I spend a lot of time with agency principals at very different stages of growth. Some are running lean operations in a single market. Others are managing multi-state books across several product lines. One thing I hear consistently, almost regardless of scale, is some version of this sentence: "We had our best enrollment year ever." My next question is always the same: What did that actually do for the health of your business?

The insurance distribution industry has spent years treating enrollment volume as the definitive signal of agency health. For most of the last decade, the underlying economics cooperated. A prolonged hard market in property and casualty, combined with rising premiums across many health and medical product lines, created an environment where agency revenue continued to grow, even when relatively few leaders stopped to examine what was actually driving it.

Most agencies have not had a reason to look too closely at those numbers, and the market has not forced the question. That is starting to change.

The Hidden Math Behind the Headlines

The 2025 Best Practices Study found top-performing agencies posted organic growth rates consistently between 10.3% and 10.7%. Those are historic numbers. On the surface, they make a compelling case that chasing enrollment is the right strategy.

The problem is what sits underneath. Financial benchmarking data shows that for the average insurance brokerage, roughly seven percentage points of that double-digit growth figure are attributable to premium rate increases and exposure base expansion rather than new accounts or deeper relationships with existing clients. Strip out the inflationary tailwinds, and actual organic growth for the average firm lands somewhere between 2.7% and 3.2%.

That is a very different business than the one most agencies believe they are running. The industry has benefited from macroeconomic tailwinds that made revenue growth look like strategic growth; premium inflation was confused with operational excellence. When the market softens and premium inflation cools, the distance between those two numbers will become impossible to ignore.

The Retention Gap Nobody Talks About

Industry data puts the average retention rate for a single-policy client at roughly 77%. An agency whose book is built primarily on single-policy households is losing close to a quarter of those clients every year. The agency is operating an expensive treadmill, constantly spending on client acquisition just to hold revenue flat.

When an agency deepens the relationship to five or more policies per client, that retention rate climbs to approximately 85%. That increase in retention sounds modest, but the compounding math over a five-year horizon is anything but. A multi-policy client is nearly twice as likely to still be in the book five years from now, representing roughly a 60% improvement in long-term customer value driven entirely by product depth rather than acquisition.

What makes this even more striking is how pervasive the single-policy problem is. Research indicates that approximately half of the average firm's customer base holds only one policy. That means half of the average independent agency's book of business is both an untapped revenue opportunity and a serious flight risk, simultaneously.

Why the Industry Stays Stuck

In most agencies, the barrier to deeper product relationships is operational, not strategic. The independent distribution ecosystem was designed to maximize transactions, not deepened relationships. Every new product can introduce another appointment, enrollment workflow, commission schedule, and other points of friction. Eventually, the economics favor moving on to the next prospect rather than deepening the relationship with the current one.

Consider what it takes to build a comprehensive household relationship spanning a major medical plan, a hospital indemnity policy, and a term life product. An agent navigates three separate carrier portals, re-enters the same client data three times, manages three different quoting engines with three different underwriting workflows, and then attempts to reconcile three fundamentally incompatible commission structures on the back end. That extra 30 to 45 minutes per client, multiplied across an open enrollment season, is simply not compatible with high-volume production targets.

The compliance landscape compounds the challenge, particularly in the senior market. CMS Marketing and Communications Guidelines generally prohibit agents from using a Medicare sales appointment to cross-sell non-health products without a documented Scope of Appointment secured at least 48 hours in advance. As a result, an agent who identifies a legitimate cross-selling opportunity often cannot pursue it in that moment. Instead, the opportunity requires a separate appointment, additional documentation, and another workflow. Each additional step introduces friction, reducing the likelihood that a valuable client need is ultimately addressed.

None of these barriers are insurmountable. They are real, though, and they explain why agencies that understand the economics of product depth still struggle to execute against it. The gap between what agencies know they should do and what their infrastructure allows them to do is where retention gets lost.

What Capital Actually Sees When It Looks at Your Book

The insurance brokerage M&A market is as active as it has ever been, driven by private equity consolidators, national aggregators, and large brokers competing for quality books. In an M&A context, the word "quality" has a very specific meaning that has nothing to do with enrollment volume.

Institutional buyers are not purchasing a snapshot of today's revenue—they are purchasing the predictability and durability of tomorrow's cash flows. A client retention rate at or above 90% is considered the standard for premium, platform-grade valuations. Agencies that consistently hit that threshold command meaningfully higher multiples, often one to two turns of EBITDA above lower-retention books. When a buyer identifies a book built primarily on single-policy clients with historical churn running below 80% to 85%, the response is earnout structures that shift up to 40% of the total purchase price into contingent payments tied to retention benchmarks the selling agency is unlikely to meet.

This is the moment that tends to surprise founders who have spent years optimizing for enrollment. They assumed the number of clients in the book was what they were selling. Buyers see it differently. They are buying the quality of those relationships, and a book full of single-policy households is a book full of clients who are one price comparison away from leaving.

The Market is Moving Anyway

The argument for product depth has always been sound, but several structural shifts are making it urgent in a way it was not five years ago.

The traditional employer-sponsored group health model is fracturing. ICHRA adoption grew by 34% among large employers and up to 49% among mid-sized employers between 2024 and 2025, as businesses shifted from selecting group plans to providing employees with tax-free dollars to shop the individual market themselves. The expiration of enhanced ACA premium subsidies pushed average deductibles to nearly $3,800 in 2026, sending millions of individuals into the market with complex coverage needs and no institutional support to navigate them.

The senior market is moving in parallel. By 2030, all Baby Boomers will be age 65 or older, with roughly 10,000 Americans turning 65 every day. Original Medicare's well-documented gaps in dental, vision, hearing, and extended hospital stays create natural openings for the agent who takes the time to build a full household picture. Multi-product senior households are among the stickiest in the book. The relationship has real financial consequences for the client, which translates directly into retention.

Carriers are responding to these same market dynamics. The era of rewarding volume alone is steadily giving way to compensation structures that reward persistence and quality of business submitted. As acquisition costs rise and profitability comes under greater pressure, carriers are placing increasing value on agencies that consistently deliver durable books of business rather than simply higher application counts.

A Different Way to Measure Agency Health

The agencies I see creating the greatest long-term value are not necessarily the ones with the largest enrollment numbers. They are the ones that can answer two questions with confidence: how many of their clients hold more than one product and what their 12-month and 24-month retention rates look like by product line. Those metrics don't just describe yesterday's performance; they predict tomorrow's enterprise value.

The agencies leading this transition have recognized that relationship growth is an operational capability, not simply a sales initiative. They routinely identify single-policy households with unmet needs, but more importantly, they've invested in systems that make acting on those opportunities almost effortless. Quoting across product lines, managing compliance requirements, and reconciling commissions are integrated into a single workflow rather than a series of disconnected administrative tasks. When the operational friction is removed, producers stop abandoning cross-sells and the book composition changes.

The valuation gap between those two categories of agency will only widen as the hard market continues to ease and rate-driven premium inflation recedes. When that happens, growth has to come from somewhere real. The agencies that figured that out before they needed to will be in a fundamentally different position than the ones that did not.

Enrollment is no longer the end goal. It is the invitation, the beginning of the relationship. Agencies treating enrolled clients as a relationship will deepen those bonds rather than finish as a transaction. Years from now, they won't simply look back on this market as a period of strong enrollment. They'll recognize it as the period in which they built businesses that compound.

Insurance enjoys the protection of a regulatory moat. Capital requirements, reserve rules, licensing, and market oversight protect policyholders—and, by extension, legacy carriers' market positions.

But the moat is built around the balance sheet, not the operating model. It was never designed to protect expense ratios, organizational layers, underwriting workflows, claims operations, or administrative overhead.

Private equity is finding the seams—the fee-generating, capital-light nodes adjacent to the regulated core—and systematically buying them, then using AI to compress cost inside while the actual risk gets parked elsewhere. Here's where the action is happening.

1. MGA and program administration

MGAs underwrite and distribute without carrying capital—and, in excess and surplus lines, without the rate-filing straitjacket that binds admitted business. In the E&S market, no state Department of Insurance reviews an actuarial memo before an MGA changes pricing. That's not a technicality; it's the ballgame.

Roll-up money has been pouring into program administration at a pace that should alarm anyone who thought "we're regulated, we're safe." AI-driven underwriting here doesn't need to win a fight with a state commissioner—it just needs to be better and faster than the human it's replacing.

2. Claims, fraud, and subrogation

Loss costs and loss adjustment expense consume roughly 60–70 cents of every premium dollar. That's the single largest pool of spending in the industry, and it's almost entirely a process problem—not a regulatory one. A state insurance commissioner has opinions about your rate filing, not about whether your computer vision model estimates hail damage better than an adjuster with a clipboard, or whether an natural language processing (NLP) model flags subrogation opportunities your staff missed while chasing cycle-time bonuses.

This is the cleanest PE trade there is: buy or build the platform, automate the workflow, collect the toll. No rate filing required, no market conduct exam in the way. Claims processing today is like bank credit card processing 30 years ago—bespoke and fragmented. Look for rapid consolidation. Think: Visa and Mastercard. Blackstone, Apollo, and KKR certainly are.

3. Fronting

Fronting carriers exist so an MGA or insurtech can write business without holding a balance sheet. They rent a fronting carrier's paper and lay the risk off to a reinsurer (often also private capital). It's elegant financial engineering, and it's grown explosively because it lets everyone upstream of the actual risk-bearing get paid without ever touching the risk. Though regulators have started naming fronting arrangements specifically in their review of PE ownership structures, real action may be years away.

4. Agency roll-ups

This isn't new, and it isn't really disruption any more so much as consolidation. Acrisure, Hub, BroadStreet, AssuredPartners—PE has been buying up the independent agency channel for 15 years, riding a demographic wave of retirement-age owners with no succession plan and no appetite to fight for a better multiple. It works. It will keep working.

AI helps at the margins—better cross-sell, better retention scoring—but the trade was never about the tech. It was about the math, and the math has gotten more expensive as everyone's figured it out.

5. Data and risk analytics

Everybody wants to be Verisk. Almost nobody gets to be, because Verisk, CoreLogic, and LexisNexis Risk Solutions already occupy the high ground, and moats in data businesses are real. The greenfield here is narrower than the hype suggests—you'll see PE money chasing climate-risk modeling and computer-vision property inspection at the edges, not a wholesale takeover of the analytics layer.

Note: If your strategy deck has a slide about "becoming the data platform for the industry," ask whether you're actually building a moat or just donating R&D spending to a market that's already been won.

6. Reinsurance and alternative capital

Cat bonds, ILS, sidecars—genuinely useful capital efficiency tools, and genuinely attractive to institutional and PE money looking for returns uncorrelated to public markets. But this is also the layer drawing the most direct regulatory heat right now, because it's structurally identical to the arrangement that has state regulators and the NAIC nervous about affiliated reinsurance in the life and annuity world: related-party transactions, opaque asset-liability matching, risk-based capital that may not be pricing the actual risk. These are known trades, under active review.

7. The balance sheet itself

And here, finally, is the piece PE mostly leaves alone: the actual risk-bearing carrier. Full statutory capital requirements, rate filings, market conduct exams, risk-based capital rules that don't care whose name is on the equity. This is the one link where regulatory protection still functions as advertised, and it's not an accident that PE has mostly declined to fight it head-on. Instead, private capital rents access to this layer—through fronting, through reinsurance, through MGA fee arrangements—rather than trying to own and run it directly. The fortress holds. Under siege are the lands that sustain it.

Eighty-four percent of large brokers with over $100 million in revenue now use generative AI. Claude, ChatGPT, and Copilot save producers hours every week on prospecting and reporting. But these tools are trained on public internet data, not insurance data.

That gap matters more in insurance than in almost any other industry. Regulation changes state by state. One wrong sentence in a submission or coverage recommendation can trigger an E&O event. And one in two agency records is missing critical fields, so generic AI often builds a confident answer on an incomplete picture.

The agencies pulling ahead are not the ones using AI first. They are the ones grounding AI in insurance-specific data. This whitepaper shows you what that gap costs and what to look for in a platform built to close it.

Come visit Zywave's website to learn:

• Why Insurance Needs Specialized AI Context
• The Hidden Cost of Incomplete Records
• What Generic AI Gets Wrong About Insurance
• The Difference Between Data and Intelligence
• Four Questions to Ask Before You Trust an AI Platform

See the difference>>

In 2012, the world avoided catastrophe by nine days. 

A solar storm emitted a plasma cloud of gigantic proportions that summer. Had it hit the Earth, it would have done trillions of dollars in damage, wiping out communications and GPS satellites while causing so many transformers to explode that it would have taken down much of the electric grid for weeks, months or even a year. But the plasma cloud passed through the spot in the Earth's orbit where it had been nine days earlier and did almost no damage. Nine days....

The Earth wasn't so fortunate in 1859, when what's now known as a Carrington Event — named after one of the astronomers who saw the solar eruption — wiped out the world's telegraph system. And scientists say such a massive flare will surely hit the Earth again, with its now exponentially more wired and, thus, vulnerable electrical and electronic infrastructure. The only questions are: when, and how devastating will the flare be?

Models suggest that the likelihood of a Carrington Event is only about 1% in any given year, but the effects would be so devastating that we need to at least be running scenarios to see how we can help clients be more resilient while also making sure insurers would survive such a catastrophe.

Let's have a look.

The Carrington Event itself isn't what a reporter would call new news. While the near miss in 2012 received remarkably little coverage, we ran an article in 2014 on the potential danger that holds up quite well, and have been periodically raising the issue since then. This article in 2021 goes deep into the implications for insurers, partly by looking at a solar storm in 1989 that, while far less severe than a Carrington Event, showed in miniature the sort of damage one might cause. A solar storm in 2024 — again, far less severe than a Carrington Event — prompted me to revisit the issue, and, for those looking for a truly deep dive into the history of solar storms and their implications, our colleagues at the International Insurance Society published this piece that same year. As a solar cycle peaked last year, making intense storms more likely, we ran another piece on the potential implications.

What is new is a recent study that found, according to the subhead of a Wired article: "Scientists have long assumed that there’s an upper limit to the intensity of the solar winds that reach Earth. New research suggests there’s not—and the implications are alarming."

The article says:

"For years, scientists have believed that there is a natural limit to the intensity with which Earth responds to the most extreme solar storms. According to that idea, when the solar wind reaches very high values, Earth's magnetic field stops reacting proportionally, and its response enters a kind of 'saturation.' However, this new study suggests that perhaps that limit never existed. What appeared to be a physical phenomenon could, in reality, be an illusion caused by the way the measurements are analyzed."

In other words, our magnetic field may not offer the protection we long thought it did. 

A Carrington Event is still seen as a truly rare occurrence — some models say one will hit Earth every 100 to 250 years, while I've seen models that say such an event is even rarer, coming just once a millennium. But the threat can't be dismissed as a black swan. When one hits, it won't be a total surprise. We know another Carrington Event — or even worse — will occur. So the threat is more of a grey swan, and there's no excuse for not planning at least at some level for when it hits. 

The effects of a massive solar event would be beyond anything we've seen in the modern day. This wouldn't just be an extended blackout. The shutdown of the grid could be so total that we'd lose access to the financial system, meaning no ATMs or credit cards. We would lose much of our communication network because of damage to satellites as well as to the cell towers and WiFi that need electricity. Water purification systems would be down. Farmers would lose access to many needed supplies, endangering our food supply. 

So insurers must help clients map out how they would keep their businesses operating or at least recover quickly — in the U.S., parts of the Atlantic Coast and upper Midwest seem to be most vulnerable, so backup capabilities located elsewhere might be wise. Generators, batteries (though many will be overwhelmed by a Carrington Event's electric surge), water supplies and so on should also be part of a plan, keeping in mind that something that may be routinely available now will be in excruciating demand if the electric grid goes down even for weeks, let alone months.

I think there are technology opportunities here, too. Solar power and batteries are improving so fast and dropping in price so quickly that they are providing unprecedented, decentralized power sources that could allow for considerable resilience even with the grid down for an extended stretch. I don't know just what would be involved in hardening them enough to withstand a massive electric shock, but doing so would greatly diminish the length of the damage from a Carrington-level event. The good news about such a solar storm is that we get about 18 hours of warning — the time between when astronomers spot a solar flare and when the storm hits the Earth — so there is time to switch power sources into bunker mode. 

Insurers obviously also have to protect themselves so they can reliably pay claims and help the world get back on its feet. Insurers should cover as much risk as possible but have to stay in business. So they need to be careful as they underwrite risks that could be beyond measure when the next Carrington Event hits.

I'd suggest also working with government to develop a recovery plan, though I'm not optimistic. I suspect that even a White House that believed in FEMA, as the current administration clearly does not, would be vague and noncommittal. So I suspect that coordination between insurers and governments will largely have to be arranged after the fact — and that the finger-pointing and legal action will be mind-bending.

I hope I don't come across as a boy crying "wolf," having repeatedly raised an alarm for a dozen years that has yet to be borne out. But remember: The fable ends with a wolf appearing and eating all the sheep.

Cheers,

Paul

Artificial intelligence (AI) has rapidly become one of the most important global business topics and risks. This is clearly reflected in the Allianz Risk Barometer, where AI rose from 10th place worldwide in 2025 to 2nd place in 2026 – the largest increase of any risk category. Across regions, AI now ranks among the most relevant risks, highlighting the speed at which it has moved up the corporate risk agenda.

A gap between perceived risk and current losses

From a claims perspective, however, the current loss situation appears relatively calm. The number of clearly confirmed AI-related losses remains limited, and if assessed purely on today's claims data, AI would not yet rank among the most significant loss drivers. This apparent gap between perceived risk and actual losses requires closer attention.

A large "grey area" exists in which AI involvement is difficult, if not impossible, to prove. In many incidents – whether cyber-attacks, business decisions or operational failures – AI is likely to play a role, even though it cannot be conclusively identified. As a result, current claims data may underestimate the true level of underlying risk.

AI risk emerging across multiple lines of business

What is already evident is that AI-related exposures are emerging across multiple lines of business simultaneously, but in very different forms. In cyber, AI is increasingly assumed to be used in areas such as phishing or malware development, even if its involvement cannot always be verified. At the same time, intellectual property disputes related to the training of large language models are gaining prominence, alongside data protection issues where AI is deployed without sufficient safeguards.

In liability insurance, risks are particularly relevant in connection with autonomous systems and AI-supported decision-making. One of the most visible areas currently is directors & officers insurance, where so-called "AI washing" – the overstatement or misrepresentation of AI capabilities – is already leading to claims. In addition, AI is significantly enhancing the effectiveness of fraud schemes, from business email compromise to advanced social engineering and voice-based attacks.

AI is not a risk that is limited solely to certain business areas and applications. It is emerging across many insurance products simultaneously – often not as the direct cause, but as an aggravating factor.

Why losses are expected to increase

Looking ahead, there are strong indications that AI-related losses will increase substantially. AI acts as a powerful risk accelerator, with the number of incidents and related litigation rising globally. External observations point to a clear upward trend.

What sets AI apart from many traditional risks is its probabilistic nature. Errors are not exceptions but an inherent feature of the technology, even when systems are properly designed and implemented.

When such systems are deployed at scale and embedded into everyday processes, even a small error rate can translate into a significant number of incidents within a short period of time. Combined with the speed of deployment, evolving governance frameworks and the widespread use of AI tools across organizations, this creates a risk environment in which losses can escalate rapidly once they begin to materialize.

AI fundamentally changes how risk behaves. Unexpected output and errors are not an exception; they are inevitable. When not addressed and managed properly, even small deviations can lead to significant losses quickly.

Understanding the roots of AI risk

At its core, AI risk operates on several levels. One lies within the technology itself. AI systems often generate outputs with a high degree of apparent confidence while remaining sensitive to small changes in input and prone to various forms of bias. This can have significant implications in areas such as decision-making, risk assessment or negotiation.

A second level arises from the way AI is implemented within organizations. While experience with AI continues to grow, many companies are still developing the necessary governance, controls and expertise, often under considerable pressure to adopt the technology quickly. Even under responsible use, the probabilistic nature of AI means that errors cannot be fully eliminated.

A third level relates to indirect effects. In many cases, AI is not the direct cause of damage, but rather a force multiplier that enhances existing risks by making attacks more convincing and fraud more effective.

Implications for risk management

Against this backdrop, it becomes clear that AI risk is not purely a technological issue. It is a business, governance and leadership challenge that requires a holistic approach. The fact that losses remain comparatively low today should not lead to complacency. Historically, losses tend to lag behind adoption – but once they emerge, they can develop quickly and at scale.

Effective risk management therefore starts with a clear understanding of the fundamental characteristics of AI and their translation into concrete business risks. Organizations need to make deliberate decisions about which risks can be mitigated, which can be accepted, and which should be transferred. At the same time, the opportunities offered by AI remain significant. Realizing these opportunities sustainably depends on addressing risks in a transparent and proactive manner.

AI offers enormous opportunities, but those opportunities can only be realized sustainably if risks are understood, addressed early and managed proactively.

A risk that is only just beginning to materialize

As AI continues to be embedded more deeply into business operations, its effect on the risk landscape is expected to grow further. What is observable today is still only an early stage of a broader transformation – one in which the true scale of AI-related risk will become more visible over time.

My son Riley burned to death pinned inside his Honda Civic in a side underride crash in 2015. His death was part of a documented crash pattern that the trucking industry and federal regulators have studied, debated, and ignored since the 1960s.

Side underride crashes happen when a passenger vehicle, cyclist, pedestrian, or motorcyclist goes under the side of a semitrailer. The consequences are often devastating because the trailer frame bypasses the vehicle's safety systems and strikes the occupant space directly. Riley's collision occurred at a shallow angle and a relative speed of about 14.5 mph, lower than speeds at which tested side-guard designs had prevented underride.

The litigation that followed showed how a fatal truck crash can affect the entire transportation chain. The truck driver's insurer and motor carrier settled early. A motor carrier that had served as an unlicensed broker settled after a few months of discovery. The lawsuit against Utility Trailer Manufacturing, a major refrigerated-trailer manufacturer, went to a jury.

Riley's crash was one of roughly 300 deaths and 400 serious injuries that side underride causes each year, with an estimated societal cost of $7.4 billion. In 2023, 5,472 people were killed in crashes involving large trucks, about 30% more than a decade earlier. Seventy percent of those killed were occupants of other vehicles; another 12% were pedestrians, cyclists, or other nonoccupants.

Mistakes happen. Side underride guards, like seat belts and airbags, reduce the severity of consequences regardless of fault.

The cost objection

NHTSA has denied four formal petitions seeking side guards on semitrailers since 1979. The 1979 denial (FR 44:55077-55078) closed with: "If the evidence gathered by the agency indicates that side underride rulemaking could contribute significantly to safety, the agency will commence rulemaking."

The trucking industry's cost objection over whether to require side underride guards on semitrailers has a long history but a thin evidentiary base. In 1969, the industry's trade association told federal regulators that side underride "is not a hazard that warrants some kind of regulatory remedial action."

In 1991, NHTSA declared without evidence that side underride guards were not cost-effective. A 2021 Freedom of Information Act request confirmed the agency held no data or analysis to support that assertion. The industry clings to the same claim in regulatory comments and litigation: guards would be too expensive, add weight, and increase fuel use. Juries are unconvinced. 

I tested those assertions.

In my 2026 peer-reviewed cost-benefit analysis, side underride guards produced positive net economic benefits under every modeled scenario, ranging from roughly $137 million to $2.8 billion.

The break-even threshold in my analysis is that modest: guards need to prevent as few as 24 deaths and serious injuries per year to be economically justified. The analysis also excluded litigation, settlements, defense costs, and insurance losses. A side guard with an aerodynamic skirt costs approximately $2,500 per trailer.

For insurers and fleets that bear the litigation costs, the actual break-even is lower still.

What the record shows

Discovery in Riley's case showed that Utility Trailer had known for decades that side underride was a serious hazard but had not designed, built, or tested its own side guard before the lawsuit. The company criticized the Angelwing, a side guard that an independent inventor had already designed, built, and crash-tested without the engineering resources of a major manufacturer, and made no attempt to develop its own solution. Two years before the verdict, IIHS independently crash-tested a side guard at 40 mph and confirmed it prevented underride. Wabash National successfully crash-tested its own design the same year. The jury found Utility negligent and returned an $18.9 million verdict. After the verdict, Utility publicly maintained that it had not negligently designed its semitrailers and argued that side guards would make them more dangerous.

Then, after arguing at trial that side underride guards were impractical, Utility developed and patented one. The company now offers a Side Impact Guard, describing it on its website as evidence of a "long-standing commitment to the safety of every motorist sharing the roadway" and claiming its innovations "set the gold standard for performance and reliability." As of 2026, Utility has delivered nearly 70 semitrailers factory-equipped with the guard.

The technology was never the obstacle.

Nearly every major semitrailer manufacturer now holds a side guard patent. The impracticality argument was an industry position, one the industry's own trade association funded specifically to develop defense strategies against side underride lawsuits, with manufacturers suppressing information from the public under a joint defense agreement.

The costs of inaction

The Utility verdict was not a one-off. Vehicle side underride cases have produced $32.5 million in Georgia for a fatality, $26.1 million in Iowa for a debilitating injury, and $13 million in a second Georgia case. Pedestrian and cyclist cases have produced an $81 million verdict in Utah, $11.5 million in Virginia for a bicyclist, and $8.5 million in New Hampshire for a bicyclist fatality, among others. Many more cases resolve confidentially. These are the hidden costs the industry never mentions when it argues against requiring guards.

The pattern is consistent. When side underride is not addressed before a crash, the costs appear afterward in verdicts, settlements, defense expenses, and rising insurance premiums. The math does not favor waiting.

Those verdicts trace back to a specific, identified crash mechanism with a documented fix. Side underride is not an anomaly. It follows from a design decision, the absence of a guard, that is identifiable, verifiable, and correctable. Insurers are not pricing it as one. Commercial auto insurance has posted 14 consecutive years of underwriting losses. Raising rates does not fix that. Preventing fatal and serious injury from the collisions that generate the claims does.

The insurance decision

Fleets already get underwriting scrutiny for driver safety programs, telematics, dash cameras, maintenance practices, and loss history. Side underride protection is a documented liability exposure that belongs in that same conversation.

Insurers are not encouraging side guard adoption. They should be, through underwriting questions, premium credits, or better retention terms.

Testifying before the Senate Commerce Committee, the American Trucking Associations asked Congress in June 2026 to preempt state tort liability for vehicles that meet federal safety standards, while opposing the side guard mandate that would create the standard preemption requires. The industry wants the litigation exposure eliminated, but opposes the only mechanism that would justify the change. In the meantime, fleets and their insurers absorb the verdicts.

None of this requires waiting for an NHTSA side guard rule. Regulation is not a prerequisite for action.

The Supreme Court recently underscored a related point in a freight-broker liability case, Montgomery v. Caribe Transport II (2026). The unanimous decision recognized that common-law duties and standards of care are part of state safety authority. Justice Kavanaugh wrote that "truck safety is a matter of life and death," and that "Not all truck accidents can be prevented. But some can." He also acknowledged that litigation and insurance costs can be significant even when brokers prevail. The lesson is broader: truck safety decisions become liability decisions.

The question for fleets, insurers, brokers, shippers, and manufacturers is not whether federal regulations will eventually require side guards. It is why the industry keeps defending inaction in court instead of addressing a known hazard. The verdicts have been answering that for years, and the amounts keep adding up.

On July 18, 2026, New York City experienced another severe flash flood. In just a few hours, intense rainfall overwhelmed transport networks, forced ground stops at JFK, LaGuardia, and Newark airports, flooded subway stations, closed major roads, and disrupted countless businesses. For one of the world's largest financial centers, the effect was devastating.

In Previsico's New York Flash Flood report, we included a reconstruction of the event, which estimated economic damage of between US$200 million and US$610 million, with US$70 million to US$214 million potentially preventable through earlier, more actionable warnings.

For insurers, brokers, risk managers, and infrastructure operators, the event offers several crucial lessons. Perhaps the biggest of these is that while weather forecasting continues to improve, understanding precisely where flooding will occur, and acting on that intelligence, is now the real competitive advantage.

Lesson one – flash floods are operational events, not simply weather events

The NYC rainfall itself was remarkable, but the operational consequences were what made headlines in the US.

Within a matter of hours, subway stations filled with water, motorists required rescue, major highways closed, and all three of New York's principal airports suspended flights. Cultural venues also suffered cancellations, leaving thousands stranded across the city.

For organizations, these aren't simply meteorological incidents. They become business continuity events affecting employees, customers, supply chains, logistics, service delivery, and revenue.

Events like these are leaving organizations increasingly worried about their security, with uncertainty over which of their sites will flood next, when, and what they can do about it.

Lesson two – geographic precision matters more than ever

While traditional flood warnings remain an important public safety tool, their breadth means that the insights are often not actionable.

During the July event, the National Weather Service issued warnings covering entire counties across Kings and Queens (an area exceeding 170 square miles). Those alerts correctly identified the threat but could not distinguish which roads, buildings or transport assets would actually experience flooding.

Previsico's flood intelligence reconstructed the event at site level, forecasting approximately 68 square miles of flooding across the Northeast Corridor, with 37 square miles concentrated within the NYC metro core, identifying expected water depths as well as locations.

At a time when every minute of downtime carries financial consequences, this level of insight is far more valuable than broad awareness. For example, knowing a particular distribution center will see 20 inches of floodwater by mid-afternoon gives people crucial time to relocate stock, move vehicles, protect equipment, and activate contingency plans before disruption occurs.

Lesson three – early warning creates measurable financial value

Perhaps the most compelling insight from the report concerns preventability.

Previsico estimates that between US$70 million and US$214 million of damage from the July event could potentially have been avoided through earlier, site-specific warning combined with operational action.

This moves flood forecasting beyond risk assessment into risk reduction. Historically, insurers have concentrated on pricing flood exposure and settling claims afterwards. Increasingly, technology allows the industry to intervene before losses occur.

Moving vehicles out of underground car parks, temporarily shutting vulnerable facilities, protecting critical equipment or delaying deliveries by a few hours may dramatically reduce ultimate claims costs.

Lesson four – infrastructure thresholds still determine losses

One interesting finding from the July storm is that rainfall did not need to reach record-breaking levels to generate significant disruption.

Peak hourly rainfall reached 2.04 inches per hour. This is well below Hurricane Ida's 3.15 inches per hour in 2021, but still exceeded New York City's sewer design capacity of approximately 1.75 inches per hour.

That relatively small difference matters enormously. Urban flooding is often driven less by total rainfall than by the point at which drainage systems become overwhelmed. Once that threshold is crossed, relatively modest increases in rainfall can produce disproportionately large increases in disruption.

For insurers, this reinforces the importance of understanding infrastructure vulnerability alongside traditional catastrophe modelling.

Lesson five – climate adaptation requires operational intelligence

Climate change is undoubtedly increasing the frequency of intense rainfall events across many parts of the world. Yet adaptation cannot rely solely on larger drainage systems or more resilient infrastructure; operational resilience must become equally important.

This requires a combination of live rainfall data, hydrodynamic modeling, and probabilistic forecasting capable of extending warning times beyond conventional alerts. As a result, businesses can get sufficient notice to make practical decisions before water arrives, transport operators can identify which assets require intervention, and emergency planners can prioritize resources where flooding is genuinely expected rather than across entire administrative regions.

From reacting to preventing

Events such as these reveal a 'new normal'. Extreme rainfall is no longer exceptional enough to be treated solely as an emergency response issue. Instead, it has become an operational business risk demanding continuous monitoring and proactive management.

This requires moving beyond traditional weather warnings towards location-specific intelligence that identifies exactly where flooding will happen, how severe it is likely to become, and how much time organizations have to act. As flash flooding becomes more frequent, the competitive advantage for insurers will lie not only in understanding risk, but in helping customers prevent losses before they occur.

That shift, from paying claims to enabling resilience, may prove to be one of the industry's biggest opportunities, both in supporting its clients, but also improving its bottom-line.

Every property and casualty claim hinges on testimony. Not documents, though documents matter, and not data fields in a claims system, though those matter, too. What moves a claim from first notice of loss to final resolution is what people say under oath and on the record.

How well we create and manage testimony is often the difference between a good outcome and a poor one—a fairly resolved claim or an overpaid claim. This includes the insured's recorded statement, an examination under oath, the treating physician's account of causation, the accident reconstructionist's opinion, and the corporate representative's answers in a bad-faith suit.

Testimony—the oral record—is the currency we use to analyze risk and construct the narratives that support our positions. Reserves are set on it. Settlements are priced against it. Juries decide on it. When a claims organization is good at its work, it understands, dissects, connects, and redeploys testimony. When it is not, it overpays, settles cases it should have tried, and tries cases it should have settled.

If testimony is the currency, most claims organizations are managing their finances by hand, using an abacus. A deposition is taken, read once by assigned defense counsel, and summarized in a report that lands in a claim file, then effectively disappears. The knowledge inside it—how a particular plaintiff's expert testifies about future medical care, which questions or contradictions unsettle a professional expert, and how a repeat-player plaintiff firm builds its damages case—is gathered once and then thrown away. The organization paid for the transcript and the hidden data it contains. Yet almost none of that value is used beyond the single matter that produced it.

The Moneyball Parallel, One Step Further

One of us argued in a prior piece, "Moneyballing Litigation," that litigation teams still select witnesses and lawyers on gut impression, much as baseball general managers once selected players based on how they looked in a uniform rather than on objective data. That argument holds for claims, as well. The vast trove of deposition data that could inform claims decisions remains largely unmined. This article extends that assertion.

To be fair, this kind of data mining was impossible not long ago. Nobody could read across 10 years of transcripts from every case a carrier had handled, pull out every instance of a given expert opining on lumbar disc herniation, and compare those instances for consistency. The labor was prohibitive.

Today, however, we have moved from manual human review as the only option to machine-first processing and analytics as a true capability. This expands both what we can understand and what we can do with the most important currency we manage—testimony. The emerging field of testimony analytics is creating opportunities for insurers to capture both efficiencies and strategic advantages. Organizations that learn to analyze that medium at scale will price claims and risk more accurately than those that do not. Let's look at how.

Two Levels of Value: The Case and the Portfolio

Testimony analytics creates value at two levels: the individual case and the broader portfolio. Both levels produce efficiencies, but the portfolio level also unlocks strategic advantages unavailable within a single case.

The first level is the individual case. Obtaining, reviewing, and analyzing testimony consumes an extraordinary amount of billable attorney time. Yet much of that work still relies on tools and methods that have changed little in decades. AI and testimony analytics reduce that burden by helping counsel search and summarize transcripts, identify admissions, and compare testimony with medical records and other evidence. For claims organizations embracing AI, tasks that once required hours of billable time can now be completed in minutes, producing faster turnaround and lower attorney fees. Most industry attention remains focused on efficiencies at this level because the savings are both conspicuous and tangible.

The larger opportunity lies at the portfolio level: leveraging aggregate data. By treating all of an organization's testimony as a single, queryable body of knowledge, transcripts become more than case files—they become institutional memory. Across matters, they record the statements, strategies, and behaviors of insureds, company witnesses, retained experts, opposing experts, defense counsel, and adverse attorneys.

By extracting and leveraging that aggregate history, a claims organization can identify patterns that no single case reveals. It can better prepare witnesses, evaluate repeat experts, profile recurring firms and attorneys, detect anomalies and contradictions across claims, and improve outcomes across its portfolio. By mining information hidden in testimony, organizations can use previously ignored data not only to increase efficiency, but also to gain a strategic advantage through better-informed decisions and increasingly reliable predictions. In testimony, the past is often prologue.

It is now possible to turn testimony into a searchable body of institutional knowledge and use it to generate a wide range of analytics. As data is added, the value compounds: every new transcript strengthens the system, transforming testimony the organization has already paid for into a reusable data asset rather than dead weight in a file room. For a large insurer responding to a disaster, that could mean identifying recurring participants, uncovering potential fraud, avoiding improper payments, and recovering millions.

The Early-Mover Advantage Matters

Claims organizations that build a portfolio-level testimony capability will out-execute those that do not, and the gap will widen rather than close.

Managing a claim well means optimizing the creation of testimony and then using it effectively—in motion practice, settlement negotiations, or before a jury. An organization that grounds its decisions in its accumulated testimony data can make a better-informed judgment.

An organization relying on the assigned adjuster's memory and the handling attorney's instinct is guessing. On one claim, the guess might beat the model. Across 5,000 claims, it will not. As in baseball, better information yields more wins on average, and claims is a business of averages.

A second, equally important reason to act is the meaningful risk that the plaintiff bar will deploy these capabilities at scale sooner. Plaintiff firms understand the power of technology and are using it to widen the intake funnel and increase case volume. Far more investment is flowing into plaintiff-side technologies than into defense-side technologies. The hundreds of millions of dollars invested across a growing group of plaintiff-side AI platforms illustrate the scale of that effort.

Where This Leaves Claims Leadership

In the end, organizations that learn to manage and analyze testimony at scale will handle claims more efficiently, price risk more accurately, deploy better strategies, and improve outcomes across their portfolios.

"Moneyballing Litigation" imagined sealed envelopes containing hidden statistics about witnesses and attorneys. The data in those envelopes already existed; it was simply scattered across transcripts, matters, firms, and years. Testimony analytics makes it possible to open those envelopes at scale—to learn from every witness, expert, attorney, and firm an organization has encountered and apply that knowledge to every matter that follows. That is the opportunity now before claims leadership.

When I taught my older daughter to drive, my (overly) nervous tendency to constantly kibitz caused her to blurt out: "You have to let me make my own mistakes." 

As she drove my sportscar on a winding backroad with narrow lanes and no shoulder, and as she occasionally drifted toward the middle even though oncoming traffic could appear suddenly over a rise, I assured her that she was, in fact, not allowed to make any mistakes. 

Yes, I would try to take a hands-off approach about school, friendships, soccer and so on. But driving? Nope. That was off-limits.

While I'm happy to report that, at age 32, she has never had an accident — not even a moving violation — the tension we worked through springs to mind as I think about the deployment of AI. There is a fundamental tension between having AI improve as fast as possible — to get as many benefits out to as many people as soon as can be done  —and a legal system that will hold the AI accountable for each and every harm it causes, no matter if that loss is in the service of a long-term gain for society writ large.

The underlying tension between statistics and accountability goes even beyond the usual issues that accompany the rise of a revolutionary technology. So AI faces a hard question: When is good enough good enough?

Let's have a look.

The clearest example of the tension is probably in autonomous vehicles, where Tesla took the speed route while Google's Waymo took the careful one.

Tesla relies just on cameras and radar as the sensors in its AVs, largely because they are much less expensive than the Lidar that Waymo and others use, meaning that Tesla could deploy what it calls Full Self Driving (Supervised) quickly. (The "supervised" label refers to the fact that, while drivers are told they can trust their cars fully, they are legally required to stay alert and be able to take control of the car instantly.) CEO Elon Musk also encouraged aggressive use of the technology so he could gather as much data as possible on problems that needed to be ironed out before the cars could be fully autonomous. 

The result is that Tesla's cars have logged nearly 13 billion miles in FSD mode — but also that there have been all sorts of complaints about problems with the software, as well as numerous accidents and even fatalities. Tesla has mostly avoided legal liability because of the fine print about drivers retaining responsibility for their vehicles even in FSD mode. Musk has said that problems are inevitable but that, in the long run, his rapid deployment of AI will reduce the total number of car accidents and fatalities.

While Musk has been promising that full autonomy was just around the corner for a decade, his vaunted fleet of robotaxis has logged just 380,000 paid miles without a safety driver in a car. He and his supporters still argue that his advantage in generating real-world data on driving, based on all the cameras and radar systems he has in the nearly 10 million Teslas on the road, will eventually make him the winner. But recent declines in the stock price for Tesla are being attributed to growing concern among his investors about his ability to deliver, after so many promises went nowhere. We'll see. 

Meanwhile, Waymo took the go-slow approach, using a full array of sensors on the assumption that they would ride the exponential Moore's law cost curve and become inexpensive enough soon enough. Prices have, in fact, cut the cost of a Lidar from about $75,000 per vehicle to $7,500, and technologies are out there that could take the price down into the hundreds of dollars per car. Waymo has rolled out the cars cautiously enough that it only has about 4,000 robotaxis on the road, but they have logged 200 million paid, unsupervised miles — so more than 500 times Tesla's total. There have been glitches with Waymo, such as with misunderstanding signs warning of construction zones, but nothing like what Tesla has faced, so Waymo is the clear leader on AVs at this point.  

Software developers take an approach like my daughter's: focusing on learning quickly even if the speed leads to some mistakes along the way. The legal system, however, takes my approach: Even if fast learning reduces the total number of lives lost and total damage that occurs over the lifetime of an AI, those who deploy it are liable for each and every mistake along the way. 

Basically, the legal system says: Don't tell me that you constructed a model and optimized it for gains to society writ large. If an AI hurt my parent/child/friend, I demand accountability.