Writing a newsletter on a morning when the president of the United States threatens that "a whole civilization will die tonight" strikes me as a fool's errand. Whatever I write will quickly pale in comparison with what happens -- or, I hope, doesn't happen -- in Iran in the next 24 hours. 

So I'll keep it short this week, just pointing out something I've been tracking for a while: that a "super" El Niño has become increasingly likely. An El Niño increases surface temperatures in the ocean, leading to higher temperatures worldwide and exacerbating just about all the sorts of natural disasters that have been producing record claims for property/casualty insurers (with the notable exception of Atlantic hurricanes). And the El Niño that's now forming looks like it will produce a far greater increase in ocean temperatures than normally occurs -- perhaps the greatest in 140 years. 

I'll take a quick look at the likely effects -- and at why U.S. insurers need to be even more careful than usual about their public statements and handling of claims, given that the insurance industry is an easy target for politicians looking for scapegoats in an election year.

Then we can all get back to our doom scrolling. 

A Washington Post article does a thorough job of laying out the risks of the El Niño that is forming. It says a "super" El Niño is one in which a key part of the Pacific Ocean sees surface temperatures increase by more than 2 degrees Celsius (3.8 degrees Fahrenheit) above average. The El Niño now taking shape could see the temperature rise 2.8 degrees Celsius (5.04 degrees Fahrenheit) above average, breaking the record set in 2015.

The result, the article says, could be: 

• "Reduced hurricane activity in the Atlantic Ocean and possible drought in the Caribbean islands. Increased hurricane and typhoon risk in the Pacific Ocean....
• "Potential drought in central and northern India....
• "Above-average summer temperatures and humidity in the Western United States, possibly coming with unusual downpours, which may reach into the Plains and extend severe thunderstorm season.
• "Developing droughts in portions of Central Africa, Australia, Indonesia, the Philippines, some South Pacific islands, Central America and northern Brazil, particularly later in the year. Flooding downpours in Peru and Ecuador, parts of northern and eastern Africa, the Middle East and near the equator in the Pacific.
• :Higher frequency of heat waves across large parts of South America, the southern United States, Africa, Europe, parts of the Middle East, India and eventually Australia.
• "New global temperature records — especially in 2027 — probably breaking records set in 2024."

These threats come as high temperatures and low precipitation have created drought conditions across more than half the continental U.S. The problem is especially severe in the West, where devastating wildfires have become all too common. 

So, while the lack of a landfalling hurricane in the U.S. last year meant insured losses from natural catastrophes were below the average for the past decade, claims could well soar this year.

Natural disasters always draw complaints about insurers. Everybody wants the recovery to be fast and smooth -- in situations where it's almost impossible for anything to happen fast or smoothly. Politicians, always looking for a way to position themselves on the side of voters, will home in on problems and publicly scold insurers -- a week ago, President Trump made a social post calling State Farm "absolutely horrible" for its handling of the Los Angeles fires early last year, and we can expect a lot more sniping at insurers from all sides in the run-up to the fraught mid-term elections this November. 

So everyone in the industry needs to be on high alert as the El Niño develops, helping people make their homes and properties more resilient and then, when the inevitable losses occur, acting as swiftly and empathetically as possible to help people recover.

In the meantime, let's all hope and pray for a sane resolution to the man-made disaster taking place in the Middle East.

Cheers,

Paul

The race to fill senior leadership roles—and to do it faster, more competitively, and with greater diversity—has become a defining challenge for CEOs and CHROs. Across industries, organizations report a limited pipeline of "ready-now" talent, particularly at the highest levels. This raises an important question about whether the gap is one of supply or of visibility.

At the same time, women's advancement into senior leadership continues to lag despite years of focused initiatives and investments. Recent Women in the Workplace research from McKinsey and LeanIn.org attributes part of this to a shift in ambition.

But what if the issue isn't ambition at all? What if the signal is being misread?

What the Data Shows and What It Doesn't

For more than a decade, McKinsey's annual research has shaped the national conversation on women's advancement, offering critical visibility into representation and career progression. The data reveal a consistent pattern: Women enter the workforce in strong numbers, advance early, and then experience slower progression at senior levels. In fact, recent reports have sparked a broader discussion on whether gender parity will be reached in 50 years and whether there is an ambition gap.

Yet this survey research data tells only part of the story. It maps where women are and the movement, but it does not fully illuminate the experience of leadership once they arrive. That deeper understanding requires a different lens—one that brings into focus how advancement actually unfolds.

What the Experience Reveals

As part of the research for SelfPowerment: The Inner Shift for High-Achieving Women Who Want More Than Just Success, a qualitative study was conducted with 52 senior women leaders and 10 male executives across industries in the United States, including meaningful representation from the insurance sector across property & casualty and life & annuities.

Rather than focusing on representation alone, this research examined lived leadership experience—how careers begin and evolve, and ultimately how advancement unfolds once performance has already been proven.

What emerged is a pattern that is widely experienced yet rarely articulated. High-achieving women are consistently delivering at the highest levels. They are leading enterprise transformation, running complex organizations, and driving the outcomes companies rely on for both growth and performance.

And yet, many describe a subtle but consistent shift. Performance continues while advancement slows. What surfaces is not a question of capability; it is one of visibility.

As outlined in the SelfPowerment white paper, "What No One talks About—But Women Know," this dynamic forms what is defined as the Invisible Advancement Cycle—a repeatable pattern in which leaders become indispensable to execution while authority, sponsorship, and progression fail to keep pace.

The Misinterpretation of Ambition

From the outside, this dynamic often presents as disengagement. Over time, some women step back from pursuing the next role and become more selective in the opportunities they consider. In some cases, they choose to leave the organization altogether.

This shift is frequently interpreted as a decline in ambition. A closer examination of the lived experience reveals something far more grounded: When sustained high performance no longer consistently leads to advancement with greater influence or authority, leaders recalibrate how they engage. They become more intentional about the roles they accept, seeking opportunities where responsibility is matched with decision-making authority, where visibility translates into influence, and where increased scope aligns with how they define success.

Ambition remains. But it evolves, becoming more focused, more deliberate.

Where Alignment Changes Everything

One of the most important insights from the research is this: Not all women stayed in this invisible advancement cycle. Those who were able to sustain both influence and fulfillment did so because they had made a shift. They moved from endurance to alignment.

These women took ownership of their careers rather than wait for validation. They set clear, strategic boundaries around roles that expanded responsibility without corresponding authority. They repositioned their leadership from execution alone to enterprise-level impact, and they defined success on their own terms—beyond title or traditional progression.

This is the foundation of SelfPowerment—a return to purpose and a renewed ownership of one's career on one's own terms. It reflects a fundamental shift from, "Will they choose me?" to "Do I choose this?"

Alignment changes how women engage—with greater clarity, confidence, and conscious choice.

Why This Matters to CEOs and CHROs

This realignment extends beyond a women's issue; it is an enterprise leadership imperative. When this alignment pattern persists, organizations begin to absorb hidden costs that often go unrecognized until they become systemic.

Leadership capacity becomes underleveraged as the leaders closest to execution—those who understand how strategy truly operates—remain outside core decision-making circles. Succession pipelines narrow over time, with organizations looking externally for leadership while proven internal talent remains underused. At the same time, dependence concentrates within a small group of high-performing leaders who carry disproportionate responsibility for outcomes.

As alignment erodes, experienced leaders begin to step back or disengage, and gaps emerge between an organization's stated commitments to leadership development and the reality of lived experience.

As the research makes clear, these are not abstract dynamics—they are operational, financial, and strategic in their impact.

The "Operational Leader" Blind Spot

One of the most consistent insights across industries is how leadership continues to be evaluated. Strategy is often defined through vision, narrative, and positioning, while execution—where strategy becomes real—is frequently categorized as operational, tactical, or supportive.

In today's environment, however, execution is where complexity resides. Leaders who integrate technology and operations, guide transformation, and deliver enterprise outcomes often hold the deepest understanding of how the business truly functions. But when execution is undervalued in advancement decisions, organizations inadvertently overlook the very leaders they depend on most.

A More Accurate Question

Rather than asking whether ambition is shifting, organizations are better served by examining how leadership is defined and rewarded. This invites a more precise set of questions: whether visibility is being equated with leadership, whether performance is being recognized or simply relied upon, and whether the leaders most critical to outcomes are also those advancing into positions of influence.

When the answers begin to diverge, the issue becomes clear. It is not ambition that is changing. What is changing is the alignment between performance, authority, and advancement.

A Call to Action

This moment presents a powerful opportunity for both organizations and the leaders within them.

For CEOs and CHROs, the imperative is to make the invisible visible. This begins with a deeper examination of how advancement decisions are truly made, by elevating execution leadership into core strategic conversations, and by ensuring that authority aligns with demonstrated impact rather than perception alone.

For women leaders, the opportunity is equally significant. Recognizing this pattern creates the ability to respond to it thoughtfully. When something has felt misaligned despite continued success, it often reflects a structural dynamic rather than a personal one. Alignment—SelfPowerment—becomes the pathway forward, enabling leadership with greater clarity, confidence, and conscious choice.

The Leadership Shift Ahead

The next decade will require a more integrated model of leadership—one that values not only vision but also execution; not only strategy but also translation into outcomes; and not only performance but also alignment.

In many organizations, this leadership already exists. The capability is present, the experience is proven, and the impact is measurable.

The question is not whether the talent is there.

The question is whether it is fully seen—and whether women are fully choosing it.

You can preorder the book here:. You can download the white paper here.

What if your car insurance reflected how you actually drive, not just who you are? That question is no longer hypothetical. In 2024, more than 21 million U.S. policyholders shared telematics data with their insurer, according to IoT Insurance Observatory research. That reflects a 28% compound annual growth rate since 2018. Usage-based insurance (UBI) is no longer a niche; it’s a mainstream strategy reshaping our industry.  

For years, competitive pricing drove adoption. But today, something deeper is at play: Trust and perceived value are fueling the next wave of growth. This isn’t just about saving money; it’s about believing the insurer will use sensitive driving data responsibly and deliver tangible benefits in return. 

Source: IoT Insurance Observatory field customer surveys

According to a recent consumer survey by Arity and the IoT Insurance Observatory - sampling 2,059 personal auto policyholders representative of the U.S. market - 82% of policyholders would recommend a telematics app that rewards safe driving, offers feedback, provides crash assistance, and delivers other valued services. Among drivers under the age of 53, that number exceeds 90%. Positive sentiment toward telematics has steadily increased over the past decade, as shown in the chart above.

Trust isn’t a buzzword here, it’s the foundation of adoption. Consumers share data only when they believe insurers will protect it and use it to create real value. In fact, 53% of respondents expressed high trust in insurers’ handling of personal data, ranking insurers second only to banks. That trust translates into action: willingness to switch plans, share driving scores, and pay for connected services. 

The willingness to adopt UBI is strong: 60% of policyholders are open to switching, rising to 72% among younger drivers. This level is consistent with the evidence from recent TransUnion surveys showing that 60% of people reported being offered telematics opted-in.

When consumers see clear benefits, privacy concerns fade. They want pricing that reflects lifestyle, rewards for safe driving, and features like automatic crash assistance. Three-fourths are open to sharing their driving score for a personalized quote. More than half of those willing to switch prefer pricing models that offer bigger potential savings, even if it means some risk a surcharge. 

This is where telematics shines. Insurers can deliver compelling offers because telematics unlocks incremental economic value by sensing events, transmitting data in real time, and applying AI-driven analytics to understand, decide, and act. This enables smarter underwriting, faster claims processing, and more proactive risk management. By sharing part of this value with policyholders, insurers create a win-win scenario that makes UBI not just viable - but mainstream.

Source: IoT Insurance Observatory

Over the past decade, insurers have proven the power of telematics data to transform core functions: 

• Continuous Underwriting: Telematics data enables more accurate risk assessments and selection, allowing insurers to better match rates to actual risks. This leads to more sophisticated pricing, improved retention and effective acquisition of good risks, and reduced premium leakage from riskier drivers. Insurers can also use telematics-based data to make portfolio-level decisions regarding risk appetite and reinsurance.
• Enhanced Claims Management: Real time crash detection is a game-changer for claims management. Insurers can trigger proactive responses, notify emergency services, and initiate the claims process. Insights about crash events support timely claim handling and help minimize potential fraudulent or inflated requests.
• Connect and Protect: International telematics-based experiences demonstrate effectiveness in mitigating risks by identifying risky situations in real time and intervening before accidents occur.  Behavioral change programs promote safer driving, leading to fewer accidents and lower loss ratios. 

Policyholders are willing to reconsider their insurer when pricing reflects how they actually drive and live: More than half of policyholders would switch for a product with the premium defined by telematics data. 

Consumers aren’t just looking for lower premiums; they want features that matter. Rewards for safe driving and automatic emergency assistance in severe crashes rank among the top preferences across all generations - from Gen Z to Traditionalists. And the appetite for innovation doesn’t stop there: More than half of policyholders would pay $4.99 per month for a connected dashcam service that offers emergency assistance, video recording for protection against unfair complaints, and real-time safety feedback.

The impact goes beyond individual policies. When the usage of telematics data is holistically adopted across the entire insurance organization, this improves pricing accuracy, reduces losses, and makes insurance more affordable, all while promoting safer roads. Fewer accidents mean fewer injuries and lives saved. That’s why telematics is more than a business strategy, it’s a social good.

The time to invest in telematics mastery is now. Insurers that fully embrace the connected paradigm in all their core processes and responsibly use data with customer consent can unlock greater value—delivering fairer pricing, personalized experiences, and safer roads. This broader usage of data enables higher value creation and sharing, benefiting policyholders and society as a whole.

Fraud is hardly a new problem, but it is a serious issue, and recent fundamental changes in societal norms are exacerbating fraudulent conduct and making detection and deterrence less of a priority than warranted. The scope and scale of fraud are truly shocking, especially among government-funded medical and social programs currently under scrutiny, where enormous costs are somehow tolerated.

Fraud not only creates significant economic loss but also undermines confidence in its public and financial institutions, including insurance. Yet preventing and combatting fraud is seemingly episodic and random. 

All of this serves to bring renewed attention to the long-standing concerns about ever-expanding fraud in general – and specifically insurance fraud. Insurers need to heed the wake-up call.

COST OF INSURANCE FRAUD

Quantifying insurance fraud's impact is difficult and spans from premium fraud to claims fraud, whether opportunistic or through deliberate scheme. According to the Coalition Against Insurance Fraud (CAIF), insurance fraud costs American consumers more than $300 billion a year. This amounts to an individual policyholder $900 annual “tax,” as insurer costs are passed on in form of premiums. Claims fraud is said to occur in about 10% of property-casualty insurance losses. Medicare fraud alone is estimated to cost $60 billion every year.

There are also several limitations when it comes to detecting fraud. According to the National Association of Insurance Commissioners (NAIC), there are key differences between “hard” and “soft” types. Soft forms of fraud are widespread and can be a common exaggeration of a legitimate claim. Hard types are described as intentional acts to create or fabricate “damages” and claims. Still, these general headers fall short of telling the whole story. 

Claim fraud can be perpetrated by an individual or involve others including organized crime rings recognizing there are entire ecosystems designed to inflate, embellish and even fake an accident. Billing for unperformed medical procedures pales in comparison to fake “victims” being paid to undergo surgery. A single case in New York uncovered a $31 million scheme between a doctor and lawyer in trip-and-fall “accidents,” paying "victims" to endure surgery, simply to initiate a claim, justify damages or both. So-called runners are paid finder-fees to produce participants.

Further, many frauds go undetected for long periods or are missed altogether because there is much reliance on the “honor system,” whether at point of sale in which premiums are based or when making a claim. Although any healthy system checks and verifies, it is impractical, unnecessary and risky to deeply investigate a large percentages of cases. Insurers balance customer service, state regulatory requirements involving timeliness and potential complaints that can escalate to lawsuits. 

Meanwhile, internal special investigative units (SIUs) likewise have finite resources and bandwidth, only concentrating on the most actionable cases. Law enforcement agencies have similar constraints, and insurance fraud is a lesser priority than other crimes. Altogether, this dilutes the efficacy of combatting fraud, leading to uncaptured and under-reported figures.

Instead, anecdotal case examples tend to do the best job of illustrating the magnitude of fraud. Phony medical clinics, staged auto accidents, even faked deaths demonstrate the amounts at stake and the lengths fraudsters will go. More frustrating is how obvious some of the schemes are, revealed as in the infamous empty day care center stories. 

But what happens when technology pushes the boundaries beyond such traditional fraud methods?

The Yin and Yang of AI and Insurance

The rapid emergence of artificial intelligence has brought greater business risks, and the financial services industries are among the largest victims of related fraud. Ironically, business is quickly learning to harness the power of AI to fight fraud more effectively – but so are the fraudsters. 

The potential of AI in claims fraud detection is among the most powerful applications, and particularly so in life & health and accident, according to a February 2026 report from Gallagher Re and CB insights: "Global InsurTech Report."

AI has many benefits. It can improve efficiency, help make better decisions, and encourage innovation across different industries. But these advantages also come with serious risks – especially the potential for misuse in fraud or deception.

Like any powerful technology, AI can be used for both helpful and harmful purposes. This makes strong and thoughtful governance essential to maximize its benefits and protect against misuse.

Hackers and other criminals can easily commandeer computers operating open-source large language models (LLMs) outside the guardrails and constraints of the major artificial-intelligence platforms, creating security risks and vulnerabilities, researchers said.

Hackers could target the computers running the LLMs and direct them to carry out spam operations, phishing content creation or disinformation campaigns, evading platform security protocols, the researchers said. Roberto Copia, director at IVASS Inspectorate Service, spoke about this issue at the 4th National Congress of the CODICI Association in 2025. He pointed out a growing concern: While AI can improve the efficiency of the insurance industry, it can also give fraudsters more advanced tools to commit fraud.

AI and Insurance: An inseparable alliance

AI is cautiously becoming an indispensable tool in the insurance sector. Its applications range from risk assessment to customer services, claims processing and fraud detection. Predictive algorithms, neural networks, and machine learning models allow the processing of vast datasets, improving underwriting accuracy, accelerating claim settlements and strengthening insurers' anti-fraud capabilities.

But these very tools – powerful, scalable and increasingly accessible – are also being weaponized by fraudsters. “Those who seek to commit fraud are often skilled innovators – frequently one step ahead of those tasked with stopping them,” Copia has said.   

A quantum leap in criminal sophistication

Insurance fraud has always been a structural problem in the sector. Yet today, it’s undergoing a qualitative shift. We’re no longer dealing solely with fraudulent damage to property or fictitious claims. Modern fraud is digital, automated and highly sophisticated. AI has become a powerful enabler for those seeking to manipulate data and images, forge documents or create false digital identities.

A March 2026 report, Verisk State of Insurance Fraud Study, finds, based on surveys of 1,000 U.S. consumers and 300 insurance claims professionals:

• 55% of Gen Z say they would consider editing a claim photo or document
• 98% of insurers say AI editing tools are fueling digital fraud
• Only 32% of insurers feel very confident about detecting deepfakes
• 69% of consumers believe fraud will raise premiums for all policyholders

A paradigmatic example is the Ghost Broker scam: insurance websites that appear legitimate, often employing advanced social engineering techniques, real logos, and data stolen from unwitting intermediaries. AI allows these fraudulent portals to appear increasingly credible, complete with chatbots simulating customer service, AI-driven profiling of potential victims, and the delivery of highly personalized fake offers. The result is a seemingly flawless customer journey. But the buyer is left uninsured and unknowingly defrauded until subsequent inspection reveals the deception.

Another example involves "synthetic" identity fraud, in which fraudsters create an identity with a mix of fabricated credentials. According to Lexis Nexis, fraudsters may create synthetic identities using potentially valid Social Security Numbers (SSNs), with accompanying false personally identifiable information (PII). This newer challenge raises the bar for insurers to validate identity at point of sale and other policy lifecycle stages.

THE FRAUD FIGHTING IMPERATIVE

We believe that insurers have an obligation to prioritize fraud detection and avoidance in this growing, too-big-to-ignore dynamic. This obligation is moral, economic and legal. An insurer’s duty to its policyholders includes protecting their investment while managing fair and accurate premiums alike.

Paul Carroll 

With AI transforming underwriting, some say the function is entering a new phase. Do you agree? 

Katie Klutts Wysor 

When you look at what property and casualty carriers are saying and what brokers are starting to say, there’s broad recognition that generative AI could reshape risk assessment and underwriting in meaningful ways. It’s becoming an enabler. It can help underwriters make better decisions and work more effectively. 

Carriers are focusing on underwriting as a function and investing heavily in it. They’re talking about that focus in investor days and earnings calls, and they’re doing significant work internally to organize data and update processes—improving speed, increasing automation, accelerating turnaround times and supporting more informed decisions with better data. 

What’s even more significant than the process improvements is what AI could do more broadly. Think of the underwriter as managing capital and trying to direct it to the ideal place. How can AI help define the parameters around what the carrier wants to write so it can deploy capital where it is targeting stronger returns? From there, you can align appetite and business mix with the underwriting process. 

For example, if I’m a regional workers’ comp player and I want to expand into other lines of business or other states, how can I use AI to support that portfolio management decision and direct capital more effectively? Then, how do I identify the necessary distribution partners to find the business I want? How do I create the proper incentives for distribution partners to bring forward that business, so I have a submission to underwrite? And then, how do I make sure the underwriting process, and decision-making aligns with my appetite? 

I think that’s where a great deal of value could be created from an underwriting perspective, looking at how AI can help inform research on the front end, and how you then use something like a GenAI-enabled underwriting platform to begin systematically embedding strategic capital decisions into appetite, process and guidelines at scale, so underwriters evaluating risks are working from more relevant information. 

Then you can use AI to respond to new business decisions more quickly, respond to renewal decisions more effectively, and potentially take certain actions during a policy’s term to support risk mitigation conversations. 

If you can start mastering that link—how you’re deploying capital and setting appetite, all the way down to those micro process decisions—that represents a new level of maturity. 

Paul Carroll 

Speed has become a significant competitive factor in insurance underwriting. If you’re slow to quote, even with a slightly better price, you may lose the business. What’s happening in terms of speed in the underwriting process, and how do processes need to change—not just the technology—to take advantage of the speed AI can offer? 

Katie Klutts Wysor 

Speed to quote and bind means something very different across varying lines of business. In auto insurance, you need to be able to deliver a quote in seconds. So, you see many personal lines players focusing on quote simplification. In auto, it is close to a mature problem, and many carriers are following established market patterns to stay competitive. 

But the speed question gets harder as you move into more specialized or complex lines in personal insurance and small commercial, middle market and large commercial. 

What we’re seeing there is impressive. Capabilities are now emerging to triage submission intake. From a technology perspective, carriers increasingly can take a submission no matter how it comes in—via email, a platform or another channel—and combine it with what they already know about that risk, along with relevant third-party data. 

At that point, it becomes an execution challenge. How can you more systematically get to a quick yes, no, or maybe on appetite, and then move effectively  toward a quote? 

How fast that can happen depends on the line of business. But that point is right: Carriers should move as quickly as their competitors. If you’re slow, distribution may not be willing to wait. 

And the benchmark will continue to move. 

Paul Carroll 

How far along is the insurance industry in using technology to allocate capital more intelligently, and what needs to happen to reach the next level? 

Katie Klutts Wysor 

It’s less a technology challenge and more a business decision-making challenge. Some players in the market are especially strong at this, and you can see that by looking at underwriting returns over time. Those companies have consistently used technology and data to manage their portfolios and allocate capital with greater precision, and they will likely continue to adopt new approaches as the tech capability improves. The timeline for broader adoption of newer technology, including generative AI, is harder to predict because it comes back to how quickly "the pack" of carriers can  evolve how they manage the profit and loss across the portfolios. 

Paul Carroll 

What is an example of how insurers can improve their capital allocation? 

Katie Klutts Wysor 

The fundamental approach is to look at your underwriting returns against the capital you’re deploying to the business, map that out, compare outcomes, and decide where you want to grow and where you may need to pull back. Improvements in technology may allow carriers to do that analysis more frequently. Instead of doing it once a year as part of strategic planning, you could be looking at a refreshed view every month using more current data. 

Many carriers may be able to move from annual reviews toward monthly or weekly review cycles, depending on how they make decisions. They may also be able to do the analysis in a more automated way and make decisions more intentionally on micro-segments of the business (by geography, class, line, etc.) that would have been too time-consuming to identify and react to previously. 

Maybe a competitor enters the restaurant space aggressively and undercuts on price. You may decide not to follow them down that path because you believe the pricing is unattractive, so you slow growth in restaurants. 

Or take the opposite scenario: A trend affects restaurants and causes the market to become more cautious. You may conclude that the market reaction has gone too far and decide if this is the right time to pursue restaurant business. 

Today's leadership reviews may only look at class code-level data monthly or quarterly, and at frequency and severity trends in a backward-looking way. But if you can automate how, you assess that information at a portfolio level, then you can decide whether to lean in or lean out of a class like restaurants more quickly.  

Paul Carroll 

What about the execution side of this—how do insurers actually act on these faster insights once they’ve identified an opportunity? 

Katie Klutts Wysor 

The second half of the equation is exactly that. Say you’ve been able to automate and generate more timely underwriting data, so you can make portfolio decisions weekly or monthly instead of quarterly or annually. That’s a meaningful shift. The next step is execution. 

Say you decided to lean into restaurants. You want the market to know. You want your agents and distribution partners to know you’re interested in that business, particularly if another carrier has started to pull back or take rates. That’s the business you want to enter the pipeline. 

Then you want to set up your underwriting process so you can pivot quickly. Maybe you were not prioritizing that business to get it to an underwriter’s desk and streamline escalation paths to support faster turnaround. 

Of course, once submissions are flowing in, and the process is in place to evaluate and price the business you want competitively, you also need the proper governance and controls, so you don’t end up writing risks that fall outside appetite. 

The big difference this year versus a year ago is the ability to put agentic AI workflows in place and that support faster transaction-level decisions. Humans are still in the loop, but they are not necessarily slowing down the process in the same way they did when carriers relied more heavily on manual referral and escalation processes to respond to market changes. The next frontier I expect to see in the coming year is using agentic AI workflows to help improve portfolio-level decisions.

Paul Carroll 

Would you talk a bit about some of the process efficiencies from generative AI as underwriters make their decisions? While those efficiencies aren’t as strategic as the portfolio-level decisions you’ve described, they still seem substantial.  

Katie Klutts Wysor 

Underwriters face a series of yes, no, and maybe decisions, and much of the friction sits in the maybes. You can automate obvious yes-or-no decisions. The maybes are the gray area where you bring in a person. 

Over time, we may be able to bring in a person less often because of agentic AI and other decision-support tools, while maintaining appropriate human oversight. 

Some maybes exist simply because a piece of information is missing. A file gets routed to an underwriter to obtain one additional data point. Once that data point is available, a rule can be applied, and the case can become a yes or a no. In many cases, that is increasingly solvable today.  

You should identify those cases in your portfolio, then use AI to obtain the data point and apply the rule. 

There are also maybes that are more judgment-based, where you’ve created a manual review because you want someone to look at it who has seen this kind of case many times before. Maybe they’ve seen a six-figure loss in a similar situation, so you ask, “Would you write this again knowing what you know now?” 

Agentic AI workflows can help by bringing more context to the situation and supporting more informed underwriting judgment.  

Paul Carroll 

Based on what you’re seeing, how much are underwriters working with brokers and clients to provide guidance on risk reduction—essentially telling them, “you’re getting dinged for this, why don’t you fix it to reduce your risk?” 

Katie Klutts Wysor 

Right now, it’s predominantly brokers and distribution partners that are providing that first line of risk management advice. But there’s also a meaningful role for underwriters and carriers. 

The concept is there, the question is how consistently it can be translated into actionable guidance. 

Paul Carroll 

What final advice would you offer to readers? 

Katie Klutts Wysor 

What my clients care about is taking some of the bigger ideas and translating them into what to do right now and how to respond in practical terms. So, what I’d leave readers with is this: Keep thinking about the art of the possible but also focus on what you can implement now to strengthen performance this year, and start bringing those two together. 

Understand what technology, data, and AI capabilities are available to you. But more importantly, identify which ones you can deploy quickly while you continue building toward the more complex architecture and data challenges you should address over time. 

Paul Carroll 

So you can create a cycle: make targeted investments that create near-term efficiencies, then use those gains to support the next wave of investment. 

Katie Klutts Wysor 

Exactly. Don’t spend three years trying to build the perfect solution. There’s a lot you can do right now. Deploy something practical that can create value, then use those gains to support larger investments over time. 

Paul Carroll 

Thanks, Katie. 

About Katie Klutts Wysor

Katie Klutts Wysor is a Principal with PwC who advises insurance leaders on strategy, growth, and transformation. She focuses on analyzing evolving market dynamics to shape perspectives on the future of insurance and translating those insights into practical, outcome-driven growth strategies and transformation programs for carriers and brokers/distributors.

Here's a thought experiment for you: What if Elon Musk ran an insurance company?

Just imagine how regulators would react to his brash, visionary ideas wrapped in disdain for tradition and a belief that rules don't apply to him. 

But what if you could bottle the good parts of his iconoclasm and leave out the parts that would scare policyholders about the reliability of their insurance carriers? A former president of Tesla just published a book that might allow for that. It describes the five-part "algorithm" that he and Musk used to manage the company during a transformative stretch in the mid-2010s. 

I don't think insurers should go full force, a la Musk's "hardcore" mode--you could wind up with an embarrassment like DOGE and never recover--but his algorithm does offer a playbook for radical simplicity and for what I think is the right way to approach automation. 

Jon McNeill, author of "The Algorithm: The Hypergrowth Formula That Transformed Tesla, Lululemon, General Motors, and SpaceX," says the method has five steps:

• Question every requirement.
• Delete every possible step in the process.
• Simplify and optimize.
• Accelerate cycle time.
• Automate.

Question Every Requirement

McNeill writes about how Tesla, for instance, questioned China's requirement that it own a piece of any company operating in the country and eventually negotiated a deal that let Tesla own 100% of its Chinese subsidiary. He also writes about deciding that cars didn't need to be assembled out of so many parts, even though they had been since the days of Henry Ford. Instead, Tesla began experimenting with casting bigger and bigger pieces of the car and eventually succeeding, greatly reducing the need for assembly.

For insurers, though, I'm thinking the real benefit would come in more modest ways that track more closely with an anecdote McNeill told in a podcast with the Wall Street Journal. He talked about how much trouble Tesla had designing and manufacturing a part that was supposed to sit between a battery and the chassis. The problem became so important that Musk got personally involved and haunted the factory for weeks. Eventually, Musk and McNeill asked if the part was really necessary, and the battery people told them it had been mandated by the folks responsible for damping noise. When McNeill went to them, he was told that, no, the battery folks had mandated the part to minimize danger in the case of a battery fire. McNeill decided to track down the engineer who had signed the order requiring the part -- and learned he couldn't reach the person because he was a summer intern who no longer was at Tesla.

Insurers already question what they believe to be undue regulation, but I think they could benefit more broadly from asking employees across the business to question everything they're told to do, whether by someone inside the organization or outside it. Even if you just do this as a one- or two-month exercise, I'd bet you'll find you're doing lots of things just because they've always been done that way, not because they deliver any value.

Delete Every Possible Step in the Process

At Tesla, McNeill said in the podcast, he deleted several steps, and Musk asked whether he'd broken the process as a result and received some severe pushback. When McNeill said he hadn't, Musk told him he hadn't gone far enough. He needed to keep pushing until he not only got close to the bone but cut into the bone -- at which point, he should back off and find a sustainable approach.

McNeill said the rule of thumb was to only deliver what the customer directly paid for: the car. Customers didn't pay directly for manuals, for documentation, and so on, so Tesla would spend as little effort as possible in those areas.

Again, I don't think that approach would survive at an insurance company. Cut-until-you-break-something can happen in a manufacturing process, behind the scenes, but it didn't even work at the Department of Government Efficiency (DOGE), which Musk ran in the early days of the second Trump administration. Even with the slash-and-burn ethos of Trump 2.0 a year ago, Musk cut too deeply and caused problems both for those receiving government services and for Trump. 

Still, insurers can suffer from a sort of data and process bloat. Given the industry's abundance of caution, it's easy to ask for more questions, to gather more data, and to require another guardrail in the process. Life insurers have shown that it's possible to do the same with less, given the success of fluidless underwriting, and other lines could surely scale back some requirements, too -- becoming more efficient while making customers happier.

Simplify, Accelerate, and Automate

I'm combining the last three parts of the Tesla algorithm because, at least for insurers, they all fit under one mandate: "Automate last."

McNeill said Tesla learned the value of these three steps when it was having so much trouble manufacturing the Model 3 that it was running out of cash and was in danger of bankruptcy. The company stopped running its highly automated manufacturing line, set up a big tent outside the factory and started making the cars by hand. Once management figured out the best process, it began speeding up. Only once they saw that they could run the process at speed did they start bringing in the machines that would automate it -- scrapping the entire production line that they'd set up before fully understanding what was needed.

"Automate last" fits with the approach the computer industry has taken for decades: Once a manual process is fully mapped out, it can move into software and then, when you're sure you have everything nailed down, you can hard-wire the work by moving it into the silicon. 

That approach makes sense for insurers, too. When you see the possibilities of AI, for instance, you should map out a potential new process, implement it manually, speed it up -- and only then let the machines take over.

There are plenty of things about Musk's approach to business that I wouldn't recommend. For more than a decade now, I've been mocking his annual claims that he'll have millions of Teslas functioning as robotaxis, that he's going to colonize Mars (we won't even land someone on Mars in his lifetime), that he's about to unleash an army of humanoid robots, and so on. Those of us without his massive wealth would lose all credibility overnight if we pushed a similar sort of sci-fi dream. Insurance, as an industry built on trust, can't afford anything close to the wild claims that Musk makes routinely.

But I do think it's worth giving his algorithm serious consideration because it can reduce complexity and lead to effective automation. If nothing else, reading about the bold moves at Tesla might inspire some new thinking and resolve in the insurance industry. 

Cheers,

Paul

P.S. "The Algorithm" reminds me of one of my favorite geek jokes:

Q. How do we know that Al Gore actually invented the internet?

A. It runs on Al-Gore-ithms. 

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The insurance claims industry sits at an inflection point. Medical records are more complex, nuclear verdicts are rising, and the workforce is changing faster than most organizations can adapt. AI promises to help — but most implementations have fallen short. We sat down with Mark Tainton, senior vice president of data solutions at Wisedocs, to talk about what's actually working, what isn't, and why the industry needs to move from document management to true decision intelligence.

Paul Carroll

The insurance claims industry has been talking about digital transformation for years. What's actually changed in the last 18 to 24 months, and what's still stuck?

Mark Tainton

Having worked in the insurance industry for over 30 years at the intersection of technology and claims operations, I've certainly seen infrastructure change. But the bigger question now is the operating model that can actually leverage that infrastructure. And the operating model is not so much around storing documents in claims management systems or document management systems—it's about how we take advantage of that data asset. We’re essentially moving from document storage into effective decision-making.

Over the last five years, there has been an acceleration in the technology, in particular with large language models. Technology is not the problem.

It's really about taking advantage of the individual pieces of information in the world of unstructured data. That's the next wave we should be focusing on: How do we operationalize the assets so they’re part of the DNA of insurance processes?

Paul Carroll

Medical record review is at the heart of so many claims decisions, yet it still appears remarkably manual at most organizations.

Mark Tainton

I’ve certainly seen large carriers that have introduced AI but haven't introduced the process changes or changed how people can take advantage of the insights as the claim goes through its lifecycle. Carriers are using ineffective decision making approaches that continue to mirror what we saw 10, 15, 20 years ago. 

There needs to be a conversation around how adjusters work, especially because of the change in their age demographic. New people coming into the claims industry consume data completely differently. We have to adjust. 

You have to also understand the psychosocial aspects of the workforce, where COVID accelerated change. You need to cut across multiple claims at any given time and look for triggers that are prevalent by a treatment provider, or at risk indicators that suggest psychosocial issues—they are top of mind for a lot of claims teams right now.

Paul Carroll

There's always a tension between speed and defensibility in claims, especially given the high stakes. How do insurers resolve that tension?

Mark Tainton

Claims are getting more complex, and we've seen a lot of legislation that makes it very clear that if someone's making a decision solely based off AI output with no human in the loop, that's going to be a problem.

When you tie that concern into the expansion of traditional fraud and increases in nuclear verdicts, the defensibility question becomes critical. There needs to be a human in the loop.

Several states are already drawing that line legislatively. California's SB 574 and a growing number of AI governance frameworks now require that AI-assisted decisions in insurance and legal contexts be documented, auditable, and explainable. That is not a future concern; it is a present operating requirement for carriers doing business in those jurisdictions. The organizations that build defensibility infrastructure now will not be scrambling to retrofit it later.

Paul Carroll

There are a lot of solutions out there these days, but they seem to largely be point solutions—summarization tools, triage tools, document processors, and so forth. What's missing from the point solution approach?

Mark Tainton

First, they don't fit into the ecosystems of clients and large carriers. They don't work alongside platforms like Guidewire where they can function as a module and help make those decisions effective.

The point solutions also aren’t really end-to-end. They're focusing on a point in time on a particular claim. That produces what I call a silent failure. The AI processes the document and returns a summary, and the claim moves forward. But the anomaly that should have triggered a flag, the treatment pattern that does not match the diagnosis, the billing inconsistency that signals a problem: None of that surface because the tool was never designed to look across the lifecycle. The claim does not fail loudly. It just quietly travels in the wrong direction for months. 

Think about first notice of injury as a claim goes through the life cycle, and all of a sudden you get a demand package or a treatment package coming in. What are the decisions you want the adjuster to make?

You need intelligence that cuts across the full lifecycle of the claim in terms of other claims with certain characteristics. And I think that's where point solutions really come up short.

Paul Carroll

I assume that thinking is why you took a platform approach with WiseShare.

Mark Tainton

Very much so. We have the sorting and summarization solution that we just renamed WisePrep. It includes WiseChat, where users can save all the insights they generate from a large language model. We've introduced WiseInsights looking at litigation trends, looking at treatment patterns and how they develop, looking across claims that an adjuster who's got a workload of 200 or 300 claims cannot identify on their own. These insights reveal similar characteristics across claims. For example, we looked at one portfolio and identified that a particular treatment provider, over a 12-week program, consistently prescribed a higher and more severe medication at the four-week timeline. 

WiseShare is important, too.  Far too often, a summarized document gets passed from the adjuster to inside counsel, then to external counsel, and eventually to an IME [independent medical examiner]. A lot of the time, we see slip-ups—documents go missing, misinterpretations occur, different versions of the truth emerge. WiseShare brings everything together into one consolidated environment where all of those entities can actually share, review, and export the claim file. 

From a legal defensibility standpoint, that consolidation is not a convenience; it is a chain-of-custody argument. The defense bar needs to see a complete, unbroken record: the medical record chronology, the time series of decisions made, and documented consistency in how AI processed the underlying materials. When a claim ends up in litigation, the question is not just what decision was made; it is whether that decision can be reconstructed, sourced, and defended at deposition. WiseShare is built for that standard.

You have to be able to wrap intelligence around a decision, and that requires a platform. 

Decision intelligence needs to be comparative. You have to be able to see the claim you're dealing with in the context of other claims. The intelligence also needs to be sequential. Are we seeing similar patterns starting to develop on other claims in certain jurisdictions? Are we starting to see certain seasonal trends? Are we starting to see different types of treatment coming through? Finally, the intelligence must provide accountability. Is every inference sourced and every decision point documented? 

The defense bar needs to see that audit trail. They need to see the medical record chronology, the time series, and the consistency in terms of best practices for how AI actually processes documents and insights for better outcomes. From 2023 to 2024, nuclear verdicts rose 52%. Thermonuclear verdicts are up 81%, and overall verdicts are up 116%. 

You need one single environment where you store the materials, one single process that's consistent across an organization.

Bottom line: if you can't show defensibility, you're in a world of trouble.

Paul Carroll

There's discussion about AI replacing many human workers in the insurance industry. What is your perspective?

Mark Tainton 

There's this notion that AI is going to replace people at the desk. From my perspective, that's totally inaccurate. And I think that mindset sets back adoption.

But here's the inflection point: We're dealing with an aging workforce. Insurers and TPAs are struggling to attract talent. Why? Because some of the tools and technology have not evolved as quickly as in other industries. When you can walk hand in hand with AI and the person at the desk and show them all the benefits, that’s exciting. 

Paul Carroll

If you could change one thing about how the insurance industry is currently approaching AI adoption in claims, what would it be?

Mark Tainton

For me, it's what I call the evolution framework. AI is a journey, not a one-time event. Far too often, what I've seen is large organizations—mid-tier, tier two, tier three—treating this as basically an implementation. It's almost like they're going in, turning the light switch on and walking out.

I spend quite a bit of time working with clients all the way from inception to asking: Where are we actually going to implement this? What's the impact we're expecting? How does this align with strategic objectives? What are some of the key measurements we want to see in terms of adoption, change, and, ultimately, having the AI start to hit the hard dollars—reduction in litigation, average duration, and things like that.

I'll give you an example. I worked with a large carrier that wanted to implement AI across the entire organization. But they have an aging demographic in certain lines, and getting them to adopt AI would be difficult. They've also captured a lot of information very poorly in their systems—it's very much in their heads.

I said, Let's focus on the younger generation. They’ll adopt AI, and we’ll create a best practice, one that we can use when we bring in new talent. So we built a three-year program focused on them. Ultimately, the program was so successful that the older generation said, We want to be part of that, too. 

For me, the next window for anyone embarking on an AI journey is to focus on embedding it upfront—knowing, of course, that the process will evolve over time. 

Begin with what we call an EDA—exploratory data analysis—to determine what the baseline is. That way, you can prove that you’re opening and closing claims far more quickly and can see the change quarter over quarter. That data helps sell the journey. We've also done quite a bit of work around what we call data quality programs, where we assess the quality and change behavior at the desk in terms of how people are capturing data—all the way from structured to unstructured and, more importantly, in the adjuster call notes. That program embeds the solution into the fabric of the organization.

I think that's the next wave. 

Paul Carroll

Thanks, Mark.

Sponsored by Wisedocs

About Mark Tainton

Mark Tainton is the SVP of Data Solutions at Wisedocs, bringing over 30 years of AI, data and analytics transformation expertise in insurance and financial services. Having served as Chief Data Officer at multiple leading organizations, Mark understands the critical intersection of medical intelligence, litigation strategy, and claims outcomes. He advises Wisedocs on data and product strategy, go-to-market positioning, and the deployment of AI-powered solutions that address the most pressing challenges facing claims and legal professionals today.

Brute force has been the corporate response to the normalization of nine-figure payouts—build taller insurance towers. But by 2026, we've reached the breaking point of that strategy. Adding more capacity is no longer a hedge; it's a target. Leaders who continue adhering to a "wait-and-see" strategy will likely hand over their negotiating power to plaintiffs' counsel. It's time to stop reacting and shift to a more aggressive tactic of data-driven litigation counter-anchoring, a tactical maneuver that uses historical benchmarks and hard modeling to ground a case's valuation.

The Psychology of the First Number

Refusing to name a number isn't a denial of liability; it's a tactical surrender. When we stay silent and treat it as a problem for later, we leave a vacuum that the plaintiff is only too happy to fill. This is the psychology of anchoring: the first number heard becomes the mental hook upon which all subsequent negotiations hang. If the opening bid is a $100 million "lottery ticket," even a successful defense that cuts it in half results in a $50 million disaster.

Counter-anchoring disrupts this by providing a grounded alternative before the plaintiff's number can take root. This isn't a guess; it is a calculated figure backed by historical industry benchmarks and internal safety data. By presenting a credible, data-backed valuation early, we offer juries a "safe harbor."

Most jurors are actually overwhelmed by the emotional volatility of nuclear-risk cases; they want to be fair, but they lack a yardstick. When the defense provides that yardstick—derived from logic rather than emotion—it grants the jury the permission they need to reject an inflated demand without feeling they are dismissing the injury itself.

Deployment: When to Anchor (and When to Pivot)

Counter-anchoring is most effective in "gray area" liability cases—scenarios where the question isn't if the company is responsible, but for how much. In these high-value moments, the goal is to cap the ceiling before it vanishes. By introducing a data-backed valuation early in mediation, you effectively narrow the range between "reasonable" and "astronomical."

However, data is a double-edged sword. The greatest risk in this strategy is the "Cold Corporation" trap. If your counter-anchor looks like a sterile spreadsheet in the face of a human tragedy, you don't just lose the argument; you lose the jury.

There is a razor-thin line between being "grounded in reality" and being "callous to suffering." The math must be the foundation, but the delivery must be human. If the jury perceives your data as a tool to devalue a life rather than a method to find a fair resolution, the anchor will drag your defense to the bottom.

When executed with empathy, speed becomes your primary weapon. By removing the "valuation fog" early in the process, counter-anchoring forces both sides to deal with reality. It strips away the performative inflation of the discovery phase and gets to the heart of the settlement, often shaving months—and millions—off the litigation lifecycle.

The 2026 Toolkit: Credibility Over Calculation

In 2026, a spreadsheet is not a strategy. While internal loss runs are necessary, they are rarely sufficient to move a jury. To make an anchor stick, you must look beyond internal data. A jury will instinctively view a company's own historical figures as self-serving; to achieve true "safe harbor" status, your numbers must be validated against industry cohorts. Credibility is built on external benchmarks—proving that your valuation isn't just what you want to pay but what the broader market defines as objective reality.

The most critical hurdle, however, is the communication gap. Raw modeling is the foundation, but the courtroom narrative trumps all. If you cannot translate a complex actuarial model into a story about fairness and community standards, the data will be dismissed as "corporate math." The numbers provide the boundaries, but the narrative provides the "why."

Finally, this strategy demands a collapse of the traditional corporate silo. We are seeing the rise of the general counsel/risk manager nexus. In the past, Risk bought the insurance, and Legal fought the claims. Today, these two must merge their datasets well before a summons is served. By aligning on valuation models during the underwriting phase, the defense is armed and ready on Day 1 to set the anchor before the ink on the complaint is even dry.

The Underwriting Reality: From Defense to Differentiation

Adopting a counter-anchoring strategy does more than win cases; it fundamentally shifts the power dynamic at the renewal table. In the 2026 market, excess underwriters are no longer just looking at loss history—they are scrutinizing a firm's "litigation maturity." When you can demonstrate a repeatable, data-backed method for suppressing social inflation, you move from being a commodity risk to a "preferred risk."

The conversation with underwriters changes the moment you move beyond passive risk transfer. Instead of simply presenting a tower of limits, you are presenting a proactive defense framework. Underwriters are tired of "blank check" litigation; showing them that you have the tools to anchor damages early provides them with something they value more than anything: predictability. By proving you can cap the ceiling of a potential nuclear verdict, you provide the actuarial certainty that justifies lower attachments or more competitive pricing.

The ultimate result is a stronger strategic partnership with your carrier. You aren't just buying paper to cover a potential disaster; you are demonstrating a sophisticated operational control that protects the carrier's capital as much as your own balance sheet. In an era of escalating awards, the companies that thrive will be those that prove they aren't just insured against the storm—they have the data to ground the lightning.

A Grounded Future

The era of "buying our way out" of litigation risk is over. In a 2026 landscape where $100 million is the new baseline for a nuclear verdict, silence on damages is a luxury no risk team can afford. By embracing data-driven counter-anchoring, general counsels and risk managers can reclaim the narrative, providing juries and mediators with a logical "safe harbor" before the emotional tide takes over.

Success now requires a fusion of math and empathy—a strategy where the data is the foundation, but the story is the house. Ultimately, those who anchor early won't just lower their payouts; they will redefine what it means to be a resilient, data-forward organization in an age of outsized expectations.