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ZestyAI Product Insights

Winter Storm Fern has evolved into a historic catastrophe for the U.S. insurance industry. Between January 23-27, 2026, the storm shattered records by placing over 230 million Americans under severe winter alerts, with a death toll of 85 as of February 3rd. 

Preliminary industry estimates place insured losses at $6.7 billion, potentially making Fern the third-costliest U.S. winter storm on record, trailing Elliott (2022) and Uri (2021). The crisis is far from over. The National Weather Service warns of a "historic duration" of extreme cold, with temperatures 15 to 25 degrees below average, that continues to hamper mitigation efforts.

For carriers, Fern is a complex, multi-peril challenge. Claims teams are navigating a surge of freeze-related losses, ice-driven structural damage, and widespread business interruptions across 34 states. 

To understand the stakes, one needs to look no further than February 2021, when Winter Storm Uri brought Texas to its knees and generated over $11 billion in insured losses from a single state. Fern’s footprint is broader, and its secondary effects are still unfolding.

The Cold Hard Numbers from Storm Uri: Why Claims Explode Below 5°F

Our analysis of the 2021 Storm Uri reveals a striking relationship between temperature deviation and claim frequency for the non-weather water and freeze perils. Using data from multiple carriers, we tracked daily claim rates against minimum temperatures: before, during, and after the storm window (February 11-20, 2021).

The results show how rapidly falling temperatures can transform a routine winter pattern into a systemic loss event, allowing us to monitor the market’s response in real-time as conditions deteriorated, peaked, and normalized.

The results are dramatic:

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Figure 1: Daily claim rates (blue line) surged 126X above the baseline in a temporal spike as temperatures (orange line) plunged below the 20-year average (dashed green line) during Winter Storm Uri.

The chart reveals a clear inverse relationship: as minimum temperatures dropped from the mid-40s°F to below 5°F, daily claim rates didn’t just rise, they increased 126X, from a baseline of 0.04% to 0.46% at the peak. This dramatic surge underscores the significant consequences of extreme cold events on insurance liability.

Figure 2: ZestyAI’s Z-WATER™ demonstrated an 11X increase in claim frequency between ‘Very High’ and ‘Very Low’ risk tiers during Winter Storm Uri

We used ZestyAI’s Z-WATER™ to segment the property-specific non-weather water risk across the 10-day storm window. Z-WATER™ is a risk model that accounts for how plumbing design, local climate, and infrastructure reliability interact to drive non-weather water and freeze losses. By capturing real-world dynamics, such as temperature swings that stress pipes and electrical grid failures that amplify claims, the model delivers a scientifically grounded view of property-level risk.

The results were definitive: properties that Z-WATER™ scored as ‘Very High’ risk filed 26 claims per 1,000, compared to just 2.2 claims per 1,000 for those scored as ‘Very Low’, an 11X increase in claim frequency.

This accurate segmentation reveals a clear path to managing volatility. Z-WATER™ provides a deep understanding of a home’s resilience across the full spectrum of loss mechanisms, from everyday plumbing failures to expensive outlier events like Storms Uri and Fern. By enabling precise intra-territory risk splitting, the model allows carriers to price and underwrite more reliably, ensuring premiums reflect the true risk profile while protecting the portfolio against systemic losses.

The January 2026 Storm: History Rhyming?

While we can already see the immediate impact of Winter Storm Fern, the primary difference between Fern and Winter Storm Uri is the duration of the freezing event itself, rather than any changes in how quickly policyholders are filing their claims.

As shown in Figure 1, NWW claims rise rapidly as temperatures fall and taper off quickly once conditions normalize. The risk in prolonged cold events lies in how long properties stay below the Plumbing Design Temperature; the longer the freeze, the greater the likelihood of systemic plumbing failure.

During Winter Storm Uri, extended sub-freezing conditions significantly increased the number of days in which vulnerable properties were exposed to frozen pipe failures, driving aggregate losses to historic levels. Fern is now exhibiting a similar duration profile, with sub-freezing conditions persisting for up to 10 consecutive days across parts of the Northeast. The National Weather Service has warned this “could be the longest duration of cold in several decades,” raising the likelihood of elevated losses even if individual claims remain tightly clustered in time.

For carriers, the warning signs are already flashing:

• The Power Failure Multiplier: During the storm's peak, over 1 million customers lost power. In the South, where homes lack the heavy thermal insulation of northern properties, a power outage is the primary driver of catastrophic pipe bursts. Without active heating, a property can reach the "burst threshold" within hours.
• The $30,000 Claim Severity Benchmark: Recent State Farm data underscores the high stakes of these events. Winter water damage claims totaled over $628 million, with the average claim payment now exceeding $30,000. For carriers, this high per-claim severity means even a moderate frequency surge can quickly erode Q1 margins.
• Regional Fragility in the South: While the initial assessments are still surfacing, early industry estimates for privately insured losses from Winter Storm Fern puts the damage at $4 billion to $7 billion. With Texas and Tennessee identified as the hardest-hit states, carriers are facing a "Uri-style" scenario in which infrastructure wasn't designed for a 10-day deep freeze.

From Reactive to Predictive: Solving the $6.7 Billion Freeze Risk Equation

The 2021 Texas freeze taught us that traditional approaches to freeze risk are highly insufficient. Many properties that experienced burst pipes were in areas that rarely see extended freezing temperatures, meaning they lacked adequate winterization. 

This is where predictive analytics becomes essential. By modelling the interaction between property-level vulnerabilities and local temperature thresholds, carriers can better identify which properties are most vulnerable to freeze events before the damage actually occurs.

Key Risk Drivers Identified in Our Latest Analysis:

• The Design Mismatch: The greatest risk isn't just the cold; it's the sudden change in temperature. Properties in states like Texas or Tennessee face a higher risk because they are built to release heat, not trap it. They lack the heavy insulation and deep-buried pipes needed to survive a 10-day freeze.

• The Power Grid Vulnerability: Our analysis shows that areas prone to power outages face a compounded risk. In the South, a home’s primary defense is its heating system so when the power fails and the heater stops, the "burst threshold" can be reached in just a few hours.

• Building Vulnerabilities: Our analysis shows that older homes and properties with plumbing routed through exterior walls are disproportionately represented among $30,000 non-weather water losses.

The Bottom Line for Carriers

The 2021 Texas freeze was a pivotal moment for the industry, generating more than 500,000 claims and $11.2 billion in insured losses in a single state. Today, Winter Storm Fern represents an even broader systemic threat, with weather alerts impacting 230 million people across more than 30 states.

While the final tally for Fern is still developing, the data is already clear: temperature shocks drive claims at exponential rates. With early industry assessments estimating privately insured losses between $4 billion and $7 billion, it is evident that the prolonged duration and geographic anomaly of extreme weather events are the primary drivers of this volatility.

For carriers looking to protect their Q1 margins, predictive analytics are no longer a luxury; they are a requirement. By analyzing property-level characteristics, regional vulnerabilities, and historical temperature deviations, you can move from reactive claims handling to proactive risk management. 

The question isn't whether another major freeze will occur, but whether your portfolio is prepared for the next 126-fold surge.

Learn More About Z-WATER 

ZestyAI’s Z-WATER™ provides the industry’s most granular view of interior water risk, helping carriers accurately and reliably assess properties in areas prone to temperature shock events. By analyzing detailed property-level characteristics alongside historical weather patterns and regional risk factors, our advanced models predict the likelihood of Non-Weather Water (NWW) and freeze claims as well as their associated severity. This deeper level of analysis empowers carriers to make smarter pricing decisions before the next major storm hits.

Methodology: Analysis based on aggregated claims from multiple Texas carriers during Winter Storm Uri (February 2021). Temperature data reflects mean daily minimums across the exposure footprint, weighted by ZIP Code to account for geographic density. The claim/exposure ratio was calculated by dividing daily claims by the average policy-day exposure.

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¹CNN Weather, "More than 230 million people under alerts for potential ice, heavy snow and extreme cold," January 2026. [link]

²Fox News, "Noem coordinates with Mississippi officials as state recovers from deadly winter storm," January 2026. [link] 

³Insurance Innovation Reporter, “KCC Estimates $6.7 Billion in Insured Losses from Winter Storm Fern,“ February 2026 [link]

⁴Texas Department of Insurance, "Insured Losses Resulting from the February 2021 Texas Winter Weather Event," March 2022. [link]

 ⁵Fox Business, “More than 1 million Americans lose power as monster winter storm sweeps across the US,” January 2025 [link]

 ⁶Carrier Management, “Frozen Pipes Lead to $628M in Losses for State Farm,” January, 2026 [link]

⁷ Barrons, “Winter Storm Fern Packed a Wallop. Now the Cost Estimates Are Rolling In.,“ February 2026 [link]

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Wildfire risk in the United States is no longer confined to the edges of forests or traditionally high-risk zones. New analysis using ZestyAI’s property-level wildfire models shows that millions of homes classified as low or no wildfire risk under federal assessments face elevated wildfire danger when evaluated at the property level.

This analysis was recently featured in Vox, which examined how wildfire behavior is evolving — and why broad, backward-looking risk maps are increasingly misaligned with how fires spread today.

👉 Read the full article on Vox → https://www.vox.com/climate/476932/california-wildfire-los-angeles-risk-ai-housing-climate

Wildfire risk is closer — and more granular — than most maps show

Many homes damaged or destroyed in the 2025 Los Angeles wildfires were still classified as “low risk” under federal wildfire assessments. ZestyAI’s property-level analysis provides a different perspective.

By evaluating individual structures — including vegetation proximity, defensible space, building characteristics, and neighborhood-level fire dynamics — ZestyAI identified more than 3,000 properties worth approximately $2.4 billion in areas impacted by the Palisades and Eaton fires that showed elevated wildfire risk despite being classified as low or no risk under FEMA’s census-level assessments.

Across California, the classification gap is even broader. Approximately 1.2 million properties, representing roughly $940 billion in residential property value, are designated as low or no wildfire risk under federal maps, despite AI-driven property-level models indicating elevated wildfire danger.

Why census-level wildfire maps fall short

Wildfires do not spread evenly across census tracts or counties. Ember-driven ignition, structure-to-structure spread, wind conditions, and localized vegetation patterns create uneven outcomes, where one home survives and the next is destroyed.

Federal wildfire assessments are designed to provide a baseline view of community-level risk. FEMA has noted that its National Risk Index is not intended to serve as a property-specific risk assessment. When risk is evaluated at the individual property level, meaningful differences emerge that aggregated maps are not designed to capture.

What more granular wildfire risk intelligence enables

More detailed wildfire risk data can support:

• Targeted mitigation efforts at the property and neighborhood level
• More informed rebuilding and land-use decisions
• Clearer, more defensible underwriting and portfolio strategies
• Improved dialogue between insurers, regulators, and communities

A shift in how wildfire risk is understood

Wildfire risk is evolving faster than the systems built to measure it. Homes are no longer just adjacent to wildfire hazards; they increasingly influence how fires ignite, spread, and intensify, even in dense urban environments.

Property-level risk intelligence does not remove hard decisions. But without it, those decisions are made using an incomplete picture of where wildfire risk truly exists.

Read the full Vox article here.

In property insurance, roof age is one of the most critical factors in assessing risk. Yet too often, carriers rely on self-reported or agent-supplied data that is incomplete or inaccurate.

ZestyAI’s recent analysis of 500,000+ properties revealed widespread discrepancies in reported roof age. The result? Mispriced policies, unexpected losses, and operational inefficiencies that impact the bottom line.

As climate volatility grows and reinsurance pressure intensifies, overlooking the true condition and age of a home’s largest, most exposed surface is a risk no carrier can afford.

What’s Inside

• Uncover the biggest myths and blind spots in roof age records.
• Understand why traditional data sources, like claims systems and permits, fall short in providing accurate roof age.
• Learn how a multi-source verification strategy, combining aerial imagery, permits, tax records, and AI, offers a blueprint for improvement and 97% national coverage.
• Explore why roof age is a predictor of losses across multiple perils, not just wind and hail.
• Discover the one-two punch of verified roof age and real-time condition insights, delivering a complete view of risk, even for young roofs with hidden problems.
• Align your roof age data with growing regulatory expectations, particularly in states like Florida.

Access the Guide.

New research from ZestyAI reveals that 62% of U.S. homeowners are deferring critical home maintenance, adding up to $317 billion in potential claims exposure for insurers.

These findings come as Severe Convective Storms (SCS) caused an estimated $58 billion in insured losses in 2024, surpassing hurricane-related losses and marking the second-costliest SCS year on record.

Tornadoes, hail, and wind events now account for over 60% of all U.S. catastrophe claims, and research from the Insurance Institute for Business & Home Safety (IBHS) shows that roof damage accounts for up to 90% of residential catastrophe losses.

Key Findings from ZestyAI’s Homeowner Survey

According to ZestyAI’s nationally representative survey, 62% of homeowners have delayed essential repairs due to budget constraints, representing nearly 59 million U.S. homes with unaddressed vulnerabilities. Forty percent said they would rely on an insurance claim to cover major repairs like roof replacement, adding up to an estimated $317 billion in potential exposure for carriers.

Alarmingly, 63% of homeowners who weren’t living in their home at the time of the last roof replacement don’t know how old their roof is, making it even harder to detect aging systems before they fail. Meanwhile, 12% admitted they would delay repairs indefinitely, further increasing their risk of property damage.

Severe Convective Storms: The Growing Catastrophe Risk

This blind spot compounds known risks: prior ZestyAI analysis has identified over 12.6 million U.S. properties at high risk for hail-related roof damage, representing $189.5 billion in potential roof replacement costs.

“Deferred maintenance has long been a known risk factor, but today the stakes are higher than ever,” said Kumar Dhuvur, Co-Founder and Chief Product Officer of ZestyAI. "With claim severity rising and storm losses compounding, insurers need more than hazard maps to navigate this landscape."

"Property-level insights allow carriers to proactively address known vulnerabilities, improve underwriting precision, and work with homeowners to reduce losses before they happen.”

ZestyAI’s findings support a growing push toward data-driven, preventative underwriting strategies, especially as carriers face rising claim severity and pressure to improve combined ratios across storm-prone states.

With insured losses projected to exceed $30 billion, the recent Los Angeles wildfires rank among the costliest in U.S. history—reshaping how insurers think about risk, resilience, and readiness.

Watch the Full Webinar → LA Fires in Focus: What Insurers Need to Know

In this on-demand webinar, experts from the Insurance Institute for Business & Home Safety (IBHS), the Western Fire Chiefs Association, Cal Poly’s WUI Fire Institute, and ZestyAI unpack what really happened—from frontline response to lab-based research and model performance—and share critical strategies insurers can use to prepare for what’s next.

Watch this session if you’re a Product Managers, Underwriters, Actuaries, and Risk & Innovation leaders looking to make informed decisions in an increasingly volatile wildfire landscape.

What You’ll Learn

• Key takeaways from the Los Angeles wildfires
• Research on structure-to-structure fire spread and resilience factors
• How wildfire risk models performed—what we got right (and wrong)
• Practical strategies to reduce exposure and strengthen resilience

Meet the Experts

• Anne Cope, Chief Engineer, IBHS
• Bob Roper, CEO, Western Fire Chiefs Association
• Frank Frievalt, Director, WUI Fire Institute at Cal Poly
• Kumar Duhvur, Co-Founder & CPO, ZestyAI

With insured losses projected to exceed $30 billion, the recent Los Angeles wildfires rank among the costliest in U.S. history—reshaping how insurers think about risk, resilience, and readiness.

Watch the Full Webinar → LA Fires in Focus: What Insurers Need to Know

In this on-demand webinar, experts from the Insurance Institute for Business & Home Safety (IBHS), the Western Fire Chiefs Association, Cal Poly’s WUI Fire Institute, and ZestyAI unpack what really happened—from frontline response to lab-based research and model performance—and share critical strategies insurers can use to prepare for what’s next.

Watch this session if you’re a Product Managers, Underwriters, Actuaries, and Risk & Innovation leaders looking to make informed decisions in an increasingly volatile wildfire landscape.

What You’ll Learn

• Key takeaways from the Los Angeles wildfires
• Research on structure-to-structure fire spread and resilience factors
• How wildfire risk models performed—what we got right (and wrong)
• Practical strategies to reduce exposure and strengthen resilience

Meet the Experts

• Anne Cope, Chief Engineer, IBHS
• Bob Roper, CEO, Western Fire Chiefs Association
• Frank Frievalt, Director, WUI Fire Institute at Cal Poly
• Kumar Duhvur, Co-Founder & CPO, ZestyAI

ZestyAI, the leading provider of AI-powered property and risk analytics, today announced that its Severe Convective Storm suite, including Z-HAIL™, Z-WIND™, and Z-STORM™, has received regulatory approval from the Minnesota Department of Commerce.

This milestone supports Minnesota insurers in improving storm risk assessment, enhancing underwriting precision, and supporting proactive risk management strategies.

Minnesota has seen significant losses from severe convective storms. According to data from NOAA’s National Centers for Environmental Information (NCEI), the state experienced three billion-dollar weather events in 2024 alone, with hail and wind causing extensive damage. A July storm in the Twin Cities resulted in more than $1.8 billion in insured losses, highlighting the need for innovative solutions to manage storm-related risks.

ZestyAI’s Severe Convective Storm suite delivers property-specific risk insights by combining climatology analysis with granular property data. Built on extensive loss data and validated by regulatory authorities, the suite equips insurers to assess and address storm risks with a higher level of accuracy and confidence. Key features include:

• Z-HAIL: Evaluates each roof’s unique characteristics, including accumulated damage, to predict which properties are likely to file a claim, even in the same neighborhood.
• Z-WIND: Uses AI-generated 3D analysis revealing pivotal insights about roof condition, complexity, and potential points of failure.
• Z-STORM: Predicts the frequency and severity of storm damage claims, examining the interaction between climatology and the unique characteristics of every structure and roof
  These models allow insurers to move beyond reactive damage assessments, helping them identify high-risk properties, allocate resources effectively, and support policyholders in reducing risks.

Bryan Rehor, Director of Regulatory Affairs at ZestyAI said:

“Minnesota’s exposure to hail and wind damage underscores the importance of property-specific insights. With this approval, insurers can access validated models to deliver precise underwriting and rating decisions and encourage risk-reduction measures among policyholders.”

This approval builds on a series of regulatory endorsements in key wind and hail-prone states across the Great Plains, Midwest, and U.S. South, including Texas, Colorado, Indiana, Missouri, and Iowa, among others.

National insurance carriers have long relied on sophisticated models to drive underwriting accuracy and profitability.

But even the most advanced systems can benefit from fresher, more granular, and unique data inputs.

That’s where ZestyAI comes in. With 97%+ U.S. property coverage and exclusive data points—like roof condition and building permits—ZestyAI provides national carriers with the data needed to supercharge their existing models and achieve unparalleled accuracy in risk assessment.

Every insurance model thrives or falters based on the quality of its inputs. Using computer vision and AI-powered insights, ZestyAI captures property-specific features with unmatched precision and is updated multiple times annually. This allows carriers to access insights they’ve never had before, including:

• Roof Condition and Complexity: ZestyAI’s 3D analysis evaluates every facet, penetration, and angle of a roof, providing a complete view that powers complex rating models.
• Parcel-Level Features: Detailed property-level data, such as driveway condition, building permits, lot debris, and overhanging vegetation, reveal nuanced risks for underwriting.
  Climate, Geography, and Infrastructure Variables: Comprehensive data encompassing topography, slope, climate factors, and critical infrastructure.

Why National Carriers Choose ZestyAI as a Data Partner

Adopting an off-the-shelf solution isn’t always the right fit for national carriers with robust internal modeling teams. Instead, these insurers benefit from ZestyAI’s ability to integrate powerful datasets into their existing infrastructure seamlessly. Key advantages include:

• Data Uniqueness: Proprietary insights like property updates and nuanced parcel-level conditions unavailable from public or conventional sources.
• Broad and Deep Coverage: 97%+ U.S. property coverage ensures nationwide relevance.
• Change Detection and Data Recency: AI-driven updates keep your models ahead of evolving risks with near-real-time insights.

By integrating ZestyAI’s data, carriers can complement their models and ensure their outputs are informed by the latest, most comprehensive property-level information.

Driving Precision with a Science-Driven Approach 

At ZestyAI, science and a hypothesis-driven approach form the foundation of our offerings. Hundreds of variables are tested for each model, carefully selected and validated to ensure they meet both logical and causal standards—not just correlations. This rigorous methodology ensures compliance with regulatory scrutiny and real-world risk prediction.

Immediate Benefits, Long-Term Value

• Enhanced Model Accuracy: ZestyAI’s data serves as a diagnostic lens, revealing what’s missing in your existing frameworks and sharpening predictions.
• Operational Efficiency: Recent upgrades to ZestyAI’s API infrastructure, including a 50% reduction in response times and a 10x increase in data processing power, ensure carriers can seamlessly integrate real-time insights into their workflows. These enhancements enable faster decision-making and improved scalability, helping insurers stay ahead in an evolving risk landscape.
• Regulatory Readiness: Transparent, explainable data sources ensure compliance with even the most stringent underwriting regulations.

ZestyAI, the leader in AI-powered property and climate risk analytics, today announced a partnership with THORE Insurance, a Texas-based company.

Johnathan Yazdani, President and CEO of THORE Insurance, said:

"After evaluating several options, ZestyAI was the clear choice for our underwriting needs. Far too often, our industry suffers preventable, foreseeable losses and chalks it up to a cost of doing business. No more. Zesty's comprehensive property insights and roof age solution stood out, offering the precision and scalability we need to grow our business in Texas year over year and maintain low prices for our members through underwriting excellence."

"Their data-driven insights and depth of 40+ property features made the decision easy for us, and we’re confident ZestyAI will be a key partner as we build for the future."

ZestyAI’s Roof Age insights, derived from building permits, aerial imagery, and advanced AI analysis, provide 97% data coverage across the U.S., addressing inaccuracies in self-reported roof data.

‍Recent research shows that 15% of roofs are at least eight years older than reported, highlighting the need for reliable, data-driven solutions.

The Digital Roof™platform uses AI-generated 3D analysis to assess roof attributes like condition, complexity, and potential failure points, while Z-PROPERTY™ Location Insights identifies property features such as vegetation overhang, swimming pools, and solar panels. Together, these capabilities deliver deeper insights to refine risk assessment and pricing.

“In addition to their appetite for innovation, THORE’s leadership clearly communicated a vision to serve Texas homeowners with fairly-priced, best-in-class insurance products,” said Sebastian Kasza, Director of Strategy and Business Development at ZestyAI.

Leveraging AI-powered, property-specific insights in underwriting and pricing is the best way that a carrier can achieve that ambition sustainably.

We are thrilled to partner with Jonathan and his team as they serve the Texas insurance market.”

By Attila Toth, Founder and CEO, ZestyAI 

“SaaS is dead.” 

With these three words, Microsoft CEO Satya Nadella sparked a global debate, challenging the foundation of enterprise operations. His prediction? AI agents will soon replace traditional SaaS workflows, moving business logic to a dynamic AI layer and leaving legacy tools behind. 

For property and casualty (P&C) insurers, this prediction is more than a tech trend—it’s a wake-up call. Carriers have invested heavily in SaaS platforms to modernize underwriting, claims, and risk management. But with AI agents poised to dominate, are these investments like castles built in the sand, vulnerable to the rising tide of AI? 

Let’s unpack what Nadella’s claim means for P&C insurers and explore how to prepare for a future where agentic workflows reshape this $2.6 trillion global industry. 

From Static Systems to Agentic Workflows

Over the past decade, P&C carriers have focused on modernizing their technology stacks, moving
to SaaS-based systems for policy management including underwriting, claims, and billing. These platforms promise efficiency gains, streamlined workflows, and improved business intelligence. Yet Nadella’s vision points to a future where AI agents bypass these systems altogether, directly interact- ing with data to execute tasks dynamically. 

Enter agentic workflows. 

Agentic workflows are powered by AI agents—autonomous systems that can analyze data, make decisions, and execute tasks in real-time without rigid reliance on predefined rules or interfaces. Unlike traditional workflows that depend on user interaction, agentic workflows dynamically adapt to the situation, accessing real-time data and leveraging advanced decision models to solve problems creatively. 

Let’s Break it Down With Examples:

• Underwriting: Traditionally, underwriters rely on policy management systems to assess risk, manually inputting and analyzing data. In an agentic workflow, an AI agent pulls data from internal and external sources, such as property imagery or weather patterns, and evaluates risk in real time, and proposes pricing autonomously.
• Claims: Instead of adjusters triaging claims by reviewing data and making decisions step by step, AI agents analyze First Notice of Loss (FNOL) data, cross-reference it with historical patterns, flag potential fraud, and recommend payouts or next steps—all in seconds. 

Think of agentic workflows as moving from a ‘static map’ to a ‘smart GPS.’ Traditional SaaS systems provide fixed routes, like a printed map or a AAA TripTik, where users must plan and follow a predefined path. In contrast, AI agents function like a GPS that dynamically adjusts to roadblocks or detours, guiding you in real-time to reach your destination more efficiently. 

This doesn’t eliminate the roles of underwriters or adjusters—it amplifies them. With agentic workflows, professionals transition from being data processors to strategic decision-makers, supported by AI agents that execute repetitive and analytical tasks. 

The question is not if, but how fast this shift will occur. In P&C, where SaaS investments are relatively new, the transition may take time. But the direction is unmistakable, and forward-thinking executives should prepare now.

How Insurance Leaders Can Prepare for the AI Era 

The shift to AI-driven workflows brings both challenges and opportunities. To stay ahead, insurance leaders must act now. Here’s how: 

1. Build AI-First Architectures 

Insurers must prioritize modular, API-driven platforms that enable seamless integration with AI agents. An AI-first architecture treats applications as interchangeable layers rather than static end- points, ensuring adaptability to future innovations without extensive system overhauls. 

2. Unify Siloed Data 

AI agents thrive on data, yet fragmented, siloed data remains a significant challenge for insurers. It’s not about choosing the “right” database—AI agents can interact with any data store. What matters is creating a unified and federated data structure that breaks down silos and provides AI agents with a cohesive view of organizational information. 

CIOs should prioritize data integration, ensuring underwriting, claims, customer, and risk data are accessible across the enterprise. A federated approach bypasses the need for lengthy consolidation projects while enabling AI-driven insights. 

3. Engage Regulators Early

AI workflows will only succeed if regulators are on board. Departments of Insurance (DOIs) need to trust the decision-making processes of AI agents and ensure they meet standards for transparency, fairness, and compliance. 

At ZestyAI, we’ve worked with state Departments of Insurance across the U.S. to gain approval for our AI models. Building trust with regulators requires proactive engagement, clear communication, and ongoing education. Insurers that lead in this area will not only gain competitive advantages but also shape the regulatory frameworks that govern the use of AI. 

4. Pilot Agentic Workflows 

Start small, but start now. Deploy AI agents in low-risk areas like claims triage or fraud detection. Early pilots provide valuable lessons and build organizational confidence in agentic workflows. 

5. Expand ROI Thinking 

AI agents are poised to fundamentally transform operations, requiring a broader perspective on ROI. Beyond traditional metrics like cost reduction or workflow efficiency, consider strategic gains such as: 

- Faster speed to market.
- Improved customer satisfaction. - Enhanced risk segmentation. 

6. Put Technology Partners to the Test

Carriers should evaluate their SaaS providers on their readiness to transition to agentic workflows. Ask pointed questions: What is their AI strategy? How do they plan to integrate AI agents into their products? - Are they prepared to support modular, dynamic workflows? 

The Bottom Line 

The future Nadella outlines—a world driven by AI agents—is as disruptive as it is exciting. For P&C insurance executives, it’s a call to action: the technology stack of today may not meet the demands of tomorrow. Preparing now by investing in AI-first architectures, building unified data structures, and engaging with regulators will position insurers to thrive in this new era. 

SaaS isn’t dead yet, but the writing is on the wall. The question is, are you ready to embrace the future? Are you building a castle ready to weather the waves?

Missouri’s severe convective storms are growing more destructive, with hailstorm-related claims skyrocketing by 245% in 2024 alone. To address this rising risk, ZestyAI has secured regulatory approval from the Missouri Department of Insurance for its Severe Convective Storm suite, including Z-HAIL™, Z-WIND™, and Z-STORM™.

Missouri’s Rising Storm Losses

Missouri’s vulnerability to severe convective storms is well-documented. Since 1980, 82 weather events have each caused over $1 billion in damages. In 2024, a March hailstorm—dubbed the “Gorilla Hail” storm—resulted in nearly 7,000 claims, a dramatic increase from just over 2,000 hail claims the previous year.

How ZestyAI’s Models Make a Difference

ZestyAI’s Severe Convective Storm suite provides property-specific risk assessments, enabling insurers to predict and manage extreme weather impacts with precision.

Key Features:

• Z-HAIL™: Identifies a roof’s susceptibility to hail damage and estimates potential claim severity using property-specific attributes like roof complexity and historical losses.
• Z-WIND™: Predicts wind claim frequency and severity by combining climatology with property-specific data such as roof structure and damage history.
• Z-STORM™: Delivers granular risk scores for storm claim frequency and severity, factoring in climatology, building characteristics, and roof design.

These AI-driven models help insurers proactively manage storm-related risks, allocate resources effectively, and encourage policyholders to take preventive measures.

Empowering Insurers with Advanced Risk Insights

Bryan Rehor, Director of Regulatory Affairs at ZestyAI, said:

Missouri’s exposure to tornadoes, hail, and damaging winds makes advanced risk assessment tools essential. By streamlining the regulatory process, we enable insurers to focus on protecting policyholders while reducing losses.

With regulatory compliance and validated loss data at its core, ZestyAI’s suite enables insurers to:

• Enhance underwriting precision and optimize deductible strategies.
• Provide policyholders with actionable insights to reduce risks and prevent losses.
• Move beyond reactive damage assessments to proactive storm risk management.

Looking Ahead

ZestyAI’s Severe Convective Storm suite has already received regulatory approvals in Texas, Colorado, Illinois, Indiana, and Iowa, with additional filings in progress. By equipping insurers with AI-powered tools, ZestyAI is modernizing the way storm risks are assessed, ensuring communities and insurers are better prepared to weather the storm.

Who We Are: Revolutionizing Risk Management with AI

ZestyAI is the leading property and climate risk platform, leveraging advanced AI and data science to provide property-level risk insights to insurers across the United States. Our mission is to redefine how insurers assess and manage risk while fostering a healthier, more sustainable, and more affordable insurance market.

At its core, ZestyAI empowers carriers to assess risk with unmatched precision, aligning premiums with actual property-level data. Our data-driven recommendations help insurers improve underwriting, streamline operations, and optimize portfolios—all while ensuring transparency and regulatory compliance.

ZestyAI’s solutions are rigorously validated by state departments of insurance, including in California, Texas, and Colorado. Our wildfire risk model became the first to gain approval as part of a carrier rate filing from the California Department of Insurance (CDI), setting a benchmark for the responsible adoption of AI in the industry.

With over 200 billion data points analyzed and nearly 100% aerial imagery coverage across the contiguous U.S., ZestyAI transforms vast amounts of data into actionable intelligence. From roof conditions to vegetation density, our platform enables insurers to reduce loss ratios, improve profitability, and increase the availability of insurance to underserved markets.

Proven Performance in the Toughest Conditions

ZestyAI's AI models have been tested and trusted under the industry’s most challenging scenarios:

• Z-FIRE™: The gold standard for wildfire risk, adopted across all wildfire-prone markets in the U.S.
• Z-HAIL™: Predicts hail claim frequency and severity with up to 58X greater accuracy than traditional models, as validated by IBHS research.
• Z-WIND™ and Z-STORM™: Deliver granular risk assessments for wind and storm frequency and severity, with regulatory approval across several states.
• Z-WATER™: Our newest model predicts non-weather-related water damage claims using insights from property construction, local water systems, and environmental factors.

Powered by 30+ proprietary computer vision algorithms, our peril-specific models provide carriers with the flexibility to use ZestyAI’s risk scores or integrate detailed property insights directly into their own workflows.

The Origin of ZestyAI: A Bold Pivot

ZestyAI’s journey began not in insurance, but in clean energy. Founded as Powerscout, we made a pivotal shift in 2017 when the devastating California wildfires revealed a critical gap in how insurers assess risk. Recognizing the potential of our imagery and AI models to become a lifeline for carriers and communities alike, we refocused our mission. By 2018, ZestyAI was born, dedicated to revolutionizing insurance through AI-driven insights.

Why ZestyAI?

Today, our platform empowers carriers to achieve transformative outcomes:

• Set Precise Rates: Move beyond broad territory-based pricing to property-level assessments, attracting low-risk customers with precision pricing while helping high-risk properties mitigate exposure.
• Enhance Risk Selection: Leverage comprehensive risk profiles for smarter underwriting, optimizing combined ratios and profitability.
• Improve Product Fit: Tailor coverage options with appropriate deductibles and endorsements, reducing losses and improving customer satisfaction.
• Optimize Inspections: Direct resources to properties that need attention, reducing unnecessary on-site inspections and cutting costs.
• Streamline Operations: Automate approvals for low-risk properties, freeing underwriting teams to focus on complex cases.
• Optimize Portfolios: Reassess books of business to identify accumulated risks and adjust premiums or coverage based on real-time data.

At the heart of these capabilities is our commitment to delivering a 10X return on investment (ROI) for our customers. By combining precise risk assessment with actionable insights, ZestyAI helps carriers achieve measurable results.

Building a Healthier Insurance Ecosystem

ZestyAI’s impact extends beyond carriers—we’re reshaping the insurance ecosystem to be more accessible, equitable, and efficient.

• Fair Pricing: Align premiums with actual risk to attract low-risk customers while offering guidance for mitigating high-risk exposures.
• Proactive Risk Mitigation: Empower policyholders with tailored guidance, like improving roof conditions or clearing vegetation, to reduce exposure.
• Operational Efficiency: Streamline processes to lower costs and focus resources on the most impactful areas.

Leading the AI Revolution in Insurance

As climate risks grow more intense, insurers must adopt innovative tools to assess and mitigate these challenges. ZestyAI is at the forefront of this transformation, enabling carriers to not only adapt but thrive in an evolving market.

The future of insurance lies in AI-driven precision, and with ZestyAI, that future is already here.

Schedule a demo today to see how ZestyAI can transform your risk strategy in 2025.