The frequency and intensity of severe weather events have shifted dramatically, making it harder for insurers to predict and price risk. Once-seasonal catastrophes like wildfires, hurricanes, and hailstorms are now occurring more often and in areas previously considered low-risk.

Devastating events such as the Paradise and Marshall wildfires, Hurricane Helene, and recent widespread hailstorms in cities like Denver and San Antonio illustrate the expanding spectrum of climate-driven losses. According to a major P&C public insurer, in the first half of 2023 alone, there were 43 separate catastrophe events — many below reinsurance thresholds. Texas, Florida, and Colorado accounted for roughly half of all losses, while only six states nationwide went untouched.

Combined with rising costs and population growth in high-risk regions, this volatility is putting mounting pressure on insurers. Many property and casualty (P&C) carriers are scaling back or exiting markets like Florida and California, highlighting how climate risk is fundamentally reshaping the insurance landscape.

To respond effectively, insurers are increasingly turning to hyperlocal weather data.

The Trust Imperative

Weather intelligence at the local level has the potential to improve underwriting, accelerate claims, and reduce uncertainty. Yet adoption remains uneven. Translating raw data into actionable insights, without overcomplicating pricing or eroding policyholder confidence, is still a challenge.

Both insurers and policyholders need to trust the data underlying coverage decisions. Faster, data-driven claims processes depend on confidence that the evidence is accurate and objective. The next wave of innovation is likely to come from bridging this trust gap, making weather analytics transparent, verifiable, and practical across all stages of insurance operations.

A Turning Point for Insurers

At the heart of this challenge is uncertainty. Forecast accuracy has always been central to insurance modeling, but its importance grows as climate volatility rises.

Insurers rely on precise forecasts to anticipate losses, manage exposure, and guide portfolio decisions. Yet weather can change in an instant, and accuracy today goes beyond hit rates. It requires understanding the probabilistic nature of events and quantifying the likelihood of different outcomes.

A small snowstorm may be manageable in Chicago but catastrophic in Atlanta, affecting auto claims, emergency response, and ultimately premiums. By combining AI-driven ensemble modeling with expert meteorological analysis, insurers can produce forecasts that are both precise and actionable for their unique geographic footprints. Probabilistic forecasting turns such uncertainty into measurable, manageable risk.

These advances come at an inflection point. While insurtechs have introduced new technologies, scaling these innovations depends on the financial strength and operational expertise of traditional P&C insurers. Established carriers have the capital, data infrastructure, and institutional knowledge to withstand climate volatility and actively drive innovation.

Historically, commercial insurance centered on reducing risk for business policyholders. Today, AI and expansive weather and property datasets bring that same precision to highly localized levels. Insurers can tailor policies to the risks of individual communities, or even individual homes, shifting from reactive recovery to proactive risk reduction.

This transition marks a broader transformation in the industry: moving from managing losses after they occur to anticipating and mitigating risk before it happens.

Faster, More Predictable Coverage

Parametric, or event-based insurance exemplifies this evolution. Once considered niche, it has gained prominence as extreme weather becomes more frequent. Parametric coverage provides faster, more predictable payouts while limiting insurers' downside risk and keeping coverage affordable. Unlike traditional policies, parametric products are triggered by measurable thresholds — such as rainfall, wind speed, or hail size — rather than the extent of physical damage.

Trusted, high-resolution sources with deep historical records ensure fair and accurate payouts. When executed correctly, parametric coverage eliminates the need for on-the-ground adjusters and accelerates claims, offering policyholders transparency and speed when every hour after a loss matters.

Forensic meteorology, long a trusted courtroom tool, is also evolving. Certified meteorologists have historically reconstructed weather events to corroborate claims. Today, timestamped, high-resolution data from lightning sensors, mesonets, radar, satellites, LiDAR (Light Detection and Ranging), synthetic aperture radar, and drones enhances precision and objectivity.

Ground truth remains critical. Many modern models approximate conditions, but insurers need verifiable, physical data for high-stakes decisions. Combining human expertise with advanced sensing technology accelerates claim validation, detects potential fraud, and ensures fairness for both policyholders and carriers.

Bridging Tech, Science, and Strategy

The challenge isn't just having data; it's translating it into action. Too often, information is delivered in an "over-the-wall" fashion, leaving insurers struggling to interpret or integrate insights effectively.

By combining deep meteorological knowledge with an understanding of operational realities, carriers can identify the signals that matter most for underwriting, claims, and risk modeling. When science and strategy align, insurers can move from reacting to proactively managing the weather's impact. Structured, repeatable processes allow carriers to deploy data where it matters most, improving decision-making and strengthening customer outcomes.

Insurers are investing heavily in AI, machine learning, cloud computing, data acquisition, APIs, and complementary technologies like robotic process automation, low-code platforms, IoT, and blockchain. Together, these systems create a more connected and intelligent insurance ecosystem.

Generative AI can simulate countless weather and loss scenarios, enabling insurers to anticipate risks, prevent claims before they occur, and uncover patterns human analysts might miss. AI-driven analytics also transform personalization and fraud detection. From targeted marketing campaigns to prescriptive insights for individual policyholders, these tools allow carriers to evolve from broad risk models to precise, data-informed strategies. Agents can make faster, smarter decisions, while direct-to-consumer carriers can craft hyperlocal policies reflecting a property's true exposure profile.

The Era of Climate Resilience

The convergence of hyperlocal weather data, probabilistic forecasting, forensic meteorology, parametric insurance, and AI-driven analytics is ushering in a new era of climate resilience. Insurers today are actively shaping how society anticipates, prepares for, and responds to extreme events.

Precision, trust, and actionability have become the backbone of modern risk management, enabling both policyholders and carriers to navigate an increasingly volatile climate with confidence.

The insurance industry's attention to the financial consequences that catastrophic climate events have on large businesses is well documented. Industry reports, financial analyses, and even media coverage all focus on hurricanes, wildfires, and floods that can cause billions in insured losses. And it's understandable - major disasters grab headlines, while losses at large firms are big enough to influence market confidence and insurers' overall risk exposure.

At the same time though, another weather-related issue is going under the radar: power outages caused by less severe, yet far more common, weather events, such as high winds, rain, and thunderstorms.

While electrical disruptions in general may not affect large businesses as severely as catastrophic events do, the impact accumulated from power outages is a problem big enough to sink small companies.

Just one event causing business interruption can seriously affect a company, leading to loss of revenue, productivity, foot traffic and reputation, all of which have a trailing effect on the bottom line.

Now, consider this happening more frequently. Right now, we're seeing weather events - wind, hail, rainfall, freeze, heat - becoming more common and affecting more areas of the country, especially as population shifts into geographies that are more at risk of being affected by these events (California's wildfires, for example). Recent data backs this up, showing power outages in the U.S. have risen an astronomical 74% over the past decade compared with the decade before that. What's more, smaller recurring power outages that are no more than 24 hours account for 90% of all outages, according to Adaptive Insurance data.

Along with these smaller climate-related events becoming more common, the U.S. consumption of electricity is projected to increase by 25% by 2030 and by 78% by 2050, all while the country's existing electricity grid continues to age.

For U.S. firms - 99.9% of which are considered small - the financial losses caused by the above combination is staggering. In fact, the Department of Energy estimates that electricity blackouts cost U.S. businesses $150 billion every year, only 60% of which is covered by insurance.

The protection gap

So why then is so much of this still going uncovered by insurers? For me, it's not that traditional insurance models are neglecting smaller, high-frequency disruptions, it's that they're simply constrained by design. Carriers must balance attritional losses with the risk of catastrophic events, the latter of which are volatile and notoriously difficult to price.

Without sufficient data or reliable modelling, many insurers either limit coverage or withdraw from certain markets altogether. For example, with power outages, food spoilage in homes is getting removed from coverage, and only kicks in for commercial businesses after more than 72 hours.

While charging market-value rates might offset the risk, premiums at that level would be prohibitively expensive for most businesses. So, the result is significantly less coverage available in traditional policies, a very real protection gap between what businesses lose and what their policies actually protect, and finally, businesses failing not from a single catastrophic event, but from accumulated losses of numerous smaller disruptions.

The shift to parametric insurance and a resilience mindset

With this imbalance clearly unsustainable, a shift is already underway, with the once-overlooked risk category of non-damage business interruption now drawing new attention. As a result, more parametric insurance providers are emerging.

While traditional models haven't been able to use real-time data (because it hasn't been available on the more granular level that data providers are starting to deliver), parametric is different.

By offering faster, data-driven payouts that require less adjudication and can better address these under-served exposures, parametric coverage is becoming the blueprint for these types of events.

This real-time data and predictive modeling, which is growing increasingly more effective, is able to create new opportunities for previously uncovered risk. If and when done correctly, it allows the insurers to collect new premiums and spread the risk across a more manageable and visible risk profile - including no moral hazard and less fraud, another feature of parametric coverage.

At the same time, businesses themselves are rethinking resilience. With federal and state resources stretched thin, companies can no longer rely on government support to bridge the gap caused by these repeated, smaller disruptions that chip away at cash flow and profitability.

Alongside steering toward adequate parametric insurance protection, the future will also likely see businesses form more formal resilience plans, which may even become a prerequisite for securing a loan, license, or certain types of coverage.

For more traditional or larger insurers, I see a future with a wealth of opportunity. Partnerships with insurtechs, which tend to be faster, more agile, and better equipped to develop innovative coverage models, will allow incumbents to deliver greater value to businesses in this changing risk environment.

Meanwhile, the excess and surplus lines market is expected to double in size. Its lighter regulatory framework not only enables more flexible product design, but also makes it an attractive space for top talent eager to shape the next generation of insurance solutions.

The bottom line here is that small businesses don't have to die by a thousand cuts. By addressing the cumulative impact of frequent, small-scale power disruptions through parametric insurance and proactive resilience planning, we can transform what has been a slow bleed into a manageable risk. This isn't just an operational challenge—it's an opportunity to redefine what effective, modern protection really means and to ensure that the everyday weather events that once threatened small businesses' survival become risks they can confidently weather and recover from.

The MGA market is outpacing the broader P&C sector, fueled by specialized expertise, speed, and innovation that help close the protection gap. But as next-gen, AI-enabled MGAs raise the bar, many still rely on legacy systems that slow growth and limit agility. To stay competitive, MGAs need modern foundations that drive scalability, efficiency, and innovation.

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Sponsored by ITL Partner: Majesco

Paul Carroll

At ITL, we've been encouraging the insurance industry to move to a Predict & Prevent model and away from the traditional repair-and-replace approach. Workers' compensation has been a poster child as organizations make remarkable strides in reducing workplace injuries. But there's significant complexity below the surface. What are the key challenges around volumes, documentation, staff shortages, and legacy systems?

Connor Atchison

I think you summed it up right there. It's the culmination of all of these things over decades that are making things slower and more cumbersome. We have gaps in knowledge as we strive for better care outcomes—to get that worker back to work and make sure we're spending the right amount of money on the right treatment to make that happen.

There are definitely issues around legacy systems. Workers' comp, even more than other insurance lines, is still a little bit behind. But they're catching up and adapting, and they're seeing the need, which is great.

The volumes are certainly high. When I was doing adjustment work during my time in health administration serving in the Canadian Armed Forces, I'd be sitting there highlighting and tabbing documents. You can't scale that, and that's why we need more technical innovation.

Then there's the knowledge loss. How can we retain the knowledge from someone who's been in the industry for 30 years and is retiring? Younger generations aren’t filling the gap, so technology needs to help retain that knowledge.

We're also seeing a lot of changes at the state and federal legislation level. They're getting more stringent on costs and more stringent on audits and understanding where the money's going.

You put all those together, and it's almost like a perfect storm. Technology is definitely needed right now, more than ever.

Paul Carroll

You recently commissioned a survey of claims professionals with PropertyCasualty360 “AI in claims and the 4x trust effect of human oversight” focusing on AI’s rising role in claims and wrote an article for us about how important human oversight is for generating trust. While I’ve certainly heard lots about the importance of a human-in-the-loop, your survey still surprised me.

Connor Atchison

Yes, we found that human oversight increased trust in AI by up to 4X. People are concerned about quality, accuracy, outcomes, and liability if human oversight isn’t there. Once you have an expert-in-the-loop, you build trust for the end user. They can see why the machine learning is processing the document the way it is, while being trained on industry domain knowledge and over 100M+ claims documents. That allows them to say, "This makes sense. I can understand it, and I can make my own inferences."

The other thing that really stood out to us was that 75% of the claims professionals we surveyed said they believe AI can improve efficiency through better speed and resource optimization. So on one hand, they’re clearly seeing the upside. But at the same time, there’s this capability–trust gap: they know AI can help them, they’re just not fully comfortable trusting it on its own yet.

What they do trust is themselves — and human oversight. When an expert is in the loop, they feel confident that the AI is being guided, validated, and grounded in real industry knowledge. That’s why the combination matters so much. As our Head of Machine Learning always says, we use AI for scale and humans for accuracy. That pairing is what closes the gap and ultimately builds trust.

Paul Carroll

In insurance, in general, and workers' comp, in particular, there are numerous silos—employers, bench adjusters, nurse case managers, medical examiners, treating physicians, vocational rehab specialists, and legal counsel. How does AI help resolve the silo problem in workers' comp?

Connor Atchison

I don't believe it's a silver bullet. There’s no point solution that can do it all. But AI can plug into your day-to-day workflows and take 15, 16, 20 steps and shrink that process to half or maybe a third. 

No one wants to sit there for eight hours going through a 2,000-page document. It just doesn't make sense to do this clerical work any more. It makes sense to elevate the human to make better use of their time and spend more time analyzing and making expert decisions. 

Paul Carroll

You said at a recent conference with the Division of Workers’ Compensation in California that AI can cut claims processing time in half, making claims documentation platforms essential. But speed raises the stakes—how do you ensure compliance and trust keep pace with automation?

Connor Atchison

I think there are two answers to that question. First is the human in the loop, which we’ve already talked about. Beyond that, it's about building really good technology.

Going back to what I said about point solutions: Just putting data into an LLM [large language model] or SLM [small language model] or a foundational model isn't going to give you a result that helps the injured worker or the adjuster. It's going to give you something very high-level. When you go deeper and build all the different configurations around that data and the workflows, that's where you actually start getting leverage.

When you go to the next step, you have to look at the compliance standpoint. You have to know, and be able to demonstrate: Why is the model providing that information the way it has, based on the data it's been trained on?

I remember talking with the Honorable Judge Rassp from California about this. A judge doesn’t want a black box. They need to know objectively why something happened. If you can't objectively define where you've gotten the information, you run into problems. 

That's why point solutions are not the definitive answer. It's about building the entire workflow—creating transparency, understanding what legislation means, and why you have to follow different audit guidelines, time periods, or reviews.

Paul Carroll

Large language models started as generalists, ingesting all available information. Now it's possible to train AI on specialized information to produce vertical AI, providing more precise results in complex fields like workers' comp. What does this verticalization involve?

Connor Atchison

We don't have to use an LLM to do everything. We can find models that train on extractive data and give a higher confidence score and better outcomes. If all you have is a hammer, everything looks like a nail. But sometimes there's an easier, more cost-efficient way.

When we train in a very vertical way, you have to start looking at what the governments and states are doing. With on-prem deployments, we're starting to see regulated industries needing more and more of their own models on their own data to understand outcomes more effectively. There are a lot of reasons for that—compliance, security, risk—but there's a huge opportunity here if you have the data and know how to orchestrate it the right way and build it with the right outcomes.

These organizations can actually accelerate, and I don't think we can use generalist models. If you don’t fine-tune your technology and don't understand what it's trying to do, you're just going to have garbage coming out. Garbage in, garbage out.

Paul Carroll 

What does the feedback loop look like for humans to refine AI over time when it doesn't get something quite right?

Connor Atchison

Without getting technical, you can think of the top, middle and bottom of a workflow. You ingest information at the top and spit out a result at the bottom. The question is: Where do you want the human to come in?

That really depends on what you're trying to build and on the complexity of that data. At the ingestion point, for example, is the data curated? Is it clean? If you have chicken scratch from a doctor and can’t read it, the machine can’t read it, either. 

Maybe it's in the middle where you want things fine-tuned. If your models are confident and you understand the scoring—the actual mathematics—then you can have a human review at the bottom.

I think you need a combination of all of them.

Paul Carroll

Given the unique dynamics of workers' compensation—from complex injury types to vocational factors and return-to-work timelines—how do you see AI reshaping the medical record review process? And what are the key operational or regulatory hurdles insurers and TPAs must overcome to realize those efficiency gains?

Connor Atchison

The way I see it, you need a platform, not a point solution. You can’t just focus on workflows and configurations. You have to understand your data and use those unique datasets to get cross-case analysis and different inferences on how current injuries are being treated. Are they being efficiently treated? Is there waste? Are there better treatment outcomes? When you can surface all of that—and there are so many states and organizations sitting on this information but not really getting it surfaced—that could help immensely.

To really stay in front, you need to always see where things are moving from a state-by-state jurisdiction and also from a federal level. Texas has new laws coming into place. Louisiana just passed a few bills. And so on. You don't want to build something and then realize, "Whoa, this doesn't work," and have to retool your solution stack. 

I think this is where users and buyers are getting fatigued. It's so noisy out there, and only a handful of companies really know what they’re doing. Everyone's got an AI solution with an LLM. 

Wisedocs put together a buyer's guide to evaluating AI-powered claims documentation platforms to help people ask the right questions. Some issues are really important to understand, such as security and compliance primers or whether you should build vs buy. You need to get this right the first time, not the second or third time.

Paul Carroll

Returning to the recent survey you commissioned of claims professionals with PropertyCasualty360, yit found that 75% of respondents believe AI can improve efficiency in claims, yet 58% aren't using it. As we wind up here, what's your vision for where the industry could be in two to three years in terms of both technology and adoption?

Connor Atchison

I've seen studies showing that anywhere from 60% to 90% of AI initiatives are still in pilots and proofs of concept. The adoption isn't there. AI is great, but it's not connecting with the actual business need.

If I buy a Bugatti, I don’t want it to drive like a golf cart, and I think many people have been disappointed because they need to better understand their needs and the parameters of how to use AI. There are different needs for every business and use case.

But this is a really exciting time. Success is all going to come down to data. Understanding your data and then building around that—that's what's going to win, and that's why I think there will be organizations that are the haves and others that are the have-nots. 

Paul Carroll

Thanks, Connor. 

About Connor Atchison

Connor Atchison is the Co-Founder and CEO of Wisedocs, the AI-powered claims documentation platform purpose-built for medical records and insurance files, trained on 100 million+ documents. Connor is an experienced founder with a demonstrated history of working in health services, information technology, and management consulting. He aims to digitize a formerly manual industry through the adoption of artificial intelligence with expert human oversight to support manual claims processes — turning complex unstructured records into defensible outputs. As a former veteran with 12 years of military service under the Department of National Defence, he strives to change the process for filing health insurance and disability claims for top-tier carriers, federal agencies, legal defense firms, healthcare providers, and their claimants.

Matteo Carbone's latest look at a high-profile, full-stack insurtech demonstrates a point that is too often missed amid the adulation of breakout start-ups: 

Incumbents should beat insurtechs in the innovation battles far more often than they do.

While Matteo actually congratulates the insurtech, Root, for profitably growing its number of auto policies 16% over the past six quarters, he notes that Progressive grew nearly twice as fast, largely based on the same telematics innovations that are Root's calling card. 

Why should other incumbents win more often?

As Chunka Mui and I wrote in our 2013 book, "The New Killer Apps: How Large Companies Can Out-Innovate Start-Ups":

"Yes, small and agile beats big and slow, but big and agile beats anyone — and that combination is now possible.... Big companies have everything they need to continue to dominate: unmatched people, resources, supply and distribution capabilities, brand power and customer relationships.... Incumbents have growth platforms that would take start-ups years to build."

Although the insurtech glow has faded some over the past decade, as we've all realized that insurance won't be turned upside-down in the way that, say, retail has been, a sort of "arms dealer" model has developed. Insurtechs don't overwhelm incumbents but develop novel weapons that incumbents buy, often by acquiring the whole company. The model is akin to what happens in pharmaceuticals, where little guys develop new drugs and Big Pharma then jumps in, buys them and runs them through the incumbents' massive marketing and distribution networks. 

But Progressive shows how incumbents can even out-innovate start-ups in-house. It followed the model that Chunka and I have long referred to as "Think Big, Start Small, Learn Fast." (He writes about our mantra in some detail here.) 

Progressive had huge plans when it introduced its Snapshot telematics offering in 2008 but didn't try to do everything at once. It experimented at low cost, letting it learn fast and adapt as technological capabilities developed — for instance, when it became possible in the mid-2010s to use smartphones as data sources and dispense with the dongles that previously had to be plugged in underneath cars' dashboards. 

In his LinkedIn post, "When the 'Dinosaur' Laps the 'Disruptor,'" Matteo estimates that a third of Progressive's auto business now comes through its telematics offering and says it grew its base of policyholders by 28% over the past year and a half (to Root's 16%). Progressive greatly outpaced Root even though its base of policyholders is about 80 times the size of Root's.

I realize that Progressive has long been a shining example of innovation in the insurance industry, but you can also look to Geico, which has mostly caught up on telematics, far faster than most competitors, including start-ups. Or look at the failures of Amazon and Google in insurance, which Matteo dissects here and ascribes to the power of incumbents. 

I'd also suggest investigating Walmart, whose CEO recently announced retirement plans following a career that will be taught in business schools for years to come. Chunka and I actually singled out Walmart a dozen years ago as a behemoth that would have to work hard to defend its market in the face of technological change. Our book came out just as Doug McMillon was becoming CEO and blew away all expectations through super-smart use of technology.

He continued to lean into the sorts of supply chain technology advancements that Walmart has long been known for, while also experimenting with technologies that let Walmart keep pace with the ease-of-use, product selection and home delivery that gave Amazon such an edge and that it has continued to drive as hard as it can. I don't know about you, but I get a lot delivered from Walmart these days — not as much as I get through Amazon, given its hold on me through Prime's free shipping, but still plenty.

Yes, an awful lot of work has to be done to overcome the internal antibodies that can kill innovation and that become more potent the bigger and more successful a business becomes. But by now have a lot of examples of companies that have fallen by the wayside because of corporate atherosclerosis. We also have some examples of companies that, like Progressive and Walmart, have put themselves at the cutting edge of innovation despite their heft. 

Many insurance companies will do just fine by relying on insurtechs to innovate and then absorbing those new capabilities into their products, services and operations, but there is also bigger game afoot, as Matteo shows. It's possible to produce breakthrough innovation in-house if you think big, start small and learn really fast.

Cheers,

Paul

P.S. Here is a piece on the methodology that Chunka and I have developed over the decades about how to identify and handle the strategic options that can turn into breakthroughs. 

Auto insurance in the United States is under immense pressure. As 2026 approaches, auto insurers face a perfect storm of rising repair costs, increased claims severity, inflationary pressures, and shifting consumer expectations. Many consumers are questioning the value of their policies, while insurers are struggling to maintain profitability.

This evolving crisis presents both a challenge and an opportunity—one that requires bold innovation, operational agility, and a renewed focus on customer-centric strategies.

The Cost of Claims is Surging

One of the most pressing issues affecting auto insurance in 2026 is the rising severity of claims. This is not just a short-term spike—it's a structural shift.

Key contributors include:

• Advanced vehicle technology: Modern cars now feature complex sensors, cameras, and electric systems that are expensive to repair or replace.
• Labor and parts inflation: Supply chain disruptions and skilled labor shortages have driven up repair costs dramatically.
• Medical inflation: Healthcare costs tied to bodily injury claims continue to outpace general inflation.
• Litigation and legal trends: Increased legal involvement in personal injury claims has led to higher settlements and longer claim durations.

As a result, insurers are paying significantly more per claim—even as the frequency of claims remains stable or slightly declines.

Affordability Is Reaching a Breaking Point

As insurers attempt to recoup losses, premium hikes have become unavoidable. For many American drivers, especially those in urban areas or lower-income brackets, auto insurance is becoming unaffordable.

By 2026:

• The average auto premium has risen by over 20% since 2023.
• States like California, Florida, and Michigan report even steeper increases due to regulatory restrictions or localized risk.
• Many consumers are shopping around more frequently, increasing policy churn and straining insurer retention efforts.

The core problem: The gap between cost and perceived value is widening. Consumers are paying more, but don't feel better protected or supported.

Technology-Driven Solutions

To address affordability and fairness, insurers are turning to usage-based insurance (UBI) powered by telematics. These programs base premiums on driving behavior—such as speed, braking, and mileage—rather than traditional demographic factors alone.

Benefits of UBI include:

• Lower premiums for safe or low-mileage drivers
• Enhanced pricing accuracy and risk segmentation
• Greater transparency and engagement for customers

However, adoption has been uneven. Privacy concerns, lack of customer awareness, and inconsistent user experiences have slowed broader acceptance.

In 2026, the opportunity lies in making UBI the default for personal auto policies—combined with stronger education, clearer benefits, and seamless onboarding.

Another key solution lies in AI-driven claims automation, which improves efficiency and customer satisfaction while lowering costs.

By 2026, leading insurers are:

• Using computer vision to assess vehicle damage from photos in minutes
• Automating first notice of loss (FNOL) through mobile apps and virtual assistants
• Implementing fraud detection algorithms to flag suspicious claims
• Streamlining repair approvals and payments through connected platforms

The result is faster resolutions, lower operational costs, and better experiences. However, the human touch remains vital in complex or emotionally charged situations—highlighting the need for a balanced, hybrid model.

Regulatory Pressures and Market Disparities

Auto insurance affordability is also a regulatory issue. Several state governments are imposing tighter oversight on rate filings and premium increases, attempting to protect consumers from excessive pricing.

Challenges include:

• Balancing insurer solvency with consumer protection
• Inconsistent regulation across states, creating fragmented market dynamics
• Political pressure to curb rising premiums during economic downturns

Regulators and insurers must work together to create sustainable pricing models, promote innovation, and ensure equitable access—especially for high-risk or underserved drivers.

Climate Change and the Future of Mobility

Climate change is now a factor in auto insurance. In 2026, the rise in extreme weather events—from floods to wildfires—has increased vehicle damage claims.

Insurers are responding by:

• Adjusting risk models to include geographic climate data
• Offering weather-linked alerts and early warnings to policyholders
• Revising underwriting criteria in high-risk areas

While the focus has traditionally been on property and catastrophe insurance, auto insurers must now account for environmental volatility as a growing risk driver.

Emerging trends in mobility are also reshaping the risk landscape:

• Electric vehicles (EVs), while environmentally friendly, are costlier to insure due to expensive battery systems and limited repair networks.
• Autonomous driving technologies have not yet delivered the expected reduction in accidents, and liability questions remain unresolved.
• Car-sharing and subscription models are complicating ownership-based insurance frameworks.

Insurers in 2026 must adapt their products to match new patterns of vehicle use—offering flexible, modular, and pay-as-you-go options that align with the future of mobility.

Restoring Trust and Rebuilding Value

At its core, the crisis in auto insurance is about trust. Consumers feel they're paying more for less. Insurers, meanwhile, are battling rising costs, regulatory scrutiny, and customer churn.

To succeed in 2026, insurers must:

• Invest in transparency: Clear communication about pricing, claims, and policy changes
• Improve digital experiences: Easy-to-use apps, quick claims processes, and responsive service
• Embrace innovation responsibly: Use technology to enhance—not replace—human-centered care
• Prioritize fairness: Personalize pricing while protecting vulnerable customer groups

By redefining their value proposition, insurers can move from being seen as a financial burden to becoming trusted partners in mobility safety and risk management.

Conclusion

In 2026, the rising cost of claims, growing affordability concerns, and changing mobility trends will present serious challenges—but also a chance to rethink the system from the ground up.

The insurers who emerge stronger will be those who embrace digital transformation, personalize their offerings, improve transparency, and work collaboratively with regulators and consumers alike.

In this time of disruption, innovation is not a choice—it is a necessity. 

Insurers are racing to modernize their operations, yet the greatest constraint isn't the technology itself. It's whether teams are prepared to use it. According to KPMG, 62% of insurance CEOs believe talent gaps could hinder growth over the next three years. Automation, data tools, and AI only move the needle when employees understand how to work with them, make informed decisions, and adapt to new ways of delivering value.

That's why workforce development has become central to every corporate transformation effort. Carriers can't rely on hiring alone. To grow sustainably, insurers must inspire and engage existing employees to grow into the roles today's advanced technologies require. How to do this? Focus on strengthening digital confidence, designing clearer pathways for career advancement, and creating a culture of learning.

Building digital confidence

Modernizing the underwriting, claims, and operations functions begins with preparing employees for the new responsibilities and capabilities that come with AI, analytics, and automation. These technologies are reshaping how insurers operate, but they only deliver value when employees feel equipped to use them effectively.

In underwriting, for example, AI can complete a first pass on risk assessments, while analytics highlight patterns across portfolios, but underwriters still need the knowledge to interpret those insights and determine how they influence pricing and policy structure. Claims teams face similar changes. Image-recognition tools, natural-language systems, and fraud-detection models can streamline intake and flag anomalies, yet adjusters must develop the ability to read alerts, investigate exceptions, and focus on cases that call for negotiation or empathy.

Operations teams are moving from executing individual tasks to overseeing automated pipelines. Renewals, notifications, compliance checks, and payment routing now run in the background, which means employees need both the skills and assurance to monitor workflows, troubleshoot issues, and work with data or IT teams to refine rules. This shift from doing to overseeing requires a new kind of capability. One built on understanding systems, not just completing tasks.

All of this makes workforce development essential. Employees need learning opportunities that include guided practice, coaching, and real-world examples to boost their confidence and ability to use these tools to best help the business overall.

Designing clearer paths for career advancement

The confidence employees develop with AI and digital skill-building only translates into business value when it connects to career growth. Insurers need structured career and education pathways that prepare their talent for critical roles.

A strong approach is to create role-specific learning tracks to make it clear to employees what they need to know. Programs that focus on AI, business acumen, and strategic thinking help employees build the exact competencies needed for senior underwriting roles, claims management positions, or operations leadership. For example: an underwriting analyst who completes a structured program in AI-driven risk assessment and predictive modeling positions themselves for advancement into roles that shape pricing strategy and portfolio management.

These programs work best when they combine university rigor with practical application. Employees learn foundational concepts, then immediately apply them to real business challenges in their departments. Cohort-based learning supports better learning outcomes too, creating room for peer collaboration and support. More employees will enroll in the development, and more will finish, when they are part of a group doing it together. When designed well, these learning experiences can deliver capability building at a pace fast enough to address urgent needs, while thorough enough to prepare people for genuine responsibility.

Creating a learning ecosystem

Structured education programs can be important for addressing AI-related talent gaps, but they work best when supported by a broader learning ecosystem. Hiring employees is expensive, and insurers often get more value from developing the people they already have across all levels of the organization. The first step is acknowledging that learning isn't a perk. It's a foundational business strategy that helps teams adapt to modern tools and workflows.

Position learning as part of a larger culture of growth. Mentorship programs, internal mobility pathways, and clear recognition for the development of new skills or earned credentials show employees that the organization values them and their long-term development. When people see that their effort leads to new opportunities, learning becomes something they pursue willingly rather than something assigned to them.

Making modernization work

The pace of digital adoption inside many carriers today reminds us that insurance isn't as slow to evolve as some might think. As companies integrate AI, automation, and new data tools, success depends on whether employees feel prepared to use those tools in meaningful ways. Technology can accelerate decisions and streamline workflows, but people translate those advances into better service, stronger risk assessment, and more efficient operations.

Investing in workforce development is the key to these modernization efforts. Existing teams already understand the business, the market pressures, and the needs of policyholders. When they have access to learning programs that help them grow into new responsibilities, insurers strengthen talent retention and avoid costly rehiring. A workforce equipped to adapt moves ahead with the industry, while building a stronger foundation for the future.

As the insurance industry enters 2026, AI is no longer a futuristic concept; it's embedded in underwriting, claims, fraud detection, and customer engagement. Yet, as technology accelerates, the real differentiator for insurers will not be how advanced their algorithms are, but how effectively they keep people at the center of innovation.

The Core Insight: AI Isn't the Strategy—Human Experience Is

For years, insurers have focused on digitization and automation to reduce costs and improve efficiency. Those gains are now table stakes. The next frontier is strategic integration of AI that enhances—not replaces—the human experience. Policyholders expect empathy, transparency, and tailored solutions. AI can deliver these outcomes only if deployed thoughtfully and with ethical rigor.

Why People-First Matters in an AI-Driven World

Insurance is fundamentally about trust. Customers rely on insurers during moments of vulnerability—after an accident, a health crisis, or a natural disaster. If AI-driven decisions feel opaque or impersonal, trust erodes. Conversely, when technology empowers human judgment and improves responsiveness, it strengthens relationships and loyalty.

Consider claim processing: AI can triage and flag anomalies in seconds, but the final conversation with a policyholder should reflect empathy and clarity. Similarly, predictive analytics can identify coverage gaps, but agents must translate those insights into meaningful advice.

Three imperatives for insurers in 2026

1. Reframe AI as an enabler, not a replacement

AI should augment human expertise, not eliminate it. Automation can handle repetitive tasks—document verification, fraud scoring, risk modeling—but complex decisions require human oversight. This hybrid approach ensures accountability and preserves the human touch that customers value.

AI-driven underwriting systems for small commercial policies now routinely process the majority of submissions autonomously, with underwriters stepping in to review edge cases and maintain direct communication with brokers. This approach has led to faster turnaround times while still preserving essential human judgment.

2. Invest in ethical and explainable AI

Regulatory scrutiny is intensifying, and consumers demand fairness. Black-box algorithms won't cut it. Insurers must prioritize models that are transparent, auditable, and bias-tested. Explainable AI isn't just a compliance requirement, it's a trust-building tool.

Action Steps:

• Establish governance frameworks for AI deployment.
• Conduct regular bias audits across demographic and geographic data.
• Provide clear explanations of automated decisions to customers and regulators.

3. Design for empathy at scale

Personalization is more than product recommendations—it's about anticipating needs and communicating with care. AI-driven insights should empower agents and brokers to deliver proactive, humanized interactions.

Example: Predictive analytics can flag life events—such as home purchases or family changes—that trigger coverage needs. Instead of sending generic emails, insurers can equip agents with scripts and resources for empathetic outreach.

Emerging opportunities

• Generative AI for customer engagement: Chatbots and virtual assistants can handle routine inquiries, freeing agents for complex conversations. But tone and transparency matter—customers should always know when they're interacting with AI.
• AI in risk prevention: Beyond claims, AI can help policyholders avoid losses altogether. Think IoT-enabled sensors for property monitoring or telematics for safer driving. These tools create value by reducing risk and enhancing customer experience.

The bottom line

The winners in 2026 won't be those with the most advanced tech stack, but those who marry innovation with empathy. AI can transform risk management and operational efficiency, but only if insurers remember that trust—not technology—is the ultimate differentiator.

As we look ahead, the mandate is clear: build systems that serve people first. In doing so, insurers will not only harness the power of AI but also reinforce the human values that define the industry.

Insurance has always been about adapting to uncertainty, but the pace of change sets today’s risk landscape apart. Economic volatility, behavioral shifts, geopolitical tensions and technological disruptions now evolve in real time. Insurers aren’t just assessing risk anymore — they’re trying to keep up with it.

That’s where artificial intelligence steps in. More than just a technology investment, AI is a response system; it can help carriers adapt at the speed of risk itself.

However, AI’s impact isn’t uniform across all lines. How AI adds value depends on whether an organization’s goal is efficiency or effectiveness.

Personal Lines and Small Commercial: Boosting speed and accuracy

In personal lines and small commercial, volume is king. Profitability is highly dependent on a number of factors including speed, accuracy and consistency across thousands of transactions each day. For these carriers, AI delivers measurable results by streamlining operations. By automating repetitive tasks and enhancing decision consistency, AI enables personal lines insurers to boost throughput, cut costs and elevate service levels without compromising accuracy.

A U.S.-based digital insurer recently deployed AI across policy issuance, claims and customer service. The results were dramatic:

• Policy turnaround times dropped by more than half, allowing near-instant processing.
• AI-led fraud detection improved claims accuracy while accelerating payouts.
• Chatbots reduced call wait times by 70%, freeing up human agents for more complex needs.

These changes didn’t just boost efficiency — they reshaped the customer experience. AI helped the insurer achieve scale without sacrificing precision, turning standardization into a strategic advantage.

Large Commercial and Specialty Lines: Sharpening human expertise

Unlike low-complexity, fast-issue lines, large commercial and specialty insurance operate not on speed but rather on insight. Whether it’s large property, marine, energy or cyber risk, each policy is unique, high-stakes and data-intensive.

An Australian specialty insurer integrated AI into its workflow, reducing underwriting cycle times by 35%, improving pricing accuracy across multi-jurisdictional portfolios and accelerating regulatory reporting with automated compliance tracking.

Rather than replacing human expertise, AI sharpened it, aggregating disparate data, modeling complex scenarios and providing context-aware recommendations. The result was not simply faster decisions but smarter ones, making complex risk more manageable and measurable.

The new equation: Efficiency + effectiveness

The real transformation in insurance won’t come from choosing between efficiency and effectiveness. It will come from knowing when to lead with each and support with the other.

• Efficiency ensures scale, speed and consistency.
• Effectiveness ensures sound judgment in complex, high-stakes decisions.
• AI’s true potential lies in bridging the two, creating systems that adapt to both.

As governance and compliance frameworks evolve, insurers must ensure that AI-driven acceleration doesn’t outpace accountability. The leaders of tomorrow will be those that use AI to enhance human decision-making.

The future of insurance is adaptive

The next generation of insurance will be defined by who enables smarter decisions. Personal lines and small commercial will continue to harness AI for operational leverage. Specialty and large commercial insurers, including brokers and MGAs, will rely on it for analytical depth and precision. But the real winners will be those who blend both approaches, creating hybrid models that can flex between speed and sophistication as the situation demands.

The goal isn’t just faster insurance: It’s smarter insurance built on systems that think, learn and evolve alongside the risks they’re designed to protect.

Partnering for intelligent ops

For insurers seeking to harness AI without overhauling their internal infrastructure, Cogneesol offers a scalable bridge between innovation and implementation. Our insurance solutions support everything from underwriting and policy administration to claims and analytics. By combining data, automation and industry expertise, Cogneesol helps insurers reduce operational friction, enhance compliance and turn digital transformation into measurable performance gains.

About the author

Ilya Filipov is a strategy-driven insurance and technology executive specializing in growth, partnerships, and operational transformation across the P&C and legal ecosystems. As Head of North America at Cogneesol, he leads go-to-market, alliance development, and client success for brokers, MGAs, carriers, TPAs, and law firms — helping them modernize operations through AI-enabled intake, back-office automation, system integration, and scalable BPaaS solutions.

With more than 15 years of experience spanning carriers, insurtechs, and distribution networks — including leadership roles at Total Expert, Talkdesk, and Westfield — he brings deep expertise in product strategy, commercial partnerships, revenue operations, and complex service delivery. Filipov is known for simplifying operational chaos, architecting data-driven transformation, and building durable growth engines for mid-market and enterprise clients.

For more thought leadership from the Cogneesol team, please visit our blog at Cogneesol Blog – For an Ecosystem of Digital Transformation

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