Even as I've tracked every twist and turn in the technology for autonomous vehicles for going on 15 years now, the key development I've been waiting for occurred, not in the lab or on the road, but in a recent op-ed in the New York Times. In it, a neurosurgeon made the case for AVs as a "public health breakthrough."

He said he was horrified by the more than 39,000 deaths from motor vehicles just in the U.S. last year, "more than homicide, plane crashes and natural disasters combined.... These crashes are also the leading cause of spinal cord injury. We surgeons see the aftermath of the 10,000 crash victims who come to emergency rooms every day. The combined economic and quality-of-life toll exceeds $1 trillion annually, more than the entire U.S. military or Medicare budget."

Then he made the sort of case technologists have been making for years about the potential for AVs to drastically reduce death, injuries and property damage... but this time it came from a doctor. 

And he framed his case as a public health issue, the sort of effort government gets behind and citizens appreciate, even if they may discount the claims they hear from those they see as techno-optimists and rapacious capitalists.

Based on other favorable press in recent weeks and on the relentless rollouts of robotaxis planned for this year, I think we're seeing a sea change. Arthur C. Clarke famously wrote that "any sufficiently advanced technology is indistinguishable from magic," but, over time, the magic wears off, and wildly advanced technologies begin to seem almost normal.

AVs are now being normalized to the point where I think the clock has started ticking on what will be a fundamental rewiring of the auto--and auto insurance--landscape. 

While I've long been an enthusiast about driverless technology, I've always been worried about winning over public sentiment. The techies initially argued that they just needed to be demonstrably better than human drivers and felt that was a fairly low bar, given that more than 100 people a day die in car crashes just in the U.S. But that's not how people look at technology. 

When a driver causes a crash, we may be understanding. We've all done careless things and had near-misses. But if software causes a crash, it wasn't making a mistake in the heat of the moment. Someone designed that software, and they screwed up. The huge company employing that coder somehow missed that mistake, too. 

Machines aren't supposed to make mistakes. Ever. So the bar for AVs' safety is actually higher than it is for human drivers.

Uber stopped its efforts to develop autonomous vehicles after a single fatal accident in 2018 caused a PR disaster. GM's Cruise halted its robotaxi development after one of its cars hit a pedestrian in 2023. The collision was a freak accident, in which another car hit a jaywalking pedestrian and flipped her in front of the robotaxi. But Cruise's AV, programmed to get out of the way after an accident, pulled off to the side of the road--not understanding that the pedestrian was caught underneath the car. The multibillion-dollar AV program couldn't survive the bad PR and scrutiny that followed.

The Cruise debacle left a bad taste. Subsequent press played up resentment of AVs, such as by people who learned they could paralyze a driverless car by putting an orange traffic cone on the hood.

But the press has gradually been shifting. Recently, for instance, Tesla got some nice publicity because one of its cars drove the famous Cannonball Run route between Los Angeles and New York entirely in self-driving mode. (CEO Elon Musk had promised one of his cars would do so by the end of 2017, but still....) Tesla got more attention when Lemonade said it would offer steep discounts to Tesla drivers for miles they traveled while in so-called Full Self-Driving mode, based on the belief that Tesla's AI is much safer than human drivers are, at least in certain circumstances. 

Public opinion has seemed to shift, too, both based on the press and on the growing familiarity with the cars. Yahoo! Finance reports that a survey "conducted in San Francisco this past July found that ​​67% of San Francisco residents now support the operation of driverless robotaxis, up from 44% in 2023, with 'net favorability' of robotaxis swinging from -7% in late 2023 to +38% in mid-2025."

The New York Times piece by the neurosurgeon pulls all those threads together for me and suggests that the public is ready to accept whatever the technologists can deliver. 

Yes, there is always a danger that a car will do something catastrophic. And we'll still see the occasional story about an embarrassing glitch, such as the recent one where a power outage in San Francisco knocked out all the traffic lights, and Waymo's cars just stopped, citywide, because they didn't know what to do.

But, barring a disaster, people are certainly going to see a lot more robotaxis on the road this year. Google's Waymo is already up to about 250,000 paid, fully autonomous rides a week and aims to quadruple that by the end of the year. Waymo already has fleets in San Francisco, Phoenix, Los Angeles, Austin, and Atlanta and plans to add 20 markets this year--including Miami, Dallas, Houston, San Antonio, Orlando, Las Vegas, San Diego, Detroit, Washington, D.C., Baltimore, Philadelphia, Pittsburgh, and St. Louis. Waymo is testing in New York and plans to test in London soon, too.

Waymo is doubling its production of AVs and expects to build more than 2,000 this year. 

And that's just Waymo. Tesla has big plans to expand this year--though any prediction from Musk must be viewed with skepticism, given that he's been consistently overpromising about AVs for more than a decade. Amazon says it will build 10,000 robotaxis a year starting in 2027. Some smaller companies say they're testing robotaxis in Tokyo and Southeast Asia. A host of Chinese companies have pursued autonomous driving aggressively, though generally at the driver-assist level, and the government recently became more cautious after a gruesome accident. Baidu says it will test in London this year, and Europe is shaping up as a battleground. Most of the players there figure to be Chinese and American companies, but Mercedes just announced an autonomous venture with Nvidia, and Nvidia hopes to provide the technology, including simulators, in similar ventures with other manufacturers. 

Insurance won't feel the effect right away, by any means. The tectonic shift won't happen until individuals start buying driverless cars, and when that happens is anybody's guess. But even robust adoption of robotaxis, which should happen over the next couple of years, could be material. If Waymo is really doing 1 million paid rides a week by the end of this year, that's maybe $1 billion of revenue, if annualized. That revenue would have gone to gig drivers and taxi drivers, who buy traditional insurance, but instead will go to a corporate behemoth that self-insures. 

That shift in revenue will mean maybe the loss of just $100 million of premium for auto insurers (based on my back-of-the-envelope calculation). That's a drop in the bucket in a U.S. market measured in the hundreds of billions of dollars of premiums. But exponentials are crazy things. If Waymo quadruples its size this year, what will it do next year? The year after that? And after that?  It's pretty easy to imagine Waymo having 20X its current presence within a few years. And if Tesla, Amazon, Baidu and other Chinese behemoths can deliver, too....

It's worth watching, especially if the public health argument really gains traction.

Cheers,

Paul

Out Front Ideas with Kimberly and Mark kicks off annually with the "20 Issues to Watch" webinar. While there are certainly more than 20 issues to discuss, the focus is on high-impact matters in risk management and employee benefits that require more attention. These are essential issues for every risk manager, HR manager, and insurance professional to monitor in 2026.

1. Connected Risks

Risk does not happen in a silo, requiring assessment across the business and agile planning. Risk managers and their partners must be more diligent than ever in evaluating overlapping risks, including environmental, technological, and human factors. Most organizations recognize their risks, but few are fully prepared to tackle them.

2. Fraud as a Systemic Risk

The Coalition Against Insurance Fraud estimates that fraud costs the insurance industry over $308 billion per year. It can take the form of unethical physicians performing unnecessary surgeries, medical providers billing for services never rendered, and plaintiffs fabricating or exaggerating injuries. Fraud must be met with meaningful consequences, and the industry must actively identify fraud, share intelligence, and demand prosecution.

3. AI Lessons Learned from Early Adoption

Many early AI adopters initially focused solely on platform deployment, only to learn that success hinges on a clear use case tied to measurable business outcomes and a return on investment. Goals for adoption must be clear and concise. Additional considerations include stakeholder engagement, alignment and execution, data readiness, and governance.

4. Industry Engagement

In-person industry events bring colleagues together to help solve problems, exchange ideas, and learn from one another. Conference attendance has not returned to pre-pandemic levels, and that shift has come at a cost to the collective learning and collaboration that strengthen our industry. Reconsider the value of active industry participation and, if given the opportunity, attend a conference.

5. Healthcare Trends

Access to care remains a critical concern, particularly for rural healthcare entities at risk of closure due to continuing physician shortages. As pandemic-era waivers expire, telehealth opportunities are also ending, as physicians can only treat patients within the states where they are licensed. However, AI continues to drive health technology innovation, offering early diagnostic testing and opportunities for self-guided care. Wearables continue to gain popularity, with more industries deploying them to enhance workplace safety.

6. Insurance Market Pressure Points

Legal system abuse continues to worsen the development of liability claims, keeping commercial auto unprofitable despite a decade of premium increases. In response, there is growing interest in quota-share liability towers and captives. The property market avoided hurricane impacts last year, but those benefits were offset by wildfires, with hail and severe convective storms now driving most global catastrophe losses. Workers' compensation remains competitive, yet deteriorating claims have pushed combined ratios above 100% in California and Nevada, signaling an end to the prolonged soft market and flatter rate expectations ahead.

7. Catastrophe Risk Becomes Baseline Planning

For risk managers, what was once an ad hoc emergency response has become a structured playbook to follow in the event of a catastrophe. Some are even shifting from a coordinated team to a business unit that oversees climate, business continuity, and catastrophes. This approach outlines clear roles, responsibilities, and consistent expectations in the event of an incident.

8. Claims Insights

Medical inflation in workers' compensation has historically lagged behind broader healthcare inflation due to fee schedules, but those pressures are now clearly emerging. The National Council on Compensation Insurance (NCCI) reported a 6% increase in both indemnity and medical claim severity in 2024, while the Workers' Compensation Insurance Rating Bureau (WCIRB) in California noted a 9% increase in medical costs. Additionally, expanded mental health claims, catastrophic injuries, and cancer presumptions for first responders are affecting long-term costs.

9. AI in Business Transformation

Fluency in AI is becoming essential for organizations as they adapt to challenges. When paired with user-centric design, AI can drive transformation by improving efficiency while still relying on employees' critical thinking. Organizations that hesitate to adopt automation risk falling behind, especially as time savings can be reinvested in innovation.

10. California Workers' Compensation

California remains one of the costliest workers' compensation states. Savings from the 2012 reforms have been eroded by rising litigation and medical inflation, driving a 127% combined ratio in 2024 and prompting renewed reform discussions. The primary cost driver is cumulative trauma (CT) claims, which broadly cover degenerative conditions and account for over 21% of claims and 38% of litigated cases. While meaningful reform would require addressing CT claims, political resistance makes significant change unlikely.

11. Employee Benefits

Employers are prioritizing engagement, retention, and culture through continuous employee listening and lifecycle surveys. At the same time, health plan costs continue to rise, with projected increases of 6.5–7.6% in 2026, according to Mercer, and growing concern over GLP-1 drug spending. While point solutions remain popular, complexity is increasing. Employees increasingly value purpose, belonging, well-being, and psychological safety as organizations brace for tighter budgets and slower pay growth.

12. Legal System Abuse and Tort Reform

Between 2023 and 2024, the number of verdicts exceeding $10 million increased by 50%, while verdicts over $100 million surged by 68%. These trends can be exceptionally difficult to reverse. However, there has been incremental success, with Florida and Georgia enacting reforms to curb litigation abuse, and several other states considering similar legislation. For the impact to truly resonate with the public, the focus must shift to how these verdicts affect everyday life, including lost jobs, higher prices, and reduced access to services.

13. Workplace Mental Health and Well-being

Supporting psychological well-being is a strategic imperative for engagement, productivity, and safety. Burnout and mental health directly affect business performance and recovery outcomes. Mental health claims now rank second only to pregnancy in leave and disability, surpassing musculoskeletal injuries, and are increasingly recognized as barriers to injured worker recovery. Early identification and targeted support, including behavioral health resources and virtual care options, can improve outcomes and shorten recovery timelines.

14. Cyber Risk

Cybersecurity remains a top concern as ransomware attacks scale through ransomware-as-a-service, expanded attack surfaces, and third-party vulnerabilities. Many breaches go undetected for months, and repeat attacks are common when weaknesses persist. AI-driven tactics, including deepfake executive scams, are increasing risk. Human error remains the weakest link, making continuous employee training, phishing simulations, and healthy skepticism essential to an effective cybersecurity strategy.

15. Workforce Considerations

Retaining today's workforce begins with understanding employee expectations around growth and flexibility. Employees increasingly value career mobility, skills that support current roles, and opportunities to build future capabilities. In hybrid environments, organizations are expanding virtual reality training and self-paced, high-impact "burst" learning programs. Clearly defined career paths are more important than ever, particularly as expectations for flexibility rise. PwC's 2025 research found that 58% of employees would rather quit than return to full-time office work, up from 35% in 2023.

16. Public Entity Challenges

Workers' compensation presumptions and heightened law enforcement liability exposures present unique risks for the public entity sector. At the same time, public entity risk managers face constrained budgets, limited staffing, and aging infrastructure. Pension liabilities remain a significant concern, with recent estimates placing nationwide unfunded public pension obligations at $1.2 trillion. As claim costs rise, higher taxes are likely to follow, and when revenues fall short, essential services are reduced. Because taxpayers ultimately bear these costs, these challenges should matter to private sector businesses and risk managers as well.

17. Reputational Risk in a Real-Time World

Reputational risk is a concern throughout organizations, whether a social media post misses the mark or an operational blunder occurs. Reputational events may affect business success, customer relationships, and growth opportunities. When a crisis occurs, preparation is critical. The response often determines the extent of reputational damage and risk exposure. Knowing how to frame that response for the intended audience is essential and requires understanding stakeholder, employee, and customer perceptions in advance.

18. Regulatory Overreach and Unintended Consequences

Overregulation continues to create significant challenges for businesses and risk managers. In-state physician licensing requirements temporarily waived during the pandemic improved access to care, but those requirements have since been rolled back. Similar regulatory friction exists for claims professionals, particularly in the handling of in-state workers' compensation claims. While well-intentioned, these regulations can fail to keep pace with technology, market realities, and evolving risks.

19. Operational Readiness in the Age of AI

As technology reshapes business models, organizations must ask whether their operations are ready for the future. Without adaptation, some business models risk becoming obsolete within the next decade.

20. Critical Digital Infrastructure: Data Centers as a System Risk

None of today's AI-driven innovations are possible without the massive data centers that power AI and cloud-based systems. These facilities have come under increased scrutiny as some communities court them, while others resist due to strain on critical infrastructure, particularly electrical grids and water supplies. Data centers also create substantial downstream risk due to their role as critical service providers. A single outage can disrupt operations for thousands of businesses.

Listen to the archive of our complete Issues to Watch webinar here. Follow Out Front Ideas with Kimberly and Mark on LinkedIn for more information about coming events and webinars.

What if we have been looking at AI from the wrong angle? What if it is not a magic fix for the insurance industry’s legacy issues but is an unlock for the next generation of growth through insurable risks? 

AI is emerging alongside forces already reshaping the global risk fabric: the rise of intangible assets and cyber exposure, mounting climate volatility, shifting global demographics, and an entirely new class of technologies. These are not distant scenarios, they are today’s realities. 

The IIS Innovation Report reflects an industry in transition, a theme underscored during our executive working group session at the Swiss Re Centre for Global Dialogue in Rüschlikon. Leaders recognized that early AI efforts often focused too narrowly on efficiency and missed the broader strategic opportunity emerging across the global economy. 

The discussions made it clear that the next decade will divide the sector between organizations making marginal improvements and those rebuilding their operating models around proprietary knowledge graphs, reengineered data flows, and augmented human judgment. 

These foundations enable stronger risk selection, superior service performance, and loss prevention in a far more dynamic risk environment, while preserving what remains fundamentally human in our business: trust, advice, and long-term client relationships. AI agents can deliver transformative gains, but only for firms prepared to rethink governance, decision rights, talent, and data strategy. 

This is the strategic inflection point. If we mobilize for it, insurance will not simply adapt, it will become one of the defining stabilizers of an increasingly connected and AI-enabled world!

--George Kesselman

Executive Summary

AI transformation is sweeping the insurance industry

The IIS Report on Innovation, which draws from a diverse respondent pool across insurers, reinsurers, insurtechs, and consultancies, finds that, while enthusiasm for AI is high, maturity levels vary significantly by company size and type. Larger firms are generally further along in production deployment, while smaller firms are focusing more on exploration and customer-facing innovation. 

Efficiency remains the primary driver of AI adoption 

Operational efficiency and workflow optimization dominate current AI priorities, with 53% of respondents citing them as top focus areas, followed closely by underwriting, pricing, and claims management. These findings indicate that insurers are initially using AI to strengthen core processes rather than disrupt existing models. Smaller firms, however, show a stronger tendency toward leveraging AI for customer service and market expansion. Metrics of success largely center on productivity gains, data accuracy, and improved customer experience, though formal frameworks for ROI measurement are still evolving across the industry. 

Experimentation is widespread but deployment maturity is limited

Adoption data reveal that about 87% of companies are pursuing GenAI initiatives, though only around a quarter have reached production-level implementation. Budgets dedicated to AI average 3.9% of overall spending. Most firms rely on third-party general-purpose large language models like ChatGPT, while larger organizations increasingly explore first-party or industry-specific models. Leadership of AI innovation typically originates at the executive level – especially CEOs, boards, and CTOs/CIOs – indicating strong top-down strategic ownership of AI adoption.

Key challenges focus on governance, data, and talent

This report also identifies major challenges that can temper progress. Chief among these are concerns over data privacy and integrity, security, and bias management, as well as the difficulty of measuring ROI. Talent shortages and the lack of formal governance frameworks also impede scalable AI integration, especially for small firms. Most companies rely on human oversight rather than structured governance systems, though larger insurers are beginning to formalize processes through ethics committees, audit trails, and explainability standards.

Innovation is balanced with risk in the era of AI agents 

Looking forward, the report highlights both excitement and caution surrounding the rise of autonomous AI agents in insurance. Top concerns – such as hallucinations, validation difficulties, and regulatory compliance – reflect an industry still grappling with trust and accountability in automated decision-making. Overall, the findings portray a sector experimenting, learning, and building the foundations for responsible, scalable AI adoption that enhances both operational excellence and customer experience

To download the full report, click here.

Catastrophic events no longer feel rare, and the trend has moved far beyond what businesses once planned for. Over the last decade, the United States averaged nearly 19 billion-dollar disasters each year, but recent seasons have pushed well past that baseline. NOAA’s National Centers for Environmental Information recorded 28 such events in 2023 and 27 in 2024, a stark shift from the 1980s when the country saw only about three annually. The financial toll has climbed just as quickly, with losses from 2020 through 2024 averaging $149 billion per year, about 50% higher than the previous decade. These patterns are redefining what catastrophic risk looks like and challenging long-held assumptions about how often businesses can expect major disruptions.

As the ground shifts, companies that once planned around familiar cycles of storms, fires or floods are now confronting events that fall outside historical patterns. The frequency, intensity and unpredictability have changed, and the insurance system built on those past patterns is adjusting in real time. Carriers are reassessing their ability to absorb these losses, and many businesses are finding that the coverage they relied on a decade ago is no longer guaranteed.

A market adjusting in real time

The commercial market isn't simply "hardening." It's recalibrating. Carriers are confronting losses that strain decades of assumptions, and the adjustments are landing squarely on policyholders. Reinsurers increased rates after several consecutive years of heavy catastrophe losses, and carriers passed those costs down the line.

Models that once guided underwriting with confidence now struggle to predict what a season will look like. As a result, insurers have taken steps that affect companies of every size, even those with long relationships and disciplined risk management histories.

Those steps include:

• Cutting limits or declining coverage in regions prone to catastrophic weather
• Raising premiums for property and business interruption programs
• Adding exclusions tied to infrastructure failures, utility outages or wide-area events
• Requiring higher deductibles that shift more exposure back to the business

These changes reflect market realities, not a lack of commitment from carriers. But the outcome is the same: businesses face a widening gap between the risks that threaten their operations and the coverage that remains available.

Catastrophe looks different than it did even a decade ago

Today's catastrophic events aren't limited to the storms or wildfires that dominate headlines. Businesses are experiencing losses through disruptions that don't always produce physical damage but still create significant operational fallout.

A regional power grid failure can shut down production for a week. A port closure can stall shipments and freeze revenue. Smoke from distant fires can force evacuations or limit facility access. Even a minor storm can disrupt transportation networks enough to halt essential deliveries.

For some companies, especially mid-size operations, even a short disruption can derail revenue targets or strain cash flow. Many later discover those losses don't fit the triggers in their commercial policies.

When coverage no longer matches the risk

The shift in catastrophic risk has exposed a structural gap. Traditional policies were designed for events linked to clear physical damage. But the biggest financial pressures today often stem from indirect losses: supply chain interruptions, extended downtime or infrastructure outages that fall outside standard policy language.

Companies now face the possibility of:

• Uninsured business interruption when utilities fail
• Supply chain breakdowns that halt production but do not trigger property coverage
• Delays caused by transportation failures that fall outside conventional business interruption terms
• Vendor or contractor failures that create cascading operational consequences

The result is a landscape where businesses carry far more risk on their balance sheets than they did a decade ago, often without realizing how exposed they are until a disruption occurs.

How businesses are adjusting their approach

No single strategy solves this challenge, and companies are not walking away from the commercial market. They still rely on traditional policies for core protection. But many are broadening their risk management approach to address exposures the market can't or won't take on.

One of the clearest trends is that more companies, including mid-size businesses, are evaluating ways to finance retained risk. That includes expanding deductible layers, building internal reserves and, for many, exploring captive insurance structures that allow them to address operational exposures that are difficult to insure elsewhere.

A captive isn't a replacement for commercial insurance. It's a tool that helps companies take control of risks that fall through the cracks. When designed correctly, it supports recovery from disruptions that create financial pressure even without physical damage.

Why this shift matters

Businesses operate in an environment where catastrophic events have outpaced the insurance system built to cover them. The market is doing what it needs to do: adjust, correct and protect solvency. But that correction forces companies to rethink what resilience looks like.

Executives are asking new questions:

• What happens when a catastrophe affects operations but does not trigger a claim?
• How much financial exposure sits outside the commercial program?
• What mechanisms exist to fund losses that commercial policies exclude?
• How can the business recover quickly without waiting for external aid or slow claim processes?

These questions are driving strategic conversations that didn't exist a decade ago, especially among companies that cannot afford extended downtime.

Planning for volatility, not predictability

The path forward requires a more flexible approach to risk financing. Businesses are developing programs that combine traditional coverage with internal mechanisms designed to respond to catastrophic exposures the market excludes or restricts.

For many, that includes:

• Assessing where catastrophic risk exceeds available coverage
• Quantifying operational vulnerabilities that don't trigger standard policies
• Considering alternative financing tools, including captives, to bridge the protection gap
• Building long-term strategies that reduce reliance on unpredictable market cycles

Companies that take these steps move from hoping the market will respond to preparing for a reality where disruptions are more frequent, more complex and more costly.

The bottom line

Catastrophic events are no longer rare, and insurance structures built around predictable patterns can't always keep pace with today's volatility. Businesses that want to remain resilient must look beyond traditional coverage and consider additional strategies that help them withstand disruptions that threaten operations.

Captives play a role in that shift, not as a cure-all, but as a practical tool for financing the risks the commercial market can't absorb. The companies that adapt now will be better positioned to stay operational in a landscape where catastrophe is a continuing part of doing business.

Executives in every industry grapple with fragmented information streams that obscure the full picture of customers, competitors, vendors, and risks.

They want pictures of places with perils, proximity, price, sanctions, anti-corruption, regulation, crime, and compliance. Those map the playing field.

They need profiles of players on that field. Best, worst, and next customer, competitor, consortium, and criminals. Active, passive, and latent friction on the field of business and nature.

What are the risks they need to manage themselves? Which risks can be transferred with insurance? How do things change over time? When and why should they adopt new tactics?

Nobody builds or operates anything without risk management and insurance.

Some risks are well-enough understood that there are formulas on maps with data that explain:

• rate to risk – distance to coast, nearest fire line, closest body of water, feet to fire hydrant
• rules of risk – sovereign borders, zoning, taxation boundaries, legislative hellholes, politics
• range of risk – proximity to population, nearness of combustible materials, crime indexes

Some risks are still being grappled with:

• "invisible" risks – the internet is not a place, criminals lie, organized criminals lie better
• "known unknowns" risk – pandemic, war, supply chain, cyber, lawsuits, climate, tech
• "emerging" risks – aging infrastructure, connectivity, AI, casualty CATs, land use

Entity resolution and digital entity resolution are two key dimensions where massive progress is being made in transforming our understanding of the players on the field as well as navigating current and foreseeable changes in the field of play itself.

Adding entity data with geo-digital entity attributes is now a new avenue for putting risk on the map.

Are my neighbors my adversaries?

"See the battlefield, know your enemy" is a strategic imperative in conflict – some see that business competition, combating criminals, and complying with rules, regulations, and laws as necessary conflict, akin to war. Sun Tzu's "The Art of War" makes compelling sense in creating an awareness about your own situation and a diligence in understanding others with whom you interact or that are in your environment.

Tactically, assigning ultimate owner entity resolution to all the places and resources on your business battlefield makes a frustrating legacy of poor data a daily problem. Strategically, you can add more sustainability and resilience to your business by improving your knowledge of "brick and mortar, with click and order" data and clearly mapping your known trusted customers and partners as well as those you do not trust. Then everything else is open for business, with trust unknown, yet not unknowable.

Blending hyper-local with hyper-linkable on a map

The risks you can see and those you can only know as relatable can be illustrated visually on a map now.

Visible overlap of the world and the e-world can take a picture where two worlds interact - physical names and addresses and internet names and addresses pool into entities and relationships. Perils and problems in either or both can create business risk, but a peril on the internet might manifest at multiple physical locations. A duality of risk with asymmetric shapes.

You can play these visuals forensically and in a forward-looking fashion to understand risks, supply chains and single points of failure to improve your sustainability and resiliency. It's a new frontier of both understanding risk as well as a new entrée for Predict & Prevent initiatives.

Tracking relatedness on a map when water is rising, the wind is blowing, a freeze is coming, the earth is shaking, or when a fire is raging are traditional processes now. But today's risks now include more layers of risk that extend to digital assets and brand reputation as well as cyber exposures and regulatory and compliance requirements tied to knowing your business, your customers, your vendors, and your physical/digital/legal/cyber ecosystem.

When is a bunch of dots on a map really a single organization with legitimate purpose? If you don't tie them together appropriately, then you create aggregation and accumulation risk.

When are those dots nefarious sanctioned shells, all being operated in shadowy collusion? If you don't find these accurately, then you are dealing with the wrong customers.

Only entity resolution can help you sort it and keep it sorted.

A company in New York running on servers in North Korea owned by companies controlled by criminal cartels in sanctioned and unsanctioned countries is different than a legitimate NYC business entity. The same for Frankfurt, London, Quebec, Sao Paulo, Mexico City, or any hub.

Knowing what's behind the dots on a map matters.

Entity resolution and digital domain entity mapping emerge as pivotal technologies, bridging disparate data points to reveal actionable insights.

From unmasking fraudsters and untrustworthy entities, we can now blend data and view them in maps and graph analytics like never before. These connected and resolved entities can show what is otherwise hidden – how "click&order" meets "brick&mortar" – and then relates these to maps and graphs that bring entity resolution data and GIS tools together as new ways for reshaping how businesses operate. In some regards, a GIS coordinate or polygon is the same as a street address in creating a unique identifying reference. In other regards it may be even better.

Classic geospatial information systems are mashing up with federated streams of disparate identities getting resolved with industrial grade entity resolution engines on names and addresses from the real world and modernized digital entity names and addresses from the e-world.

Unraveling Entities: The Foundation of Clarity

Entity resolution, at its core, is the art and science of identifying when different records refer to the same real-world entity, despite variations in naming, addresses, or other attributes. Think of it as a digital detective work: matching "Acme Corp." in one database with "Acme Industries" in another, accounting for typos, abbreviations, or mergers. This process relies on advanced algorithms, machine learning, and sometimes geospatial data to link entities across sources like customer databases, transaction logs, and public records.

In the business world, poor entity resolution leads to potentially costly blind spots—duplicate customer entries inflating marketing budgets or missed connections in supply chains. But when done right, it creates a unified view, often called a "Customer 360," enabling personalized experiences and efficient operations. Financial institutions, for instance, use it to consolidate profiles from multiple accounts, spotting patterns that standalone data might overlook.

Business leaders face a perennial challenge: How do you connect the dots in a sea of disconnected data? Consider a scenario where a financial institution spots unusual web traffic patterns on its site. Is it a legitimate corporate inquiry or a sophisticated fraud attempt? Or imagine a real estate firm assessing a commercial property—does the tenant's online activity signal stability or hidden vulnerabilities? These questions underscore the power of entity resolution and digital domain mapping, two opportunistically intertwining techniques that transform raw data into strategic advantage.

Mapping the Digital Footprint: From IP to Insight

Companies are complementing entity resolution with digital domain mapping, particularly in the practice of tracing web traffic back to specific companies through reverse IP lookups. When a visitor lands on your site, their IP address can be cross-referenced against databases of corporate networks, revealing not just location of the domain server, but also the operating organizational identity - using B2B signals to understand transactional behavior.

Tools like reverse IP tracking turn anonymous visits into named prospects, enriching CRM systems with firmographic data such as company size, industry, and revenue. When integrated with entity resolution, it resolves ambiguities—ensuring that traffic links correctly to the parent corporation, even if subsidiaries are involved.

Key Use Cases: Where Resolution Meets Reality

The true value shines in practical applications. Here are a few ways businesses are leveraging these technologies to drive decisions and mitigate risks.

Fraud Detection: Spotting the Anomalies

In fraud prevention, entity resolution and digital domain mapping form a dynamic duo. Banks analyze transaction data alongside web traffic to detect mismatches—say, a login from an IP tied to a known risky entity, or duplicate profiles attempting wire transfers. For example, if multiple accounts share an email but originate from disparate company IP addresses, it could flag account opening fraud. Anti-money laundering (AML) teams use this to uncover hidden networks, reducing false positives and accelerating investigations. Real-time resolution cuts fraud losses by identifying suspicious patterns across channels more accurately and faster than other means.

Property Due Diligence: Assessing Digital Vitality

For real estate investors and developers, due diligence extends beyond physical inspections. Entity resolution helps verify tenant identities by linking lease records to corporate filings, while digital domain mapping evaluates a company's web traffic footprint. High traffic from reputable IP addresses might indicate a thriving business, boosting property value; conversely, erratic patterns and patterns with "bad actors" could signal instability. In M&A contexts, this combo accelerates reviews, slashing due diligence time from weeks to hours by automating entity matches and traffic analysis. OSINT techniques further enhance this, pulling in public web data for comprehensive risk profiles.

Marketing and Lead Generation: Targeting with Precision

B2B marketers thrive on digital domain mapping to identify anonymous site visitors as potential leads. By resolving these entities, teams may personalize content—serving tailored ads or emails to decision-makers at visiting companies. Account-based marketing (ABM) benefits immensely as well, prioritizing high-value prospects based on traffic intent.

Charting the Future: Integration and Innovation

As data volumes explode, entity resolution and domain mapping will evolve with AI, incorporating real-time geospatial layers for even richer insights—think mapping traffic to physical locations for everything from trusting a transaction to supply chain optimization. Executives invest in resolving uncertainties while positioning their organizations for what's next – the unknown to the knowable.

The global insurance market is used to discussing war risks in terms of coverage, limits, and pricing.

In Ukraine, war risk is no longer a theoretical construct or a niche extension of property insurance. It is a daily operational reality.

Over the past few years, Ukrainian insurers have gone through a learning curve that most markets only explore through stress tests or academic scenarios. This experience is not about heroism or communication. It is about how claims are actually handled when war becomes a physical risk environment.

When PVI stops being theoretical

In stable jurisdictions, political violence insurance is typically perceived as:

• an add-on to property coverage,
• a tool for large infrastructure or cross-border projects,
• a low-frequency, high-severity product.

In Ukraine, this logic no longer holds.

War risks here:

• materialize with high frequency
• take multiple forms — from direct hits to secondary damage,
• overlap with active production, energy, and logistics processes.

As a result, the key question is no longer whether war risks can be insured, but whether insurers are operationally capable of settling such claims in a controlled and professional manner.

Claims ≠ payment

One of the most common misconceptions outside Ukraine is the idea that war risk claims follow a linear process:

incident → report → payment.

In reality, war-related losses are rarely simple.

Assets affected by attacks — power plants, manufacturing facilities, logistics hubs — have multi-layered technical structures, including:

• core equipment,
• auxiliary systems,
• cable networks,
• control and monitoring systems,
• infrastructure elements with indirect or secondary damage.

Each component requires separate technical assessment, and standard claims-handling templates are largely ineffective.

In practice, war risk claims become engineering-driven analytical projects, not administrative exercises.

The limits issue: why "EUR 250,000" or "UAH 10 million" is not underinsurance

A frequent question from international partners is why war risk limits in Ukraine often appear modest.

The answer lies in reinsurance availability and affordability.

After every major attack, insurers receive a surge of requests from corporate clients. International reinsurers — including the Lloyd's market — are formally willing to quote. In practice:

• quotes are valid for hours or days,
• pricing can reach 10–15%,
• terms fluctuate significantly depending on the phase of the conflict.

Under such conditions, full risk transfer frequently becomes economically unviable for insureds.

As a result, Ukrainian insurers have developed an alternative model — providing war risk coverage backed by their own capital, within limits that are financially sustainable.

This is not a compromise.

It is pragmatic capital risk management.

Speed versus accuracy

Another underestimated dimension is claims settlement timing.

War risk claims require a delicate balance:

• excessive speed increases the risk of technical or legal errors,
• excessive delay jeopardizes business continuity for insureds.

In the Ukrainian context, 30–40 days from incident to payment is not slow. It reflects:

• comprehensive documentation,
• multi-level technical expertise,
• decision-making under non-standard operational conditions.

This balance is difficult to model theoretically but emerges through practice.

The human dimension of claims handling

An often-overlooked element of war risk claims is the human factor.

Claims teams operate:

• on physically damaged sites,
• in constant interaction with clients facing business disruption or loss of critical infrastructure,
• under intense responsibility for accuracy, timing, and capital impact.

In such conditions, policy wording alone is insufficient.

Effective claims handling requires the ability to combine technical expertise, expectation management, and professional restraint.

This dimension is largely absent from traditional claims-handling frameworks in peaceful markets.

What global markets still underestimate

The core lesson from Ukraine is uncomfortable but clear:

War risk is not a standalone insurance line.

It is a systemic stress test for underwriting, capital adequacy, claims handling, and human management.

Ukrainian insurers are currently accumulating experience that:

• cannot be fully replicated through simulations,
• is not captured in standard methodologies,
• will, unfortunately, become relevant for other markets sooner or later.

Ideally, such experience would never be needed.

But since it exists, it deserves to be discussed professionally and without illusion.

For many in the insurance industry, 2025 was the year of the "AI Reality Check." After a whirlwind of excitement surrounding generative models, many carriers found themselves navigating a landscape cluttered with broken promises and stalled pilots. As we look toward meaningful innovation in 2026, the path forward requires us to address the "key myth" of AI: the seductive, yet ultimately destructive, belief in the end-to-end magic pill.

Believing that AI can or should replace human judgment at scale is disconnected from the reality of what the technology is. It's far more nuanced and, ultimately, more valuable. AI excels at specific, well-defined tasks: parsing documents, extracting structured data, identifying patterns in large datasets. Humans excel at everything else: understanding context, applying judgment, managing relationships, and making decisions that balance competing priorities.

AI in insurance isn't about doing it all at once. It's about deploying AI module by module, connecting thoughtfully, and staying grounded in what the technology can and cannot do today. That's how AI moves from hype to durable business value.

This distinction matters enormously, especially in insurance, an industry that has been swept up in the promise of AI-powered transformation. Over the past few years, insurance companies have invested heavily in "end-to-end AI systems," ambitious platforms that promise to automate entire workflows, from document intake through underwriting decisions to claims processing. The pitch is compelling: let AI handle the complexity so your teams can focus on strategy. The reality, however, tells a very different story.

The Gap Between Hype and Production

The most significant barrier to durable business value has been the industry's obsession with "end-to-end" solutions. We have seen insurers attempt to buy "AI underwriters" with the expectation that the model will handle everything from initial intake and actuarial analysis to final premium pricing.

There's significant noise around concepts like "AGI" (artificial general intelligence) which creates unrealistic expectations about what AI can accomplish today. This prevailing narrative obscures a critical truth: we're nowhere near the kind of AI that can independently manage the nuanced, multifaceted work that insurance professionals do every day.

An AI cannot replicate 20 years of an underwriter's experience or possess the nuanced context of a specific account. When these "do-it-all" systems attempt to underwrite a complex entity like a national car rental fleet, they often produce inaccurate results because they lack the human context to understand the specific distribution of vehicle types or local risk factors.

When these end-to-end systems fail to deliver, adoption plummets, and frustrated teams retreat to their old manual ways of doing things. This is a failure of strategy, not technology. The myth that AI can do it all has led many to overlook the "hidden costs of delay"—the thousands of touchpoints where humans are forced to review the same long documents and messy email threads over and over again.

This observation cuts to the heart of the key myth that has driven billions in insurance AI spending: the belief that you can build a single system to handle everything.

The Human Touch

Another critical truth? People want to know there is a human hand guiding the decision-making, particularly in an industry as important as insurance. Insurity's 2025 AI in Insurance Report revealed that just 20% of Americans say it's a good idea for P&C insurers to leverage AI, and 44% of consumers are less likely to purchase a policy from an insurer that publicly uses AI. In a 2025 Guidewire survey, 40% of respondents said they would feel more confident in insurers' AI if decisions could always be referred to a human when challenged. Finally, a 2025 survey conducted by J.D. Power showed that insurance customers are most comfortable with AI when it is used to automate routine aspects such as sending claim status updates (24%), managing their billing (23%), and answering basic customer service questions (21%).

So what insight can we gain from these numbers? People are more wary of the insurance industry's use of AI when there isn't a human available to speak with or in control of ultimate decision-making. It seems that customers are far more comfortable with insurers using AI in their workflows when it is deployed for automatic, manual processes embedded with human oversight.

The Failure of End-to-End Automation

Many insurers bought AI underwriting or claims products with high expectations. These systems promised to intake documents, evaluate risk, and generate underwriting decisions and pricing. It seemed the entire underwriting process would be fully automated. What happened next was instructive.

In one recent example, a large insurer deployed an "end-to-end" AI system to handle renewal underwriting for a major account. The AI evaluated the client's profile and recommended a specific premium. But when the human underwriter, who had managed that account for years, reviewed the recommendation, the flaws became obvious. The AI had missed critical nuances about the client's composition and risk profile. The underwriter knew from years of professional experience that this contextual information fundamentally changed the risk calculation. The AI system had the same information as the human underwriter, but the AI's recommendation was simply wrong.

The outcome was predictable: the insurer stopped using the system and went back to manual underwriting. With one major near-miss, "people just go back to the old way of doing things," the expert said.

This represents a profound failure in the AI industry. After this experience, the underwriter noted "It's better to do it manually than to use an AI. Something seriously has gone wrong here."

The Real Innovation: Modular AI

If end-to-end systems fail, what actually works? The answer lies in a fundamentally different approach: "modular AI deployment." Rather than trying to automate entire processes, successful organizations break complex workflows into smaller, well-defined components and apply AI where it genuinely adds value.

Instead of attempting to automate every aspect of a human's job, AI initiatives should focus on eliminating one extremely tedious and time-consuming task.

This philosophy is particularly powerful in document-heavy operations like insurance. Rather than developing an AI that promises to fully contextualize an underwriting submission and make complex recommendations, a more effective strategy is to concentrate on a single, crucial pain point such as accurately extracting and classifying documents. This is a genuinely difficult challenge. Insurance submissions often contain mixed document types, irrelevant supplemental data, and complex tables that general-purpose AI models frequently fail to process correctly because they are not designed to do so.

This is precisely where focused AI adds clear, measurable value. Once documents are properly classified and key data is converted into structured formats, human underwriters operate with far greater efficiency. Their time is spent reviewing pre-processed data and applying their judgment, experience, and understanding of company-specific risk appetite, not manually hunting through dozens of PDFs for critical information.

Building Digital Transformation Through Integration

The path to meaningful AI advancement in insurance isn't about finding the perfect all-knowing system. It's about thoughtful integration of specialized components to increase efficiency and letting professionals get back to the real work at hand. Organizations should consider which capabilities to buy (like document extraction), which to build internally (like risk models specific to your business), and how to orchestrate them effectively.

This is building AI one small piece at a time. You might deploy document classification as a module. Then add information extraction. Then integrate those outputs into your downstream systems. Each step is validated, each component is understood, and each addition genuinely improves the workflow for the humans who ultimately make the decisions. No "end-to-end" black box AI.

Admittedly, this approach requires discipline and is less exciting than the promise of end-to-end automation. But it actually works and leads to full adoption, rather than initial experimentation and inevitable abandonment when reality fails to match the pitch.

Since 2019, several casualty lines of business have shown a consistent pattern of adverse development through year-end 2024. Preliminary third-quarter 2025 disclosures signal that this trend is not yet reversing. The underlying experience, however, differs significantly by line and by carrier. This article focuses on where and why reserve shortfalls are occurring. We also provide some high-level suggestions to adjust actuarial methods for more adequate reserves.

Using Annual Statement data from S&P Global Market Intelligence, we examined:

• Commercial auto liability (industrywide), and
• Other liability—occurrence experience for 20 writers concentrating on excess or umbrella coverage.

Across accident years 2016–2024, published ultimate loss ratios have increased almost every calendar year. With the benefit of hindsight, initial and subsequent reserves were inadequate.

From Annual Statement data via S&P Global Market Intelligence Industry Commercial Auto Liability

From Annual Statement data via S&P Global Market Intelligence based on Other Liability Occurrence results from 20 companies that predominately write excess and umbrella business.

What is driving this pattern of inadequate reserves? We believe that the following factors are the most significant:

1) Extended Litigation: The expansion of third-party litigation funding and the improved capitalization of certain plaintiff firms mean more lawsuits proceed to trial. This causes challenges with traditional actuarial methods. Actuaries often use the past patterns to predict future patterns; however, if the environment changes significantly the methods become less reliable. With an increasing percentage of claims being litigated, historical loss emergence patterns are less reliably predictive of the future patterns. The industry has observed both longer cycle times (from claim report to claim settlement) due to more litigation and increased settlement costs as jury outcomes increasingly favor plaintiffs.

2) Backward-looking benchmarks: Actuaries often use older years' loss ratios to estimate loss ratio results for more recent years (after adjusting for premium changes and loss trends). However, if the older years' loss ratios consistently increase, the initial assumptions for the newer years start too low.

3) Under-estimated trend in a rising-cost environment: In an environment of increasing costs, it is difficult to estimate trend factors. For example, if average claim costs are increasing, some companies may believe that case reserves are more adequate and therefore not reflect the higher trends in the projections.

4) Management optimism. After the large rate increases and underwriting tightening during 2019-2022, some management teams find it hard to believe that loss ratios are not dramatically improving. This belief can delay the recognition of continuing adverse development.

The published industry results for the last few years clearly indicated adverse industry development as illustrated in the graphs above. Preliminary data published through the third quarter of 2025 indicates adverse development is continuing for some companies.

The table below displays development through the third quarter for all lines of business, separated by companies that indicated favorable development for accident years 2022 and prior and those that indicated adverse development.

*Based on Accident Years 2022 and Prior

From Annual and Quarterly Statement data via S&P Global Market Intelligence

For the companies we have summarized that reported third-quarter data, this industry composite displayed little change in prior year reserves for accident years 2022 and prior, with favorable reserve development for accident years 2023 and 2024. 53% of the companies indicated favorable development and 47% of the companies indicated adverse development for accident years 2022 and prior. We note that reserve development differs by company in the amount and magnitude due to the lines of business written.

The quarterly data reported to the NAIC is not presented in the same level of detail as the year-end data, as Quarterly Statements display development for all lines of business combined. Therefore, we segregated the companies into different groupings based on our assessment of the type of business the companies write. Additionally, the quarterly development is only available for accident years 2022 and prior, 2023 and 2024.

The cohorts of companies that primarily write personal lines business, workers compensation business, medical malpractice business and mortgage insurance displayed favorable reserve development for accident years 2022 and prior, and also for accident years 2023 and 2024. Personal lines business as well as workers compensation business are lines generally less affected by social inflation. For accident years 2022 and prior, the total combined reserves for these cohorts of companies developed favorably by approximately 3%.

From Annual and Quarterly Statement data via S&P Global Market Intelligence

However, the cohort of companies that write primarily commercial insurance, companies in run-off, and reinsurance companies displayed adverse development for accident years 2022 and prior.

From Annual Statement data via S&P Global Market Intelligence

Drilling down within the commercial lines writers provides additional insights. The following table displays the reserve development for commercial lines writers that:

• write limited amounts of workers compensation;
• write both commercial and personal lines;
• are excess and surplus lines companies; and
• are writers of other commercial lines of business including workers compensation (i.e., "other commercial writers").

The cohort of companies that primarily write commercial lines with limited workers compensation business displayed higher adverse development (2.6% of adverse development for accident years 2022 and prior) compared to their more diversified peers that also wrote either workers compensation or personal lines (these cohorts displayed 0.3% of adverse development for accident years 2022 and prior). It is reasonable to assume that commercial lines carriers that are more diversified (e.g., write workers compensation or personal lines business) are benefiting from the favorable development on these lines which mitigates the development they may be experiencing in their commercial business.

The cohort of commercial companies with limited workers compensation business also write commercial automobile liability. The companies that primarily write commercial auto liability are displaying higher adverse development. We did not separately segregate these companies as the reserve base is limited and the development is driven by a few companies. Commercial auto liability is a line of business more affected by social inflation that has had significant rate increases and re-underwriting over the past few years, which increases the uncertainty in the reserve estimation process.

We note there is variability within the various cohorts and for certain cohorts of companies, a few large carriers had a significant effect. Within the "favorable" cohorts, 41% of companies posted adverse development for accident years 2022 and prior. Conversely, 49% of insurers in "adverse" cohorts reported favorable development for those same accident years. For the lines of business affected by social inflation, prior years' development hinges on how effectively each insurer has captured social inflation effects in past analyses and how aggressively they are recognizing those pressures today.

Favorable cohorts: Companies writing personal lines, workers compensation, medical malpractice and mortgage insurance

Adverse cohorts: Companies writing commercial business, companies in run-off and reinsurers

Given the factors outlined, we expect unfavorable reserve development to persist for certain lines of business and companies. However, favorable and adverse development will affect insurance carriers differently depending on the lines of business they write and their prior recognition of social inflation in the actuarial methods.

Although accident years 2023 and 2024 are generally indicating a favorable run-off, we have a concern that adverse development will occur in these accident years as the historical adverse development may not be fully reflected in the actuarial assumptions.

To reflect social inflation in actuarial methods, we recommend companies:

• Reevaluate the expected loss ratios that are used in actuarial methods to not only reflect historical adverse development but also current claim activity; and
• Separate lines of business into more granular groupings which segregate those segments more affected by social inflation and those less affected by social inflation (e.g., litigated versus non-litigated claims).

After year-end 2025 data is released, we will publish a companion article that presents updated results with more details by line of business, along with greater discussion on how to adjust actuarial methods.

A year after the devastating Los Angeles wildfires of January 2025, the insurance and reinsurance industry is still absorbing the scale of their impact, as well as the lessons learned for future risk assessment. What is now clear is that these fires were not an anomaly but a warning signal for how wildfire risk is evolving in a changing climate.

The financial consequences of the LA wildfires were significant. To date, insurers have paid out approximately $22.4 billion, with total insured losses reaching around $40 billion overall. These figures place this firmly among the most costly natural catastrophe events in recent US history, reinforcing wildfire's status as a primary driver of loss rather than a secondary peril.

The sparks behind the blaze

In the year since, investigators have been able to piece together a clearer picture of how the fires began and why they escalated so rapidly. The initial blaze, the Palisades Fire, was started by human activity. Emergency services believed the fire had been successfully extinguished, but a combination of Santa Ana winds and exceptionally dry conditions caused it to re-ignite, with devastating consequences.

The second major event, the Eaton Fire, was ignited by a nearby power line. While these two fires inflicted the most severe damage, they were only two of 14 separate wildfires that occurred across the region during January 2025.

Climate change also played a central role in amplifying their severity. Heavy rainfall during 2022 and 2023 drove extensive vegetation growth across California. This period of abundance was followed by a prolonged drought, which dried out that vegetation and transformed it into highly combustible fuel. In effect, climate volatility, not just warming, created ideal conditions for wildfire spread.

A global shift in wildfire behavior

The human cost of these events has also been profound. While 31 deaths were attributed directly to the fires, a medical study published in JAMA (The Journal of the American Medical Association) estimates that up to 400 additional deaths may have been caused indirectly, driven by poor air quality and reduced access to healthcare during and after the events. Further, more than 100,000 homes were evacuated, disrupting communities and livelihoods on a massive scale.

Taken together, these impacts underscore a sobering reality: wildfire risk is no longer confined to historically defined burn areas or traditional seasonal expectations. The LA fires echoed patterns seen elsewhere, including the Australian bushfires of the same year, where successive wet years followed by extreme heat produced similarly combustible landscapes.

The diversity of ignition sources - human activity, infrastructure failure, weather-driven re-ignition, and climate change - highlights a critical challenge for risk modelling: wildfire cannot be understood through a single causal lens and the parallels seen across hemispheres point to a global shift in wildfire behavior.

What this means for our industry

For underwriters, the key takeaway is clear and urgent: past bushfire and wildfire burn areas are no longer a reliable predictor of future fires. Historical loss data, while still of value, cannot on its own capture the rapidly changing interactions between climate, vegetation, weather extremes, and ignition sources.

These distinctions matter. The latest wildfire models, such as BirdsEyeView's, for instance, explicitly focus on different ignition mechanisms, recognizing that the probability, timing, and severity of fires vary materially depending on how they start and how environmental conditions evolve around them. Treating wildfire as a homogeneous peril obscures these dynamics and increases underwriting blind spots.

This has significant implications for pricing, accumulation management, and capital allocation. Wildfire has firmly shifted from a 'secondary' peril into a core driver of portfolio performance. Models calibrated on assumptions of climate stationarity risk lagging reality at precisely the moment when precision matters most.

Progress lies in prediction

Looking ahead, the industry's ability to adapt will depend on its willingness to embrace climate-aware, data-driven analytics. Advances in satellite observation and machine learning now allow us to monitor fuel load, vegetation stress, and environmental conditions in near real time, enabling earlier detection of emerging risk patterns and more responsive underwriting decisions.

The lessons from the LA wildfires, one year on, are therefore not only about loss – they are about learning. If reinsurers and insurers can move beyond retrospective modelling and adopt adaptive intelligence, they will be far better positioned to navigate the growing volatility of wildfire risk.

In a world where climate dynamics are rewriting the rules, resilience will belong to those who can see risk forming before it ignites.

When the Buffalo Bills fired head coach Sean McDermott on Monday, that brought the number of top jobs vacated this season to 10. That struck me as a huge number, out of just 32 such positions. I'm accustomed to six or seven, maybe even eight. 

It turns out that the 10 departures this year are the most since... the end of the 2022 season. And eight of the 10 hires from three seasons ago have already been fired. 

I realize I have the luxury of surveying the frenzied hiring and firing as a lifelong fan of the Steelers, who have had precisely three head coaches since 1969. (There have been six popes in that same stretch.) The first two of those Steeler coaches are in the Hall of Fame, and Mike Tomlin, who resigned last week after 19 seasons, will surely join them when he becomes eligible. 

It's obviously not helpful to tell teams that they, too, should hire Hall of Fame coaches, but I do think lots of teams are getting one thing very wrong — and anyone leading an innovation effort, including at an insurance company, may be tempted to make the same mistake. 

Basically, too many teams don't have a long-term vision. They think they do, but they don't. So they lose patience too quickly. They get twitchy when the results aren't there immediately and move on to the next coach or general manager or both, only to pull the plug on them too quickly, too. As I wrote two weeks ago — in what I thought would be my one NFL reference for the year — the impatience is partly because owners get focused on the outcomes of their choices, rather than on whether they made a good bet. 

Owners ask: Did we win the Super Bowl this year? They don't ask: Did we put ourselves in a good position to have a chance to win? They don't ask: Are we putting together the pieces for the next several years? They ask: Did we win this year?

That sort of thinking is how you become the Cleveland Browns. Since the 1999 season, when the team was reconstituted after the original franchise moved to Baltimore and became the Ravens, the Browns have had 12 head coaches and are about to hire their 13th. In those 27 seasons, they've won 33% of their games and zero titles in the four-team AFC North. They've played in all of four playoff games, winning one.

Yet the Bills have decided to follow suit. They fired McDermott even though he took the Bills to the playoffs in the last seven seasons and in eight of his nine seasons as head coach. They made it to the conference championship twice, losing both times to the formidable Chiefs. The Bills hadn't been to the playoffs in the 17 years before McDermott arrived. Who do they think they'll get who'll be better? 

Firing Pete Carroll as the head coach of the Las Vegas Raiders after one season? Sure, the Raiders were an awful 3-14 this season, but that's only one game worse than their record for the 2024 season, and they'd had losing records in 2022 and 2023, as well. You're telling me they didn't know what they were getting in a 74-year-old coach who, among many other things, had just coached for 14 seasons in Seattle? I have no idea whether he was the right fit in Las Vegas, but either Raiders ownership was too quick to commit to him as the turnaround guy a year ago or was too quick to bail after this season. In either case, the Raiders showed they're dysfunctional.

By contrast, when the Steelers hired Chuck Noll in 1969, he was a 37-year-old with no track record as a head coach and went 1-13 in his first season. He had losing records in his second and third seasons, too. But he was putting the pieces together, the Rooney family stuck with him, and the magic started happening in season four.

When the Cowboys hired Jimmy Johnson as head coach in 1989, he went 1-15 his first season. But he and owner Jerry Jones had a long-term vision largely based on the eight draft picks, including three first-rounders, they got for trading Herschel Walker to the Vikings. By season four, Dallas was winning the first of the three Super Bowls it took in four years.

The Cowboys actually demonstrate both patience and impatience. After Johnson led the team to two Super Bowls, Jones felt Johnson was getting too much credit. So — in what I believe is the dumbest decision ever by the owner of a sports franchise — Jones fired Johnson and brought in another college coach who had won national championships to try to show that just about anyone could win with the juggernaut Jones, the self-proclaimed genius, had put together. The Cowboys did, in fact, win one more Super Bowl, but then got twitchy as Jones took more control and have never recovered. The Cowboys have won five playoff games in the last 30 seasons and have never even made it back to a conference championship game. 

What Should Insurers Do?

Insurance companies have a luxury that NFL franchises don't: They don't have to deal with hundreds of podcasts by rabid fans who want to fire everybody any time someone fumbles a football. 

Still, insurance companies have to answer to shareholders, and they do have to succeed. That means innovation efforts, especially related to generative AI, need to fit into a long-term vision. They can't be one-offs, because those are too easy to kill. And the efforts can't be judged based just on whether they succeeded or on any other short-term indicators. 

The right questions are: Were they good bets? Did we learn something important? What do we do next to build on what we just learned?

Early on, it was at least okay to do broad experiments with Gen AI. People needed to get comfortable with the concept, and the applicability was somewhat nebulous. But we're more than three years into the Gen AI revolution now, so it's time to do more long-term planning about how Gen AI can both make your organization more efficient and about how it might even let you make more radical changes to your business model. 

Once you've laid out that vision, you have to stick with it. None of this firing the coach or heading off in a new direction the first time something unexpected happens. And the commitment has to be communicated from the top of the organization, repeatedly, so people know this isn't just a phase that they can assume will pass them by if they just keep their heads down. 

I can't guarantee success. Even my Steelers haven't won a playoff game since 2017, and there's no guarantee we won't pick a dud as head coach this time. Dan Rooney played a major role in hiring all three of our coaches since 1969, and he died in 2017. But I can guarantee that taking a stable, long-term approach means you won't be the Cleveland Browns. 

Cheers,

Paul

P.S. How committed have the Steelers been to their head coaches for the long term? My father once told me an illustrative story that was passed on to him by a friend who was the PR guy for the Steelers in the 1970s and 1980s. 

Now, my father was a hail-fellow-well-met, Irish storyteller type, but his stories always started out based on something that actually happened, and I choose to believe this took place just as my father described: 

The PR guy said he was sitting in Noll's office at the end of a workday, when Dan Rooney stuck his nose in. 

Rooney said, "Chuck, I put your contract in your in-box. I left the numbers blank because it's your turn to put them in this year." 

Noll responded, "No, no, it's your turn. I put the numbers in last year."

Rooney said, "I checked. I did the numbers last year. Just put the contract in my in-box when you're done, and I'll sign it in the morning. Have a good night."