While graduates recently booed the mere mention of AI at commencement ceremonies in Florida and Arizona, to wild applause, Google's head of search unveiled "the biggest upgrade to our search box in over 25 years."

In a nutshell: Google has moved from being a search engine — a tool that helps humans find links — to being a search agent: an autonomous system that researches, synthesizes, and concludes on behalf of the human, often without the human ever visiting a website.

They even introduced a feature called "Human Not Present" payments — which is, if you squint, the most honest branding in Silicon Valley history.

Here's the thing about technology revolutions that people keep forgetting: Consumer sentiment and consumer behavior are two entirely different animals. People hated ATMs and used them. People hated self-checkout and used it. People write long X and Reddit screeds about how AI is destroying everything, then ask ChatGPT to fix their cover letter 20 minutes later. The outrage and the adoption happen simultaneously, in the same brain, without apparent contradiction.

Google's AI Overviews are already used by more than 2.5 billion monthly users, and AI Mode has more than 1 billion monthly users. What percentage of those booing graduates are among them? Let's set the line at 80% — and I'll take the over.

The hard business reality is this: Whether you decide to use AI in your operations is up to you. Whether AI is used to evaluate your business for marketing purposes is up to them.

The existential question isn't whether to participate in an AI-mediated marketplace. The question is whether your business is legible to the machines that will increasingly decide who gets recommended, who gets trusted, and who gets the transaction.

Most businesses, if they're honest, are not legible.

They're running on claims systems from 2009, CRM platforms that don't talk to each other, PDFs that contain institutional memory no one has ever indexed, and tribal knowledge sitting in the heads of people who are 18 months from retirement.

For decades, this created internal inefficiency — higher costs, more escalations, customers on hold listening to music they didn't choose. Annoying, expensive, but survivable.

Now businesses face a more fundamental issue: discoverability itself.

Google is explicitly trying to turn Gemini from a chatbot into a distributed agent runtime — a system that doesn't just answer questions but routes decisions, recommends vendors, and executes multi-step commercial transactions.

An AI agent deciding which carrier to recommend, which doctor to surface, or which vendor to integrate is going to favor organizations whose operations are coherent, structured, and machine-readable. It has no patience for ambiguity. It won't retry. It won't call customer service. It will simply move on to the competitor whose data makes sense.

Which means a structured operational model isn't just an IT project anymore. It's the difference between being findable and being invisible in a world where the searcher is a machine with no tolerance for mess.

Operations and marketing have spent decades pretending to be separate disciplines. AI is about to collapse that distinction. Every fragmented workflow, every ambiguous data state, every avoidable customer complaint — all of it now has a marketing consequence.

The machines experiencing your company aren't reading your brand guidelines. They're reading your operational reality.

That was the announcement that got lost in the recent hoopla — the quiet, structural truth beneath it all: In the agentic era, your operations are your marketing. Operational excellence is marketing excellence.

Data governance in P&C insurance is largely a documentation discipline. Regulatory examinations are increasingly asking for control execution records, quality assessment artifacts, and lineage documentation, not policy statements describing the intent to produce them.

The gap between a governance program and governed operations is the difference between what is written and what can be demonstrated on demand for any data asset at any point.

Why the Stakes Are Rising

Five forces are raising the cost of that gap.

1. Decision accuracy depends on data quality enforced at the point of production. Pricing errors, adverse selection, and claims leakage frequently trace back to data quality failures introduced upstream and compounded through analytical layers.
2. Analytical efficiency suffers when lineage is undocumented. Actuarial, underwriting, and data science teams repeatedly re-validate the same data assets, producing duplicated effort and inconsistent conclusions from identical source data.
3. AI and algorithmic operations have created a new tier of data obligation. When data feeds a pricing model or claims scoring system, its provenance, quality, and sensitivity classification become examinable. Colorado SB21-169 requires documentation of external data sources used in underwriting decisions. The NAIC AI Bulletin requires data quality assessments for model inputs.
4. Audit readiness now demands contemporaneous evidence. The NAIC AI Systems Evaluation Tool, active in a 12-state pilot as of 2026, includes a dedicated exhibit on data governance evidence, asking specifically for data source documentation, quality controls, lineage records, and third-party data assessments. Evidence assembled during a response window carries far less weight than records produced in the ordinary course of operations.
5. Regulatory change velocity has accelerated. Draft MDL-672 revisions, expanding NAIC AI Bulletin adoption, and new state privacy laws are advancing simultaneously. A governance model built around periodic policy reviews cannot keep pace.

The Engineering Distinction

The difference between a governance program that describes controls and one that enforces them is an engineering distinction.

The NAIC AI Systems Evaluation Tool makes this distinction operational. Exhibit B asks for a governance risk assessment framework. Exhibit D asks for data source documentation, quality controls, and lineage records. Carriers with documentation programs will find the gap between what they have and what the exhibit requires.

The foundation is a canonical requirements model. Every data governance obligation, across applicable regulatory frameworks, is expressed as a precise, technology-agnostic requirement defined once. NAIC MDL-668, the GLBA Safeguards Rule, Colorado SB21-169, and industry best practice reference overlapping obligations. The canonical model rationalizes them into a stable center. When a new regulation arrives, its provisions map to existing requirements. Coverage gaps close without restructuring the underlying governance framework.

Each canonical requirement is enforced through controls at four phases of the data lifecycle: at design time before a data asset is defined, at deploy time before a pipeline reaches production, at rest on stored data, and in motion during pipeline execution. Controls produce structured audit facts. These facts link directly to the canonical requirements they evidence. When a regulator requests proof that access controls operated on claims data during a specific period, the answer is a query against the platform.

Exceptions are handled as structured records in the same platform: the specific requirement being waived, the approving authority, the compensating controls in place, and an expiry date. The audit trail carries the full exception transparently.

Where to Start

The insurance industry has already solved an analogous problem in actuarial pricing systems. Rating algorithms are versioned, changes require documented approval, prior versions are retained for audit, and the system generates its own compliance record. That standard of infrastructure applied to the broader data estate is the direction data governance is heading.

Start with one domain and build it completely: canonical requirements, controls at each lifecycle phase, and the evidence each control must produce. Access control and entitlement is the natural starting point. It has dense regulatory citation, direct examination relevance, and well-defined enforcement mechanisms.

Governance built as infrastructure, one domain at a time, produces durable examination readiness and measurable improvement in the quality of every data-driven decision made from it.

Insurance has always been grounded in a quiet but powerful discipline. At its core, the industry was never just about transferring risk. It was about understanding which risks deserve to be transferred in the first place.

For decades, the logic held firm. Risks had to be measurable, diversifiable, supported by capital, and structured with aligned incentives. That discipline is what made insurance one of the oldest continuously profitable institutions in modern finance.

Today, that foundation is being tested — and in many cases, quietly abandoned.

The Industry Is Scaling Faster Than Its Assumptions

In the race toward growth, digital distribution, embedded products, and AI-led underwriting, insurance is increasingly operating at the edges of its own logic. We are no longer just insuring factories, fleets, and homes. We are underwriting cyber ecosystems with non-linear aggregation risk, climate exposures with deep correlation across geographies, businesses built almost entirely on intangible assets, embedded products that blur the line between insurance and software, and a fast-emerging category of AI-agent liability that no traditional form was designed for.

These are not incremental risks. They are structurally different risks. And yet many of them are being evaluated using frameworks designed for a very different world.

The Real Problem Isn't Data. It's Discipline.

The common narrative says we need more data, better models, smarter AI. Here is the uncomfortable truth: the challenge is often not a lack of data or technology. It is a failure to revisit the first principles of insurability.

Before asking "Can we price this?", we should be asking:

• Can this risk actually be measured with confidence?
• Can it be pooled without hidden correlation breaking the model?
• Is there sufficient capital to absorb tail events?
• Are incentives aligned — or are we underwriting behavior we cannot control?
• Where are the boundaries of this risk under stress?

If these questions are not rigorously answered, pricing becomes an illusion of control.

The Five Foundations of Insurability

First-principles thinking is about stripping a problem down to its irreducible truths and rebuilding from there. In insurance, those truths have not changed. I refer to them as the *Five Foundations* — and every underwriting decision, every product launch, and every AI investment should be tested against them.

1. Risk Must Be Measurable

If you cannot define frequency and severity with reasonable confidence, you are not underwriting. You are speculating.

This is where many emerging risks struggle. Cyber loss data exists, but threat vectors evolve faster than the data describing them. Intangible-asset valuations have no historical benchmarks to anchor severity. AI-agent liability has neither —frequency is unknown, severity is open-ended, and the causal chain runs through a model nobody can fully audit.

2. Pooling Must Work

Insurance depends on the independence of risks. But today's risks are increasingly correlated. A single cyber event can affect thousands of firms simultaneously. A wildfire season can trigger losses across entire regions that were modeled as independent. A single cloud provider outage can take down policyholders across industries that share no other common feature. When correlation increases, pooling breaks. And with it breaks the economic engine of insurance itself.

3. Capital Must Be Adequate

Every risk ultimately resolves to a capital question. Are insurers holding enough capital for extreme scenarios? Are they accurately modeling tail risk? Are they accounting for systemic shocks that cross lines of business?

If not, growth today becomes solvency pressure tomorrow. The history of financial crises is the history of institutions that mistook a benign environment for permanent capital adequacy.

4. Incentives Must Be Aligned

Poorly structured products create moral hazard at scale. Cyber policies that pay without enforcing basic hygiene. Parametric triggers that pay when there was no real loss — or refuse to pay when there was. Embedded covers sold at checkout to confused buyers. When incentives are misaligned, losses stop being random. They become predictable — and expensive.

5. Tail Risk Must Have Boundaries

Not all risks have containable downside. Some exposures cascade across industries, amplify through shared technology, and escalate faster than capital can respond. These are not traditional insurance problems. They are systemic risk problems, and they require structures — government backstops, mandatory mitigation, layered retentions — that no single carrier should attempt to absorb alone.

Where the Foundations Change Day-to-Day Decisions

Underwriting

First-principles discipline reframes the underwriter's job from "approve or decline at a rate" to "decide whether the structure fits the risk." An underwriter who can articulate which foundation a risk strains — and propose a structural fix — is far more valuable than one who simply applies a guideline. This is also where AI tools earn or lose their keep. A model that surfaces correlated exposure across a book is strengthening the foundation. A model that simply accelerates quote turnaround on a structurally fragile risk is accelerating the problem.

Product Design

Not every emerging risk should become a product. Before launching, leaders should ask whether the coverage structure itself needs to change — sub-limits, exclusions, parametric layers, captive participation, mandatory mitigation, or co-insurance with the insured retaining meaningful skin in the game. Sometimes the answer is that the risk is better retained by the customer than transferred to the carrier. That is not a failure of innovation. It is innovation doing its job.

Regulatory Strategy

Regulators are increasingly focused on aggregation risk, capital adequacy, climate stress testing, and systemic exposure. Carriers that have already internalized the Five Foundations are several steps ahead of their filings — because they can show why a product is structured the way it is, not merely that it complies.

Technology and AI Adoption

AI can optimize pricing, but it cannot fix flawed assumptions. If the underlying risk is not measurable, not poolable, and not bounded, then AI simply scales the error faster. The most useful question to ask of any AI investment is not "does it make us faster?" but "which foundation does it strengthen?" Faster bad decisions are not progress.

The Hard Truth

This is where the industry needs to be brutally honest. Some risks should not be insured in their current form. Some require entirely new structures beyond traditional insurance. Some demand collaboration across public and private sectors that no carrier can build alone.

Trying to force-fit these exposures into existing models does not create innovation. It creates fragility — the kind of fragility that does not show up in a quarterly result but is sitting in a portfolio waiting for one correlated year.

First principles are not about slowing innovation. It is about making innovation durable.

Most failures in financial history share a pattern. They are not caused by a lack of information or a shortage of intelligent people. They are caused by the quiet conviction that a new structure, a new technology, or a new market condition has somehow repealed the old foundations. The foundations do not get repealed. They get violated — and the violation only becomes visible when a correlated event arrives that the model had assumed away.

Insurance is now operating in exactly that kind of environment. Correlation is rising. Capital is under pressure. Innovation is accelerating faster than the assumptions underneath it can be retested. The temptation to write everything, embed everywhere, and let AI sort it out is real, and the short-term economics often reward it. The long-term economics are governed by the foundations whether the market acknowledges them or not.

For years, cyber insurance discussions focused on ransomware, phishing, social engineering, and data breaches. The assumption underneath most underwriting models was relatively consistent: cyber events, while serious, would still unfold within human and operational constraints.

That assumption may be starting to change.

Recent discussions surrounding Anthropic's Claude Mythos and related frontier AI cybersecurity systems are beginning to raise a different concern across financial institutions, regulators, and cyber markets: what happens when vulnerability discovery and exploitation start operating at machine speed instead of human speed?

The concern is not theoretical anymore. Regulators, central banks, and major financial institutions have already begun discussing the potential systemic implications of AI systems capable of autonomously identifying and chaining software vulnerabilities at unprecedented scale and speed.

For the property and casualty insurance industry, this matters far beyond cybersecurity headlines.

A Different Kind of Cyber Risk

Traditional cyber underwriting was largely built around environments where threats evolved incrementally. Vulnerabilities were discovered gradually. Patches were released over time. Organizations generally had some opportunity to respond before exploitation became widespread. Frontier AI compresses those timelines.

Systems like Mythos reportedly demonstrate the ability to identify unknown vulnerabilities across software environments far faster than traditional human-led security processes.

That changes the shape of cyber exposure itself. The larger issue is not simply that attacks may become more sophisticated. It is that cyber risk may become increasingly correlated across interconnected systems and infrastructure dependencies.

Many commercial insureds already rely on shared cloud environments, software providers, managed service vendors, and common technology stacks. A single vulnerability tied to a widely used dependency can already create accumulation concerns for cyber carriers. AI-driven vulnerability discovery could intensify that problem significantly.

What previously unfolded over weeks or months may eventually unfold over hours.

Where Insurance Models Begin to Struggle

Most commercial insurance underwriting still operates through periodic snapshots of risk.

Applications are completed annually. Supplemental questionnaires capture point-in-time controls. Cyber posture is often evaluated during renewal cycles rather than continuously. But AI-driven cyber environments may not evolve on annual timelines anymore.

A company's attack surface can shift rapidly through vendor integrations, software dependencies, cloud architecture changes, and emerging vulnerabilities. If offensive capabilities accelerate faster than underwriting visibility, insurers may find themselves evaluating cyber risk using processes designed for a slower environment. That creates a growing mismatch between underwriting cadence and risk evolution.

This is part of what makes systemic cyber risk different from traditional independent-loss assumptions. One exploit path, one software dependency, or one infrastructure weakness could potentially affect thousands of organizations simultaneously across a carrier's portfolio.

For insurers, the concern increasingly becomes portfolio interconnectedness rather than isolated policyholder events.

What the Industry May Need to Rethink

The industry has spent years modernizing workflows, digitizing underwriting, and improving operational efficiency. But AI-driven cyber environments may require something deeper than workflow modernization alone.

They may require continuous visibility into changing infrastructure risk. That could gradually push cyber underwriting toward:

• more dynamic monitoring
• stronger software dependency mapping
• infrastructure-aware accumulation modeling and
• underwriting approaches tied more closely to telemetry and operational signals rather than static questionnaires alone.

At the same time, insurers themselves may become part of the exposure story. Many carriers operate on layered technology environments built over decades through acquisitions, vendor integrations, and legacy infrastructure. Advanced AI systems capable of identifying weak links across interconnected systems could expose vulnerabilities not only within insured organizations, but within the insurance ecosystem itself.

Therefore, conversations around systems like Mythos are drawing attention from regulators and financial stability groups—not simply because of cybersecurity, but because of the potential for correlated operational disruption across interconnected industries.

The broader issue is not whether AI will improve cyber operations.

It almost certainly will.

The deeper question is whether insurance systems can adapt to environments where cyber risk evolves faster than traditional underwriting and portfolio management structures were originally designed to handle. That may become one of the defining insurance challenges of the AI era.

Commercial auto rates have been climbing. Every market participant knows this. The standard explanation involves nuclear verdicts, social inflation, and litigation funding. Those factors are real.

What gets less discussion is the operational side of the loss equation. Not the litigation. Not the verdict environment. The claims management practices that run between first notice of loss and final settlement, and what those practices cost on a book-level basis when commercial trucking is handled like any other commercial auto line.

It's a different animal. The industry broadly acknowledges this. But acknowledgment hasn't produced widespread changes in how these claims get handled.

The Supplement Rate as a Performance Indicator

Supplement rates on commercial trucking and heavy equipment claims average between 20% and 25% industry-wide. A supplement is a revised repair estimate — the initial figure gets approved, disassembly begins, and the shop returns with a higher number.

A 20% to 25% rate tells you something specific. It tells you the first estimate was wrong at a high frequency. That frequency isn't random. It reflects a systematic gap between the complexity of the equipment being assessed and the expertise of the person writing the first estimate.

A general auto adjuster reassigned to a Class 8 truck or a piece of construction equipment doesn't know what to look for. A specialist does. The operations using appraisers with dedicated heavy equipment expertise consistently hold supplement rates between 10% and 14%. That 10-point gap on a large commercial trucking book represents a material dollars-and-cents difference in indemnity spending. It shows up directly in loss ratios.

Most program administrators and MGA executives can't tell you their supplement rate on trucking claims.

Towing and Storage as Indemnity Leakage

Towing and storage on commercial vehicles is a significant and largely unmanaged cost category on most trucking programs. Storage fees of $125 to $200 per day accrue from the moment a vehicle is taken to a yard. Claims that sit unworked for 30 to 60 days generate thousands in storage exposure before a single repair decision is made.

The towing invoice itself is a second problem. Inflated mileage, charges for equipment that was dispatched but not deployed, fees for services not rendered. These line items go on the invoice and, in most cases, get paid without challenge because the adjusting operation doesn't have the market knowledge to identify what a reasonable commercial tow should cost.

One carrier reviewing its annual towing spending found it had overpaid by more than $650,000 in a single year. That's not an outlier. That's what happens when commercial vehicle towing invoices go through a general claims operation that doesn't specialize in this exposure.

On a book of any meaningful size, towing and storage leakage is a line item that belongs in loss ratio conversations. It rarely appears there because nobody is measuring it separately.

Subrogation Recovery as Underpriced Leverage

Commercial trucking subrogation is a specialty within a specialty. The values are high, liability is typically contested, and the file has to be built correctly from day one of the incident. When it is, win rates above 80% are achievable on eligible files.

Most general TPA operations don't run dedicated commercial trucking subrogation programs. The case complexity is high relative to the volume they handle in that category. Recovery rates on trucking subrogation through general programs reflect that mismatch.

For MGAs and program administrators with meaningful trucking exposure, subrogation recovery represents a straightforward improvement to the economics of the book. It doesn't require renegotiating terms. It requires routing eligible files to a team that knows what it's doing with them.

What the 2026 Claims Conversation Is Missing

The industry's attention in 2026 is rightly focused on AI-assisted claims processing, faster FNOL response, and data-driven loss analytics. The consensus view entering 2026 was that commercial auto rates would continue rising while claims automation would begin generating measurable efficiency gains. That framing is correct as far as it goes.

What it misses is that technology-assisted claims handling applied to a general adjusting model doesn't solve the expertise problem on specialized equipment. A faster general adjuster writing estimates on a crane or a loaded semi is still a general adjuster writing estimates on a crane or a loaded semi. Speed doesn't compensate for the knowledge gap that produces 22% supplement rates.

The gap between strategic intent and claims execution is where loss ratios on commercial trucking programs get made or broken. The intent to manage this exposure well is almost universal. The execution requires domain expertise that most general operations don't have and can't develop at a sufficient depth for an exposure this specialized.

The Program Design Question

For MGAs building or managing commercial trucking programs, the TPA selection question deserves the same analytical rigor as rate adequacy or reinsurance structure. The right question isn't which TPA can handle the claims. It's which TPA has the specific expertise to handle these claims at the supplement rates, towing spending, and subrogation recovery rates that a profitable book requires.

The specialty exists because general operations don't produce the outcomes this exposure demands.

The performance data from specialty operations — the supplement rates, towing savings, subrogation win rates — is publicly available for comparison. The loss ratio improvement potential is real and measurable. The question is whether program design conversations are treating claims expertise as a first-order variable or an afterthought.

For most trucking programs, it's still the latter.

Other Resources From Insurance Thought Leadership

1. "Insurance 2026: Progress Via Technology, Collaboration" (Jan. 8, 2026): "The consensus view entering 2026 was that commercial auto rates would continue rising while claims automation would begin generating measurable efficiency gains."
2. "4 Key Trends Reshaping P&C Insurance" (Feb. 5, 2026): "The gap between strategic intent and claims execution"

In today's P&C insurance market, profitability no longer depends solely on underwriting discipline or claims management. Increasingly, it hinges on how effectively and how quickly insurers can price risk. With margins under pressure from inflation, rising claims severity, and climate-driven catastrophe losses, insurers are increasingly compelled to modernize the pricing function.

And yet, despite the critical role pricing plays and advances in technology, many insurers still manage it with the same spreadsheets and disconnected workflows they relied on 20 years ago. While core systems and claims have seen modernization efforts, pricing remains a new frontier for digital transformation.

Pricing as a Strategic Lever

The pressures reshaping the market are forcing insurers to treat pricing as more than an actuarial exercise. It has become central to profitability, where even small missteps in rates can quickly multiply across entire portfolios. It is equally critical to competitiveness, as more agile rivals can undercut static carriers, particularly in commoditized personal lines. And it is now inseparable from customer experience, with policyholders demanding personalized, transparent pricing that reflects their usage, geography, and unique conditions.

Boardrooms are starting to discuss pricing in the same way they discuss claims, distribution, and customer experience. The realization is simple: Pricing has shifted from a back-office function to a frontline driver affecting business strategy and success.

Pricing Solutions Driving the Future

Increasingly, insurers are turning to dedicated pricing engines, platforms that centralize pricing logic, enforce governance, and accelerate deployment. Unlike spreadsheets or legacy raters, these systems provide:

• A single source of truth for all rating and pricing logic and assumptions
• Auditability and governance to ensure regulatory compliance.
• Integration with external data for more granular pricing.
• Automation and APIs that connect pricing directly to core systems and distribution channels.

Vendors such as Earnix, hyperexponential, Akur8, Quantee, and Radar are redefining what insurers can expect from their pricing capabilities:

• Earnix focuses on multi-line insurers, offering strong capabilities for SME and standardized products. It includes built-in predictive modeling and statistical tools, supports model migration, and provides pre-built connectors for major policy administration systems such as Guidewire. Earnix has also developed a wide range of AI-driven capabilities within its platform.
• Hyperexponential (hx Renew) is a strong fit for insurance pricing operations where actuarial models need to be operationalized for underwriting teams. hx Renew does not provide built-in predictive modeling capabilities and is typically positioned more as a model execution and decisioning platform than a full machine learning development environment, with advanced modeling usually performed externally. Python is a core component of the hx platform, with most processes, workflows, model logic, and integrations configured using Python, providing a high level of flexibility and customization.
• Akur8 was initially developed as a modeling-focused solution. It has a strong emphasis on predictive modeling capabilities, providing AI-powered tools that allow actuaries to build statistical models quickly while retaining the ability to inspect, adjust, and refine them as needed. The platform supports the pricing lifecycle from risk model development to pure premium aggregation and rating plan definition. It has also expanded into integration and deployment areas, including its own pricing engine and the ability to integrate with existing legacy systems.
• Quantee is focused on the end-to-end pricing lifecycle, including predictive modeling, what-if analysis, testing, documentation, and integration. The platform supports the independent work of pricing teams and provides a stable environment for core pricing processes, along with a range of additional AI and advanced modeling features. Following its acquisition by Guidewire, the platform is expected to become more deeply integrated into the Guidewire ecosystem, enabling a more streamlined integration approach. As integration with the Guidewire ecosystem evolves, elements such as proration, cost breakdown structures, and operational process management may become more streamlined and centralized.
• Radar (by WTW) offers a comprehensive suite of pricing and underwriting tools covering the entire insurance pricing process. Its modular platform includes Radar Base for tariff setup, Radar Live for integration with point-of-sale systems, and Emblem for rapid predictive modeling on large datasets, along with several other specialized tools. Radar enables actuaries to build, test, and optimize pricing models with real-time portfolio insights.

While their approaches differ, the common thread is clear: Pricing engines enable insurers to move at market speed with more control, accuracy, and transparency.

Five Strategic Advantages of Modern Pricing Solutions

The business impact of modern pricing goes far beyond operational efficiency. Insurers that deploy dedicated pricing engines can:

1. Improve profitability through better segmentation and more precise risk adjustment.
2. Accelerate speed-to-market for new products, endorsements, and rate changes.
3. Enhance customer experience by offering personalized, transparent pricing aligned with expectations.
4. Enable innovation and product flexibility by testing new pricing models, micro-products, and bundles without overhauling core systems.
5. Strengthen regulatory readiness and compliance with built-in governance, transparency, and audit trails.

Capturing Advantage Through Modern Pricing

Modernizing pricing is not without hurdles. Legacy system dependencies, fragmented data structures, and organizational silos can slow progress, and actuaries and IT teams often struggle to align on ownership. Yet these challenges are surmountable, especially when insurers work with the right consulting partner and pricing solution.

Successful transformations are built on clear business alignment, executive sponsorship, and phased rollouts that deliver quick wins.

Looking ahead, the pricing function will evolve beyond faster rate changes and centralized governance. The next generation of pricing will harness AI, behavioral analytics, and real-time data streams to create adaptive models that continuously evolve with market conditions. At the same time, regulatory scrutiny will intensify. Fairness, transparency, and explainability will become mandatory, not optional. Insurers that can demonstrate accountability and governance in their pricing processes will not only earn regulatory approval but also build customer trust.

Modern insurance pricing is about improving how risk is measured and priced, with greater accuracy, speed, and transparency. In a volatile and changing market, the ability to price with agility, precision, and auditability will be a difference-maker and separate the leaders from the laggards.

In a recent interview, Steve Hasker, chief executive of Thomson Reuters, said that in fiduciary professions like law, tax, audit, and compliance, what matters is not just speed but whether the output is authoritative, traceable, and accountable to professional standards. The same applies to insurance claims.

For a while, much of the conversation around AI focused on efficiency. Could it help insurers process claims faster? Could it reduce manual work? Could it improve consistency? Those are still important questions. But they are no longer the only ones that matter. The harder question now is whether the decisions AI helps shape can be understood, defended, and trusted.

This matters even more in claims, where a decision is rarely truly final when it is first made. It may be questioned by the policyholder, examined by regulators, revisited in an audit, or challenged in court. Under that kind of scrutiny, a system cannot just give an answer. It also needs to make clear how it reached that conclusion.

This is why explainability has moved from a mere technical concern to a business requirement. Explainability now sits at the center of governance, compliance, and customer trust. The broader market is also moving beyond initial excitement about what AI can do in theory and toward a more practical question: What does it take to use it responsibly in real operations? A recent Gartner report points to the same trend, highlighting governance, data readiness, and operational discipline as the factors that will separate early excitement from lasting value.

In claims, explainability is where that shift becomes real.

AI Does Not Change the Insurer's Responsibility

AI may be changing how work gets done, but it does not change the insurer's underlying obligations. Claims laws, consumer protections, and standards for fair treatment still apply, whether a decision is made by a person, a model, or a third-party vendor. If a claim is denied, delayed, escalated, or flagged for possible fraud, the carrier is still responsible for being able to explain why.

This is becoming even more important as regulators pay closer attention to how AI is used in insurance. In Europe, the EU AI Act raises the bar for transparency, explainability, and human oversight in high-risk uses of AI. In the United States, the regulatory picture is less uniform, but the direction is similar: insurers are increasingly expected to show that AI-supported decisions are fair, accountable, and subject to oversight.

For claims leaders, the takeaway is simple. The issue is no longer whether AI is allowed in claims. The issue is whether the carrier can stand behind the decisions it helps produce.

The Real Divide Is Not Who Uses AI

Across property and casualty insurance, the appeal of AI is easy to understand. Carriers want to improve consistency, reduce handling costs, and help claims professionals manage growing complexity. Used prudently, AI can support all of these goals.

But the real divide in claims is no longer between insurers that use AI and insurers that do not. It is between systems that produce outputs and systems that produce decisions the business can actually explain and defend. This difference matters because a decision made today can come back months or years later in a complaint, an audit, or a lawsuit. A recommendation that looks efficient in the moment can become a serious problem later if no one can clearly explain why it was made.

This is where black-box neural network systems begin to create friction. Models built for speed and prediction may perform well in testing, but if their reasoning cannot be understood in plain terms, every downstream review becomes harder. Claims teams are left trying to reconstruct logic after the fact from technical outputs or vendor explanations. By then the problem is no longer just technological; it becomes operational, legal, and reputational.

Why Explainability Cannot Be Added Later

One of the most common misperceptions among carriers is that explainability can be dealt with later, after a model is already in production and the business value has been proven. In practice, that is far more costly than it sounds.

Once a claims process is built around systems that do not make their reasoning easy to follow, real transparency is difficult to add later. You can layer on reports, write summaries with LLMs, or use tools such as SHAP values to suggest which factors may have shaped the outcome, but these are still only approximations. They are not the same as being able to see the path to the decision from the beginning.

In the real world, repair costs change, fraud tactics evolve, and what counts as normal in one region may not hold in another. These shifts can easily affect a black-box AI system, and the problem is often hard to see until real harm has already been done. By then, it may appear as a rise in customer complaints or as questions from a regulator about why similar claims are being handled differently.

The Gartner report makes much the same point in broader terms. The companies most likely to get lasting value from AI are the ones that build the right structure around it: good data, clear accountability, and controls that remain dependable over time. In claims, explainability is part of that structure. It is not something you can add later. It has to be there from the beginning.

The Problem Often Starts with the Data

Explainability is often talked about as if it lives only inside the model. In reality, it depends just as much on the data feeding the system.

A claims system cannot produce trustworthy reasoning if the underlying data is incomplete, poorly structured, weakly governed, or disconnected from the market where it is being used. If the inputs are flawed, the explanation may sound polished while still hiding the real problem. A carrier may think it has a well-controlled system, but if claim data varies widely across geographies, repair networks, documentation practices, or policy types, the system can still produce unstable or unfair outcomes.

Explainability helps bring those problems to the surface. It gives the business a way to spot when the model is leaning too heavily on the wrong signals, when patterns do not fit local conditions, or when assumptions that made sense in one setting are being carried into another where they no longer belong.

This was illustrated in an auto claims fraud model deployed in a Middle Eastern country. The legacy model consistently flagged accidents occurring after midnight on weekends as high risk. That logic had been inherited from North American and European training data, where late-night weekend accidents often correlate with alcohol-impaired driving. But in the market where the model was being used, alcohol consumption was prohibited and families commonly stayed out late on weekends. The signal was not just weak; it was systematically wrong. Because the reasoning behind the model's behavior was not visible in a usable way, the problem went unnoticed until it showed up in regulatory scrutiny and customer dissatisfaction.

What Explainability Should Look Like in Practice

The first question claims leaders should ask about any system influencing claim outcomes is not just whether it is accurate. It is whether the people responsible for the claim can understand the reasoning well enough to use it responsibly.

If a claim is delayed, escalated, denied, or flagged for possible fraud, the adjuster should be able to see the factors driving that result in language that makes sense in the context of the claim. That could include missing documents, timing issues, policy conditions, behavioral patterns, or other relevant signals. The point is not that every decision becomes simple. The point is that the reasoning should be visible enough for the business to evaluate it.

Just as importantly, the adjuster should be able to challenge the recommendation when it does not fit the facts. Human oversight only means something if the person reviewing the claim can see why the system is pointing in a certain direction and can document a reasonable override when needed. Otherwise, "human in the loop" becomes little more than a formality.

Good explainability also helps the organization over time see when outcomes are drifting, when similar claims are being treated differently, or when a signal that once seemed useful is starting to distort results. In that sense, explainability is not just about answering questions after a problem appears; it is also about spotting problems early enough to do something about them.

A Practical Test for Claims Leaders

For claims executives, the best way to judge whether a system is ready is to ask a few simple questions.

Can we explain how this AI-generated recommendation was produced?

Can we trace the data and reasoning behind it without relying on a technical team to rebuild it from scratch?

Can we show that similar claims are being treated consistently?

Can we detect when the AI model's behavior starts to shift?

Can an adjuster disagree with the AI-generated recommendation and explain why in a credible way?

These are not abstract governance questions, rather practical tests of whether the AI model can survive real-world scrutiny.

The Choice Facing Claims Leaders

Claims leaders now face a much clearer choice than many realize.

The question is no longer whether AI can improve speed, support triage, or strengthen fraud detection. In many cases, it can. The real question is whether insurers are building those capabilities on foundations strong enough to hold up when the decision is reviewed, challenged, or questioned later.

That is why explainability matters so much now. It is not just a safeguard for compliance teams, but also the practical link between AI and accountability. It connects decisions to oversight by helping expose weak data, hidden bias, and changing patterns before they become bigger problems.

The wider business conversation is moving in the same direction. As the early excitement around AI gives way to a more realistic view, companies are becoming clearer about what lasting value actually requires. The real issue is no longer just what AI can do, but whether it can be trusted, governed, and used responsibly over time. In claims, explainability is where that becomes visible in everyday decisions.

Consider what 2025 demonstrated about the insurance industry's risk assumptions. No major hurricane made landfall in the United States. By the logic of traditional catastrophe modeling, which has always placed tropical cyclones at the center of loss scenarios, 2025 should have been a manageable year. Instead, global insured losses hit $107 billion. 

Secondary perils that catastrophe models have historically treated as background noise, including wildfires, severe convective storms and floods, accounted for a record 92% of that total, up from a 56% average over the prior decade. Severe convective storms alone delivered their third-costliest year on record.

The industry did not have the wrong year. It has the wrong product architecture.

The secondary perils mismatch hiding in plain sight

For decades, catastrophe reinsurance was built around a defensible logic: The events that would truly threaten the balance sheet were episodic, high-severity, well-modeled primaries, like a Category 5 hurricane or major earthquake. Secondary perils existed, but they were attritional, manageable, and amenable to the law of large numbers. That assumption is no longer valid. Secondary perils such as hailstorms, flash floods, wildfires, severe thunderstorms, and freezing events, produced $136 billion in total losses in 2024, well above their ten-year inflation-adjusted average of $110 billion.

The more important question is not why secondary perils are growing, but why, after a decade of this data, the market has not produced instruments adequate to transfer the risk. The answer is structural, and it is uncomfortable: The institutions with the capital and sophistication to absorb the frequency of secondary peril risk have rationally opted not to.

After 2022 and 2023 - years of punishing secondary peril losses - reinsurers raised attachment points sharply. Reinsurers redesigned their treaties to keep secondary peril frequency off their books. That was a rational response for their balance sheets, but it created a structural vacuum. Hailstorms, flash floods, wildfires, freeze events mark losses that aggregate across a portfolio but never reach a single-event treaty threshold. They now sit almost entirely on primary carriers, who lack the capital efficiency to hold them and are responding the only way their product architecture allows: raising premiums, tightening underwriting, and in some markets, leaving altogether.

What carriers' market exits actually signal

The consequences of this structural mismatch are accumulating in observable ways. In California, standard carriers have non-renewed more than 1 million wildfire-exposed policies since 2018. The California FAIR Plan, the state's insurer of last resort, grew from around 200,000 policies in 2020 to more than 450,000 by late 2024, a 123% increase driven almost entirely by wildfire-related withdrawals from the standard market. Nationally, approximately one in seven owner-occupied homes is now uninsured, a figure that jumped more than 6% between 2023 and 2024 alone as rising premiums priced households out of coverage. The E&S market has absorbed the spillover, reaching $86 billion in direct premiums in 2023, growing for a fifth consecutive year. But E&S is a pressure valve, not a solution. And 70% of residential flood losses go uninsured annually in the United States, representing roughly $17 billion in losses absorbed by households and taxpayers each year.

The instinct is to read this as a pricing problem: If the industry just charges enough, it will re-enter. But that logic misses the target. Premium increases are not restoring market access. They are accelerating the concentration of risk in residual markets that are structurally worse at absorbing it than the private market they replaced. Market exit is not a correction mechanism. It is the protection gap widening in real time, underwritten by public balance sheets that were never designed for the purpose.

Closing the gap between the trigger event and the realized loss

Traditional indemnity insurance requires an adjuster, a loss assessment, and a claims process calibrated to a world where individual events are large, distinct and infrequent. That workflow is expensive even when functioning correctly, and it was never designed to handle the accumulation of dozens of mid-severity events per year across a portfolio. Parametric structures remove that friction entirely. A defined trigger, such as hail accumulation exceeding a threshold, wildfire perimeter within a defined radius, flood depth at a gauge station, or freeze degree-days above a specified level, is met or not met. Settlement is rapid. There is nothing to negotiate.

There is a further irony that the insurance industry has been slow to absorb: Secondary perils are more parametrizable than primary ones, not less. Hurricane track and wind-field modeling involve genuine uncertainty that makes trigger design difficult. Hail accumulation, flood depth, wildfire proximity, and freeze intensity are all measurable in near-real-time from satellite and ground-based observation networks. The basis risk problem that has historically constrained weather derivatives - the gap between the trigger event and the realized loss - closes considerably when AI-driven models can calibrate triggers at the property level rather than the regional index level. The technical barriers to frequency-risk transfer are lower than they have ever been. The remaining barrier is product design inertia.

Where the unpriced accumulation is building

The geographies that have already experienced market disruption are not the only exposures deserving attention. The next unpriced accumulation is building in the Midwest and upper South, where severe convective storm frequency has been running at record levels for three consecutive years and reinsurance treaty structures still treat hail and tornado losses as below-threshold attritional items.

The carriers and risk managers who treat secondary peril accumulation as a known quantity that can be managed through pricing and underwriting tightening alone will find, in the next five years, that they have been solving the wrong problem. The cat model was built for the kind of disaster that makes the front page. The losses that will define the next decade are the ones that happen every season: individually unremarkable, collectively devastating, and structurally unhedged by the instruments the industry currently relies on.

Today's family offices know they face a growing list of climate-related risks. From catastrophic wildfires in California to severe wind and hail events across the Midwest, losses are increasing at an unprecedented scale. According to Aon's 2026 Climate and Catastrophe Insight, the Palisades and Eaton Fires were the costliest events of last year at $58 billion, while severe convective storms – associated with thunder, lightning, heavy rain, hail, strong winds and sudden temperature changes – resulted in the highest aggregated losses at $68 billion. While no hurricanes made landfall in the United States in 2025, the previous eight years saw an average annual economic loss of over $75 billion from named storms.

In high-threat zones, rising insurance premiums and shrinking coverage options are making it more difficult for family offices to rely on traditional risk transfer alone. While awareness of climate risks and their related coverage issues is widespread, the real challenge is to move into strategic action. A clearly defined risk philosophy can help lead the way.

What is a risk philosophy?

A risk philosophy defines how a family approaches risk tolerance, risk transfer, and risk mitigation across their portfolio of assets. It guides decisions around deductibles, insurance structures, and capital investments in property protection.

The value of a risk philosophy

While the insurance market is starting to soften, property owners in some regions are seeing a 20% increase in premiums based on the individual characteristics of the location. Family office managers reviewing their insurance spend are seeing a clear and significant upward trend with premiums.

Coverage options are also becoming more limited. In regions prone to wildfires, hurricanes or severe convective storms, insurers are pulling back. Limits are declining, and deductibles are increasing – or insurance carriers are exiting these locations altogether. This has prompted family office managers across the country to consider the question: What can we do to better manage and mitigate these growing risks? A thoughtful risk philosophy can help answer that question.

How to design a risk philosophy

Every family will have their own risk philosophy and tolerance. For some, this means prioritizing lower deductibles on primary or high-use properties. It could also involve taking a self-insured approach for certain properties, redirecting savings into resiliency strategies. Ultimately, it is a balancing act that comes down to determining what matters most and applying that perspective consistently across a family's property portfolio.

That consistency is key, especially for family offices managing multiple homes. However, a strong risk philosophy is not rigid – it should allow for nuance and evolution. For example, a portfolio of houses may generally favor higher deductibles, but a single property located in a high-risk wildfire or hurricane zone might warrant a different approach. In that case, a family might take the self-insured route and invest more in mitigation. It all comes down to each homeowner's unique risks.

Depending on a property's location, there are many different strategies a homeowner could put in place to help improve resiliency. In coastal flood-prone areas like Florida, this may include evaluating elevation, improving drainage and installing flood vents. In wildfire-exposed regions like California, creating defensible space, installing ember-resistant vents and adding roof sprinkler systems can all help reduce risk.

Conclusion

Building a risk philosophy is not a one-time strategy. Risk is dynamic, and both environmental conditions and the insurance market continue to evolve. It is important to re-evaluate risk philosophies annually and think about what can be done differently to help make a property more resilient.

For family office risk managers, staying ahead of emerging risks and solutions is essential, and a clear risk philosophy can inform a more strategic approach. If you are not already having these discussions with an advisor, now is the time.

This article is provided for general purposes only and does not provide individual advice. This article should not be viewed as a substitute for the guidance and recommendations of a retained professional. Readers should seek advice from their own qualified professionals before making decisions based on this article.

In April, something significant happened in the auto industry: Tesla confirmed that production had begun on its Cybercab, a fully autonomous vehicle with no steering wheel, no pedals, and no human in the loop. Until now, the conversation has focused on what this means for Uber and Lyft and on whether robotaxis are going mainstream.

But perhaps there's an equally consequential question. What happens to the insurance industry once the driver has gone the way of the Edsel? Unfortunately, the industry has not seriously tried to answer it.

The Model Was Built Around the Human

Commercial auto insurance was designed around a single variable: the person behind the wheel. That is why insurance prices reflect driving behavior; liability follows whoever was driving, and policy language assumes a human making decisions on the road in real time. The full architecture of risk assessment, premium calculation, and claims resolution rests on the assumption that human judgment is what gets priced.

Open almost any commercial auto policy today, and the human driver as the unit of risk appears on nearly every page. But remove the driver, and pricing assumptions, liability triggers, and claims logic all rest on a human variable that no longer exists. So the language built for that world has to be rewritten.

Autonomous vehicles are no longer theoretical. From Level 3 consumer vehicles to more than 700,000 weekly robotaxi rides globally, deployment is moving faster than the regulatory frameworks meant to govern it. With that comes an even deeper anxiety the industry rarely discusses openly - autonomous vehicles are much safer than vehicles with human drivers. Research in Traffic Injury Prevention found Waymo cut injury-causing crashes by 79%, with intersection crashes down 96%. Tesla reports Full Self-Driving (Supervised) improves U.S. road safety by over 80%.

On its face, all of this is nothing but good news. But for an industry where roughly half of all premiums are tied to auto, those numbers describe an existential shift. Fewer claims are indeed good for society, but they also represent a fundamental challenge for a business model never redesigned to reflect it.

The Transition Is the Real Challenge

The most challenging chapter is perhaps underway, in the chaotic middle ground before full autonomy becomes the norm.

Waymo's current operating model shows how messy this can be. In Austin, it has partnered with Uber, while in San Francisco it competes directly against Uber and Lyft. In both markets, it works with maintenance fleets including Hertz, Avis, and new AV service companies. Each raises different insurance questions.

Once a Waymo comes off the road and a human driver takes it in for service, there is no settled answer for what is being insured. These vehicles can be worth hundreds of thousands of dollars due to their embedded sensors and software. If a maintenance technician damages a radar unit and that vehicle later causes an accident, is the resulting liability an auto insurance issue or product liability? Current policies do not offer a clean answer.

Mixed-fleet operations carry that ambiguity: overlapping liability, unclear ownership of risk, and policy language written for a world that no longer exists. The work ahead, therefore, is a fundamental redesign of how liability gets assigned in multi-party autonomous operations. When something goes wrong, the question of responsibility, whether the OEM, the platform, the maintenance fleet, or the software provider, has no clean answer.

Data is the starting point, and fleets like Waymo and Tesla are sitting on enormous amounts of operational data that could reshape how risk is understood and priced. But that means insurers need access to that data, and the frameworks to build products around how these vehicles actually operate.

Regulators have a significant role to play, too, because the state-by-state patchwork that just about worked for rideshare will not scale for autonomous vehicles. Federal coordination on liability standards and minimum insurance requirements for AVs would give the industry a target to build against.

The Window to Get Ahead Is Narrower Than It Looks

The rideshare era offers a partial template. When Uber arrived, insurance took years to catch up, but the industry muddled through. However, the trajectory this time looks faster. Nevertheless, unlike the rideshare era, the industry already knows how to build insurance products for markets without a rulebook.

But the scale is different, the liability questions more complex, and the next major AV incident will create enormous pressure to fix things quickly, in public, under scrutiny. Waiting for that moment is the wrong strategy.

Insurance has to shift from static to dynamic, using real-time data to map how risk is distributed across platforms, fleets, maintenance partners, and technology providers. Liability has to follow that data through every link in the chain.

Adapting will not be enough, because a model that priced human behavior for a century is finished. What replaces it will look almost nothing like today's commercial auto insurance. Carriers treating this as a rebuild will define the next era of mobility risk. Everyone else will be left writing policies for a road that no longer exists.