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.

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

An underwriter writes a $5 million commercial auto liability policy for a large trucking fleet and sees it as a big win, especially if the fleet doesn't incur any losses until the second, third or fourth year. However, the insurer hasn't really made $5 million. Instead, it has assumed years of potential liability that could ultimately make the policy woefully unprofitable.

Research from the American Transportation Research Institute (ATRI) released in May shows why. Per-mile liability losses rose an average of 33% between 2021 and 2024 due to a sharp rise in crash claims expenses, despite a 2.6% reduction in crash rates involving heavy-duty trucks.

It only takes one serious accident to turn a sure win for a carrier into a stinging underwriting loss. That is why insurers should take these three steps to assess a commercial auto client's risk management practices and determine whether its fleet demonstrates the operating discipline required to reduce its risks throughout the policy term.

Step 1: Look Beyond Historical Loss Runs

One of the biggest challenges underwriters face when reviewing commercial fleets is selecting and pricing a policy based solely on past performance. While historical loss runs may seem favorable, subpar claims processes will amplify loss severity in the event of a reportable incident.

Strong claims management is an equally strong indicator of future performance. Fleets that recognize an incident, report it and respond by improving both operational and risk management practices will perform better over time.

During underwriting, insurers should review each prospect's claims processes in detail. Ask how quickly they identify and report claims and whether they have any policies in place for investigation and escalation. Explore whether they use data to recognize recurring accident patterns and look for evidence the fleet has changed its procedures or behaviors based on their claims experience. Use the answers to objectively assess how responsive the fleet is likely to be throughout the policy period.

Step 2: Review How Fleets Use Data

Telematics, on-board camera systems, electronic logging devices (ELDs) and other monitoring systems provide fleets and carriers with a wealth of operational data. Yet the mere presence of these technologies alone does not necessarily make a fleet more insurable.

To see why, consider telematics devices. They deliver a seemingly endless stream of data points, from GPS location and vehicle speed to engine hours, fuel consumption, hard braking, harsh acceleration and following distance. There is so much data, however, that fleets can become overwhelmed quickly. They may not know which data is most important, and they might not have a set process for using that data to improve driver behavior and reduce their risk for accidents.

For these reasons, both carriers and brokers should look beyond a fleet's technology adoption when assessing a client's insurability. Underwriters should ask which data points a fleet monitors, who reviews them and what triggers an intervention. Fleets that manage their risks well will focus on a small handful of meaningful data points, then use them to tailor their continuing training efforts to each driver based on their individual behaviors.

Brokers have a complementary role. If they find a prospective client is experiencing data paralysis, they should partner with carriers with a proven track record of helping fleets make their vehicle safety data actionable. Doing so will position brokers as trusted advisors to their clients while also helping their carrier partners write profitable business.

Step 3: Use Technology to Identify Leakage

A fleet can look like a favorable risk when the policy is bound. It can also become unfavorable six months later. To understand why, consider a carrier that insures 100 trucks, but the fleet actually has 150 power units operating on the road. If the policy is priced on a per-power-unit basis, the carrier is taking on extra exposure it never priced.

The challenge for most insurers is finding those extra vehicles before a claim or renewal exposes the problem. Some carriers are solving this by developing proprietary AI tools that can compare scheduled vehicle data with inspection and operational records. If a vehicle identification number (VIN) shows up in the records but not on the policy, the carrier can talk with the client, find out why and add the vehicle to the policy midterm if applicable.

While AI can stop leakage, carriers should also realize technology should not replace human judgment. Predictive models can help insurers analyze more accounts and surface risk signals faster, but greater underwriting volume does not necessarily mean better underwriting. Experienced professionals must still determine which specific underwriting criterion matters the most.

Insurability Is More Than Pricing Risk

With premiums and claims costs continually rising, carriers can no longer focus solely on rising rates. They must also reduce their losses. Insurers that thoroughly assess a fleet's loss history and claims processes, help it use data to improve driver safety, and check in regularly to prevent leakage will write good business and reduce claim frequency and severity, thereby creating more value than pricing adjustments alone.

The growth of cloud computing and the amplified use of artificial intelligence across different industries has fueled the creation of massive, state-of-the-art data centers, and with that, increased the need for innovative property/casualty industry solutions to meet nontraditional insurance exposures.

Data centers, which process and store large amounts of essential information, tend to be warehouse-sized facilities that house computer servers and require significant cooling, power backup, fire suppression and security systems. Initially, their proliferation was seen in the 1990s with the rise of the Internet, but the current expansion is unfolding at a much larger scale and even faster pace, fueled by billions of dollars in private investment and federal directives to fast-track certain projects.

Larger data centers, or "hyperscale" data centers, are drawing the greatest attention and scrutiny because of their size, rapid growth and substantial impact on local communities. Given the customized technology that's in these data centers and the fact that they'll be very hard to replace and difficult to repair, any kind of covered cause of loss that keeps any of these data centers out for any significant period of time would likely cause a significant financial loss, and business interruption is one of the main prospective causes of loss. Contingent business interruption comes into play as well as in these scenarios: it's not just AI companies that would be losing revenue it would also be the clients that are leasing services from these AI companies that are losing their business. Disruptions to data center operations would create a domino effect.

Insurance considerations should come into play from the very start of the planning and building of a data center. Builders' risk exposure is high with the potential for physical damage to the data center during its construction. Supply chain issues or labor shortages that affect the construction industry, especially acute issues in a specific state or region, could be problematic for insurers of data center builders or owners of completed data centers. Depending on the area where these facilities are constructed, they could put a significant drain on locally available skilled labor or construction expertise.

Once built, a data center could experience first-party financial loss stemming from a direct, covered cause of loss; for example, a fire that damages the physical structure, and its interior components (e.g., computer servers and other equipment) and other contents. Because the data centers use servers that generate unprecedented heat loads, the risk of ignition is higher than in traditional large commercial facilities such as warehouses. Physical damage from failed systems outside the data center, such as an off-premises power failure, could create an insured loss as well depending on the property coverage extensions within an insured's policy. Commercial property coverage would also be necessary to cover damages from other natural or man-made disasters.

From a general liability standpoint, any bodily injury or property damage to third parties from a fire or explosion at the facility could create major losses, and from an environmental liability standpoint, if there's any discharge associated with the operation of the center that contaminates the water supply of the surrounding communities, that is another liability exposure.

Another obvious potential exposure to loss concerns cyber. Losses from failed or inadequate cybersecurity could be costly, especially if any of these data centers are housing proprietary, private or sensitive information of any type.

At the same time, data center development can create significant economic opportunities, including numerous jobs for technicians, electricians, facility managers and other local building trade workers. State and local governments can also benefit greatly from property and sales taxes associated with these centers. Overall, the benefits of data centers should ultimately be weighed against public costs.

The risks posed by data centers apply beyond the United States and are generally the same in international markets. These data centers built internationally will also need to satisfy domicile-specific regulations, such as those related to cybersecurity, the environment and diverging online content regulation.

Time will tell if insurance supply can keep up with demand. The sheer size of hyperscale data centers necessitates (re)insurers making determinations concerning their risk tolerance and where their preferred placement is within a layered property insurance program. Innovative structures such as captive formations could become part of the picture, but clearly, as data center development and construction spreads, required insurance coverage should evolve as it is currently beyond what the traditional property/casualty industry has previously experienced. Setting risk tolerances and building a history of loss experience are all factors in developing needed underwriting expertise.

Given the significant capital required to finance data center infrastructure projects, insurers may have exposure on the asset side of their balance sheets through private credit investments, private equity partnerships or other financing arrangements. Insurance investment managers also will need to monitor associated risks closely, including regulatory changes or legislative developments that could affect the long-term performance and viability of these investments.

With AI becoming a more critical component for society in general, how well the headwinds associated with the availability and affordability of energy to power data centers are addressed will be of major importance. Insuring these facilities will continue to be a complex undertaking involving an integration of multiple coverages, some of which will be needed in phases as different stages of data center development are completed.

A head of claims operations walks the executive team through the results of an AI-assisted document review rollout: a sharp, real cut in average review time. The room comes alive over it: not skeptical, enthusiastic. Someone's already running expense-ratio math out loud before she's finished the slide. Someone else wants to know if the same approach works in underwriting. The head of claims tries to steer the conversation toward what else those hours might be worth doing instead of cutting them, but there are three more items on the agenda, 15 minutes left in an hour that's already running long, and a room half-checked-out toward the next meeting.

The cost-savings read isn't wrong. It's just the only idea that got any airtime.

By the following week, the number is sitting in the forecast as a bottom-line improvement, and HR has scheduled a meeting to talk through the actual headcount reductions. Nobody chose this outcome over another one; it's just the one with momentum, and momentum in a room with an hour on the clock beats an idea nobody had time to finish developing. The room did what rooms do under time pressure: it ran with the fastest, most defensible read, and by the time anyone might have asked a second question, the decision was already operational.

This is the mechanism I keep running into with insurance and financial-services leaders working through AI at scale. It isn't that no one owns the question of what to do with freed capacity. Someone owns it, in exactly the moment described above: usually whoever's in the room when the result lands, or Finance once it's in their model. The problem is they're answering a question nobody asked out loud: is this time worth more as a saving, or as something redirected? By default, the answer is savings, because savings is the fastest, most comfortable story a room on the clock can agree on.

There's a harder truth underneath the reflex, too. Even if the head of claims had been able to keep the floor, she probably couldn't have made the case: nobody had set up a way to measure what those hours would be worth doing something else. Under real forecast pressure, with downside risk already sitting in the numbers, the room isn't choosing the fast story over the slow one. It's choosing the only story it actually has data for.

The default doesn't hold up, and two firms now say so independently

Gartner surveyed 350 business executives this spring at companies with at least $1 billion in revenue. Eighty percent of the organizations that had piloted an AI or autonomous technology followed with a workforce reduction. But there was no meaningful ROI difference between companies that cut staff and those that didn't. The strongest returns weren't at the companies that cut deepest; they were at the ones using AI to make people more productive, not to replace them. Gartner's Helen Poitevin put it directly: chasing value through headcount reduction alone leads most organizations toward limited returns, not the returns they modeled.

Deloitte's research puts a number on the other half of that same gap. Eighty-four percent of organizations are increasing their AI investment. Only 20% report meaningful revenue impact. But organizations that redesign the work itself, rather than just removing roles, are roughly twice as likely to exceed their AI ROI expectations, and nearly two and a half times more likely to see real financial gains.

Two firms, two survey populations, the same finding: cutting the role doesn't reliably convert a technology gain into a business gain. Redesigning the work does, by a wide margin. This isn't a values argument about protecting jobs. It's a return argument, made with the same data the cost-saving case leans on.

Why the default survives unquestioned

Part of why this default never gets challenged is that the dashboard isn't built to challenge it. A rollout dashboard answers one question the week the tool launches: did the tool work? It was never built to ask the second question: now that the tool freed something up, what's the best use of it? Nobody updates the dashboard to ask that, because nobody built a version where the question has a place to live.

The result is a familiar split. Leadership sees a rollout that delivered the projected time savings and reads that as ROI, declared and closed. The people managing the work six months later are living inside whatever happened to that time, usually nothing or usually cost-cutting, and see a different number entirely. Both readings are accurate: they're reading two different moments, off two different instruments, and only one of those instruments ever looks past launch week.

This lands harder in insurance and financial services for a specific reason: the executive team is tracking results weekly, which is exactly the speed that produces a room like the one above. A real redesign case takes longer than a week to build. By the time underwriters or claims reviewers could show what redirected hours would be worth against loss ratios or retention, the quarterly external reporting cycle has already locked in the launch-week number as a win, and the budget conversation has moved on.

What interrupts the default

Naming the pattern doesn't fix it, so here's what I'd tell a claims or underwriting leader sitting inside this right now.

Put the redirection case on the roadmap when the rollout is designed, not after the results land. That claims leader's slide only had one story on it because only one story had data behind it. Building the other one is real work: further experimentation and analysis, beyond what it takes to simply validate that the tool works. Naming that work up front, as part of the rollout plan rather than an afterthought raised in the meeting, is what gives the room something to weigh against the savings read before momentum decides for everyone.

Reuse a review cycle you already trust. Regulated organizations already run disciplined quarterly and annual reviews for compliance and audit. Add one standing question to that cadence: what did we say this rollout would free up, and which path, saved or redirected, did it take? You don't need a new process. You need one more question inside a process that already has teeth.

Track what changed in the work, not what changed in usage. A login count says the tool got turned on. It doesn't say whether a reviewer, underwriter, or claims handler is doing anything differently on Thursday than in January: the real measure of whether freed time went anywhere.

The open question underneath this one

There's a third factor sitting under both of these that I don't think the data above fully answers, and I'd rather name it honestly than force a tidy conclusion: why doesn't anyone stop to ask the redirection question at all? Do organizations reward speed and visible activity over the slower work of finding a better use for freed time? Is the savings number just easier to defend than a redirection bet that might not pay off for multiple quarters? I don't have a clean answer, and I'd rather leave the question open than manufacture certainty. It's worth its own room, not a paragraph tacked onto this one.

What the data above does settle is narrower, and it's enough: cutting the role may feel like the safe, obvious way to capture value from AI. Two independent studies now say the opposite is the better path. The next time a rollout result lands in a room, and everyone reaches for the cost-savings read in the same breath, it's worth asking whether anyone had a number for the alternative, or whether the room just did what rooms do.