively uninsured at the moment of highest physical risk.

Fixing it does not require new insurance law. It requires carriers with personal lines and commercial lines operations to stop treating delivery workers as an edge case in each product and to design specifically for the actual risk profile of the work: high mileage, urban concentration, multi-platform, commercially active for some portion of every shift.

The market is already built. The workers are already driving. The coverage that would actually serve them has not yet arrived at scale.

Sources Referenced

• Bureau of Labor Statistics Census of Fatal Occupational Injuries, 2024. Published January 2025. bls.gov/news.release/cfoi.nr0.htm
• Bureau of Labor Statistics Employer-Reported Workplace Injuries and Illnesses, 2023-2024. Published January 2026. bls.gov/news.release/osh.nr0.htm
• Stride Health Gig Worker Health Coverage Survey (4,000+ respondents). PR Newswire, November 15, 2022. prnewswire.com
• Stride Health / Noah Lang, CEO Independent Worker Uninsured Rate Update, 2023. prnewswire.com, December 2023
• McKinsey & Company American Opportunity Survey: Independent Workers. 2022, updated 2024. mckinsey.com
• Insurance Information Institute (III) Auto Insurance and the Gig Economy. iii.org
• California AB5 / Proposition 22 Gig Worker Classification Legislation, 2019-2020
• UK Supreme Court Uber BV v Aslam [2021] UKSC 5 Worker classification ruling
• Inshur Driver Research on Coverage Preferences, US and UK markets. inshur.com