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.

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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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.

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.

The insurance industry faces a key question: How can insurers successfully industrialize their AI initiatives?

Scaling AI requires a distinct approach relying on: compliance, security, and traceability. Not meeting these requirements prevents projects from moving beyond the early stages, especially when they lack clear governance, performance indicators and risk management.

AI itself is not the factor impeding or slowing down the crucial passage from POC to fully deployed projects. The real issue lies in the approach and environment in which AI initiatives are developed and industrialized. Projects are still approached from a very traditional perspective rather than as potential business use cases, overlooking the need to factor in elements such as IT infrastructures, operations and, crucially, data management.

In this case, the distinction lies in the way data is being managed. Organizations with poorly structured and scattered document silos struggle with the technological debt of outdated systems including legacy enterprise content management (ECM) platforms and archives. Data management makes a difference when it comes to successfully industrializing insurers' projects as well as being one of the main difficulties they can encounter.

Scaling up with strong decision making

To succeed, organizations must rely on clear strategies driven by high-value business use cases that show immediate and significant affect in areas that are key to the business, for example the automation of claims processing.

They should also increase the focus on modernizing the existing ECM platforms while refraining from launching a complete overhaul. As counterintuitive and challenging as it might sound, this balance is essential to success and can be achieved by intelligent information management and keeping up with the latest AI implementations.

Finally, they take into account challenges related to governance and compliance from the very beginning: data traceability, model explainability, and compliance with regulatory frameworks.

The key to success: a strong information foundation

What differentiates insurers that have successfully scaled their AI projects from others is the way they approached the issue: they started with data rather than starting with AI. Insurance is a document-driven industry; its value lies in leveraging its content: policies, claims files, contracts, correspondence, broker communications, loss reports, medical records, underwriting submissions, and regulatory documentation.

Some insurers are still dependent on legacy systems, traditional ECM/DMS platforms which are now showing their limits and slowing access to information. Their lack of flexibility, combined with the proliferation of repositories, make the use of information difficult.

To overcome these difficulties insurers must rely on technological solutions incorporating AI to automate the creation of a unified, structured, and accessible information environment. But in order to be truly impactful and bring long-lasting innovation, this can't simply be merely a new layer added on top of an existing system: what is really needed is a thorough modernization of native platforms, contextualized in real-time thanks to advanced AI tools.

Inspired industry leaders are those who know how to prioritize long-lasting sustainable industrialization over short-term and rapid changes. Integrating AI solutions is a starting point, but not the solution itself. The ideal conditions for large-scale deployment have to touch all assets of the business, from talent acquisition to fill the new skill gap to investing in research and development and, especially for a consumer-facing industry like insurance, transparency and the ability to explain the benefits stemming from the technology upgrades.

Organizations that can't align with this approach are likely bound to be left behind in the no man's land of unrealized POCs, while others successfully scale up projects and introduce innovations.

After working at just about every level of an insurance agency, you start to see M&A differently. I've been the person taking out the trash, the producer trying to win the account, the leader trying to make payroll, and now the CEO thinking about how to build something bigger without losing what made the business work in the first place.

It's easy to talk about acquisitions in financial terms. The numbers matter, of course – no one builds a successful brokerage on good intentions alone. But if you've ever sat in the producer's chair, serviced the client, worked through a tough renewal, or built a relationship with a carrier over many years, you know the numbers only tell part of the story. Sometimes, they're not even the most important part.

Insurance is a relationship business, and agencies aren't just books of business. They're local reputations, client histories, carrier relationships, producer instincts, service teams, personalities, and a few quirks that somehow become part of the culture. When an agency joins a larger organization, the transaction may close on paper within weeks or months – but the real work begins after that.

The deal isn't the finish line

One of the biggest mistakes in acquisition-driven growth is treating the close as the win. The legal documents are signed, the press release goes out, everyone shakes hands, then the team moves on to the next deal.

But for the people inside the agency, that's where the questions start.

Will my role change?

Will my clients feel the difference?

Will the culture change?

Will this still feel like the place I helped build?

Those questions are not a distraction from integration. They are integration.

If leaders don't address them, people will fill in the blanks themselves. And blank spaces rarely get filled with optimism – they get filled with rumors, assumptions, and hallway theories.

The best post-acquisition strategies are built around a simple idea: people aren't being absorbed. They're being supported, connected, and brought into something larger. That takes discipline. You have to listen before you start changing things, understand why the agency works, and learn what the team, clients, and local market already trust. Skip that work, and you can end up damaging what made the agency worth acquiring in the first place.

Preserve what makes the agency valuable

Successful independent agencies grow because they have something that works: a trusted team, a strong local brand, deep carrier relationships, a niche they understand better than most, a service model clients value, or a culture that makes people want to stay.

I'm a big believer that the riches are in the niches. In this industry, specialists matter. The best people usually have something they really know. It may be a coverage area, a region, a class of business, a client type, or a set of relationships that took years to build. A larger organization shouldn't flatten that – it should amplify it. The goal isn't to make every partner agency look, sound, and operate exactly the same. The goal is to understand what should be supported at scale and what needs to stay close to the client.

In many cases, the larger organization can take on back-office support, improve data and reporting, modernize tech stacks, expand market access, and reduce administrative burdens. This will give producers and service teams more room to do what they do best. But relationships, local market knowledge, client trust, and specialized expertise should be protected with intention.

Growth should make great agencies stronger, not generic.

Provide the clay to work with

You have to give your team the clay to work with. That means giving people the tools, resources, data, support, and freedom they need to shape something meaningful. It does not mean handing them a script and asking them to become someone else.

Insurance professionals are entrepreneurial by nature. Producers especially want to build, solve, connect, and win. Service teams want to take care of people and do right by the client. Local leaders want to protect the reputation they've built.

A larger brokerage can bring a lot to the table, but it has to show up as support, not control.

For example, better data shouldn't feel like someone is watching over your shoulder. It should help leaders make better decisions, help producers identify opportunities, help teams reduce manual work, and give the business a clearer view of what's working.

New systems, reporting, workflows, and processes can be valuable, but only if people understand the "why." If the only message is, "Here's the new way we do things," you'll lose people. If the message is, "Here's how this helps you serve clients, grow your book, and spend less time fighting the machine," you have a much better chance of earning buy-in.

Culture is built in the day-to-day

Every company says culture matters. Far fewer do the slow, practical work required to protect it during growth.

Culture isn't a set of values added to an onboarding deck. It's how people make decisions, solve problems, and treat each other when the work gets hard. It's how leaders communicate when the answer isn't clear. It's how wins are shared, how conflict gets handled, and whether employees feel respected after the transaction closes.

In insurance, culture shows up in very practical ways – affecting how producers collaborate, how teams respond under pressure, how leaders talk about clients, how quickly people adapt to change, and whether employees feel like they are part of the future, or simply along for the ride.

That's why communication after a deal can't be a one-time announcement.

People don't need every answer on day one. They know business is complicated. But they do need to know that leadership understands the questions. They need to hear what's changing, what's not changing, and why. And they need to see local leadership remain engaged.

Local leadership still matters

One of the smartest things a larger brokerage can do is listen to the leaders already inside the agency. They know the clients, the history, and the way work actually gets done. They can translate change to the team in a way that feels credible, because they've earned their trust. They can also identify when something looks good in theory but won't work in practice.

That feedback is gold, even when it's inconvenient.

A strong growth model doesn't silence local leadership. It gives those leaders better tools, more support, and a broader network while still respecting what they know.

This is especially important in today's market because insurance is becoming a war of capabilities. Agencies need more resources than ever: technology, data, analytics, specialty expertise, carrier access, compliance support, talent development, and operational infrastructure. But capabilities only matter if they actually help the people closest to the client.

Long-term value is built after the announcement

The insurance industry will continue to consolidate. There are too many structural reasons for it: succession planning, talent pressure, technology investment, carrier complexity, margin pressure, the need for scale. But long-term success won't come from acquiring the most agencies. It will come from helping the right agencies get stronger.

Growth happens when clients stay, producers keep producing, leaders keep leading, and teams believe they have more opportunity than they had before. It happens when the business becomes better, without forgetting what made it worth acquiring in the first place. That's the real work of insurance M&A.

After decades of inefficiencies, insurance carriers, MGAs, and agencies have finally begun to invest in their technology to modernize and improve distribution management. The goal is straightforward: automate producer onboarding, simplify licensing and appointments, maintain compliance, and process transactions more efficiently.

As the industry works to catch up with these investments, they've revealed a stark reality that most distribution systems were designed to execute processes rather than generate strategic intelligence. This distinction is significant because the next competitive advantage in insurance distribution won't come from simply managing producer networks more easily, though that is important; rather, advantage will come from understanding the networks more deeply.

Distribution data is the untapped goldmine in the insurance industry. The winners are already prospecting the land.

The Data Exists, You Need To Use It

Every interaction within a distribution management platform creates valuable information. Appointments, licensing timelines, agency affiliations, geographic distribution, product sales, producer tenure, renewal activity, and more data points are readily available to companies that use a centralized database.

Historically, this data has been stored to primarily support administrative functions. Once a transaction is complete, the information is saved but its strategic value goes untapped. If you ask a carrier how many appointed producers they have they can easily answer. However, if you were to ask them for more detailed insights the answers become much more difficult — or impossible — to produce.

Which newly appointed producers have generated the highest premium in their first 90 days? Which agencies consistently outperform peers in specific product lines? Where are producers successfully cross-selling multiple products versus writing only one line of business? Which states have the strongest producer recruitment outcomes relative to onboarding investment?

These are critical business questions, not operational ones. And these are the kinds of insights distribution data will be able to provide.

Reporting Vs. Intelligence

There is a distinct difference between reporting and intelligence. Reporting tells you what happened, but intelligence helps explain why it happened and what should happen next.

Let's consider producer recruiting. Many organizations measure success by the number of producers appointed each quarter. But appointments alone don't determine business value. What if data revealed that producers recruited through one regional agency network generate twice the lifetime premium of those recruited through another channel? Or that producers with certain business characteristics consistently become top performers within six months? These insights could fundamentally reshape how an organization invests in recruiting.

This isn't theoretical. One MGA connected its appointment engine directly to live production data. Instead of maintaining — and paying state fees for — appointments across its entire roster, the system now fires an appointment the moment a producer submits their first application and initiates termination when production goes dormant. Producers go from signup to production-ready in minutes instead of weeks, and state appointment fees dropped by more than 50% because the roster finally reflects reality. A report would have told this MGA how many producers it had appointed. Intelligence told it which appointments were actually earning their keep.

Identify High-Performers Earlier

One of the biggest opportunities lies in identifying successful producers much earlier in their relationship with a carrier. Many carriers and MGAs recognize top producers after they've built an established book of business and hit certain milestones. These recognitions work to build and solidify strong working relationships between top producers and carriers. This goodwill is effective, but it is only built after producers deliver large results.

What if they could identify high-potential producers within their first few months? Organizations could build these relationships earlier, and create a stronger connection with up-and-coming talent.

By analyzing historical production patterns, onboarding activity, product mix, submission behavior, and engagement trends, AI-powered analytics could recognize signals and patterns that have historically preceded long-term success. Perhaps producers who complete onboarding in less than 30 days, immediately write across multiple product lines, and maintain consistent submission activity during their first quarter have historically become top performers.

If these patterns emerge early, distribution leaders could proactively invest in those relationships through targeted marketing support, additional training, and even mentorship. Rather than reacting to success after it occurs, organizations could help accelerate it.

Opportunities Hidden in Geography

Distribution intelligence has the potential to uncover geographic expansion opportunities that may not be immediately obvious.

For example, a carrier may believe it has saturated a particular state because of the number of appointed producers operating there. However, a deeper analysis might reveal that neighboring counties with similar demographics have significantly lower producer density but higher policy growth potential.

Alternatively, the data may show that commercial lines producers are outperforming personal lines producers in a specific region, suggesting an opportunity to adjust recruiting priorities or product offerings.

These insights would allow organizations to make expansion decisions based on measurable market intelligence rather than calculated assumptions.

From Dashboards to Decision Engines

Collecting data is important, but not using it creates little value. Real opportunity comes from gleaning valuable insights and making them accessible to business leaders.

Modern dashboards need to move beyond displaying static metrics. They should benchmark producer performance, identify emerging trends, forecast recruiting outcomes, and highlight opportunities requiring immediate attention.

Imagine a distribution executive opening a dashboard that identifies states where onboarding times have increased, predicts recruiting shortfalls for the next quarter, highlights agencies exceeding profitability benchmarks, and recommends where additional field resources should be deployed.

Those are strategic business decisions powered by data, not just operational reports.

The Future Is AI

As AI continues to mature, the possibilities become even more compelling. Instead of simply analyzing historical performance, AI will increasingly help organizations anticipate future outcomes and identify lucrative opportunities.

Predictive models may identify producers who are likely to disengage before production declines become visible. They could flag onboarding delays that historically lead to lower first-year performance or detect compliance trends that indicate elevated regulatory risk before violations occur.

There will be a shift from responding to problems after they've affected revenue or operations, to intervening proactively before major harm is done.

Insurance has never lacked distribution data. Historically, it has lacked access to organized data and is now missing the ability to transform that information into strategic insight.

The organizations that gain the greatest competitive edge over the next few years won't simply automate more workflows and organize their back-office processes. They'll use distribution intelligence to make smarter recruiting decisions, strengthen agency relationships, optimize geographic expansion, and anticipate future risks before they materialize.

Distribution data isn't an administrative byproduct, but a strategic asset organizations can use to inform better decisions across every stage of the producer lifecycle.

The future of distribution isn't just better management; it's better intelligence.