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?

AI has moved past the hype stage—it's reshaping cost structures, competitiveness, and growth across insurance. Carriers who hesitate are locking in cost and risk profiles that only get harder to unwind. Drawing on original research with insurance executives, this report shows how AI, GenAI, and Agentic AI are separating Leaders from Followers and Laggards—and why 2026 is the point of no return.

AI is now a boardroom priority, tied to insurers' top 2026 goals: cutting costs, streamlining operations, and improving customer experience. Across underwriting, claims, servicing, billing, distribution, and loss control, carriers are moving from talk to active pilots, targeting friction in paperwork-heavy areas like claims and service. But appetite is outpacing the data foundation needed to support it, raising scalability and reliability risks without stronger governance.

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One of the greatest challenges facing insurers is making sure that every claim is documented consistently, regardless of who is performing the inspection or where it takes place. Smart glasses offer a clear path to standardizing this process by relaying information, guidance, and documentation tools directly within the wearer's field of view.

Rather than relying solely on handwritten notes, memory, or photos taken after an inspection, smart glasses enable adjusters and field representatives to capture POV evidence in real time while following predefined inspection workflows. Step-by-step prompts, often oral, help ensure that required photographs, measurements, and observations are collected in the same order and according to the same standards across every inspection.

The result is greater transparency for insurers, policyholders, and auditors alike. Claims files become more complete, inspection procedures become more repeatable, and documentation is captured as events occur rather than reconstructed later. This not only improves confidence in claim decisions but also creates a stronger evidentiary record should questions arise later in the claims process.

As AI capabilities continue to mature, AI smart glasses can also assist by identifying missing documentation, recognizing objects or damage patterns, and helping ensure that inspections meet established company guidelines before they are submitted.

Is it possible that AI-enabled remote assessments can reduce the need for adjusters to travel on-site? If so, could this help lower costs or improve overall response times?

In many situations, yes. While some claims will always require an experienced adjuster to visit a site in person, smart glasses make it increasingly practical to conduct guided remote assessments for a wide range of property, vehicle, and equipment claims.

Using a live video connection, a policyholder, contractor, or local representative wearing smart glasses can share a first-person view with an adjuster located anywhere in the country. The adjuster can observe conditions in real time, ask questions, request additional views, and guide the inspection without the delays associated with scheduling travel.

This approach offers several advantages. It can significantly reduce travel time and associated costs, accelerate response following widespread events such as severe storms or natural disasters, and allow experienced adjusters to assist more customers each day. It also improves access to expertise in secure, remote, or difficult-to-reach locations where specialized personnel may not be immediately available.

Faster assessments often translate into faster claims decisions, helping policyholders begin repairs sooner while enabling insurers to manage resources more efficiently during periods of high claim volume.

Can smart glasses better provide guidance to policyholders when resolving claims or billing issues?

Smart glasses have the potential to transform customer interactions from reactive conversations into guided experiences.

Rather than describing damage over the phone or attempting to follow written instructions, policyholders can receive visual guidance while completing important tasks. AI-generated prompts or remote experts can direct them to photograph specific areas, locate serial numbers, inspect equipment safely, or verify documentation without leaving the inspection process.

This guidance helps reduce misunderstandings while making the experience less stressful for customers who may be navigating an insurance claim for the first time.

The same capabilities can extend beyond claims. Customer service representatives may use smart glasses during virtual support sessions to help policyholders understand equipment, review billing questions, or verify information required to resolve an issue more efficiently. By providing contextual information directly within the user's field of view, smart glasses make complex interactions easier to complete while reducing the need for repeated follow-up calls.

How can smart glasses create a more consistent experience to guide policyholders through the documentation process when building claims?

Incomplete documentation remains one of the most common reasons claims require additional review or follow-up. While wearing smart glasses, users can be guided through a structured documentation process from beginning to end.

Instead of expecting policyholders to determine which photos to take or which details are important, the system can provide step-by-step instructions tailored to the type of claim being filed. Users can be prompted to capture specific rooms, damaged assets, identification numbers, receipts, or supporting evidence before moving to the next step.

Because the guidance is delivered in real time, the likelihood of missing critical information is significantly reduced. AI can also identify gaps in the documentation, recognize whether required images have been captured, and recommend additional photos when necessary.

For insurers, this creates a more standardized claims package regardless of who submits it. For customers, it reduces uncertainty and increases confidence that the necessary information has been provided the first time, helping claims move through the review process more quickly.

What's the hidden benefit of the insurance industry beginning to adopt smart glasses, and why is it happening now?

The most significant benefit may not be the technology itself—it is the ability to capture data and make it available anywhere.

Insurance organizations face continuing challenges associated with workforce transitions, increasing claim complexity, and rising customer expectations. Smart glasses enable experienced adjusters to extend their knowledge beyond physical travel by supporting multiple inspections remotely, mentoring less experienced personnel, and guiding policyholders through complex situations from virtually any location.

At the same time, artificial intelligence is making these systems increasingly valuable. AI can assist with documentation, recognize objects and damage, summarize inspections, retrieve policy information, and provide contextual recommendations without interrupting the inspection process. Together, AI and smart glasses create a powerful platform that augments human expertise rather than replacing it.

For the insurance industry, the opportunity extends beyond operational efficiency. Smart glasses have the potential to improve consistency, strengthen documentation, reduce unnecessary travel, accelerate claims resolution, and deliver a more transparent and supportive experience for policyholders. As insurers continue to seek ways to improve both customer satisfaction and operational performance, AI smart glasses are emerging as a practical technology capable of advancing both objectives simultaneously.

With so many smart glasses flooding the market, which is the right choice for insurers?

Not all smart glasses are designed for the same purpose. While consumer AI glasses like Ray-Ban Stories, Virtue, and Xreal have generated significant attention for features like voice assistants, messaging, and media capture, insurance organizations require a very different set of capabilities. Claims assessments, inspections, and customer interactions demand devices that are built for reliability, security, and extended professional use.

Enterprise smart glasses, such as the Vuzix M400, are specifically engineered for these environments. They offer extended-wear battery life, hands-free operation, high-quality cameras, and displays that remain visible in a wide range of lighting conditions. Just as importantly, they integrate with enterprise software platforms, support secure device management, and can be deployed and updated across large organizations.

For insurance professionals, this means adjusters can access claims information, inspection checklists, policy details, and remote expert assistance without interrupting their workflow or reaching for another device. Enterprise platforms also support compliance requirements through encrypted communications, secure authentication, and centralized IT management—capabilities that are often essential when handling sensitive customer information.

Durability is another important consideration. Field adjusters routinely work in challenging environments, including construction sites, disaster areas, industrial facilities, and severe weather conditions. Enterprise smart glasses are designed to withstand these demanding settings while delivering consistent performance throughout a full workday.

Perhaps the most important distinction is purpose. Consumer smart glasses are primarily designed to enhance everyday personal experiences, while enterprise smart glasses are built to improve business processes. In insurance, success depends on accurate documentation, standardized inspections, secure data handling, and efficient collaboration between policyholders, adjusters, and remote experts. Enterprise smart glasses are purpose-built to support those objectives.

As AI capabilities continue to evolve, the most valuable deployments in insurance will likely combine enterprise-grade hardware with intelligent software that assists users in documenting claims, identifying missing information, guiding inspections, and connecting field personnel with subject matter experts. Choosing a platform designed for professional workflows ensures insurers can take advantage of these innovations while meeting the security, reliability, and scalability requirements of the modern enterprise.

Claims handling is one of the most visible cost lines in insurance. Industry estimates consistently place 70% to 80% of claims handling costs inside routine, repeatable processes: status inquiries, documentation requests, coverage confirmations, first-notice-of-loss intake. These are the categories that make the AI business case straightforward to build and difficult to execute without hurting accuracy.

Most insurer AI triage deployments begin with a proof of concept on controlled test data. What they encounter in production is a different environment, with a different risk profile, and different failure modes. Three design decisions determine whether the transition from pilot to live operation succeeds or stalls.

The Claims Categories Ready for AI Triage (and the Ones That Are Not)

The categories that perform reliably in production share a characteristic: the resolution requires accurate information retrieval and a rule-based decision, not adjuster judgment.

First-notice-of-loss intake for standard peril types (vehicle collision, water damage, property theft) follows a structured data-collection process that maps cleanly to what AI agents do well. The agent gathers required fields, confirms coverage against the policy record, generates a claim reference, and routes to the appropriate handling queue. Intake time drops significantly with AI. Early-stage accuracy is high when the agent has direct, live access to the policy management system.

Policy status and coverage inquiries are a second reliable category. Policyholders and brokers need clear, accurate answers about what is and is not covered under a specific policy. These queries have a deterministic answer that the AI can retrieve from the policy record and communicate without ambiguity. When it does so accurately and immediately, satisfaction scores on this category improve, and the insurer avoids the misquote risk that comes from a rushed human response during peak volume.

Documentation status updates on open claims, whether a repair estimate has been received, whether a payment has been processed, where a claim sits in the workflow are the third reliable category. These interactions are high in volume and low in complexity. They consume significant adjuster time. When the agent handles them with real-time access to the claims management system, adjusters recover that time for interactions that actually require their expertise.

The categories that are not ready are those that require genuine coverage interpretation, multi-party coordination, or circumstances the policy language does not address clearly. Deploying AI on these categories in an early implementation is where most accuracy problems originate.

The Accuracy Threshold That Protects Both CSAT and Regulatory Standing

In most service sectors, a triage system that resolves 70% of queries correctly in the first months of deployment and improves from there is considered a successful pilot. Insurance applies a different standard, for two reasons that are specific to the sector.

First, inaccurate coverage information given to a policyholder at claim time creates both a CSAT problem and a potential errors-and-omissions exposure. A claimant told their loss is covered and later finding it is not does not experience this as a minor service inconvenience. Second, insurance regulators in most markets require that specific communications meet accuracy and disclosure standards that a misconfigured AI agent can fail to meet without the insurer knowing until a complaint surfaces.

The practical consequence is that the confidence threshold below which the AI escalates rather than responds must be set higher in insurance than in most service environments. A system that generates a coverage answer when its confidence score is moderate is operationally acceptable in retail support. It is not acceptable in insurance, because the cost of a wrong answer is asymmetric: a small number of incorrect coverage statements create regulatory and customer relationship problems that far outweigh the efficiency gains across the cases the system handled correctly.

Define the escalation trigger conservatively in the early deployment. A narrower AI scope with a high accuracy rate builds the internal confidence and operational track record needed to expand scope responsibly. A wide scope with a moderate accuracy rate generates precisely the incidents that slow adoption and invite regulatory scrutiny.

What Production Looks Like After the Proof of Concept

Proof-of-concept environments test the happy path. Production environments test the edge, at volume, across the full range of policy types and peril circumstances the carrier actually handles.

Three failure modes appear consistently in live insurance triage deployments.

The first is policy variant coverage. A claimant's policy may carry endorsements, exclusions, or carrier-specific modifications that are not reflected in the standard coverage language the agent was trained on. Without direct access to the full, structured policy record, not a summary, the agent falls back to standard language and produces an answer that is accurate for the base product and wrong for that policyholder's specific terms.

The second is multi-party claims. In a commercial property claim or a liability claim involving multiple parties, the intake process requires collecting different information from parties with different roles and interests. AI agents calibrated for personal lines intake do not handle this correctly without specific configuration, and the errors they generate in multi-party scenarios tend to be the most visible ones.

The third is mid-process handoff quality. When a claim requires escalation from the AI to a human adjuster, what the adjuster receives determines whether the customer experience continues or restarts. A handoff record that captures the full interaction context, what the agent understood, what was collected, and what was confirmed allows the adjuster to continue from where the agent stopped. A handoff that returns the claimant to the beginning of the intake process generates the complaint pattern that regulatory affairs teams track.

Keeping Adjusters in Control of What Matters

The framing that produces both operational results and staff adoption is direct: AI handles information retrieval and routine intake so adjusters spend their time on the interactions that require professional judgment, relationship management, and expertise. Not as a threat to the role. As a description of what the role becomes.

The adjusters who see AI triage succeed in their operation are consistently the ones who were involved in defining where the escalation boundary sits. That line is a professional judgment, not only a technical parameter. Involving the claims team in setting it and giving them a clear override path when the system routes something they believe it should not produce better-calibrated systems and faster adoption than any training program.

The insurers getting durable results from AI claims triage are not the ones that deployed the most capable model. They are the ones that were clearest about where human judgment is irreplaceable and built their system around that boundary from the first day of deployment.

A few years ago, a homeowner walking into an insurance conversation was a lot like a patient walking into a doctor's office. They knew something was wrong (or at least expensive), but they mostly deferred to the expert. The agent explained coverage options, translated policy language, and guided the decision. The homeowner nodded along and signed.

That dynamic is breaking down. A growing number of homeowners are doing research with AI before they ever talk to a human agent. According to the 2026 Hippo Housepower Report, 54% of homeowners plan to use AI to check whether they're paying a fair price, 48% to compare providers and policies, and 40% to better understand their policy.

Much like patients who Google their symptoms before showing up at the clinic, homeowners may not have the diagnosis right, but they have the vocabulary. They're arriving with more context, more specific questions, and higher expectations for speed and transparency than they had even two years ago.

The natural question is how far this goes. If consumers are already using AI to research coverage and compare carriers, will AI eventually handle the entire transaction? Some agencies are already building toward that, but the industry isn't ready for a fully autonomous buying experience. The regulatory, financial, and psychological infrastructure to support it doesn't exist yet.

The last mile stays human

Even with all of that AI-assisted research, the final purchasing step will remain a human interaction for a long time. When it comes to big purchases or signing on the dotted line, consumers want to be able to look someone in the eye. They want a person to put an arm around their shoulder and say, "Yes, this is the right policy. You're covered."

Think of it like booking a complex international trip. Most people will spend hours researching flights, hotels, and visa requirements online. But when the itinerary gets complicated, plenty of people still pick up the phone and call a travel agent for that final confirmation. The AI does the legwork, but a human closes the loop.

Beyond that psychological barrier is the legal one. In most states, core insurance functions like binding coverage, providing advice, and serving as the agent of record are non-delegable duties that require a licensed human. AI sophistication won't change that in the near term. 

The National Association of Insurance Commissioners (NAIC) adds a regulatory layer. As of early 2026, 25 jurisdictions have adopted its Model Bulletin on the use of AI systems by insurers, and another four states—California, Colorado, New York, and Texas—have their own insurance-specific regulations. 

On top of that, payment companies are still uneasy about sensitive credit card or EFT information flowing through AI, even with tokenized digital wallets. Carriers also need to modernize the underlying technology stacks, data systems, and APIs that agents already rely on. Layering agentic capabilities onto outdated or unreliable infrastructure will only create more friction and make it harder for agents to trust the technology enough to use it.

Gen Z homeowners are already far more comfortable with AI in their insurance decisions than older cohorts. But for now, the human at the end of the buying process provides reassurance that AI cannot replicate at scale.

Who's liable when the AI agent gets it wrong?

Independent agents are already hiring technology companies to build AI chatbots that handle initial consumer conversations, run quotes, and present coverage options. The technology works. What's unresolved is who's responsible when that bot gets something wrong.

Let's say an agent deploys an AI tool built by a third-party vendor. That tool runs a quote, explains coverage, and presents a recommendation to a consumer. The consumer buys the policy. Six months later they file a claim, and the AI-recommended coverage had a gap.

The mistakes are easy to imagine. AI may draw attention to key coverages, but not that requirements and policy language vary by county, or that some carriers in a region build that coverage into the base policy while others exclude it entirely. It can look up a flood zone but misinterpret whether their property sits on the edge of one, or that the distinction can change based on drainage and slope. And replacement cost—one of the most important numbers in a homeowners policy—depends on regional labor rates, local building codes, and material costs that fluctuate with supply chains. An AI tool pulling from general data is likely to get that number wrong, or differently than an insurance company.

So who owns the gap? The tech company that built the bot? The agency that deployed it? Or is there a disclaimer at the bottom of the screen that says the onus is on the consumer? For human error, the answer is settled. Agents carry errors and omissions insurance for exactly this reason. None of that infrastructure exists yet for AI agents. These questions will get worked out in the courts, slowly, and until they do, anyone deploying consumer-facing AI is carrying a risk that's difficult to quantify and impossible to fully insure against.

The new visibility problem

If you're a carrier and you're not thinking about how AI-assisted shopping will affect your visibility to consumers, think about what happened with search engine optimization. Companies that ignored SEO a decade ago disappeared from Google results. They were invisible to the vast majority of consumers, regardless of the quality of their product. The same dynamic is starting to form with AI.

As more homeowners begin their insurance research with an AI tool, carriers that win will be the ones that are prepared for it, with clearer coverage language, accessible APIs for AI tools, and structured product information. The rest risk being left out of the conversation entirely.

The homeowner sitting across from an independent agent today is more informed and more specific in their questions than they were three years ago. AI is a big part of why. The industry's job is to meet consumers where they are and to be clear about where AI ends and experienced human judgment begins.

Since the ransomware crunch of 2020, cyber underwriting has gotten steadily more external and more technical: attack-surface scans at quote, patching cadence wired into the catastrophe models, and third-party telemetry now feeding the major vendor platforms. Global cyber rates have kept falling through early 2026 even as claim frequency rises.

All of cyber underwriting measures the current state of technology. Almost none of it measures whether the organization behind those controls is deteriorating: losing the people who understand its systems, struggling through major organizational change, or cutting the resources needed to sustain operations.

Is that kind of operational strain visible from the outside and distinguishable from the ordinary noise any large company produces? To find out, we traced public signals in the year before three major breaches: TCS, CDK Global and Change Healthcare – plus a peer control study for TCS. In each case, the deterioration left a public, dated record months before the loss.

The signals are public posts: employee reviews on Glassdoor, AmbitionBox, Indeed and Blind, and practitioner and customer threads on Reddit. We ignored general dissatisfaction and looked for dated observations tied to identifiable roles, in four categories: organizational instability, security dysfunction, financial distress, service degradation.

The outsourcer

The connection is most direct at TCS, a services business where the workforce is the product it sells. Through 2025, it held an A on SecurityScorecard, a rating that spans everything from patching cadence to how exposed its people are to being targeted.

Across the 201 signals, the recurring themes were constant reorganization, experienced staff leaving faster than they were replaced, and people put on work they weren't equipped for. Four months before the M&S attack, a TCS consultant described being presented to clients as a cybersecurity analyst despite never having worked in that role. Months earlier, a SOC analyst listed "slow incident response" among his team's problems.

In April 2025, attackers called the IT helpdesk TCS had run for Marks & Spencer since 2018 and talked staff into a reset; M&S put the profit impact near £300 million. Jaguar Land Rover followed months later, at an estimated £1.9 billion cost to the UK economy. TCS ran IT there too, though the public record doesn't establish that its people were the ones socially engineered.

The obvious objection is that every Indian IT major generates this kind of noise. So we ran the same collection on Infosys, Wipro and HCLTech: 501 signals across the three, against TCS's 201. All four show reorganization noise. But security staff are about 8% of TCS's signals against roughly 3% at the nearest peers. While the baseline volume varies by firm, only TCS showed a concentration of security-role complaints describing problems within the security function itself.

The buyout

At CDK, the pressure came through ownership. Brookfield took the company private in 2022 in a deal backed by roughly $5.8 billion of debt, and public posts began documenting the operational consequences.

From June 2023 to the day before the attack, employees across engineering, support, sales and training described the same sequence: repeated layoffs, work shifted to the outsourcing partner Genpact, and the people who understood the systems walking out with each cut. A trainer who left in February 2024 traced it to the top: a 30% margin wasn't enough for the CEO, so he cut across the board to reach 40%. That same month, a director's advice to management consisted of three words: "stabilize the talent drain."

Security leadership also became harder to identify. David Hahn, CDK's first publicly identifiable CISO – a role the company first filled in 2020 – joined Ballistic Ventures as CISO-in-Residence in December 2022. We found no public evidence of a successor before the ransomware attack. Across dealership forums, users described repeated outages, with one writing during a July 2023 incident that the platform was "up for 3 minutes and then down for 30."

In June 2024, BlackSuit ransomware hit and froze nearly 15,000 dealerships, about $1 billion in dealer losses. CDK never disclosed the intrusion vector, so no causal link can be drawn. What the public record does show is that the organization operating those systems had been under visible operational pressure for a year beforehand.

The pattern is structural: across private-equity portfolios, where the incentives are the same, S-RM found 72% of firms had a serious cyber incident in their portfolio within three years. In February this year, Bloomberg reported on Ivanti, which Clearlake Capital had taken private. Cost-cutting there stripped out the engineers who understood the company's VPN code, and Chinese state hackers exploited the resulting flaws to reach US government and corporate networks. Some government and corporate buyers, Bloomberg reported, now factor private-equity ownership into how they assess security products. In the same report, Rob Leahy, former CIO of NASA's Goddard Space Flight Center, said ownership structure should be part of any product risk assessment: "Are they investing in the future or are they not?"

The rollup

Change Healthcare was assembled by serial acquisition and run for margin, where cost-cutting meant deferring integration. Optum, UnitedHealth's health-services arm, absorbed it in October 2022.

Across the 142 signals we collected from Change and its new parent, employees repeatedly described experienced staff leaving and the loss of institutional knowledge. One review summarized the effect as "letting the knowledge walk out the door." Another, posted in November 2023, three months before the breach, described acquired companies as "not integrated into network sometimes ever."

In February 2024, attackers got in through a single internet-facing Citrix portal with no multi-factor authentication (MFA). By the CEO's later congressional testimony, it ran on technology acquired in 2022 that had never been brought under the company's own MFA policy. The breach reached 192.7 million people, at a cost UnitedHealth put at $3.09 billion for 2024 alone, the largest healthcare data breach on record.

Change is one of three Optum acquisitions to have been breached since 2023; Solutran and Episource followed, the last hit twice, in 2023 and in 2025. Two years after the attack, UnitedHealth was still consolidating 18 acquired medical-record systems down to three. Acquisitiveness itself leaves a statistical trace: a 2025 study of 5,072 U.S. firms found the more a company acquires, the more breaches it experiences, the effect growing when acquirer and target come from different businesses.

Organizational deterioration is already familiar to cyber insurers. It appears in breach investigations, claims files and post-loss reviews, where staffing shortages, restructurings and operational strain often help explain how technical failures developed. By then, however, those signals are explanatory rather than predictive. The cases here suggest that some of the same patterns are visible months earlier in public operational data.

Three cases chosen for how they ended cannot establish predictive value on their own; that requires knowing how often the same signals appear at companies that never suffer a loss. The TCS peer comparison is a first attempt at that question.

Cyber underwriting has become increasingly sophisticated at measuring the state of the technology. Whether the organization behind those controls is changing in ways that affect cyber resilience is not yet part of how the risk is priced.

Forecasts suggest this year's hurricane season could bring lower than average storm activity. But disaster risk is not measured solely by the number of named storms.

It only takes one major flood to expose the vulnerabilities that persist across the US flood protection system. And increasingly, flood losses are not confined to coastal communities or storm surge alone. Flooding driven by heavy rainfall, overflowing rivers, and flash floods are increasingly affecting communities far beyond traditional flood zones, often in places where insurance take-up is low and financial resilience is limited.

This reality highlights an urgent policy challenge; America's flood protection gap continues to widen at a time when economic exposure is growing.

Flooding can happen almost anywhere, yet millions of American households and businesses remain uninsured or underinsured against flood risk. When disasters strike, the consequences extend well beyond individual property losses, which alone are already devastating. Delayed recovery affects local employers, municipal budgets, infrastructure systems, housing markets, and broader regional economic activity.

Insurance plays a critical role in helping individuals, businesses, and communities recover more quickly and reducing long-term economic disruption. Strong insurance participation supports financial stability after disasters, accelerates rebuilding, and reduces reliance on post-event federal assistance. It's an essential component of economic resilience for all Americans.

That's why Congress should advance the bipartisan Community Flood Resilience Act, introduced by Congressman Andrew Garbarino and Congressman Gregory Meeks.

The legislation reflects a pragmatic recognition that resilience requires both physical mitigation and financial preparedness. By allowing a portion of federal flood mitigation assistance funding to support community-based parametric flood insurance solutions, Congress is advancing a thoughtful public-private sector approach to disaster resilience.

This legislation does not replace the National Flood Insurance Program (NFIP). Instead, it acknowledges that public and private solutions can work together to expand protection, improve awareness, and strengthen recovery capabilities. In today's evolving risk environment, collaboration is essential.

Community-based parametric insurance provides funding when predefined conditions are met, such as measured rainfall levels, river heights, or other objective flood triggers. Because payments are tied to those triggers rather than lengthy loss-adjustment processes, communities can access funds much more quickly after a disaster. Faster access to funding can help local governments stabilize essential services, support small businesses, and assist vulnerable populations during the critical days immediately after flooding occurs.

Speed matters after disasters. Delays in recovery funding often translate into prolonged economic hardship for communities already under strain. Parametric insurance policies can deliver payments within 30 days, or less, when the funds are needed the most.

The legislation also emphasizes education, outreach, and transparency. Participating communities must describe how they promote flood insurance awareness, encourage mitigation efforts, and communicate clearly about how these products function alongside traditional coverage. These provisions recognize that resilience begins with understanding what's at risk.

The insurance industry has long played a foundational role in supporting economic growth and recovery following catastrophic events. As risks evolve, innovation in risk transfer and resilience financing will increasingly become important complements to infrastructure investment, stronger building standards, and disaster mitigation programs.

Public-private collaboration will be critical to narrowing the protection gap. Legislation like the Community Flood Resilience Act demonstrates how policymakers can encourage innovation while strengthening community preparedness and preserving the role of insurance in supporting economic resilience.

Resilience is built before disasters through smarter planning, stronger mitigation, and broader financial protection. Public policy that improves flood insurance participation and accelerates recovery better protects homes and businesses, and promotes the long-term economic stability of communities across the country.

As flood risk expands beyond traditional geographic boundaries, policymakers need tools that strengthen both physical resilience and financial preparedness. The Community Flood Resilience Act is a practical way to do both.

State Farm just told 19,000 captive agents the deal has changed. Deferred compensation? Gone. Health benefits? Reduced. Renewal commissions? Squeezed in favor of new-business production.

State Farm is a policyholder-owned mutual—the largest in the country—not a private equity play. Yet the announcement reads like it came straight out of a KKR, Apollo, or Blackstone operating playbook.

For decades, State Farm's model rested on a simple premise: a book of business is not self-sustaining. It requires labor. Agents weren't just selling policies; they were maintaining them—fielding calls, resolving issues, retaining customers, spotting risks before they became claims. Renewal commissions weren't a bonus. They were the operating system.

But operating systems get deprecated.

Every generation redraws the line between labor and leverage, between what requires a human and what can be systematized. The real question isn't whether people add value. It's whether they add the same value they once did—and whether that value supports the same cost structure.

Seen through that lens, State Farm's move wasn't surprising. It was inevitable.

Three forces have been quietly closing in.

First, competition. Progressive and GEICO operate without an agent-heavy cost base. They built direct models—leaner, faster, less sentimental. As they gained share—Progressive recently passed State Farm as the top writer of auto policies in the US—State Farm was forced to respond.

Second, management migration. Over the past two decades, executives have moved through private equity portfolio companies, internalizing a shared language—almost a mantra—of efficiency, productivity, and return on capital. What was once distinctive to private equity is becoming simply how management thinks.

Third, AI. Service calls, billing questions, renewals, first notice of loss—tasks that once justified large workforces and long-tail commissions—are increasingly handled by software that doesn't sleep, doesn't churn, and declines in marginal cost over time.

This doesn't make human agents obsolete. It makes legacy compensation models obsolete.

Human value doesn't disappear, it concentrates in complex cases, edge scenarios, trust, judgment—the hard stuff. But the routine? The repeatable? The predictable? That's already slipping out of human hands.

The private equity approach asks a relentless question of every line item: if we were building this today, would we pay for it this way? That question is destabilizing inside legacy models, because once you ask it honestly, a lot of "strategic investments" start to look like habits. And habits, over time, get expensive.

So this isn't a story about private equity taking over State Farm. It's something more consequential: the normalization of a worldview private equity helped industrialize. Nothing is sacred—except the spreadsheet. Every cost is conditional. Yesterday's logic expires faster than anyone wants to admit.

Cost cutting is the easy part. Plenty of companies are doing that—and calling it strategy.

The harder move is what comes next: reinvesting those savings to build something better. Better experiences. Stronger capabilities. New forms of growth that justify the disruption.

In the end, the winners won't be those who simply get leaner. They'll be the ones who get smarter about where humans still matter—and ruthlessly disciplined about where they don't.

That's the real ghost in the machine.