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

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.

The insurance sector is, understandably, quite risk-averse. Insurance companies don't endure and thrive by taking unnecessary risks in an environment defined by stringent regulation and nuanced decision-making. This is why it is so concerning to see companies within the sector taking unnecessary risks as they face up to the mounting pressure of demonstrating progress with generative AI.

It's easy enough to understand the urgency behind the industry-wide scramble to leverage new tools like autonomous agents and AI-powered applications. AI isn't new to the insurance business — use cases revolving around risk modeling and data forecasting were becoming commonplace in the sector before the deep learning and LLM boom in 2023. However, those technologies took years to find a place in insurers' technology stacks. They were heavily tested with strong, clear use cases. The race to adopt generative AI tools is not the same.

EIOPA's 2024 Digitalisation report found that 50% of non-life insurers and 24% of life insurers were already using AI in various areas of the insurance value chain, with applications including pricing and underwriting, fraud detection, and claims management. When it works, it really works. Research from McKinsey found that the insurance sector's AI leaders have created 6.1 times the total shareholder return compared with AI laggards. That figure doesn't just make the case for adopting AI. It makes the case that, if generative tools can be successfully integrated with insurers' tech stacks, the results are outstanding. McKinsey's report found that, in other sectors, AI leaders were generating at most two to three times the shareholder return.

Across the insurance space, there is an increasingly common fear that everyone else is somehow ahead. Companies see their competitors announce new AI pilots and products, vendors make new promises of transformative AI tools, and employees are already experimenting with agents and chatbots. No one wants to be caught standing still while the rest of the market races ahead.

The result is a growing wave of AI FOMO. Insurers are launching pilots, funding multimillion-dollar integration plans, and generally jamming AI into any perceived gap in their workflows. The problem is, many are doing this before interrogating and identifying where AI will actually create any meaningful impact for their business.

The dangers of putting AI before the business case

Last year, a study from MIT found that 95% of AI pilot projects "failed to deliver any discernible financial savings or uplift in profits," the data from which supports an earlier report released by Capgemini in 2023 that found 88% of AI pilots never reached production.

A more recent report from Simplifai found that, while 99% of insurers now have some form of generative AI in place and 83% of carriers are spending more than £3.75 million every year on tokens, subscriptions, and infrastructure, just 42% of insurers had taken the next step towards actually deploying AI into active business functions.

There is an undeniable appetite in the insurance sector for AI, but not a great deal of understanding when it comes to what the technology can do, or where it fits within existing business systems (not to mention the tech stack, which for an insurer is more likely to be some form of legacy system). This issue presents symptomatically as an abundance of AI pilot programmes that never mature into real world business solutions. Insurers know they need to act on AI, but they don't have a clear idea of where to start, which processes to prioritise, or how to evidence the value it creates.

How insurers can distinguish AI opportunity from AI hype

This raises an important question for insurers: what will it take to shift AI from isolated innovation projects to something with tangible business applications?

Scaling AI effectively can lead to substantial business value — the data supports it — but for insurers mired in expensive pilot programmes that never seem to translate into finished products, or who feel as though they're in danger of being left behind, it's essential to approach AI from a business perspective, not a technological one.

Successful AI adoption in insurance might just mean embracing more of the risk averse, methodical behavior for which the sector is sometimes criticized. It means making informed decisions regarding where AI can create real, sustainable impact. Success depends on identifying the use cases with the strongest commercial and productivity outcomes, rather than increasing the volume of AI initiatives in hope of reaching a magical, unspecified tipping point.

Insurance industry-specific AI tools are accessible across the market, doing away with any competitive advantage gained by adopting a particular platform or model. As access to the technology becomes more uniform, competitive differentiation will depend upon how organisations apply it. Successful approaches start with workflows, operating models, and business outcomes. They deploy AI across complete business processes instead of isolated point solutions and establish governance from the outset so that risk, compliance, and accountability are embedded throughout implementation.

The insurance industry doesn't have an AI problem. It has a strategy problem. FOMO is pushing insurers into suboptimal decisions, when the real challenge lies in executing a coherent transformation strategy. The next phase of insurance AI adoption won't be about buying and using more AI. It will be about making better decisions about where AI belongs.

A World Cup soccer tournament wouldn't be a World Cup without controversies, and the just-concluded event had its share. 

Then Argentina said, Hold my Fernet con Cola. 

Following the team's 1-0 loss in the finals to a clearly superior Spanish team, an Argentine player picked a fight on the field that included grabbing a Spanish player by the throat and throwing him to the ground, and Argentine teammates backed him up. Just about the whole Argentine team then acted churlish during the awards ceremony, even turning their backs as the Spanish players were awarded their gold medals and the team trophy. 

Within minutes, reporters and fans were revisiting every untoward thing Argentina had done during the tournament, then during prior tournaments, then on the team bus, then.... 

Argentina provides a great example of how actions taken even by a few in the heat of the moment can sour masses of people on a group or a brand. It's a lesson that I think insurers, in particular, should take to heart, given that our most consequential actions tend to come when dealing with people in situations where their emotions are running hot. 

Let's have a look. 

Argentina had been a possible feel-good story coming into the tournament this year. It had finally won the World Cup in 2022 for its captain, all-time great Lionel Messi. If Argentina had repeated as champion, it would have been the first to do so since Brazil in 1962. Messi, who had won the Golden Ball award in 2022, given to the best player in the tournament, was in the running for the award again. Going into the final, he also had a shot at the Golden Boot, given to the top scorer in the World Cup. At 39 years old, a beloved player was putting in a remarkable performance.

Now, Argentina is known for being chippy, even dirty, and it played to form throughout the tournament, including by having a player sent off in the final after a violent tackle. Spain, while hardly free of fouls, played a classic style that contrasted sharply with Argentina and led any number of people to post at the conclusion of the game some variant of, "Football won today." 

The history of writeups about the Argentine team suggests that would have been about the extent of the complaints about Argentina's tactics. 

Then the Argentine players started knocking around some of the Spaniards after the final whistle, and all bets were off. 

Someone quickly shared a clip of the Argentine player instigating the post-game brawl. Then people started going back through the whole game, pointing out everything even borderline that Argentine players did — here is one-such 13-minute clip. But why stop there? Here is a 5 1/2-minute clip of transgressions by Argentina that weren't penalized in the semifinal against England. Of course, there was group play, too — here is nine minutes of uncalled fouls against Switzerland. 

Earlier incidents became fair game, as well. A video surfaced in 2024 of Enzo Gonzalez, the Argentine player who drew a red card in the final, and teammates chanting racist slurs on the team bus, as posts such as this one quickly noted over the weekend. Gonzalez had apologized profusely, including personally to Black players on his club team, and surely thought the incident was behind him. No longer. Many on social media also noted that the Spanish goalkeeper had been classy in accepting the Golden Glove award, for the best at his position in this year's tournament, while the Argentine keeper had used the award to make an obscene gesture when he won in 2022.

Analysts were universally brutal about Argentina after the final. The New York Times ran a story under the headline, "Argentina disgraced themselves, and the World Cup final, with their charmless petulance." In case that wasn't enough, the NYT ran another story, full of images, under the headline, "How Argentina turned the World Cup final dirty with shoves, skulduggery and squealing."

My point being: Once sentiment turns against you, even based on an incident by one person or a small group, things can go downhill fast — and keep going.  

This surely isn't news to insurance companies, which understand that claims are the moment of truth. Everyone and everything has to line up just right when you're dealing with longstanding, loyal customers in their moment of need. They've earned compassionate, professional excellence — and they'll react in horror if they don't get it. 

But I still think object lessons like those provided by Argentina are worth noting and spreading, because it only takes a few people, or even a single person, to undercut what so many other people are doing to earn loyalty. Social media can broadcast bad actions incredibly fast these days and seems to relish doing so, especially if there is compelling video. 

And narratives are hard to shake once they take hold. The Argentine team is being cooked especially hard because it was already known as a dirty team. In insurance, if you're not known for great customer service, complaints will find an especially alert audience — I'm sure State Farm, for instance, is being incredibly careful these days, given the controversy over its handling of claims from last year's wildfires in California.

I won't suggest buying the jersey of Leandro Paredes, the Argentine player who ran up on a Spanish player from behind after the game and knocked him over, because some of the money would find its way back to him. But maybe he can be an anti-hero for anyone dealing with insurance customers. Whatever you do, people, don't earn us a reputation like that guy....

Cheers,

Paul

P.S. When I think back on the World Cup, I'll prefer to think about the positive surprises. Who knew that Costco and ranch dressing would be such delights for those visiting the U.S.? Erling Haaland? I've spent years hating on him in a Man City kit but found him impossibly charming both in a Norway jersey and in his experience with U.S. culture. Then there was Spanish star Lamine Yamal's three-year-old brother, Keyne, who stole every scene he was in throughout the tournament. 

And I'll especially cherish a moment that Jude Bellingham and Bukayo Saka and their English team had in their third-place game against the French. 

When England earned a penalty kick, Bellingham prepared to take it. He had emerged as a full-on star for England and had already scored six goals; seven would be unworldly. But he knew that Saka had scored twice against France, knew that concerns about injury had (unwisely, in my view) kept Saka out of the semifinal that England lost against Argentina, and may have been thinking about how Saka and two Black teammates had missed penalty kicks in a tournament in 2021 and had endured wildly racist criticism. 

Bellingham told Saka, "Go on and get your hat trick," and handed him the ball. Saka converted with a kick that the keeper wouldn't have touched even if he had guessed right, rather than diving in the opposite direction. Saka's goal turned out to be the winner. 

Bellingham, by the way, got his seventh goal a few minutes later with an extraordinary display of technical virtuosity. So nice guys finish.... first?

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