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With all the copy I read through every week as I decide which pieces to publish here at ITL, I'm noticing an odd trend. 

For the past couple of years, tons of the articles submitted to me gloried in the insurance industry's "transformation" and "disruption." In recent months, though, lots warn that insurance is at a "crossroads" or an "inflection point" — often dressed up with ominous adjectives so the situation becomes a "major" crossroads or a "crucial" inflection point.  

Why the doom and gloom? And is it justified?

The one issue that could potentially merit the inflection point talk for the whole industry is generative AI. In the three years since ChatGPT announced itself to the world, it has already created numerous opportunities for efficiency, and AI agents hold the prospect of far more profound change. If you can get to the point where you say to your AI, "Gather all the information I need for this claim by contacting all the relevant parties," you would, in fact, have a crossroads. Those who figured out how to take advantage of that sort of AI agent would go one way, toward paradise, while everyone else would head in the wrong direction.

But we aren't there yet, and I think it'll take time for us to get there. The Silicon Valley ethos may be to "move fast and break things," but insurance companies don't get to do that.  We're not allowed to break things. Too many people get hurt if we do. 

The insurance industry faces plenty of other big issues, too: the increased number and intensity of natural disasters, uncertainty and rising prices because of the on-again, off-again Trump tariffs, federal policy that is reducing aid to states following natural disasters and may mean the elimination of the Federal Emergency Management Agency (FEMA), and so on. 

But does that mean we're at an inflection point? I don't think so. I think those issues just show that insurance is a complex, dynamic world, of the sort we've been dealing with, mostly effectively, for a long time. The supply chain disruptions because of COVID certainly caused a crisis, for instance, but insurers have already recovered enough that I recently published an article with the title, "Are Auto Insurers Now TOO Profitable?"

Besides, most of the claims of impending crisis I see are about far less comprehensive issues than GenAI. They're about the need to update legacy systems, to clean data, to adopt some more efficient approach to underwriting or handling claims, and so on. 

I agree with all the points those thought leaders are making. I also understand the need for innovators to create what is often referred to as "a burning platform." At the Wall Street Journal, I covered IBM in the '80s and '90s, a period during which the very smart executive leaders knew they needed to change to keep up with the increasing pace of innovation in the industry but couldn't quite bring themselves to do anything radical, because IBM had been the most profitable company in the world for so long. Only once the company started taking multibillion-dollar writeoffs and laying off tens of thousands of people — having prided itself on never laying off a single person in its 80-year history — did the company have the burning platform that Lou Gerstner used so effectively to change the culture.

I even accept that some parts of the industry are at inflection points. For instance, I recently published a piece by Stephen Applebaum and Alan Demers, "Embedded Insurance Nears Tipping Point" — because they're right; embedded insurance has been percolating as a possibility for years now and may be about to have its breakout moment, especially in auto insurance. I even published a piece in September with the headline, "Insurance at an Inflection Point." That was before I started seeing the term so often that I became allergic to it, and I wouldn't use it in a headline today, but the article makes a smart point about a potential new business model for insurers. 

But we have to maintain our credibility, and we can't be deluding ourselves. We likely aren't doing that if every fifth piece or so that I read claims the industry is at a crossroads/inflection point. (I recently opened a proposed article whose first sentence was, "The insurance industry is at an inflection point," and the next article began, "The insurance industry is at a critical inflection point.") 

There is loads of important change happening in the insurance industry, and GenAI will surely get us to an inflection point. But let's not oversell what's happening now. 

Not every problem is a make-or-break moment. Not every bit of progress is a game-changer.

Cheers,

Paul

P.S. I seem to need to cleanse my soul every six to 12 months with a piece like this. Here are some of my favorite previous rants, which I think hold up just fine: "Let's Stop With the Gibberish," "May I Rant for a Moment?" and "Two Words We Must Stop Using." 

I get riled up just rereading them. Please share with any colleague you think could use a nudge — or maybe a chuckle. 

Cyber insurance remains a cornerstone for managing digital risk, yet the market is evolving in ways that may surprise many organizations. By 2026, policies are expected to provide less certainty than policyholders have come to assume. Insurers are introducing new exclusions, enforcing stricter underwriting standards and responding to the rapid emergence of complex threats such as AI-driven vulnerabilities, zero-day exploits and connected Internet of Things exposures. 

For risk managers and insurance brokers, anticipating these exclusions and developing strategies to address coverage gaps is essential. Misalignment between perceived protection and actual policy coverage can expose organizations to significant operational disruption and financial loss. 

The next section examines why insurers are introducing these new exclusions and what drives their focus on high-uncertainty, potentially catastrophic exposures.

Why Exclusions are Escalating

Claims metrics in 2025 show relative stability, with reports indicating that both the number and average severity of large cyber claims have remained largely unchanged compared with prior years. On the surface, this might suggest that insurers are not under pressure. However, the surge in exclusions is driven less by historical claims and more by emerging, high-uncertainty risks that could produce catastrophic losses. 

Insurers are increasingly concerned about exposures without established actuarial history, including AI-driven attacks, zero-day vulnerabilities, connected IoT systems and state-sponsored cyber operations, according to a 2025 report by Allianz. 

Even isolated events, such as the 2024 CrowdStrike outage affecting multiple Fortune 500 companies, illustrate the accumulation risk insurers now face—where a single incident can affect numerous policyholders simultaneously. 

This combination of unquantified risk, potential for systemic loss and regulatory uncertainty has prompted insurers to tighten coverage and add exclusions to protect against scenarios that could produce outsized financial consequences.

Emerging Exclusions to Expect in 2026

Risk managers should anticipate new categories of exclusions that will redefine what traditional cyber insurance covers. Understanding the rationale behind each exclusion and its potential impact is critical for preparing organizations.

Artificial Intelligence Risks

Artificial intelligence is becoming ubiquitous, yet insurers are increasingly excluding claims linked to its use. Policies may deny coverage for errors or omissions in AI systems, misleading outputs or regulatory violations tied to AI implementation. 

A notable concern is the breadth of some exclusions, which may apply not only to a company's own AI systems but also to third-party platforms used in business operations. This expansive scope creates uncertainty about whether claims will be honored when AI played even a minor role. Risk managers must scrutinize AI-related language in policies and assess whether existing coverage aligns with emerging liabilities, according to an article in the Harvard Law School Forum on Corporate Governance and Financial Regulation.

State-Sponsored Cyberattacks

Following global geopolitical developments, insurers are expanding war or cyberwar exclusions to cover state-backed attacks, according to Mitigata. The impact can be profound, as even incidents occurring in peacetime may fall within the exclusion if a government is implicated. This is particularly significant for organizations operating in critical infrastructure sectors or with extensive international digital networks. Awareness of the scope and triggers of these exclusions is essential for preparing mitigation strategies and considering supplementary coverage.

Catastrophic and Widespread Events

Insurers are increasingly defining "widespread events" or "catastrophes" in ways that limit aggregate exposure from systemic incidents, according to an article by Chubb. These exclusions may restrict coverage when multiple policyholders are affected simultaneously, such as through a coordinated ransomware attack targeting a popular cloud provider. For organizations, this can mean delayed payouts or denied claims when the event's scale triggers a policy exclusion. Clear understanding of these terms is necessary to plan alternative risk strategies.

Web Tracking and Regulatory Liabilities

Policies are tightening language around website tracking, data privacy and compliance with evolving regulatory regimes. Failure to satisfy underwriter inquiries regarding tracking technologies can lead to broad exclusions. Similarly, coverage for fines, penalties and reputational harm is often limited. Organizations must ensure that their security posture, privacy practices and compliance measures are fully documented to avoid coverage gaps.

Enforcement of Existing Exclusions

Even long-standing exclusions are being applied more rigorously, the 2025 Allianz report found. Insurers are denying claims for failure to meet minimum security requirements, including missing multi-factor authentication, unpatched vulnerabilities or outdated incident response protocols. Insider threats, third-party vendor risks, contractual liabilities and regulatory fines are also increasingly scrutinized. For risk managers, this means that maintaining robust, documented controls is not optional but a condition for coverage.

Managing Exclusions

To navigate this tightening environment, organizations should align coverage with actual risk. Key actions include:

• Implementing and documenting robust controls, including multi-factor authentication, endpoint detection and response systems and formal incident response readiness.
• Being transparent during underwriting by accurately representing security posture and addressing known vulnerabilities.
• Conducting regular risk assessments to ensure IT infrastructure aligns with coverage requirements.
• Reviewing policy language closely, with attention to definitions for catastrophes, state-sponsored attacks and minimum security requirements.
• Collaborating with specialized brokers who understand the nuances of cyber policies and can advocate for coverage clarity.

These measures help reduce the likelihood of denied claims and ensure policies reflect actual organizational risk. Insurance remains necessary, but it must be coupled with proactive risk management to be effective.

Filling Gaps with Alternative Risk Transfer

When traditional policies leave high-severity, low-frequency risks uncovered, alternative risk transfer solutions can provide supplementary protection.

Captive Insurance

A captive is a subsidiary insurance company established to underwrite risks for its parent organization. Captives allow coverage of exclusions such as state-backed cyberattacks, AI liabilities, or reputational loss. This approach enables customized protection, keeps premiums and underwriting profits within the organization and provides certainty where commercial markets may be constrained.

Parametric Insurance

Parametric policies pay out based on predefined triggers rather than measured losses. For example, a payout may be tied to a specific number of exposed records or a defined system downtime period. Parametric insurance ensures rapid access to capital for business interruption costs, even if the primary cyber policy contains restrictive exclusions.

Capital Market Solutions

Cyber risks can also be transferred to capital markets through insurance-linked securities such as catastrophe bonds. These instruments attract external capital to cover peak risks, including systemic cyber events, and can expand overall capacity for insuring niche exposures that traditional policies exclude.

Conclusion

Cyber insurance exclusions are expanding in response to evolving threats and increasing claims severity. By 2026, risk managers and brokers must recognize that traditional policies alone may not provide full coverage, particularly for AI-related liabilities, state-sponsored attacks and catastrophic events. Proactive strategies, including robust documentation, controls, regular risk assessments and complementary alternative risk transfer solutions, are essential to bridge coverage gaps. Aligning insurance with operational realities ensures that organizations maintain resilience, protect enterprise value, and respond effectively when cyber incidents occur.

Accelerating P&C product and rate filing is critical to meet dynamic market demands and regulatory requirements. Traditional processes are constrained by manual handoffs, fragmented data, and slow approvals, resulting in delayed product launches and constrained profitability. This article explores how data, GenAI, and Agentic AI can transform rate filing—enabling parallel execution, automated testing, and intelligent workbenches for competitive analysis.

By adopting best practices in architecture, automation, and governance, insurers can compress cycle times, enhance pricing sophistication, and improve compliance. The approach outlined empowers carriers to respond swiftly to market shifts, optimize risk management, and gain a decisive edge.

Problem Statement: What?

Property and casualty (P&C) insurers in the United States face a complex and fragmented regulatory environment when filing new products or rates. The average time to approve rate filings has increased by 40% nationwide (for the period from 2018 to 2024 for homeowners' product). The result is delayed market response, constrained profitability, missed opportunities to reflect a changing risk posture (for example: In California, Proposition 103 limits insurers to base rates on historical losses rather than current and predictive/forward looking models).

While these regulatory complexities add to the delays of rate approvals, insurers also face internal challenges. These are magnified by fragmented data assets affecting rate development/indications, weak/limited integration of policy administration systems and rating engine, manual scenario generation & validations across rating workflows, too many handoffs, and manual state filing preparation.

Understanding Regulatory Complexity in State Filing

The regulatory complexity arises from several related factors:

1. State-Based Regulation and Legal Diversity:

Insurance regulation is primarily state-based, with each state legislature enacting its own rating laws, standards, and filing requirements. These laws may be based on NAIC model laws (e.g., prior approval, file-and-use, use-and-file, flex rating), but significant variation persists in definitions, processes, and compliance expectations across states. Insurers must navigate a patchwork of statutes, administrative rules, and case law, often requiring tailored filings for each jurisdiction.

2. Multiplicity of Filing Types and Entities:

Filings may pertain to rates, rating rules, policy forms, underwriting rules, or combinations thereof. Entities making filings include insurers, advisory organizations, and third-party filers, each subject to different rules and authorities depending on the state and product category.

3. Rigorous Data and Actuarial Standards:

Regulators require extensive supporting data for rate filings, including historical premium and loss data, actuarial analysis, and justification for rating factors. Standards mandate that rates must not be excessive, inadequate, or unfairly discriminatory, but interpretations and required methodologies (e.g., loss ratio vs. pure premium methods, credibility standards, catastrophe modeling) vary by state. Data quality, segregation, and rate adjustment protocols are scrutinized, and regulators may require multi-year data, trend analyses, and loss development triangles.

4. Procedural Complexity and Review Process:

The filing process involves multiple steps and stakeholders: filers must ensure completeness and compliance with state-specific requirements, often using tools like SERFF for electronic submissions. Reviewers conduct detailed checks for statutory and regulatory compliance, issue objection letters for deficiencies, and may require hearings or amendments. The process is iterative, and delays often result from incomplete filings or back-and-forth correspondence.

5. Policy Form Review and Public Policy Considerations:

Beyond rate filings, policy forms are subject to rigorous review for compliance with mandated provisions, prohibited clauses, readability standards, and consistency with pricing memoranda. States may require additional documentation, such as actuarial memoranda or advertising materials, and enforce unique requirements for specific lines of business.

Internal Challenges in Rate Change Management

Rate change management in P&C insurance is challenged by fragmented data sources and limited clarity/disjoint in data/business requirements for rate development & analysis. Insurers must reconcile information from underwriting, claims/loss history, reinsurance, and market trends, which demands extensive data wrangling and preparation. Latency in accessing third-party data and manual handoffs between product, actuarial, and IT teams further slow the process, leading to rework and misalignment.

The absence of integrated platforms for hypothesis development, rate workups, and filing results in inefficiencies and extended cycle times. Compliance steps are repeated for each state, and technical requirements for integration are often relayed indirectly, compounding delays. Manual testing and architectural gaps—such as non-stateless rating engines and scattered product management logic—impede data-driven decision-making and actuarial rigor.

Dislocation analysis, a key actuarial process, is time-consuming due to sequential, repetitive workflows and limited automation. The challenge is to quickly identify segments with disrupted rates and adverse loss ratios, as variable-by-variable reviews are essential but time-consuming. Without robust analytical capabilities, targeted adjustments are delayed, increasing regulatory risk and reducing pricing effectiveness.

How to bridge the internal challenges?

To accelerate and improve product/rate filing for Personal Auto & Property, insurers must deploy targeted interventions across dimensions such as Planning & Communication, Platform/Architecture, Data Controls & Trust, Validation, and rate filing intelligence—ensuring each stage of the value chain is robust, data-driven, and responsive to market and regulatory demands.

• Planning & Communication: Product / Rate filing has a direct correlation to business or product strategy. Considering its significance and the complex nature of the regulatory, it requires well-architected planning and execution. More often the challenges or delays are due to siloed interactions, lack of integration, gaps in business & IT/data requirements, delayed communication etc. across teams (product management, IT, Data, Actuarial, State filing etc.). Creating a digitized & integrated master rate change plan (by state, LOB, change complexity, filing type, etc.), workflow assignments and tracking ensure timely communication, transparency in timelines, dependencies etc. and enables identifying the choke points to improve execution. For example, Shift left the production IT activities related to configuration and build (i.e., before DOI approval/state filing, pre deploy with future effective dates toggled off until approval). Use emergency change approvals for minor rate updates and enforce strict SLA/OLA for signoffs to cut internal wait times.

• Platform/Architecture: Significant data engineering and configuration effort spent during Dislocation analysis and Post approval implementation. Address duplicate efforts spent in dislocation analysis and implementation (post rate approvals) by choosing appropriate rating engines (e.g.: Akur8, Earnix) with integration accelerators and compatible with modern policy administration systems.

• Data Controls and Trust: Automated data pipeline to ingest information, third-party data from near real time sources (telematics, IoT) on loss characteristics, use of CAT models for rate filings to assess risks like wildfire in California (as part of sustainable insurance strategy) to aid rate factor selection and an Assumptions Data Hub to capture UW assumption, Pricing assumptions, loss data etc., helps to build agility. Similarly, replacing legacy /excel-based models for rate filings with python/modern platforms such as hx Renew for central, version-controlled environment helps to improve collaboration, simplification and drive accurate filings.

• Automated validation: Leverage pricing tools/platforms such as hx Renew to automate scenario analysis (what-if") scenario analysis, automate the assessment of the impact of model changes and changes to assumptions, automated validation rules. Also, pairing provisional rate implementation with automated regression and CI/CD, improves response time via elastic rating engines and enhances rate monitoring, compliance, and traceability.

• Rate filing intelligence – Build & leverage rate filing intelligence powered by insights from SNL insurance product filing datasets (from S&P Global Market Intelligence) to understand market strategies, industry trends, analyze filings/factor changes of peer insurers, insights wrt objections, approval/response timelines of DOI etc. Harnessing these insights provides a feedback loop wrt product strategy, planning & execution adaptation to market conditions and decision-making.

Potential Benefits

Adopting integrated interventions such as master rate change plans and disciplined workflows, modern rating engines and platforms, reducing/eliminating excel based rating models, third-party data integrations, CI/CD and automated regression, market aware rate filing intelligence and effective change management—can significantly increase throughput of rate changes, strengthen rating traceability, reduce refiling/rerating cycles, and leverage richer third-party data for more responsive pricing, improving conversion, loss ratio resilience, and agility to market shifts.

The Way forward

To accelerate rate filing and product launches, insurers should assess their implementation strategy across the dimension such as people, process, technology and data, to evaluate their performance and outcomes. By operationalizing some of the relevant interventions listed above, insurers can compress cycle times, respond swiftly to market shifts, and optimize risk management. Now is the time for industry leaders to champion these changes and drive better outcomes.

Ten years ago—has it been that long?—I was working with the largest insurer of churches and religious institutions in the US when we discovered they were incurring an average of $70 million in annual losses from frozen pipes.

It makes sense. Many houses of worship sit empty most of the time, and in the northern half of the country—where most of this carrier's book was concentrated—a power outage or failing furnace leads to frozen pipes, burst lines, and substantial water damage claims.

So we built an IoT service that monitored furnace activity and water pipe temperatures, complete with a call center to alert policyholders before problems escalated. It worked so well that it survives today: insureds receive annual premium discounts for enrolling, and frozen pipe claims have dropped over one-third.

That experience in continuous risk management sparked my fascination with the next frontier: continuous underwriting. In my view, there's no reason insurance premiums shouldn't fluctuate daily—like stock prices or utility bills—as new risk data emerges.

Frustratingly, there are exactly two reasons they don't: regulation and reinsurance.

Tesla Insurance: A Case Study in Market Inertia

Tesla Insurance launched in 2019 in California, leveraging real-time telematics data from connected vehicles to offer up to 30% lower premiums through a Safety Score algorithm that tracks behaviors like hard braking and collision warnings. The system performs real-time scoring—true continuous underwriting—and adjusts premiums monthly.

Today, Tesla Insurance operates in just 12 states. Twelve states in six years represent a glacial pace for a company built on speed, underscoring how state-by-state regulatory approvals and legal roadblocks stifle algorithmic pricing scalability. Elon Musk has joked that SpaceX will reach Mars before Tesla Insurance writes business in all 50 states—a sadly ironic quip, since the technology for continuous underwriting already exists.

Then there's reinsurance. Earlier this year, Tesla accelerated its pivot toward vertical integration by launching full in-house underwriting for California policies, marking a strategic departure from third-party partners like State National Insurance (a Markel subsidiary). This move gives Tesla direct control over risk assessment, pricing, and policy issuance—despite California's Proposition 103 restrictions on dynamic telematics pricing.

This operational autonomy does two critical things: it eliminates reinsurance constraints—such as conservative loss ratio caps that previously stifled Tesla FSD-linked innovations—and positions the company for national expansion, with pilots already running in Texas and Illinois. By year-end, in-house underwriting will cover 40% of Tesla's $1.2 billion premium base.

Cyber Insurance: A Case Study in Market Necessity

Cyber underwriting has traditionally relied on static annual assessments, but accelerating threat velocity—in the first half of '25, incidents grew by 49% YoY—demands a shift to continuous underwriting. Real-time data from AI-driven tools like open-source intelligence (OSINT) scanning and attack surface risk management (ASRM) enables dynamic risk evaluation and premium adjustments.

Cyber insurtechs such as Cowbell are transforming underwriting from a snapshot into a living process. They report a threefold reduction in claims through proactive remediation and adaptive policies tied to evolving security postures.

These cyber insurtechs focus almost exclusively on the SME segment—businesses with less than $1 billion in revenue, fewer than 1,000 employees, and, crucially, simpler IT environments than large enterprises. They're also proactive. Cowbell, for instance, actively monitors and underwrites risk for over 31 million SME entities using continuous external attack-surface scanning (their Cowbell Factors), often before a quote is even requested. This makes them one of the clearest real-world examples of continuous underwriting operating at scale in the small-and-mid-market commercial segment.

Regulation is actually helping here, pressuring carriers to verify real-time adherence to baseline security standards like multi-factor authentication through tools such as Endpoint Detection and Response (EDR) and Managed Detection and Response (MDR).

Reinsurance innovation is providing capacity. Leaders like Munich Re and Swiss Re are investing in advanced modeling and proportional treaties that favor data-rich, quota-share structures—lowering capital needs while supporting AI-enhanced risk portfolios.

Continuous underwriting unlocks growth. Projected global cyber premiums are expected to more than double from $14 billion in 2023 to $29 billion by 2027.

The "Big" Fight to Scale

In this corner, the champ: Big Insurance and Big Legal (has anyone not heard of Morgan & Morgan?). They'll spend upwards of $200 million this year lobbying Washington to preserve the McCarran-Ferguson Act of 1945, keeping arcane insurance regulations frozen in place.

In that corner, the challenger: Big Tech. As continuous underwriting—by definition, fully automated—consumes AI data center capacity, the AI hyperscalers are throwing untold millions into the fray.

The majority of insurance consumers—per recent surveys—are rooting for the challenger.

I'm not here to scare anyone by saying, "Tech will replace all insurance professionals." That line is boring now.

What I want to talk about is something else: Tech may not replace your job immediately, but it is slowly replacing your worth in the system.

We are entering a phase where some changes in insurance are no longer a choice. They are inevitable. I call this "inevitablism" in insurance.

What Is Inevitablism?

Inevitablism in insurance refers to the mindset that certain industry shifts — such as automation, AI adoption, data-driven decision-making, and modernization of legacy systems — are not optional but unavoidable.

It's the belief that these changes will happen regardless of current comfort, resistance, or preparedness, and that insurers must adapt rather than delay, because the future will arrive with to without them.

Tech vs Talent: The Usage Gap

There is no shortage of talent in insurance. The real problem is how that talent is being used.

Across the industry, many bright professionals spend their day on low-value tasks:

• Moving data between systems
• Updating spreadsheets
• Chasing documents
• Sitting in the same recurring meeting.

They are capable of designing better products, rethinking portfolios, and solving complex risk problems. But because technology inside many insurers is underused or outdated, people become the "glue" holding legacy processes together.

And let's be honest — we all know insurance adopts technology at a speed of 0.1× compared with the rest of the world. When the world is moving toward no-code workflows, instant software creation, and autonomous systems, insurers are only now preparing to give GenAI controlled access to production environments.

The gap is not just between tech and customers; it is between tech and talent.

Instead of using technology to free people for higher-value work, we often use people to compensate for the lack of technology. That's where the fear of AI comes from. It's not just, "Will AI replace my work?" It's also, "Have we allowed our roles to become so basic that any decent system could replace them?"

The Age of Innovation

We are already in a world shaped by Web 3.0, emerging platforms and decentralized technologies. Bitcoin's rise is just one signal of how digital value and infrastructure are shifting. On top of this, AI is accelerating innovation at a speed the industry has never seen before.

In this environment, insurers do not have the option to "wait and watch". They will be forced to adopt technology and create products that match how people actually live, work and transact today.

Innovation will not grow linearly; it will grow exponentially with the help of AI.

Automation will not be a luxury; it will be a necessity.

With open-source AI tools, startups can build, iterate and launch at a fraction of the cost and time. This new tech wave can easily create the next 10 major insurance players for the world—born digital, data-native and globally connected from day one.

In the future, most people will have their own AI agent helping them choose the right policies from hundreds of options. Most interactions—advice, onboarding, even parts of claims—could happen through VR or AR environments, especially for complex or high-value risks.

Behind the scenes, risk and portfolio decisions will rely on far more computing power than today, with advanced simulation and optimization. At the same time, connections between insurance and reinsurance will become more streamlined, with better data-sharing, real-time insights, and smarter capital allocation.

Leadership Choices in a Legacy World

Some leaders still believe that sticking to legacy systems and old processes is the safest path. They focus on short-term stability, minimal change and being answerable upwards, rather than looking ahead.

And this creates another silent problem — there is no real plan to make the transition easier for the next generation of leaders. Very few leaders think 10 years ahead. They avoid solving foundational issues like unstructured data, fragmented systems, or outdated architecture. But if today's leaders don't streamline data, modernize infrastructure, and clean the technical debt, how will the next leader build, innovate, or scale?

Without this groundwork, every new initiative becomes a retrofit, every improvement becomes a patch.

On top of that, most organizations don't have a clear plan to upskill employees before introducing new technology. Instead of preparing talent for next-level work, new tools get dropped in suddenly. This creates anxiety, resistance, and the fear of being replaced. A thoughtful, long-term upskilling roadmap not only protects employees — it empowers them to drive the transition and elevate the organization to its next stage.

Others think long term. They understand that the next generation of executives will not just "manage operations" but will be expected to embrace innovation, work with AI and data fluently, and redesign how insurance is delivered.

The organizations that win will be the ones where leaders:

• Invest in modern platforms instead of patching legacy systems forever
• Empower teams to experiment, automate and simplify
• Build long-term upskilling plans so employees grow with the technology
• Prepare future executives to operate in a world where AI, Web 3.0 and virtual interactions are normal, not experimental

The choice is simple: either leadership shapes the transition, or the transition happens to them.

A Future No One Wants to Miss

If we get this right, the future of insurance is not something to fear—it's a future no one will want to miss.

Insurance will work much more globally than it does today. Risks will not only be priced and held locally; they can be pooled globally, with capital, data and exposure flowing more smoothly across borders.

With the help of Web 3.0 and digital identity, we may see unique decentralized IDs created for individuals, businesses and even digital assets. These IDs can carry verified risk information, claims history, behavior patterns and coverage details in a secure, portable way. That means faster underwriting, smarter risk selection and better pricing for those who manage risk well.

For customers, protection becomes something that quietly works in the background—across countries, platforms and channels—instead of a one-time, paperwork-heavy transaction.

At the same time, insurers may rely on an entire army of AI agents to handle day-to-day tasks: answering queries, comparing products, monitoring exposures, flagging anomalies, and triggering workflows. These agents will effectively act on behalf of both the insurer and the customer.

That raises a new question for the industry: we won't just be insuring people and organizations — we will also need to think about how to insure the agents and the risks created by their decisions, errors, or failures.

As more processes are automated and more intelligence is built into systems, something important happens on the human side: we actually get more time and space to think.

More time to:

• Discover what risks and needs are emerging
• Innovate types of coverage and services
• Design better experiences for both physical and virtual worlds

AI, automation and advanced computing handle the volume and speed. Humans handle the nuance and direction.

Final Thoughts

The future will not argue with any of us.

We can continue to debate whether Al will really reach certain capabilities, whether regulators will permit specific models, or whether customers will fully trust automated decisions. Many of these discussions are valid and necessary.

But some trends do not wait for our full intellectual comfort. They advance quietly through small projects, pilot programs and incremental upgrades.

The future does not require large numbers of people to keep legacy processes alive. It requires fewer people doing higher-value work, supported by smarter tools and more connected systems.

As someone who has spent the last two decades in insurance, I've witnessed the perennial struggle faced by carriers and agencies alike: you have enormous volumes of data, legacy systems built on silos, and you're making many of your strategic decisions based on reports that are weeks, sometimes even months, old. The issue isn't access to data. It's the inability to analyze and operationalize that data in real time.

In the world of insurance, the ability to access and act on insights as events unfold is fast becoming the new "power center" of leadership. Why? Three key reasons: agility, precision, and control.

Agility

The pace of change in this industry is accelerating, especially since Covid-19 forced companies to take a hard look at their digital strategy. Shifting consumer expectations, compressed margins, evolving incentive structures, and new distribution models require leaders to respond quickly. Yet most still rely on batch reports pulled manually from core systems.

According to one recent insight, many insurers remain hamstrung by systems that cannot deliver analysis in real time, forcing backward-looking decisions. Real-time analytics can eliminate that delay. Instead of waiting for month-end or quarter-end reporting cycles, leaders can monitor performance as it happens, whether it's agent productivity, product mix shifts, lead conversion trends, or persistency drops.

Precision 

In today's world, precision is required to understand patterns like:

• Which products are driving the highest lifetime value?
• Which agents are trending toward lower persistence?
• Where are revenue leaks occurring?
• Where is there early evidence of chargebacks or clawbacks that will erode revenue?

These are some core questions that leaders grapple with daily, and they can't be answered with static spreadsheets. When analytics are available to them in real time, leaders can spot early signals before they turn into financial problems.

Control

For decades, core systems for commissions, reporting, policy administration, and field performance have been disconnected. The result: fractured visibility and slow response times. With real-time analytics unifying these workflows, leaders can intervene earlier, forecast more accurately and coach more effectively. Imagine giving agents visibility into their own earnings trajectory and book of business health or regional leaders having live dashboards highlighting where risks are emerging or products are outperforming.

The implication for leadership is profound. If you don't make real-time analytics a core capability, you're ceding strategic advantage.

Picture a distribution network where you don't wait for quarterly performance reviews or manual data pulls. Instead, you see live indicators of channel performance, emerging lapse risks, commission anomalies, and revenue movement. You can intervene the moment an issue surfaces, not weeks later.

This is the shift: from reacting to yesterday's insights to orchestrating today's outcomes.

Real-time analytics are no longer a "nice to have." For insurance distribution leaders, they define who will operate reactively and who will lead.

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.