Paul Carroll

Your latest economic outlook struck me as reasonably optimistic, despite the U.S. tariffs and our war against Iran, but the environment changes awfully fast these days. Where do you stand now?  


Michel Léonard

I'm sorry to disappoint. We put out our Outlook a few weeks ago, and I've already turned more pessimistic. You know me — I lean optimistic by default. But here's what happened, and it all comes down to the Iran conflict:

When we put out our last quarterly estimates, everyone was talking ceasefire — and what it would mean for the P&C industry, replacement costs especially, and for the broader U.S. economy, inflation especially. Then the ceasefire evaporated, and the consensus swung back to the war grinding on. So we started repricing growth and inflation across the board — systemic, structural, economy-wide.

I say more pessimistic because I'm not ready to call a trend yet. You've heard me say it takes two quarters to make a trend — and we're not there. We won't have two clean quarters of data until late this year or early next, on what the Iran conflict has done to the economy, or is still doing by then. But we've clearly shifted from worrying mostly about GDP growth to worrying about price stability and jobs, too.

Paul Carroll

Oil prices haven’t risen as much as many of us expected, because various countries, especially China, have cut way back on consumption or dipped into strategic petroleum reserves. As a result, the oil shock hasn’t spread throughout the economy as much as it might have. But with the resumption of at least occasional fighting in Iran and the depletion of petroleum reserves and stockpiles for fertilizer and other oil-dependent products, are we reaching a breaking point where contagion can no longer be contained?



Michel Léonard

Yes, absolutely. We've been measuring how oil-price contagion spreads into the wider economy two ways: first, the damage already done, through existing PPI [produce price index] data; second, how it could unfold from here, through stress tests.

On what's already happening: higher oil prices have jumped the fence from energy into everything else — the contagion is real. Aluminum and petrochemicals, machinery and equipment, electronics and accessories, fertilizer manufacturing, plastic resins and materials — all up an average of 34% year-over-year over the last six months.

Paul Carroll

As the conflict drags on without a clear path to negotiations or a long-term solution, what do your stress tests say might happen to replacement costs in P&C insurance?



Michel Léonard

Our P/C replacement-cost stress tests ran three conflict durations — through end-2026, into 2027, into 2028 — and zeroed in on the three lines the Iran conflict is already hitting hardest on replacement costs: farm owners, commercial property, and personal and commercial auto. For those lines, we estimate replacement costs climb an average of 13% year-over-year if the war runs into 2027, and another 20% if it drags into 2028.

Paul Carroll

How should people use scenarios like this to plan?

Michel Léonard

In our last outlook, we said the whole point of stress tests is to build consensus around managing the severity of tail events — not their likelihood. Scenarios have to be credible, but not the most likely — and almost never are. 

Let a stress-test conversation slide into political debate about how likely each scenario is, and it's dead on arrival. Pick credible scenarios, skip the likelihood argument, and go straight to impact severity — that's how you actually get people to consensus on a risk-mitigation strategy.

For P/C, that brings us to actuarial analysis — backward-looking by definition. Here, that's a feature, not a bug. You anchor to the data in the rearview mirror, then work with regulators on where reserving and rates should go. The stress tests are the headlights — you use them after, mostly to set reserving or policyholder surplus above what regulators require.

Now, if you ask which of the three is most likely, I'll say this may be the most impossible conflict to forecast I've seen in decades. Put a crystal ball in my hand instead of structured analysis, and I'd still bet on the one-year scenario — into 2027.

But I'll say it again: each scenario's exact probability is beside the point, as long as it's credible. What matters for our audience is that nobody can argue the war couldn't drag on another year or two. So my advice: reserve adequately for all three scenarios — in a way that's economically viable for your own book and how it actually behaves.

Paul Carroll

No matter what happens in terms of costs, the outlook for growth seems fairly positive. Is that correct?



Michel Léonard

The pressure point is what the Fed does. Real tell: the new chairman's very first communiqué ended with a standalone line — "The Committee will deliver price stability." That's just not how these things are written. I've never seen anything like it. It signals rate hikes for an inflationary environment, it locks the Fed in. And markets didn't like that line one bit when there was no rate increase at the next meeting.

I won't get into the dynamics between the new chairman and the president who appointed him — or what the president wants, which everyone knows. But if the Fed hikes based on today's inflation drivers, that's a basic policy mistake. Rate hikes work best on demand-driven inflation; this is supply-side, so raising rates buys you almost nothing.

As Greenspan used to say, rate hikes would be the wrong medicine for the disease. here This inflation is the White House's to cure — not the Fed's.

In my view, a 25 basis point hike in this geopolitical climate would land with several times the force it would in normal times. It would slam the brakes on growth — tipping GDP toward a contraction and pushing unemployment toward 5%.

Paul Carroll

A fair amount could change on tariff policy and other issues based on what happens in the November midterm elections. How do you see these political dynamics affecting the situation?


Michel Léonard

I'm so glad you brought that up, because the tariffs are back. It's August, and out of nowhere a few weeks ago we've got tariffs on Canada and others again. I didn't see that coming — I thought we'd closed that chapter. The only way they make sense right now is to change the subject, from Iran-conflict inflation to protectionism. But it's not all performative — tariffs are increasing government revenues, real money coming in, more than expected back in 2022. 

Tariffs are inflationary by design. They won't do affordability any favors — and insurance affordability least of all.

The midterms and the rest of the DC noise you're pointing to — that's what tips the one- or two-year scenario.

The one-year scenario gets more likely if Democrats lead the polls into the midterms and take at least one chamber of Congress — this could cause a replay of 1980, when Reagan was already offering a better deal to the Iranians before he'd even won the presidency.

The two-year scenario gets more likely if the Democrats don't win back a chamber. You'd have a lame-duck president who'd probably want to end the war he started on his own watch —  or, again, we could have a rerun of 1980.

Paul Carroll


What happens if the Iranians show for the long term that they can keep the Strait of Hormuz mostly closed and that the U.S. can’t protect our longtime allies in the Middle East as much as we and they thought we could?

Michel Léonard

That changes the whole ballgame — the repercussions would run in every direction. But now I'd be trading structured analysis for the crystal ball again, so I'll stop there.

Paul Carroll

We’ve covered a lot. How would you summarize?



Michel Léonard



First, prices are already spiking — and not evenly; some costs aren't behaving the way you'd expect. Check the actual BLS and BEA data. That part matters. Second, P/C replacement costs are climbing, too, hardest in farm owners, homeowners, and commercial property.

Finally — and I'll end on optimism, like I promised — I think this slowdown is short-lived. As long as the Fed keeps its hands off rates.

And if you’re a Triple-I member, don’t forget that you can reach out to us directly. Membership has its privileges. 

Paul Carroll

Thanks, Michel. As insightful as always.

Generic AI is reaching its limit in insurance. It can summarize a submission, draft a customer response or extract information from a claims file. What it cannot do independently is make the high-stakes decisions that determine insurance performance.

Those decisions require more than a plausible answer. They depend on an insurer's data, pricing and underwriting rules, risk appetite, regulatory obligations, portfolio position and operational context. Without that grounding, AI may produce an answer that sounds credible but cannot be trusted, explained or acted upon.

According to Grant Thornton's 2026 AI Impact Survey, 44% of insurance executives said governance or compliance challenges had caused AI projects to fail or underperform, while 56% identified regulatory uncertainty as a leading barrier to scaling AI.

This is driving the industry toward vertical AI built specifically for insurance. Unlike general-purpose tools, vertical AI can apply intelligence within the context, controls and accountability that real insurance decisions demand.

The Danger of Faster Silos

Most insurers already have vast amounts of data, but rarely a complete view at the moment a decision must be made. Policy data may sit in one system, claims history in another and customer interactions somewhere else. Portfolio information and external risk signals may also arrive on different cycles.

Each source tells part of the story, but none necessarily reflects the full business context.

When insurers place AI on top of that partial view, they may produce an answer more quickly without producing a better decision. In high-stakes decision making, speed cannot compensate for missing context.

Insurance has traditionally been organized around separate disciplines, including pricing, underwriting, claims, customer engagement and portfolio management, even though the outcomes of those functions are closely connected. A pricing decision affects conversion and retention. An underwriting decision changes portfolio quality. A claims experience can influence renewal, customer value and future risk.

The business understands these relationships, but its systems do not always reflect them.

The risk is that insurers layer AI onto the same fragmented operating model. Pricing, claims, underwriting and service may each gain tools, while the organization still lacks a shared understanding of the decision being made. Individual tasks become faster, but the underlying disconnect remains.

That is the limit of horizontal AI. It can improve an activity without improving the enterprise decision surrounding it.

Insurance AI Should Follow the Decision

Policy administration, billing, claims and CRM platforms remain essential to insurance operations. They preserve records, process transactions and provide the stability insurers rely on. AI should strengthen the work performed within and across those systems, rather than attempt to replace them.

These platforms are highly effective at recording what has happened. The greater opportunity lies in helping insurers determine what should happen next.

A renewal decision, for example, may require policy history, recent claims, customer value, portfolio concentration, emerging risk signals, a pricing model, underwriting appetite and current regulatory requirements. No single platform owns all of that context.

The architecture must therefore be decision-centered rather than core-centered. Insurers need to identify the intelligence, rules, workflows and human expertise that must come together around each decision, then orchestrate those elements across the systems they already use.

Vertical AI extends far beyond adding insurance terminology to a general-purpose model. It is designed around how insurance work is performed, how decisions are governed and how their consequences are managed.

A rate change, for example, affects more than the price presented to a customer. It can influence portfolio composition, retention, profitability, fairness and regulatory filings. The AI supporting that decision must understand those connections.

Bringing the necessary context into the workflow may require predictive AI for risk and pricing, generative AI for explanation and guidance, and agentic AI for bounded, multistep execution. The type of model matters less than the insurer's ability to orchestrate the right intelligence around the decision.

The decision must come first.

Governance Must Travel With The Action

Insurance has always delegated authority within defined boundaries. Controls throughout the process allow insurers to distribute decision-making without losing accountability.

Agentic AI changes the risk profile because it can take action rather than simply provide advice. When an AI system can initiate or execute steps in real time, reviewing its activity after the fact may be too late.

Governance must therefore be embedded within the decision itself. It should define which data can be used, which models and rules may be applied, when human review is required and how the complete decision path can be reconstructed.

Not every AI component will be deterministic, but the route from input to action must be controlled wherever the decision demands it. Probabilistic intelligence requires deterministic guardrails, including defined permissions, monitoring, escalation paths, and the ability for a person to intervene.

The regulatory direction is already clear. The NAIC's model bulletin calls on insurers to establish governance, documentation, testing, validation and third-party oversight for AI systems supporting decisions that affect consumers. It also reinforces that insurers remain accountable for those decisions, regardless of the technology used to support them.

Human oversight must be designed into the operating model. This does not mean asking someone to rubber-stamp every AI-generated output. It means defining what can be automated, what must be reviewed, when escalation is required, and who ultimately carries responsibility.

Done well, this approach also gives experienced underwriters, actuaries and claims professionals greater leverage. Their hard-won expertise can become reusable decision logic, business rules and escalation paths, rather than remaining trapped in documents, manual processes or the knowledge of a small number of individuals.

Measure Decisions, Not Deployments

The insurance industry has invested heavily in data, analytics, predictive models, cloud platforms and core modernization. The remaining gap lies between those assets and the decisions that run the business.

A vertical approach allows insurers to advance AI without waiting for a multiyear core replacement. It can draw on the systems, data, and models insurers already trust, then orchestrate them around the decisions that need to improve today.

This makes existing investments more valuable while giving the business greater flexibility to adapt, without destabilizing the systems that keep operations running.

Success should therefore be measured by the quality and business impact of the decisions being improved, not by the number of AI tools deployed. That may include stronger pricing performance, more consistent underwriting, faster product changes, improved customer outcomes, or better portfolio management.

Insurance is an industry of connected consequences. AI built for isolated tasks will miss those connections, regardless of how capable the underlying model may be.

Insurance-native, vertical AI begins with the decision. It brings together domain context, predictive and generative intelligence, business rules, workflows, governance, and human judgment at the point where action occurs.

That is how insurers can move beyond isolated AI use cases and turn AI into a trusted, scalable capability for the decisions that matter most.

When two steel columns buckled on the 21st floor of the former Pfizer headquarters in Manhattan this July, the building safety industry watched a familiar sequence unfold, and commercial property insurers should have watched just as closely. 

Reporting noted that engineers were cautioned against assuming the visible damage marked the full extent of the problem at the 37-story tower, then mid-conversion into more than 1,600 apartments. Structural elements are connected, and a failure in one area often signals stress elsewhere. Early accounts pointed toward a missing steel reinforcement plate, a detail that should have appeared in the project's engineering plans but was never installed, according to engineers cited by The Real Deal.

For a carrier underwriting that asset, or an adjuster assigned to the claim, it would be easy to call this a documentation failure. That framing undersells the problem. The tower did not lose its documentation. It lost records continuity. Every renovation, code cycle, and material change was tied to whatever system or individual happened to be tracking it at the time. When those links broke, so did the file's ability to describe the true risk on the books.

The missing layer is not another document repository. It is a persistent identity connecting every record, inspection, permit, renovation, owner, engineer, and platform to the same physical asset across its lifecycle and every policy period it will ever carry.

Documentation Answers the Wrong Question

Even carriers with disciplined underwriting files run into this. A document repository answers what records exist. It cannot answer what actually happened to a specific asset across its operating life, the question a claims examiner is really asking.

A recent industry analysis of facility continuity found that as veteran facility managers retire, decades of undocumented building knowledge often leave with them, since it lives in someone's head rather than in a system. The analysis cited IFMA projections that more than 45% of facility management professionals worldwide will retire within a decade, removing much of the institutional memory insurers have quietly relied on when pricing risk.

That is the same failure described two ways: a retiring manager who carried undocumented knowledge out the door, or a file that cannot say what a structure can support after decades of alterations. The cause is identical. The record lives with the software, the vendor, or the employee, never with the infrastructure itself.

What Claims and Underwriting Actually Require

In a post-failure scenario, structural engineers, investigators, and claims teams are not simply looking for a folder of drawings. They need a reliable, continuous chain of custody for every material change, traceable to who made it, when, and under what code cycle. A binder handed off at turnover cannot provide what's needed.

The same need extends well beyond a single claim. Fire departments, FEMA, adjusters, building officials, and search and rescue teams all depend on the same continuous history of what a building is, what it can support, and what has changed. So does every actuary modeling portfolio exposure, and every reinsurer pricing a treaty against it.

What claims and underwriting actually require is closer to a VIN for physical infrastructure, a persistent identifier every contractor, engineer, permitting office, carrier, and platform can reference over decades, regardless of who owns the data or which system created it. Imagine underwriting an aircraft fleet if every maintenance record moved to a new numbering system each time ownership changed. That is unacceptable in aviation. It remains normal for buildings.

A Survey That Is Measuring Something Deeper

Viewed this way, recent survey findings from ARC Facilities on facility leader confidence are not really measuring documentation quality. They are measuring identity fragmentation, the same fragmentation showing up as inconsistent exposure data across a book of business. The survey found many facility leaders describe only partial confidence in accessing critical building information when it matters most, one noting their organization lacks a reliable system.

Low confidence in complete records is what a carrier's risk engineers should expect in an industry where every renovation, ownership change, and software migration creates another chance for the thread between an asset and its history to snap, often just before a claim tests it.

The Layer the Industry Never Standardized

Over recent decades, the built environment has standardized layer after layer of how infrastructure gets built and managed. CAD standardized design. BIM standardized information. GIS standardized location. IFC standardized interoperability. Digital twins standardized representation.

None of those layers standardized identity, a persistent, portable reference that follows infrastructure across every system, every underwriting cycle, and every claim, throughout its lifecycle. Each platform generation has improved how information about an asset is captured, but none has solved for what happens once the platform, vendor, or risk manager who understood it moves on.

A digital twin cannot remain continuous if the identity of the asset it represents is not continuous. Neither can a book of business.

Interoperability tells systems how to exchange information. Identity tells systems what they are exchanging information about. The industry has spent 50 years perfecting the first problem while leaving the second unaddressed, and insurers have been pricing risk on top of that gap.

A Different Question for Risk and Insurance Leaders

The Pfizer conversion is useful not because documentation failed at a single point, but because continuity failed across an entire history of changes, and continuity depends on identity. That is a claims story and an underwriting story at once.

The more productive question for the insurance and risk industry isn't how thoroughly infrastructure gets documented. It's why so little of it carries a persistent identity across ownership changes, renovations, and software migrations, including changes in carrier, broker, and policy. Every industry exchanges data about buildings without a shared way to identify what it describes.

The built world has standardized nearly every way information is created, exchanged, and analyzed. The next standard will not be another data format. It will be persistent identity, and the carriers who adopt it first will be the ones who can actually price the risk they are holding.

In 2012, someone showed me a life insurance policy still in force. Issued in 1928. I'd come to the sector after a long run in credit cards, where you know what you're carrying within six to 18 months. I did the math without meaning to: that policy predated Social Security.

Insurance carries risk differently, and the difference isn't just duration — it's what either side can do about it once the ink is dry. A lender can act on an individual account: reduce a line, renegotiate, price it differently, charge it off. A life insurer cannot unilaterally touch a policy already in force; that right belongs to the policyholder, not the carrier. The industry doesn't just carry decades-long risk. It carries it locked, one contract at a time, with no lever to revisit terms even after the assumptions underneath them have quietly stopped matching reality.

That's an issue the industry's own machinery was never built to answer. Reserving, asset adequacy testing, hedging, reinsurance, the sale of legacy blocks to run-off specialists — all of it real, all of it sophisticated, all of it built to answer whether the aggregate is still solvent. None of it answers a narrower, harder question: does this one assumption, buried in a block that's still passing in aggregate, still hold? The standards touching assumption review ask for judgment, triggered by whatever surfaces the need — not a date on a calendar. The tests that do run on a schedule check the aggregate, not the assumption underneath it.

Here's the tell that this gap stopped being theoretical

In March 2026, the NAIC began piloting a structured AI Systems Evaluation Tool across 12 states. It isn't a general AI-governance gesture. Examiners using it can ask insurers to detail the oversight of AI systems used by actuarial service providers specifically, and to walk through the testing, verification, frequency, and methodology behind them. For actuaries who build or validate predictive models — which increasingly means experience studies and assumption-setting, not just pricing or claims — that means model documentation and assumption justifications becoming subject to regulatory examination, not just internal actuarial standards of practice. That's not an analogy to a proactive posture. It's regulators reaching directly into how AI-touched assumptions get tested and justified — a first, concrete step toward exactly the granular scrutiny this piece is arguing the industry doesn't otherwise have.

(A brief aside: the industry has pushed back hard on this pilot, and not merely over workload. Trade groups' formal comments call it "voluntary for regulators while compulsory for companies," note it has no defined end date, and flag that findings from what's explicitly an exploratory phase could be held against a carrier before the tool itself is finalized. Those are fair governance objections. They're also, in a way, evidence for this piece's own argument: regulators are building this proactive posture iteratively, through judgment and a pilot, before hardening it into a fixed rule — which is closer to the muscle insurers themselves need than a reason to dismiss it.)

That same year — 2012 — I watched a version of this exposure play out in real time, from inside a different seat, in an industry-wide effort that was already well underway. The 2008 crisis had left insurers holding large in-force books of annuities carrying guaranteed crediting or withdrawal rates set in a much higher-rate era. When rates fell and stayed down for years, those guarantees didn't just look generous — they became a standing liability the original pricing had never been stress-tested against. And because nobody can unilaterally rewrite a policy already in force, the fix was never going to be a model update. It was years of patient, unglamorous, one-by-one work: building offers attractive enough that policyholders would voluntarily give up guarantees the industry could no longer comfortably afford to keep. Carrier by carrier, contract by contract, across the sector — not because anyone lacked talent, but because nobody had a standing practice of asking, before the rate environment turned, whether that guarantee still made sense.

That's the exposure insurance carries now, in its own way, with its own kind of contract. It isn't new, and it was never really about AI — the gap between "the aggregate is fine" and "the individual assumption still holds" predates any model running against an underwriting file. What's changing is the cost of not knowing. Checking assumptions individually, continuously, used to be a resourcing decision nobody could justify for a block that was passing anyway. That constraint is loosening — not because the risk changed, but because the tools that make granular, continuing review economically realistic now exist. "We check the aggregate and trust judgment to catch the rest" isn't gone as an answer. It's weaker than it was five years ago. And that industry-wide effort is a preview of something worth sitting with: the fix that took years of manual, one-by-one work only got built because the rate environment forced it. It's a much better position to build that habit before something forces it than during.

What this actually asks of insurance executives

I'm not offering a view on hedging strategy or reserve methodology — that's not my lane. What is squarely in my lane: whether the organizational conditions exist for the people who already own assumption review to work well with AI — not defer to it, not ignore it, but genuinely partner with it.

• The regulator's own posture is worth studying less for compliance value and more as a design pattern — and it's only become buildable as standing practice, rather than crisis response, recently. Assumption review has always had owners. What it hasn't had is a way to run continuously rather than annually or on trigger. That's what AI now makes affordable — not a replacement for an actuary's judgment, but a way to test a guarantee, a lapse assumption, a legacy block, far more often than a person could manage alone.
• The harder question is whether the talent is being built for that partnership. Treating an AI system's output as the answer is one failure mode. Dismissing what it surfaces because it doesn't match what the model has always said is the other. The judgment in between — knowing when to challenge an AI finding, when to trust it, when to go dig further — isn't automatic. It has to be cultivated, the same way any other analytical judgment is.

Picture your next executive meeting on NAIC's AI Systems Evaluation Tool — whenever the Fall National Meeting takes it up, this becomes an agenda item somewhere. The easy question in that room will be whether you're ready for the exam. The harder, better question is whether the people who own your assumption reviews are equipped to work alongside AI — able to use what it surfaces, and just as able to push back on it when something doesn't add up.

The guaranteed-rate annuity problem wasn't solved by finding more talent — the industry had no shortage of it. It was solved reactively, once a crisis forced the question, and it took years longer than it should have because nobody was set up to ask it before the crisis made it unavoidable. That's the real choice in front of insurance leadership now: build the organizational conditions and the talent to ask that question all the time, with AI doing part of the looking and people doing the judging — or wait for the next rate environment, or the next examiner, to force the question again.

The Call

At 6:42 a.m. on a Tuesday morning in early February, Michael Grant's phone rang.

He was already awake. Public company CEOs rarely sleep well during earnings week.

The caller ID displayed David Ellis, Meridian Commercial Insurance's chief financial officer. David never called before seven unless something had gone wrong.

Michael answered immediately.

"We have a reserve problem."

The independent actuarial review had concluded the previous evening. After weeks of analysis and challenge sessions, the conclusion was unavoidable. Meridian would need to strengthen its commercial casualty reserves by approximately $500 million.

The financial implications were immediate.

Before the markets opened, the board would need to be informed. Rating agencies would request meetings. Investors would expect explanations. The earnings call scheduled later that week would no longer focus on operating performance. It would focus on credibility.

Michael listened quietly before asking a single question.

"How did we not see this coming?"

It was the question every executive, director, analyst, regulator, and shareholder would eventually ask.

It was also the wrong question.

The better question was this:

How did a long sequence of individually reasonable executive decisions become a $500 million reserve development?

The answer did not lie in a single actuarial assumption or one poor management decision.

It began nearly three years earlier.

Three Years Earlier

Three years before the reserve strengthening, Meridian looked exactly like the kind of carrier executives aspire to build.

The regional P&C insurer had earned a reputation for disciplined underwriting, conservative reserving, and consistent financial performance. Reserve development was generally predictable. Analysts viewed the company as steady. Rating agencies considered it well managed. Internally, executives regarded reserve discipline as one of Meridian's competitive strengths.

The quarterly reserve committee reflected that culture.

Led by the chief financial officer, the committee brought together senior leaders from actuarial, claims, underwriting, finance, and risk. Each function contributed a different perspective before management selected the company's carried reserve position.

The chief actuary presented reserve indications based on established actuarial methods and independent reserve reviews.

Claims executives discussed emerging litigation trends, settlement behavior, and developments within the largest claims.

Underwriting reviewed changes in portfolio composition, policy limits, pricing discipline, and business mix.

Finance evaluated the implications for earnings, capital adequacy, and external financial reporting.

The discussions were disciplined.

Assumptions were challenged.

Alternative interpretations were debated.

No one rushed to conclusions.

Looking back, there was remarkably little to criticize.

The organization had experienced executives.

It had sound governance.

It had reliable data.

It had established actuarial methods.

It had exactly the reserve process most well-managed carriers would expect to have.

And yet, within three years, those same executives would be explaining a $500 million reserve strengthening to the board and to the market.

The explanation would not be weak governance.

Nor would it be poor judgment.

It would be something far more subtle.

The organization possessed the information it needed.

It simply possessed it in pieces.

The First Signals

The first indications that something was changing did not arrive dramatically.

They appeared as routine operational observations.

During one quarterly reserve committee meeting, the chief claims officer noted that commercial auto bodily injury claims in several jurisdictions were remaining open longer than historical experience suggested. Settlement negotiations appeared less predictable. Litigation was becoming more complex in certain venues.

The observation attracted discussion but not concern.

Claims professionals encounter fluctuations like these regularly. Individual developments often reflect temporary operational factors rather than lasting changes in the underlying claims environment.

The committee agreed that the trend deserved continued monitoring.

Nothing suggested it justified changing reserves.

The underwriting discussion followed.

The chief underwriting officer described gradual changes in the portfolio. Larger commercial accounts represented a growing share of written premium. Higher liability limits had become increasingly common in several business segments. Competitive market conditions were influencing coverage structures in selected areas.

None of those developments appeared unusual.

Each reflected deliberate business decisions that management had already approved.

The business remained profitable.

Pricing discipline remained intact.

The portfolio continued performing within expectations.

The chief actuary then presented the quarterly reserve indications.

Historical development remained broadly consistent with prior analyses. Multiple actuarial methods continued supporting management's reserve position. Independent actuarial review identified no material reserve deficiency.

Viewed independently, each conclusion was entirely reasonable.

Claims observed subtle operational changes.

Underwriting observed gradual portfolio evolution.

Actuarial observed reserve adequacy supported by historical experience.

No individual observation justified changing the company's reserve position.

The committee approved another quarterly reserve selection.

It was a disciplined decision supported by the evidence available at that time.

What no one yet understood was that the organization was beginning to observe different parts of the same emerging story.

The Institutional Blind Spot

Nothing about Meridian's organizational structure was unusual.

Like most successful P&C insurers, it was organized around specialized functions.

Claims focused on claims.

Underwriting focused on risk selection and portfolio quality.

Actuarial focused on reserve adequacy.

Finance focused on earnings and capital.

Each function was exceptionally good at answering its own questions.

That specialization represented a strength.

It also created an unintended limitation.

Reserve adequacy is not fundamentally a claims question.

Nor an underwriting question.

Nor solely an actuarial question.

It is an institutional judgment that depends upon integrating evidence originating across the enterprise.

During periods of stability, that distinction rarely matters.

Historical experience aligns closely with current operations.

Independent functions naturally reinforce one another.

But when the operating environment begins changing faster than historical experience can reveal, something important happens.

Claims professionals may observe changing litigation behavior months before those developments become statistically visible.

Underwriters may recognize shifts in portfolio characteristics before those changes influence reserve development.

Finance may begin asking different questions about capital while actuarial analyses remain appropriately anchored in historical data.

Each observation is valid.

Each reflects disciplined professional judgment.

Each remains incomplete.

The reserve committee brought those perspectives together.

It integrated professional opinions.

It did not necessarily integrate the underlying evidence.

That distinction explains how well-governed institutions can still reach consequential decisions with an incomplete understanding of emerging enterprise risk.

Meridian was not suffering from a lack of information.

The organization knew many important things.

It simply did not yet know what those things meant when viewed together.

Rewinding the Story

Now rewind the story.

The portfolio is the same.

The claims are the same.

The executives are the same.

The reserve committee is the same.

The governance process is unchanged.

No authority has been delegated to AI.

No actuarial methods have been replaced.

No reserve decisions have been automated.

Only one thing changes.

The institution begins making reserve decisions with a more complete understanding of the evidence already available across the enterprise.

Instead of reviewing information that has been organized independently by claims, underwriting, actuarial, finance, and risk, the reserve committee begins with an integrated view of the emerging risk environment.

That distinction may appear subtle.

It is anything but.

In the original timeline, each function accurately described what it was observing.

Claims reported longer settlement cycles.

Underwriting described a gradually changing portfolio.

Actuarial presented reserve indications that remained within established ranges.

Finance evaluated capital and earnings implications.

Every presentation was accurate.

Every conclusion was professionally sound.

What the committee never saw was how those observations were beginning to reinforce one another.

Now imagine the same meeting supported by an AI-enabled decision intelligence capability.

Before the committee convenes, the system continuously evaluates information flowing across the enterprise—not to make reserve recommendations, but to identify relationships that deserve executive attention.

It examines claim notes, case reserve movements, settlement duration, litigation activity, jurisdictional trends, changes in policy limits, shifts in portfolio composition, external legal developments, and traditional actuarial analyses.

Its purpose is not to predict ultimate losses.

Its purpose is to answer a different question:

Are independently observed developments beginning to describe the same emerging enterprise risk?

Rather than producing another dashboard, the system highlights combinations of evidence that would otherwise remain separated by organizational boundaries.

Claims managers have independently observed increasing settlement duration within several commercial auto segments.

Underwriting has independently documented continued growth in higher-limit policies within many of those same segments.

External litigation data suggests increasing plaintiff success in overlapping jurisdictions.

Traditional actuarial indications remain within acceptable ranges, but recent development is gradually migrating toward the upper end of those ranges.

None of these observations is individually conclusive.

Together, however, they justify asking different questions before the next reserve decision.

Notice what has not changed.

The chief actuary still owns the actuarial analysis.

Claims leadership still evaluates claim behavior.

Underwriting still assesses the portfolio.

Finance still considers capital implications.

The reserve committee still debates.

Management still decides.

The board still exercises oversight.

AI has not replaced professional judgment.

It has strengthened the evidence supporting professional judgment.

Instead of asking,

"Do today's reserve indications remain reasonable?"

the committee begins asking,

"Does our historical development fully reflect what the rest of the organization is already beginning to observe?"

That is a fundamentally different conversation.

The committee is no longer evaluating isolated evidence. It is evaluating the emerging enterprise narrative that evidence collectively describes.

The Decision Changes Before the Outcome Does

The first visible change at Meridian is not a reserve adjustment.

It is the discussion preceding the reserve decision.

During the next quarterly review, actuarial indications continue supporting management's reserve position.

Historically, that would likely have concluded the discussion.

Instead, it becomes the starting point.

Claims leadership is asked whether the changing settlement patterns represent temporary operational variation or an emerging shift in litigation behavior.

Underwriting reviews whether the evolving portfolio characteristics are concentrated within specific industries, geographies, or coverage structures.

Actuarial evaluates additional scenarios while continuing to rely upon established reserving methods.

No one proposes a $500 million reserve increase.

Nor should they.

The available evidence still supports management's selected reserve position.

What changes is the institution's understanding of uncertainty.

Over subsequent quarters, as additional claim development enters the actuarial analyses, the findings no longer appear unexpected.

Management has already been investigating the underlying drivers.

Reserve strengthening still occurs.

Long-tail casualty business remains uncertain.

No analytical capability eliminates that uncertainty.

The difference is that reserve adjustments occur progressively as the organization's understanding evolves rather than emerging as a single material financial event.

Capital planning becomes more measured.

Investor communication becomes more predictable.

Management discussions become more forward-looking.

The financial impact remains. The surprise does not.

Beyond Reserving

Reserve development illustrates a broader principle that applies throughout P&C insurance.

Every consequential decision is made before uncertainty has been eliminated.

Underwriting decisions commit capital before future losses are known.

Claims decisions resolve complex situations before every fact has emerged.

Reinsurance decisions reshape retained risk before the next catastrophe occurs.

Reserve decisions recognize liabilities before ultimate claim outcomes become fully visible.

The common challenge is not technology.

It is decision making under uncertainty.

That is why the executive conversation around AI should begin to change.

Today's discussion often centers on models, copilots, agents, orchestration platforms, and automation.

Those capabilities matter.

They improve productivity.

They streamline workflows.

They reduce operating expense.

But those are implementation capabilities.

Enterprise value is created somewhere else.

It is created when institutions consistently make better consequential decisions.

That distinction changes how executives evaluate AI investments.

Instead of asking,

"How many AI capabilities have we deployed?"

they begin asking,

"Which consequential decisions have become measurably better because of them?"

That is ultimately the only business question that matters.

The Decision Before the Decision

Meridian's story is not fundamentally about reserving.

It is about institutional decision making.

The company did not experience a $500 million reserve strengthening because it lacked capable executives, disciplined actuarial methods, or sound governance.

It experienced one because individually reasonable observations remained institutionally disconnected until history had already confirmed what operations had begun to signal.

That distinction reaches well beyond reserving.

Every consequential underwriting decision reflects the institution's understanding of risk at a particular moment.

Every significant claims decision reflects its understanding of liability, customer impact, and financial exposure.

Every reinsurance decision reflects management's understanding of the portfolio before uncertainty has been resolved.

In each case, executives must commit capital before perfect information exists.

The quality of those decisions depends upon the quality of the evidence available before the commitment is made.

That is where AI has the opportunity to reshape P&C insurance.

Not by replacing executive judgment.

Not by automating accountability.

Not by predicting the future with perfect accuracy.

Its greatest contribution is enabling institutions to assemble, interpret, and challenge evidence earlier, allowing experienced executives to make better-informed consequential decisions while they still have the opportunity to influence the outcome.

For decades, reserve committees have begun with a familiar question:

"Based on the evidence before us today, what reserve should we establish?"

The AI era introduces an earlier and arguably more important question:

"Have we assembled every meaningful piece of evidence the institution already possesses before making one of its most consequential financial decisions?"

The institutions that outperform in the AI era will not necessarily be those that deploy the most AI.

They will be the ones that consistently assemble better evidence before making consequential decisions.

In the AI era, competitive advantage will belong less to the institutions that possess the most information than to those that assemble the best evidence before making consequential decisions.

As soon as I heard about Flock, the now-huge network of cameras that can automatically read license plates and instantly report their location to authorities, I wondered how long it would take before we read about police officers using the network to track girlfriends, wives and exes. 

It actually took longer than I thought, but here we are: The Washington Post reported a week ago that at least 50 officers throughout the U.S. have been accused of stalking by using Flock and similar systems. All sorts of national media have now followed the Post story, and we're in an environment of heightened sensibilities about surveillance and about how scary AI might turn out to be, so this story isn't going away any time soon. 

In the swamp that is today's social media, some conspiracy theorists are trying to drag in the insurance industry, saying it uses Flock cameras in nefarious ways. As the scandal unfolds, we need to not only make sure we avoid any unethical uses of camera networks but can respond quickly and convincingly to any online trolls alleging abuse.

Let's have a look. 

Many years ago, a good friend was driving an old car in Washington, DC, when he stopped at a stop sign and saw a pedestrian motioning to him that he needed directions. Tim rolled his window down, and the guy stuck a gun in his face, ordering him out of the car. But the car's clutch was dying, and the carjacker couldn't figure it out. He lurched a few feet and stalled. Another few feet and another stall. He stalled a third time before he even made it halfway through the intersection. He got out of the car, threw the keys into Tim's chest and ran off.

If the carjacker had succeeded and been in modern DC, he would have been a perfect candidate for Flock. Tim could have simply told a 911 dispatcher what his license plate was, and Flock's 120,000 cameras throughout the U.S. (except Alaska) would have instantly been on the lookout. Flock works with 7,000 police departments, or 40% of the total in the U.S., so once its AI spotted the license plate on the stolen car it would have been able to alert police and have a pretty good chance of recovering it. 

In theory, Flock will gradually prevent more and more thefts, because thieves will either be caught and incarcerated or will simply realize that car theft is a losing proposition.

I spend a lot of time with agency principals at very different stages of growth. Some are running lean operations in a single market. Others are managing multi-state books across several product lines. One thing I hear consistently, almost regardless of scale, is some version of this sentence: "We had our best enrollment year ever." My next question is always the same: What did that actually do for the health of your business?

The insurance distribution industry has spent years treating enrollment volume as the definitive signal of agency health. For most of the last decade, the underlying economics cooperated. A prolonged hard market in property and casualty, combined with rising premiums across many health and medical product lines, created an environment where agency revenue continued to grow, even when relatively few leaders stopped to examine what was actually driving it.

Most agencies have not had a reason to look too closely at those numbers, and the market has not forced the question. That is starting to change.

The Hidden Math Behind the Headlines

The 2025 Best Practices Study found top-performing agencies posted organic growth rates consistently between 10.3% and 10.7%. Those are historic numbers. On the surface, they make a compelling case that chasing enrollment is the right strategy.

The problem is what sits underneath. Financial benchmarking data shows that for the average insurance brokerage, roughly seven percentage points of that double-digit growth figure are attributable to premium rate increases and exposure base expansion rather than new accounts or deeper relationships with existing clients. Strip out the inflationary tailwinds, and actual organic growth for the average firm lands somewhere between 2.7% and 3.2%.

That is a very different business than the one most agencies believe they are running. The industry has benefited from macroeconomic tailwinds that made revenue growth look like strategic growth; premium inflation was confused with operational excellence. When the market softens and premium inflation cools, the distance between those two numbers will become impossible to ignore.

The Retention Gap Nobody Talks About

Industry data puts the average retention rate for a single-policy client at roughly 77%. An agency whose book is built primarily on single-policy households is losing close to a quarter of those clients every year. The agency is operating an expensive treadmill, constantly spending on client acquisition just to hold revenue flat.

When an agency deepens the relationship to five or more policies per client, that retention rate climbs to approximately 85%. That increase in retention sounds modest, but the compounding math over a five-year horizon is anything but. A multi-policy client is nearly twice as likely to still be in the book five years from now, representing roughly a 60% improvement in long-term customer value driven entirely by product depth rather than acquisition.

What makes this even more striking is how pervasive the single-policy problem is. Research indicates that approximately half of the average firm's customer base holds only one policy. That means half of the average independent agency's book of business is both an untapped revenue opportunity and a serious flight risk, simultaneously.

Why the Industry Stays Stuck

In most agencies, the barrier to deeper product relationships is operational, not strategic. The independent distribution ecosystem was designed to maximize transactions, not deepened relationships. Every new product can introduce another appointment, enrollment workflow, commission schedule, and other points of friction. Eventually, the economics favor moving on to the next prospect rather than deepening the relationship with the current one.

Consider what it takes to build a comprehensive household relationship spanning a major medical plan, a hospital indemnity policy, and a term life product. An agent navigates three separate carrier portals, re-enters the same client data three times, manages three different quoting engines with three different underwriting workflows, and then attempts to reconcile three fundamentally incompatible commission structures on the back end. That extra 30 to 45 minutes per client, multiplied across an open enrollment season, is simply not compatible with high-volume production targets.

The compliance landscape compounds the challenge, particularly in the senior market. CMS Marketing and Communications Guidelines generally prohibit agents from using a Medicare sales appointment to cross-sell non-health products without a documented Scope of Appointment secured at least 48 hours in advance. As a result, an agent who identifies a legitimate cross-selling opportunity often cannot pursue it in that moment. Instead, the opportunity requires a separate appointment, additional documentation, and another workflow. Each additional step introduces friction, reducing the likelihood that a valuable client need is ultimately addressed.

None of these barriers are insurmountable. They are real, though, and they explain why agencies that understand the economics of product depth still struggle to execute against it. The gap between what agencies know they should do and what their infrastructure allows them to do is where retention gets lost.

What Capital Actually Sees When It Looks at Your Book

The insurance brokerage M&A market is as active as it has ever been, driven by private equity consolidators, national aggregators, and large brokers competing for quality books. In an M&A context, the word "quality" has a very specific meaning that has nothing to do with enrollment volume.

Institutional buyers are not purchasing a snapshot of today's revenue—they are purchasing the predictability and durability of tomorrow's cash flows. A client retention rate at or above 90% is considered the standard for premium, platform-grade valuations. Agencies that consistently hit that threshold command meaningfully higher multiples, often one to two turns of EBITDA above lower-retention books. When a buyer identifies a book built primarily on single-policy clients with historical churn running below 80% to 85%, the response is earnout structures that shift up to 40% of the total purchase price into contingent payments tied to retention benchmarks the selling agency is unlikely to meet.

This is the moment that tends to surprise founders who have spent years optimizing for enrollment. They assumed the number of clients in the book was what they were selling. Buyers see it differently. They are buying the quality of those relationships, and a book full of single-policy households is a book full of clients who are one price comparison away from leaving.

The Market is Moving Anyway

The argument for product depth has always been sound, but several structural shifts are making it urgent in a way it was not five years ago.

The traditional employer-sponsored group health model is fracturing. ICHRA adoption grew by 34% among large employers and up to 49% among mid-sized employers between 2024 and 2025, as businesses shifted from selecting group plans to providing employees with tax-free dollars to shop the individual market themselves. The expiration of enhanced ACA premium subsidies pushed average deductibles to nearly $3,800 in 2026, sending millions of individuals into the market with complex coverage needs and no institutional support to navigate them.

The senior market is moving in parallel. By 2030, all Baby Boomers will be age 65 or older, with roughly 10,000 Americans turning 65 every day. Original Medicare's well-documented gaps in dental, vision, hearing, and extended hospital stays create natural openings for the agent who takes the time to build a full household picture. Multi-product senior households are among the stickiest in the book. The relationship has real financial consequences for the client, which translates directly into retention.

Carriers are responding to these same market dynamics. The era of rewarding volume alone is steadily giving way to compensation structures that reward persistence and quality of business submitted. As acquisition costs rise and profitability comes under greater pressure, carriers are placing increasing value on agencies that consistently deliver durable books of business rather than simply higher application counts.

A Different Way to Measure Agency Health

The agencies I see creating the greatest long-term value are not necessarily the ones with the largest enrollment numbers. They are the ones that can answer two questions with confidence: how many of their clients hold more than one product and what their 12-month and 24-month retention rates look like by product line. Those metrics don't just describe yesterday's performance; they predict tomorrow's enterprise value.

The agencies leading this transition have recognized that relationship growth is an operational capability, not simply a sales initiative. They routinely identify single-policy households with unmet needs, but more importantly, they've invested in systems that make acting on those opportunities almost effortless. Quoting across product lines, managing compliance requirements, and reconciling commissions are integrated into a single workflow rather than a series of disconnected administrative tasks. When the operational friction is removed, producers stop abandoning cross-sells and the book composition changes.

The valuation gap between those two categories of agency will only widen as the hard market continues to ease and rate-driven premium inflation recedes. When that happens, growth has to come from somewhere real. The agencies that figured that out before they needed to will be in a fundamentally different position than the ones that did not.

Enrollment is no longer the end goal. It is the invitation, the beginning of the relationship. Agencies treating enrolled clients as a relationship will deepen those bonds rather than finish as a transaction. Years from now, they won't simply look back on this market as a period of strong enrollment. They'll recognize it as the period in which they built businesses that compound.

The insurance industry has spent the last three years asking one question: where can we apply AI?

It's time to start asking the harder one: where shouldn't we?

We've seen what AI can do in underwriting, claims triage, fraud detection, and customer servicing. The efficiency gains are real. The speed is real. But as we embed AI deeper into consequential decisions — decisions that determine whether a claim gets paid, whether a risk gets written, whether a customer gets flagged — we're walking into a compliance gap that most organizations haven't fully reckoned with yet.

The definition of compliance is changing underneath us.

Traditionally, compliance in insurance was about people and processes following rules. Regulators asked: did you follow the procedure? Was the policy applied correctly? Can you show me the file?

Those questions still exist. But they're being joined by a new set:

• Why did the AI make this decision?
• What data trained it, and was that data appropriate?
• Can the decision be explained to the customer who was denied?
• Can it be audited three years from now when a regulator comes asking?
• When something goes wrong, who is accountable — the model, the vendor, or the carrier?

These aren't hypothetical questions. They're already landing on legal and compliance desks across the industry. And most AI systems aren't built to answer them cleanly.

Here's the tension nobody talks about openly.

The more we automate decisions with AI, the more we need infrastructure that is not AI to govern those decisions.

Compliance requires consistency, predictability, and an evidence trail that holds up under scrutiny. In many cases, the right tool for that job is deterministic — rules engines, governance frameworks, workflow controls, immutable audit logs, human checkpoints at defined thresholds. These aren't legacy artifacts to be replaced. They're the architecture of accountability that AI, by its probabilistic nature, cannot fully provide on its own.

This isn't a limitation to be embarrassed about. It's an architectural reality to be designed around.

The Auditor Cannot Be the Accused.

You cannot use AI to audit AI.

Yet most organizations today are doing exactly that — monitoring AI models with more AI. If the original model has a flaw, the auditing model likely carries the same one. That's not oversight. That's a mirror.

We've already seen AI fabricate citations — confidently, cleanly, and completely wrong. Now imagine that happening in a claims denial or an underwriting decision, where the AI made the call and wrote the audit trail.

Regulators will ask for explainability reports that were produced independently — not one AI explaining another. That documentation needs to be deterministic, traceable, and human-readable. Built without AI. Full stop.

The next wave of RegTech won't be smarter AI. It will be AI governance.

While carriers and insurtechs race to deploy more models, there's an equally significant — and arguably less crowded — opportunity in building the layer that sits above those models. The technology that ensures every AI-driven decision is transparent, explainable, documented, and defensible. The systems that answer the regulator's question before the regulator asks it.

Some of this already exists in nascent form. Model risk management frameworks borrowed from banking are making their way into insurance. Explainability requirements are starting to appear in state regulations around algorithmic underwriting.

The organizations that get ahead of this won't be the ones with the most AI. They'll be the ones who built the governance layer early — and can demonstrate it when it matters.

A word to operators.

If you're running an insurance business and you've deployed AI in any customer-facing or claims-facing capacity, ask your team this week: if a regulator asked us to explain the last 1,000 decisions this model made, could we do it? And could we prove that explanation wasn't itself generated by another AI?

If the answer to either question is uncertain, that's your compliance gap — and it's growing faster than most people realize.

The competitive advantage in this next phase won't come from adding more AI. It will come from making the AI you already have trustworthy enough to defend.

Insurance enjoys the protection of a regulatory moat. Capital requirements, reserve rules, licensing, and market oversight protect policyholders—and, by extension, legacy carriers' market positions.

But the moat is built around the balance sheet, not the operating model. It was never designed to protect expense ratios, organizational layers, underwriting workflows, claims operations, or administrative overhead.

Private equity is finding the seams—the fee-generating, capital-light nodes adjacent to the regulated core—and systematically buying them, then using AI to compress cost inside while the actual risk gets parked elsewhere. Here's where the action is happening.

1. MGA and program administration

MGAs underwrite and distribute without carrying capital—and, in excess and surplus lines, without the rate-filing straitjacket that binds admitted business. In the E&S market, no state Department of Insurance reviews an actuarial memo before an MGA changes pricing. That's not a technicality; it's the ballgame.

Roll-up money has been pouring into program administration at a pace that should alarm anyone who thought "we're regulated, we're safe." AI-driven underwriting here doesn't need to win a fight with a state commissioner—it just needs to be better and faster than the human it's replacing.

2. Claims, fraud, and subrogation

Loss costs and loss adjustment expense consume roughly 60–70 cents of every premium dollar. That's the single largest pool of spending in the industry, and it's almost entirely a process problem—not a regulatory one. A state insurance commissioner has opinions about your rate filing, not about whether your computer vision model estimates hail damage better than an adjuster with a clipboard, or whether an natural language processing (NLP) model flags subrogation opportunities your staff missed while chasing cycle-time bonuses.

This is the cleanest PE trade there is: buy or build the platform, automate the workflow, collect the toll. No rate filing required, no market conduct exam in the way. Claims processing today is like bank credit card processing 30 years ago—bespoke and fragmented. Look for rapid consolidation. Think: Visa and Mastercard. Blackstone, Apollo, and KKR certainly are.

3. Fronting

Fronting carriers exist so an MGA or insurtech can write business without holding a balance sheet. They rent a fronting carrier's paper and lay the risk off to a reinsurer (often also private capital). It's elegant financial engineering, and it's grown explosively because it lets everyone upstream of the actual risk-bearing get paid without ever touching the risk. Though regulators have started naming fronting arrangements specifically in their review of PE ownership structures, real action may be years away.

4. Agency roll-ups

This isn't new, and it isn't really disruption any more so much as consolidation. Acrisure, Hub, BroadStreet, AssuredPartners—PE has been buying up the independent agency channel for 15 years, riding a demographic wave of retirement-age owners with no succession plan and no appetite to fight for a better multiple. It works. It will keep working.

AI helps at the margins—better cross-sell, better retention scoring—but the trade was never about the tech. It was about the math, and the math has gotten more expensive as everyone's figured it out.

5. Data and risk analytics

Everybody wants to be Verisk. Almost nobody gets to be, because Verisk, CoreLogic, and LexisNexis Risk Solutions already occupy the high ground, and moats in data businesses are real. The greenfield here is narrower than the hype suggests—you'll see PE money chasing climate-risk modeling and computer-vision property inspection at the edges, not a wholesale takeover of the analytics layer.

Note: If your strategy deck has a slide about "becoming the data platform for the industry," ask whether you're actually building a moat or just donating R&D spending to a market that's already been won.

6. Reinsurance and alternative capital

Cat bonds, ILS, sidecars—genuinely useful capital efficiency tools, and genuinely attractive to institutional and PE money looking for returns uncorrelated to public markets. But this is also the layer drawing the most direct regulatory heat right now, because it's structurally identical to the arrangement that has state regulators and the NAIC nervous about affiliated reinsurance in the life and annuity world: related-party transactions, opaque asset-liability matching, risk-based capital that may not be pricing the actual risk. These are known trades, under active review.

7. The balance sheet itself

And here, finally, is the piece PE mostly leaves alone: the actual risk-bearing carrier. Full statutory capital requirements, rate filings, market conduct exams, risk-based capital rules that don't care whose name is on the equity. This is the one link where regulatory protection still functions as advertised, and it's not an accident that PE has mostly declined to fight it head-on. Instead, private capital rents access to this layer—through fronting, through reinsurance, through MGA fee arrangements—rather than trying to own and run it directly. The fortress holds. Under siege are the lands that sustain it.

Eighty-four percent of large brokers with over $100 million in revenue now use generative AI. Claude, ChatGPT, and Copilot save producers hours every week on prospecting and reporting. But these tools are trained on public internet data, not insurance data.

That gap matters more in insurance than in almost any other industry. Regulation changes state by state. One wrong sentence in a submission or coverage recommendation can trigger an E&O event. And one in two agency records is missing critical fields, so generic AI often builds a confident answer on an incomplete picture.

The agencies pulling ahead are not the ones using AI first. They are the ones grounding AI in insurance-specific data. This whitepaper shows you what that gap costs and what to look for in a platform built to close it.

Come visit Zywave's website to learn:

• Why Insurance Needs Specialized AI Context
• The Hidden Cost of Incomplete Records
• What Generic AI Gets Wrong About Insurance
• The Difference Between Data and Intelligence
• Four Questions to Ask Before You Trust an AI Platform

See the difference>>