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

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

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

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

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

Let's have a look.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

For at least the past two years, insurers have focused on an essential question: How can AI make us smarter?

Another concern is beginning to emerge.

Who, exactly, becomes smarter every time we use it?

The insurance industry has spent decades building intellectual capital that doesn't appear on any balance sheet. Underwriting judgment. Claims workflows. Fraud indicators. Distribution strategies. Product pricing logic. Regulatory expertise. Customer communications. The institutional knowledge that carriers, MGAs, and brokerage use to create a distinct competitive advantage from other businesses in their competitive space.

Today, much of that knowledge is quietly flowing into AI systems - oftentimes without the knowledge owners' (or originators') knowledge.

Not through a data breach.

Not through cybercrime.

But through ordinary, everyday business use.

The New Information Leak

When people think about information security, they typically picture hackers wearing hoodies in darkened rooms stealing customer records or ransomware shutting down operations. (Throw in some empty Mountain Dew cans to add extra "flavor" to the image…)

Generative AI introduces a different type of risk actor.

Insurance workers don't simply upload documents into AI systems. They explain how their business works. They refine outputs. They correct mistakes. They describe edge cases. They reveal why one underwriting decision differs from another. They basically fine-tune the model using the organization's life-blood – its processes, judgment, and expertise.

Each prompt adds context. Every correction adds nuance. Any successful interaction between a person and an insurance-trained LLM captures another piece of proprietary institutional knowledge.

Individually, these exchanges seem harmless. Collectively, they represent an organization's most valuable asset.

The Trojan Horse Problem

Spoiler alert for people who are still reading The Iliad before going to theaters to see The Odyssey: The ancient Achaeans didn't breach the gates of Troy by force.

They were invited inside.

That's why the "Trojan horse" analogy has gained traction among AI observers. The concern isn't that foundation model providers are intentionally harvesting proprietary information for competitive purposes. Rather, the architecture of AI creates a fundamental asymmetry: organizations must reveal increasingly detailed information about how they operate to receive increasingly valuable assistance.

The more context AI receives about your operations...

...the more effectively it can replicate (and share) your expertise.

Microsoft CEO Satya Nadella recently framed this as a "reverse information paradox," arguing that organizations effectively pay twice for AI: once financially, and again by revealing proprietary knowledge required to make the models useful. His observation reflects a growing debate across enterprise technology about ownership of prompts, feedback, workflows and organizational know-how.

Whether organizations agree with that assessment, it raises an important governance question for insurance CIOs/CISOs.

Insurance Has More At Stake Than Most Industries

Insurance isn't simply another knowledge business.

It is a judgment business.

Competitive advantages rarely come from a single algorithm or dataset. They're the result of thousands of successful decisions accumulated over years:

• Why adjusters escalate certain claims.
• How underwriters evaluate emerging risks.
• Which variables consistently predict profitability.
• How service teams de-escalate difficult customer situations.
• Which regulatory interpretations have proven successful across jurisdictions.

These are not trade secrets, per se, but they are critical operational intelligence.

Unlike customer data, operational intelligence often isn't classified or labeled as confidential. Employees may share it with AI tools because they're simply trying to work faster.

Yet this institutional knowledge may ultimately prove more valuable than the documents themselves. And, not to belabor the Homeric allusions, the safety of this institutional intelligence can be an insurance business's Achilles Heel…

Why Governance Must Expand its Purview Beyond Privacy

Most AI governance discussions inside insurance organizations focus on familiar concerns:

• Personally identifiable information
• Regulatory compliance
• Security controls
• Model accuracy
• Bias and explainability

These remain essential elements to a robust governance strategy.

But these may no longer be sufficient as the core of governance.

Organizations should also ask:

• Which AI platforms retain prompts?
• Can customer interactions be used to improve future models?
• What proprietary workflows are employees revealing?
• Which business processes should never leave controlled environments?
• When should an organization rely on internally hosted or private AI models instead of public services?

Such questions nudge AI governance beyond compliance and into competitive strategy.

Institutional Knowledge Is Intellectual Property

Historically, insurers have protected code, customer databases, actuarial models, and other vital information.

Tomorrow's competitive asset may be something less tangible: the accumulated knowledge generated every day through interactions between employees and AI.

The irony is striking.

Organizations adopted AI to preserve and amplify institutional knowledge.

Without thoughtful governance, they may also be exporting it.

Insurers' response should not be to slow AI adoption. The lost productivity gains will be consequential across every insurance function from underwriting to claims to policy servicing.

But as organizations race to become AI-enabled, their practices should be centered around the principle that cybersecurity communities have understood for years:

The most valuable information isn't always the data you store. Sometimes it's the knowledge your people create.

The evolution of AI governance won't be defined around cybersecurity, privacy, or compliance concerns. It will also require insurers to identify, classify and protect institutional knowledge as another key strategic asset.

Organizations that rise to this challenge will capture AI's productivity gains without inadvertently teaching competitors how they do business. Those that don't may discover that the greatest risk wasn't exposing customer data; it was giving away the expertise that made them different in the first place.

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.