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.

Explainability has become one of the most familiar promises in responsible AI. It is easy to understand why. In insurance, a customer, regulator, underwriter or claims leader may need to know why an AI system recommended a price, flagged a claim, routed a case or generated a communication. A black-box answer is difficult to trust and even harder to defend.

But explainability solves only part of the governance problem. A human-readable rationale may describe why a system produced an output. It does not necessarily prove which data was used, which version of the model or prompt was active, which tools were called, which controls passed or failed, who intervened, or what action the organization ultimately took.

That distinction matters more in 2026 because insurers are moving beyond AI that simply summarizes or recommends. AI is increasingly embedded in workflows that retrieve documents, update records, route cases, generate customer-facing material and initiate downstream actions. Once AI participates in execution, governance must cover the entire path from input to outcome, not only the explanation attached to one answer.

An explanation is not an audit trail

Consider a seemingly straightforward AI-assisted decision. The system explains that it routed a claim for additional review because the submitted information did not match the policy record. That explanation may be useful, but it leaves critical questions unanswered. Which policy version did the system consult? What source document was treated as authoritative? Was the mismatch material or merely a formatting difference? Did a business rule confirm the finding? Did a human reviewer accept, correct or override it? Was the customer notified, and was the notification based on the final reviewed result or the original AI output?

An explanation addresses interpretation. Governance requires proof.

The difference is especially important when an issue surfaces weeks or months later. The people who designed the workflow may no longer remember the specific case. The model may have been updated. The prompt, knowledge source or decision rule may have changed. A screenshot of the final output cannot reconstruct the process that produced it.

Insurers therefore need a durable reconstruction record: a connected set of evidence that allows an authorized reviewer to replay the material steps of an AI-assisted workflow. This is not a demand to store every hidden technical detail or every piece of sensitive data forever. It is a demand to preserve the business-relevant facts necessary to understand and defend the decision.

The regulatory direction is broader than explainability

The regulatory direction is already moving toward this broader standard. The NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers emphasizes written governance programs, lifecycle controls, documentation, accountability, and information that regulators may request during an examination or investigation. In 2026, the NAIC AI Systems Evaluation Tool pilot expanded that focus by helping regulators gather information about how insurers use AI, how they govern and mitigate risk, which models may be high risk, and what data those systems use. The pilot involves 12 states.

The same distinction appears in international guidance. The OECD AI Principles treat transparency and explainability as separate from accountability. Accountability includes traceability across datasets, processes and decisions throughout the AI lifecycle. The NIST Generative AI Profile similarly points organizations toward provenance, logging, monitoring, change management, overrides and incident handling. ISO/IEC 42001 frames AI governance as an organization-wide management system rather than a model feature.

None of these frameworks says explainability is unimportant. They show that explanation is one control within a larger operating system of governance. For insurance leaders, the practical message is clear: a well-written rationale cannot substitute for evidence that the workflow was properly controlled.

What a reconstruction record should prove

A reconstruction record should answer the questions that arise when an AI-assisted outcome is challenged. At a minimum, it should establish the business purpose and risk level of the use case; the identity and version of the model, agent, prompt or policy configuration; the authoritative inputs and sources used; the material steps, rules and tools invoked; the validation or guardrail results; the human approvals, edits, exceptions or overrides; the downstream action taken; and any later correction, complaint or incident linked to the case.

The record does not need to be one giant log file. In a mature enterprise, the evidence will often be distributed across workflow systems, model monitoring platforms, case-management tools, approval services and audit repositories. What matters is whether those pieces share reliable identifiers and can be assembled into a coherent timeline.

That timeline should be understandable to more than the engineering team. A compliance officer should be able to see what control failed. An operations leader should be able to identify where the case was held or released. An auditor should be able to verify who had authority to approve an exception. A customer-facing team should be able to explain the organization's final action without pretending that the AI made the business decision on its own.

More logging is not automatically better governance

The answer is not to capture everything indiscriminately. Excessive logging can create privacy, security, retention, and discovery risks. It can also bury the evidence that matters under millions of low-value technical events.

The better approach is risk-tiered evidence. A low-risk drafting assistant may require basic usage records and quality monitoring. A system that influences underwriting, claims, policy servicing or customer communications should preserve a far richer record. The higher the impact of the action, the stronger the requirements for source provenance, approval, segregation of duties, override justification and post-decision monitoring.

Insurers should also separate observability from authority. A system can be perfectly observable and still be allowed to do too much. Knowing that an agent called an external tool is not the same as controlling whether it was permitted to make that call. Evidence should therefore sit beside enforceable boundaries: approved tools, role-based permissions, transaction limits, release gates, escalation paths and the ability to stop or reverse an action.

Human oversight must be specific

"Human in the loop" is often presented as the answer to AI risk, but the phrase is too vague to function as a control. It does not identify which human, at what point, reviewing what evidence, under which threshold, with what authority.

Meaningful human oversight should be designed around decisions and exceptions. Routine, low-risk cases may pass automatically when predefined checks succeed. Higher-risk cases should pause for a qualified reviewer. Overrides should require a reason and, when appropriate, a second approval. Material changes to models, prompts, data sources or connected tools should trigger renewed testing rather than quietly entering production.

This design is more scalable than asking employees to reread every AI output. It concentrates human attention where judgment is needed and produces evidence that oversight actually occurred.

Five questions insurance leaders should ask now

Insurance executives do not need to inspect raw model traces, but they should expect clear answers to five questions:

Can we identify every material AI-assisted workflow in production?

Can we reconstruct a disputed decision from source to final action?

Can we show which controls ran and who approved of any exception?

Can we distinguish a model recommendation from an action taken by the company?

Can we do all of this without exposing more customer data than necessary?

If the answer to any of these questions is no, the governance gap is not explainability. It is operational accountability.

From understandable AI to defensible AI

The insurance industry should continue investing in explainability. Customers deserve meaningful information, employees need to understand system limitations, and regulated decisions should never be hidden behind technical complexity.

But the standard must now be higher. An insurer should be able to show not only why an AI system produced a recommendation, but what evidence it relied on, what controls constrained it, who exercised judgment and what the organization did next.

That is the difference between AI that sounds responsible and AI that can be governed in practice. Explainability helps people understand an answer. A reconstruction record helps the institution defend the entire decision.

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.

Close to 53 million prior authorization requests hit Medicare Advantage plans in 2024. No honest reader of that number can believe the current prior authorization model can be run effectively by humans alone, and no serious operator would argue that it should be run by machines alone.

As more health insurers use AI to help manage the prior authorization process, more hospitals, doctors, and patients have cried foul. Yet the interesting question isn't whether AI belongs in prior authorization. Scale alone makes AI an operational necessity.

The better question to ask is how we divide that labor between AI and clinicians, keeping human expertise, judgment, and experience in the loop throughout the process. If AI can help deliver the right outcomes for patients, shouldn't it be part of the process?

I think the answer is this: within a couple of years, prior authorization in the United States will run on a settled split. AI does the evidence work at machine speed, quickly approving requests that meet payers' guidelines, and a board-certified clinician owns every denial. Many health insurers have begun touting this split, and those that haven't done so will fall behind on turnaround and overturn rates, to the detriment of the trust of the physicians whose patients they cover.

The AI half of that split isn't a policy debate. It's already happening.

The 2026 HealthEdge Annual Payer Report puts AI adoption among payers at 91%, with prior authorization and claims adjudication among the highest-impact deployment areas. And UnitedHealth Group, which operates the nation's biggest health insurer, has said it's investing $3 billion in AI in 2026-27 to automate more processes, including speeding prior authorization decisions.

New rules from the Centers for Medicare & Medicaid Services (CMS) on prior authorization timelines and the June 2025 insurer pledge, which saw the largest payers commit to real-time processing and standardized electronic prior authorization by 2027, have arguably contributed to that uptake. Amid tightening deadlines and a growing number of prior authorization requests, AI is increasingly emerging as the practical lever insurers are turning to meet those deadlines at scale.

What's important is that this AI adoption comes with protections. That means clinician oversight and expert judgment. The buck needs to stop with humans, especially when it comes to denials. Some health insurers have quietly been treating AI as a way to automate reviewers out of the loop rather than to equip them. That is the wrong build, and it's unlikely to survive the next regulatory cycle.

The underlying principle behind all this – the idea that "AI flags, a clinician affirms" – is already emerging as the floor in prior authorization. As of April this year, nine states have enacted laws on AI and prior authorization, and while each has its differences, the common thread between them is a requirement for human review of claim denials (KFF). I think we'll see that number continue to grow, even as the Trump administration pushes to preempt state AI legislation. The administration's own framework has drawn a line in the sand. It targets duplicative, innovation-blocking state rules, not the ones that protect consumers from fraud and harm.

The best play for health insurers now is to ensure they're ahead of the curve. CMS's new rules require payers to publicly report turnaround, denial, appeal, and overturn rates. Once those numbers are comparable across payers, any reviewer operating without AI-assisted evidence synthesis will be visibly, quantitatively below the standard set by those who have built for the split.

Those who are operating with AI but without clinician oversight risk falling into a similar trap. Speed without judgment is where prior auth goes wrong, and persistently high denial rates become a financial and reputational liability long before they become a compliance one.

The winning model is a settled division of labor. AI handles ingestion, timeline reconstruction, guideline matching, and first-pass evidence synthesis. The clinician makes the determination and carries accountability for it, in their name, on the record. Administrative AI runs the pipes, clinical AI supports the call, and humans remain in the loop where the loop matters.

For health insurance executives making build decisions this quarter, the tangible moves are straightforward. Fix the process, don't throw it out. Build explicitly for clinician accountability rather than around it. Invest in the neutral review layer instead of trying to litigate it away. And most importantly, do the right thing and be vocal about what you're doing. Transparency is non-negotiable.

Today, the question we hear most often is whether AI belongs in prior authorization. In the next couple of years, the question flips: Why was this contested decision not supported by AI-assisted evidence synthesis? The insurers who can answer that question with a straight face are the ones building for the split now.

As insurance agencies continue to invest in AI, not all are seeing a return on investment. Some are seeing productivity gains right away as employees get certain work done faster. But often when leadership looks for the corresponding lift in revenue, client satisfaction, or operational transformation, the numbers aren't there yet. A recent study of new AI use cases announced by some of the world's largest insurers in the first quarter of 2026 found that only 18% reported a measurable result, and four out of five of those were productivity gains with no revenue or satisfaction figures attached.

The instinct for many organizations is to throw their hands up and label their AI investments a failure, but the delayed effect AI-driven efficiency has on measurable business outcomes is a predictable stage in a technology adoption cycle. AI is moving through a pattern that starts with broad adoption, shifts to prioritization, and eventually produces measurable results. Most agencies are somewhere between the first and second stage.

Expecting bottom-line transformation during what is still an adoption phase puts organizations at risk of making reactive decisions, such as pulling back on investments or doubling down in the wrong places. Rather than scrambling to justify AI spending or scrapping programs that haven't paid off yet, agencies should use this interim to make three deliberate moves: Focus AI on the priorities that matter most to their business, measure success with the KPIs they already trust, and keep human expertise at the center of how AI work gets reviewed and governed.

This is especially relevant given where the insurance brokerage business is in its own cycle. The industry has been acquisition-driven for the last three to five years, but the transaction pace has slowed. Firms that brought together multiple organizations are now looking inward, trying to create uniformity in process across disparate systems and teams. AI can play a large part in creating internal efficiencies, as long as organizations are deliberate in how they apply it. That standardization is what ultimately drives greater cost-effectiveness, particularly as firms look to shift toward outcome-based approaches.

The sharpest insurance brokerages right now are making those deliberate choices. Many are prioritizing front-of-office work because they want to drive greater client experience, and they know this will have a clear effect on satisfaction, NPS, and retention.

Similarly, measuring the impact of AI should go beyond speed or cost savings. Think about the shift from typewriters to personal computers. The work was done a little differently and certainly got faster, but that didn't change the standards for what qualified something as well-written. The same thing happened with the BlackBerry, and then again with the iPhone. These tools had a massive effect on how effective people were in their daily lives, but the underlying measures of success stayed the same.

We don't need to reinvent the wheel to measure AI's success. If any agency was tracking client satisfaction, retention rates, and growth before the technology, those metrics still apply. A meaningful early indicator might be straightforward: Are KPIs that the organization was previously falling short on starting to get hit? If so, the tools are working, and the agencies don't need to change how they measure success. They need to figure out what's most important to their business, aim AI at it, and measure with the instruments they already trust.

Even as agencies focus AI on the right priorities and track the right metrics, none of it holds together without human oversight. Anyone who has used an agent to summarize a meeting knows the technology doesn't catch everything. When AI agents are handling workflows that touch claims, coverage, or regulatory obligations, the margin for error shrinks considerably.

Agencies that are just bolting third-party AI onto their workflows without understanding what's under the hood are taking a big risk. When there is no ability to show what work has been done, you cede control to an AI agent. The right path is to have comprehensive quality assurance processes and regression testing capabilities around every agent doing day-to-day work, with checkpoints and compliance oversight built into the workflow.

There hasn't yet been a major AI-related claims dispute, but that day is coming. When it does, firms will need to understand exactly what happened and where. They can't just point to the black box of an agent. This is where domain expertise paired with that technology becomes the differentiator. Agencies working with partners who bring decades of insurance operations knowledge alongside the technology — and who maintain human oversight as a fundamental part of the process — can actually govern what their AI is doing. And as AI adoption grows, the demand for people with the expertise to oversee and evaluate what agents are producing will grow with it.

The effect of the decisions that agencies are making now will be clear by the first quarter of 2027. Nearly 60% of carriers and managing general agents expect AI to transform their business models within the next one to three years. That's when we'll be able to look back at the first two quarters of 2026 and see where speed and efficiency gains actually translated into revenue and operational results. That's when those lagging indicators will catch up.

I've spent most of my career building insurance companies and working with the people who build them. One thing has stayed true that entire time: almost everyone has significant ambition for what technology could do for their business, and very few have the time, expertise, and capital it takes to realize it.

Right now, the clearest example is the MGA channel. The segment has grown from $47 billion to $114 billion in written premium since 2020, yet fewer than 7% of MGAs have AI agents working in production. [Source: The Specialty MGA Operating Model Inflection Point.] It would be easy to read the lack of production AI as caution, or a lack of appetite. I read it differently. 

Every MGA operator already knows submission triage is slow, that bordereaux reconciliation eats a disproportionate share of ops headcount, that claims intake could move faster than it does. The people best at underwriting, distribution, and servicing are spending their days underwriting, distributing, and servicing, which is exactly what they should be doing. Standing up a technology function from scratch takes a year or more and a seven-figure hiring spree, and most MGAs would rather spend that time and money growing the book. Correctly, in my view.

I've seen what that tradeoff costs firsthand. We spent a decade at Clearcover investing in proprietary technology to run our business. We built it ourselves, and it worked: by early this year, more than 90% of our claims intake ran through AI agents, and 93% of our policies were bound digitally. The work was also nothing like a straight line. We built things that didn't pay off, not because they failed, but because the ROI wasn't there. Those lessons about where to invest shaped how we operate just as much as our wins did.

One of the main lessons is that the hard problems are workflow problems, not model problems. A submission arrives missing three data points: what happens next? A servicing request touches four systems that don't talk to each other: who reconciles it? A claim needs a coverage decision at 11 p.m. on a Saturday: does anything move before Monday morning? Can we accelerate the workflow using AI? 

Answers to those questions come from people who have sat inside an underwriting or claims operation, redesigned the work, and then built the system that runs it. They do not come from a better model, and they definitely don't come from a slide deck.

That points to a different shape of help than this industry usually gets offered. What closes the gap is a build partner: people who bring the insurance operating experience and the pre-built technical foundations with them, stand the system up inside the MGA's actual workflow, and leave the MGA owning it when they're done. 

That is a different deliverable than a platform license the MGA has to configure and hope fits, and a very different one than a strategy roadmap. The MGA channel has spent decades outsourcing specialized functions to people who show up with the expertise already built, from actuarial to claims administration. Technology should work the same way.

The economics have to change shape, too. If adoption requires an MGA to write a large check before seeing results, most of the market will rationally pass, including plenty of the best-run shops. Adoption moves when the risk sits with whoever is doing the building and payment is tied to outcomes the business already counts: submissions triaged, servicing events completed, claims closed. Insurance has priced plenty of vendor relationships this way for decades. The approach just hasn't been applied to AI infrastructure yet.

We believe that enough to test it with our own capital. This month, we opened applications for Launchpad, a program where we fund and build the AI infrastructure for a small cohort of MGAs, the MGA owns what we build, and we earn as it produces results. I don't know yet whether this exact model is the one that closes the gap. I do know that the ambition in this channel was never the problem and that the operators who are best at this business shouldn't have to become something else to get the technology their book deserves.

The next phase of growth in pet insurance will likely not come from selling more policies through existing channels alone. It will also come from expanding and improving the routes through which insurers reach customers. Retail partnerships, employer voluntary benefits and veterinary networks are pushing the market toward a B2B2C model. Yet many insurers are discovering that while they can support a direct-to-consumer journey, launching and scaling new partner channels remains painfully slow. That is the distribution trap.

Most pet insurance platforms were built around a primary sales channel rather than for a genuinely multi-channel distribution model. Introduce retailers, employers, affinity groups or veterinary networks, and complexity rises quickly. What should be a commercial opportunity becomes a technology program instead. In a fast-growing market, that friction becomes a competitive disadvantage.

Distribution Has Changed. The Operating Model Has Not.

Direct-to-consumer distribution is comparatively simple because it is built around one primary relationship between the insurer and the customer. The operating model can be built around a single customer journey. The insurer controls the brand, acquisition journey, payment method, service experience, and operating model.

Partner-led distribution is different. A retail partner may want its own customer-facing brand proposition and tailored product bundle. An employer offering often requires eligibility rules, payroll deduction and a clear process for employees who leave the business. Veterinary networks will seek offerings that are designed around the point of care, with distinct data-sharing, consent and servicing requirements.

These are not exceptional demands. They are the ordinary realities of distributing insurance through multiple routes. Yet many platforms were designed to support one primary channel, usually direct or broker-led. When the platform cannot accommodate partner requirements as configurable variations of the same operating model, each new relationship becomes an exception. Instead of configuring a new route to market, the insurer creates a new version of the business.

Commercial teams know what follows: requirements documents, competing technology priorities and months of delivery effort. By the time the proposition is live, the partner's appetite may have shifted, or a more agile competitor may already be in the market.

The Employer Channel Makes the Problem Impossible to Ignore

Employer voluntary benefits expose both the scale of the opportunity and the operational limitations holding it back. In most households, pets are an essential member of the family. In that regard, an employer does not need to fund the policy for benefit to create value. Simply offering employees access, convenience and choice can make the proposition meaningful.

But the mechanics differ sharply from a conventional annual policy sold online. Employees may enroll at different points in the year, insure multiple pets and choose payroll deduction, direct debit, or another payment method. They may change employers, alter working arrangements or leave the scheme altogether. Coverage may need to continue seamlessly when the employment relationship ends. The employer may need reporting, while the employee remains the customer and the insurer remains responsible for the policy.

These are the normal mechanics of an employer-led proposition. But systems designed around a single annual policy journey often support these requirements through manual intervention and exception processing, leaving operational teams to bridge the gap between what the product promises and what the platform can deliver. That is not a scalable distribution model. It is a workaround disguised as a channel strategy.

The real test is not whether an insurer can launch one employer scheme. It is whether it can launch 10, 50 or 100 without creating a new operational burden every time. Can it onboard partners quickly, configure eligibility and payment rules without changing core code, and support the customer after they leave their employer?

If the answer is no, the insurer does not yet have a scalable employer distribution strategy. It has an employer pilot program.

Why Channel-Specific Workarounds Are the Wrong Answer

Some might argue that the best response is to build a channel-specific solution: a separate portal for employers, a bespoke integration for a retailer, or a standalone proposition for a veterinary partner.

While this may solve an immediate launch requirement, each separate solution can introduce another product variant, data model, servicing process and set of technical dependencies. The insurer may appear to be expanding distribution while multiplying complexity behind the scenes. Over time, the business becomes harder to change, more expensive to run and less able to maintain a coherent view of the customer.

The alternative is a unified distribution model: a shared core platform that supports multiple partners, brands and routes to market without requiring a separate operating model for each. Each partner can have tailored journeys, propositions, brands permissions and business rules, while product, policy, customer and servicing capabilities remain connected.

This is where architecture becomes essential. APIs matter, but only as part of an open, configurable and connected operating foundation. The real test is whether an insurer can reuse proven product, policy, billing, customer and servicing capabilities while configuring the journeys, permissions, eligibility rules and payment methods required by each partner. If every new connection still triggers a bespoke technology project beneath the surface, APIs alone have not solved the distribution problem.

On this foundation, a new partner is no longer an integration problem to be solved from scratch. It becomes a repeatable route to market that can be launched, adapted and scaled without multiplying operational complexity.

The result is not simply faster partner onboarding. It is a different distribution model. One in which commercial teams can build partner ecosystems with confidence, because the underlying platform is designed to support multiple brands, journeys and channels without creating a separate business behind each one.

That changes the economics of distribution. Instead of asking whether a new partnership is large enough to justify a major technology project, insurers can ask: how quickly can we test, learn and scale this route to market?

Winning Insurers Will Treat Distribution as a Core Capability

The bottom line? The pet insurance market will not be won by the insurer with the most channels. It will be won by the insurers that can launch, operate and scale those channels effectively.

Rather than treating distribution as a series of integrations, insurers must transform it into a core business capability where they can launch new partners without destabilizing operations, support different enrolment and payment models without forcing customers into the wrong journey.

The result is an approach that gives commercial teams the freedom to pursue opportunities without negotiating a technology transformation every time. Retailers, employers, affinity groups and veterinary networks can open valuable new routes to growth. But those routes will remain theoretical for any business whose platform was designed for a single-channel world.

The question is no longer whether insurers want to diversify distribution. Most already do. The question is whether their technology stack will let them.

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Why We Are the Right Solution For Your Needs

The quality of every underwriting decision traces back to the quality of data at intake. Fenris delivers real-time data enrichment APIs that return verified, multi-sourced information the moment an address or vehicle is entered. Carriers and MGAs receive accurate, comprehensive data from the first step of the workflow. When the right data is present at intake, everything downstream improves: quotes move faster, underwriters work from a stronger foundation, and risk assessments reflect the full picture.

Fenris covers the full scope of property and auto risk intelligence: residential and commercial property characteristics, hazard and peril profiles, replacement cost estimates, vehicle and driver data, and predictive analytics to optimize acquisition workflows, reduce risk, and personalize customer experiences. Across all of it, the principle is the same: comprehensive, accurate data returned instantly so carriers and MGAs can make better decisions earlier. From intake to quote to underwriting, Fenris equips every step of the workflow with the risk intelligence needed to write business with confidence.

Three Main Benefits of the Product: 

• Real-time data at intake reduces drop-off, accelerates quotes, improves early decisioning, and creates a better experience for agents and applicants alike.
• Predictive AI improves risk assessment, optimizes acquisition, and delivers personalized experiences that drive higher conversions and reduce losses.
• Non-contributory across all products, so enriched data never feeds a shared pool. Predictive models stay specific to your book and your customers.

What Part(s) of the Insurance Industry Can Benefit From Our Product: 

• Customer Experience
• Distribution
• Operational Efficiency
• Underwriting efficiency