The property and casualty (P&C) insurance industry is approaching a tipping point in 2026. With climate risk intensifying and social inflation pressuring loss costs, insurers are grappling with mounting challenges while also exploring innovative strategies to stay competitive and sustainable. The landscape demands a transformation in how insurers assess, price, and manage risk.

Climate Risk Is No Longer a Future Concern

The frequency and severity of natural disasters are escalating, with 2024 and 2025 witnessing record-breaking events — from wildfires in North America and Europe to cyclones and flooding in Asia-Pacific. These catastrophes are not just more frequent; they are affecting regions previously considered low-risk, undermining the validity of historical models.

Impact on P&C Underwriting:

• Traditional catastrophe models are under strain, often failing to capture emerging patterns in climate volatility.
• Reinsurers are tightening terms and increasing pricing, leading to cascading effects across primary insurers' balance sheets.
• Geographic diversification is no longer a foolproof strategy. Risk zoning must become hyperlocal and dynamic, factoring in real-time climate intelligence.

In response, insurers are investing in climate-tech partnerships to refine modeling, using satellite data, AI-powered weather forecasting, and climate scenario testing to redefine risk pools and set more accurate premiums.

Social Inflation and Litigation Trends

Another less visible but equally threatening pressure is social inflation — the rising cost of insurance claims due to increased litigation, larger jury awards, and changing societal attitudes toward corporate accountability.

What's driving it?

• Plaintiff-friendly legal environments and third-party litigation financing.
• Higher medical costs and longer case durations.
• Juror sentiment increasingly siding with individuals over institutions.

These factors are particularly pronounced in liability and commercial auto segments, where loss ratios are deteriorating despite premium hikes.

Insurer responses include:

• Expanding policy exclusions or tightening terms and conditions.
• Enhancing claims triage with analytics to identify fraud or high-exposure cases early.
• Collaborating with legal experts to track regional litigation risk indicators and adjust reserves accordingly.

Evolving Regulatory Expectations

Governments and regulatory bodies are also stepping in, demanding that insurers play a bigger role in climate adaptation. In the U.S., NAIC climate risk disclosures are becoming more stringent. In the E.U., Solvency II enhancements include stress testing for environmental risks.

Insurers are expected to:

• Integrate ESG (environmental, social, and governance) risk assessments into underwriting.
• Demonstrate long-term solvency resilience under various climate scenarios.
• Offer inclusive insurance products, especially for vulnerable populations.

This shift is pushing insurers to adopt a dual mandate: protecting their own solvency while supporting societal adaptation to climate change.

Technology as a Strategic Imperative

In the face of these mounting challenges, digitization is not just about efficiency; it's a lifeline. Advanced technologies are enabling the P&C sector to build resilience and adaptability into their core functions.

Key enablers include:

• Geospatial Analytics: Delivering risk intelligence for property underwriting and claims by combining satellite imagery with AI.
• Predictive Claims Models: Reducing costs and enhancing accuracy in reserving by predicting litigation probability and settlement values.
• Blockchain and Smart Contracts: Particularly in commercial lines, streamlining policy administration and minimizing disputes.

More carriers are turning to parametric insurance models that trigger payouts based on predefined events (e.g., wind speeds, rainfall thresholds), reducing uncertainty and administrative burden.

Redefining Customer Engagement

As climate risks grow and insurance becomes more expensive, consumer trust is at stake. Policyholders expect transparency, fairness, and proactive service — especially after experiencing a catastrophic loss.

Insurers must evolve from payers of claims to partners in resilience:

• Offer risk mitigation tools like smart home sensors or wildfire defense services.
• Deliver digital-first claims experiences with real-time tracking and automated loss assessments.
• Communicate coverage limits and exclusions clearly to prevent disputes at the time of need.

Personalization — driven by behavioral data and lifestyle insights — will be the hallmark of customer loyalty in 2026.

The Road Ahead

The P&C insurance sector in 2026 is being reshaped by macro forces beyond its control — but not beyond its influence. By embracing innovation, transparency, and climate-conscious practices, insurers can transform these risks into opportunities.

Key priorities for insurers moving forward:

• Rebuild pricing and risk frameworks to reflect future climate, not just the past.
• Tackle social inflation with advanced claims analytics and legal insights.
• Drive digital transformation to enhance agility and customer experience.
• Prepare for regulatory shifts by embedding sustainability in enterprise strategy.

Those who respond with bold, data-driven strategies — while staying grounded in the principles of fairness and protection — will define the future of property and casualty insurance.

In the ever-evolving landscape of the insurance industry, 2025 marks a transformative year where artificial intelligence (AI) and data analytics have emerged as indispensable tools in redefining how risk is understood, assessed, and managed. This shift is not just incremental—it's foundational, changing the DNA of insurance products, operations, and customer experiences.

From predictive underwriting to hyper-personalized policies, the integration of smart technologies is enabling insurers to become more agile, customer-centric, and resilient in a rapidly changing risk environment. Let's explore how AI and data analytics are reshaping the concept of risk in the modern insurance landscape.

The Age of Predictive Risk Management

Traditional insurance models largely relied on historical data and actuarial tables to price risk. But in 2026, these models are being outpaced by predictive analytics powered by real-time data and machine learning algorithms.

Using vast amounts of structured and unstructured data—from IoT devices, social media, telematics, wearables, and third-party sources—insurers are now predicting not just what might happen, but when and why. This allows for real-time, dynamic risk modeling that is far more nuanced and accurate than ever before.

For example, AI models can now detect subtle behavioral cues from driver telematics to assess real-time accident risk. Health insurers, too, are using biometric data and lifestyle tracking to anticipate chronic illnesses, enabling earlier interventions and better risk pricing.

Hyper-Personalization of Insurance Products

The "one-size-fits-all" approach is quickly becoming obsolete. Thanks to AI-driven customer segmentation and behavioral analysis, insurance in 2025 is increasingly tailored to individual lifestyles, preferences, and risk profiles.

Usage-based insurance (UBI) for auto, pay-as-you-go travel insurance, or real-time-adjusted health policies are just the tip of the iceberg. Smart homes equipped with IoT sensors offer property insurers insights into how risk fluctuates over time, enabling micro-adjustments to premiums or coverage on the fly.

This not only improves customer satisfaction by offering transparency and fairness but also ensures better alignment between risk exposure and insurance coverage, reducing adverse selection and fraud.

Claims Processing Gets an AI Makeover

Claims management, historically a manual and paper-heavy process, is now being revolutionized by AI and automation. In 2025, the average claims cycle is significantly shorter thanks to robotic process automation (RPA), AI image recognition, and natural language processing (NLP).

Take, for instance, an auto accident claim. AI tools can analyze photos of vehicle damage, match them to repair estimates, and process payouts within minutes—all without human intervention. Virtual assistants, powered by NLP, handle routine customer queries, schedule inspections, and provide status updates.

Beyond speed, AI also helps reduce fraudulent claims by identifying anomalies or unusual patterns in real time, flagging suspicious activity for human review. This drives down loss ratios and builds more trust with policyholders.

Dynamic Underwriting and Real-Time Pricing

The role of the underwriter has evolved from a periodic evaluator of risk to a continuous manager of it. Thanks to AI, underwriting is no longer a static function. Instead, it is a living process, informed by real-time data and adaptive learning systems.

Underwriters in 2025 are equipped with intelligent dashboards that integrate multi-source data feeds—climate models, market trends, cyber threat intel, etc.—to adjust risk scores dynamically. AI suggests optimal pricing strategies and recommends policy changes, minimizing exposure while maximizing profitability.

In commercial lines, particularly for complex risks like cyber insurance, AI is helping insurers offer real-time risk assessments and conditional coverage models that change based on threat landscapes or company behavior.

The Rise of Explainable AI in Insurance

As AI models become increasingly complex, the demand for transparency and regulatory compliance grows. Explainable AI (XAI) is a key focus in 2026, helping insurers understand and justify decisions made by algorithms.

Whether it's denying a claim, adjusting a premium, or flagging a high-risk policyholder, insurers must now provide clear, human-readable explanations. This is crucial for customer trust, regulatory compliance (especially under data protection laws like GDPR or India's DPDP Act), and internal governance.

XAI frameworks are embedded in most insurance platforms, ensuring every decision is auditable and fair—an essential step toward ethical AI deployment in risk management.

Mitigating Emerging Risks With AI

The 2025 risk environment is marked by volatility—from climate change and geopolitical instability to cybercrime and supply chain disruptions. Insurers are turning to AI not only to assess but also to mitigate these emerging risks.

For example, AI-powered climate models help property insurers predict flood zones and wildfire risks with unprecedented precision, allowing for risk avoidance strategies. In cyber insurance, machine learning monitors clients' digital infrastructure for vulnerabilities and offers real-time recommendations to harden systems.

Thus, insurers are no longer passive responders to risk—they are becoming active partners in risk prevention and resilience.

Ethical and Workforce Implications

As smart technologies take over routine tasks, the role of human workers is evolving. The insurance workforce in 2025 is increasingly focused on strategic, ethical, and creative responsibilities—interpreting AI insights, ensuring fairness, and maintaining the human touch in digital experiences.

However, there are also challenges. Data privacy, algorithmic bias, and the digital divide raise ethical concerns. Insurers must invest in responsible AI governance and continuous upskilling of their workforce to balance innovation with integrity.

Final Thoughts

Smart insurance in 2025 is not just a digital facelift—it's a fundamental rethinking of how risk is perceived, priced, and managed. AI and data analytics are enabling insurers to shift from reactive loss payers to proactive risk partners.

The winners in this new era will be those who combine technological prowess with ethical foresight and human empathy. In doing so, they won't just reshape risk—they'll reshape trust in the insurance industry for generations to come.

International broking isn't a workflow. It's a field of tension. What matters isn't just what gets done but how pressure moves—who absorbs it, who deflects it, who delays it, who misreads it. It's not a chain. It's a net. And to understand why things hold or break, you need to see how that net is structured. That's why I started thinking about adjacency. Not in the social sense but in the structural one. Who is connected to whom? How tightly? With how much strain?

Most systems are described by function. This person issues quotes. That one tracks claims. Another handles renewals. But function hides friction. It doesn't explain why one delay ripples across five countries, or why silence in one actor causes overload in another. That's what adjacency captures. The weight between actors. The frequency of exchange. The risk of misunderstanding. When you model the system this way, pressure becomes visible. Saturation becomes measurable. And the broker's role becomes clearer—not as a manager of tasks but as a regulator of saturation.

I began sketching this model because so much of my daily work felt reactive but patterned. Problems didn't arrive randomly. They clustered. They recurred in the same configurations. A late local quote always triggered the same escalation path. A client silence always led to double communication loops. I wasn't just handling issues. I was trapped in a structure that kept redistributing the same pressure, slightly out of phase. That realization changed how I viewed broking. I stopped thinking in terms of performance and started thinking in terms of system behavior.

So I started mapping. Not to map people but interactions. Each actor in the system became a node: the client HQ, the local client, the master broker, the local broker, the master insurer, the local insurer. Then I assigned each a state. Not a rating but a vector of current conditions: interpretive clarity, communication load, response time, information coherence. And between each actor, I mapped the strength and weight of their connection. Some links were active and dense. Others were latent or volatile. What emerged was a pattern not of workflow but of structural exposure.

The adjacency matrix became a way to see how pressure might travel. It showed me where a shock—like a delayed quote or a misinterpreted clause—would move next. It showed me which links had no buffer, where signals would distort, where trust was brittle. More importantly, it showed me where I was sitting in that map: often at the junction of multiple high-weight, high-volatility links. Not by accident. By design. Brokers are where the pressure pools.

If we don't see that, we misinterpret our own experience. We treat overload as a personal failing. We treat delay as someone else's problem. We act as if the system is breaking, when it's behaving exactly as built. The adjacency model gives language to that. It doesn't assign blame. It shows flows. And in those flows, you can intervene strategically.

What does that mean practically? It means we stop trying to speed everything up. Instead, we start redistributing attention. If one node is overwhelmed, we soften adjacent links. If a local broker is under-engaged, we increase narrative contact before the next placement. If an insurer is slow, we don't escalate immediately. We look at what weight we've asked them to carry and whether that weight is legible. Pressure isn't the enemy. Disproportionate pressure is.

This changes how we train teams. We stop treating coordination as admin and start treating it as structural regulation. We ask brokers to track state, not just tasks. To notice when narrative begins to slip. To anticipate which links will hold under ambiguity and which will collapse. That kind of awareness can't be programmed. It has to be developed through structural thinking.

It also changes how we talk about performance. A renewal that happens late but with aligned expectations may be a better outcome than one that's fast but misaligned. A claim that takes time but builds trust may protect the account longer than one that's settled quickly but leaves narrative damage. When we model adjacency, we're not tracking efficiency. We're tracking coherence. And coherence is what makes systems stable.

Most of all, it reframes the broker's identity. We're not just project managers or service agents. We are interpreters of saturation. We don't control the actors. But we shape how they interact. And when we do that well, the system flows even under pressure. When we don't, the system still moves. But it drifts. And drift is harder to detect than failure.

So this is why I model. Not to simulate reality. To sharpen perception. The matrix isn't the work. But it reveals the hidden shape of the work. It reminds me that what holds a program together isn't the documents, the trackers, or the milestones. It's the logic of adjacency—the way pressure moves between people, and the broker's role in absorbing just enough of it to keep the structure intact.

When you start thinking like this, things look different. Silence becomes signal. Delay becomes weight. Misunderstanding becomes distortion. And the broker stops being the one who makes the system work. The broker becomes the one who keeps it from breaking. That shift—from control to containment, from management to interpretation—is where the real work happens. And the more we can make that legible, the better our systems will hold.

Paul Carroll

It was almost exactly a year ago that I attended a gathering you helped put together in Atlanta for a group that helps universities and insurers collaborate on research concerning climate risk, so this feels like a great time to catch up. What would you say are the major advances in the past year in making the world more resilient, and in the insurance industry’s efforts on that front?

Francis Bouchard

Things are starting to coalesce. As someone who's been active in this space almost exclusively for four years, I'm starting to see some real positive signs. Some of that is from insurers themselves, who are leading efforts on risk reduction opportunities, whether through IBHS [the Insurance Institute for Building & Home Safety] or other standards.

I see more industry activity—concrete, real activity—than I've seen at any other time in the last four years. Kudos to those companies that are really starting to look at these challenges in new and different ways. I see more and more non-insurers looking at insurance as a viable part of the solution and wanting to create an environment where homes and communities are insurable.

There are discussions happening with builders that weren't happening a year or two ago. There are discussions happening with architects that weren't happening a year or two ago. This system-level awareness that's growing is really encouraging because this is not an insurance problem—it's a risk problem and an insurability problem.

Many sectors are accountable for reducing risk before a home presents itself to an insurance company to be insured and priced. The fact that meaningful discussions about what other players in the value chain could do to reduce the risk of these homes is wildly encouraging. Some of that's happening in the context of the California rebuild, while some is happening with organizations trying to coalesce stakeholders to pursue a national or larger-scale solution.

I'm encouraged because people are talking, more people are acting, and people are starting to see the connection points more clearly than perhaps they had before.

Paul Carroll

What other programs, similar to IBHS’s FORTIFIED, are making strides in promoting resilient construction?

Francis Bouchard

I'd point to the LA Delta Fund, dedicated to the 12,000 homes burned in the Eaton fires. It focuses on closing the gap between what insurance proceeds will pay for and what it takes to achieve a truly resilient construction level. We often debate who should bear this cost—consumers or insurers. This organization has found a way to attract both return-bearing capital and philanthropic capital to create a blended capital fund that pays the difference—the delta—between insurance proceeds and the cost of resilient construction. They are close to launching the fund and beginning to facilitate a much higher level of resilient reconstruction in LA following the fires.

This initiative is, in many ways, epic. It's never been done before, certainly not at this scale. The fact that they can raise money from markets indicates that the interest in ensuring resilient rebuilding extends well beyond the insurance sector.

Paul Carroll

Any other examples leap to mind?

Francis Bouchard

There's the Triple-I project with PwC in Dallas that is aligning stakeholders to facilitate the rebuilding or retrofitting of homes to the IBHS standards. This is another concrete example of insurers coalescing to change the risk profile of a community.

Then you have individual firms pushing the envelope. Milliman is doing an immense amount of work, with Nancy Watkins focusing on the WUI [wildland-urban interface], where the interaction between communities and wildfire is the most extreme.

Mercury Insurance is engaging with communities about what it takes to convince them to take steps that would make them insurable. We're starting to see a shift from thought leadership to community engagement.

Paul Carroll

What industry-academia research projects have generated the most interest, and where do they stand?

Francis Bouchard

Nothing has been launched yet, as we are still waiting on a funding announcement from the NSF [National Science Foundation] and corresponding funding from industry partners. We’re cautiously optimistic about the NSF and think industry funding will follow. 

The project that generated the most interest last September was a platform to facilitate dialogue between the atmospheric science community and the insurance underwriting community and help both sides better understand the value and use of available data sources. Considering the recent changes and, in some cases, wholesale dismantling of government departments or capabilities, this issue has become even more pressing and will likely appeal to numerous companies.

Dialogue is already occurring in multiple forums. We're hoping to coalesce these discussions and create a trusted pipeline of information flowing between federal data sources and the insurance sector.

Another well-received proposal focused on improving decision-making by narrowing uncertainties and addressing them differently. This proposal will likely garner attention from the insurance industry as companies seek to systematically understand and address uncertainties from weather, policy, and FEMA perspectives. The uncertainties simply accumulate.

The community-based catastrophe insurance project is another initiative we'll likely pursue. This topic is particularly ripe given the need for more innovative risk-bearing solutions.

Paul Carroll

What about developments at major insurance industry players?

Francis Bouchard

We [Marsh McLennan] recently announced our participation in a carbon trading mechanism to derisk the issuance of carbon credits. You're seeing more insurers and brokers focusing on this as a way to facilitate the projects that generate the credits.

There's also a more macro-level shift emerging—a growing awareness around shared accountability for the insurability of homes. The debate today typically centers on the technical nature of pricing risk. What we're trying to do is use this notion of insurability to reframe the conversation.

The right question isn't about pricing; it's about understanding the thousand decisions made that led to a home having its particular risk profile. We in the insurance industry are not the end-all, be-all. We are simply reflecting the thousand decisions made prior to receiving the submission.

Focusing on insurability allows us to enlist other critical players in the housing space to adopt this same, shared accountability approach. Non-insurance professionals often expect mind-numbing analytics and modeling. When you simply ask, "What can you do to reduce the risk that a house faces when it's finally built?", people respond with, "Oh, that's it? That's doable." And it should be doable.

When you aggregate this approach across every player in the value chain, you create transformative results. You get architects incorporating resilience, developers considering wildfire protection, fully certified contractors who understand requirements, and properly prepared supplies that don't cause delays.

When all these stakeholders understand their role in reducing risk, it makes our role significantly easier.

Paul Carroll

Thanks, Francis

About Francis Bouchard

Francis Bouchard is an accomplished global public affairs professional who has served as an advisor, catalyst and contributor to a series of climate resilience and insurance initiatives. He is currently the managing director for climate at Marsh McLennan, and earlier served as the group head of Public Affairs & Sustainability for Zurich Insurance Group, where he focused on aligning the group’s government affairs, sustainability and foundation activities. He originally joined the insurance sector in 1989 and since has held a series of industry-focused advocacy, communications, sales, citizenship and public affairs roles, both in the U.S. and in Switzerland. Francis also chairs the board of directors of SBP, a national non-profit focused on disaster resilience and recovery, serves on the board of the climate-focused insurtech incubator InnSure, and is a member of the advisory council of Syracuse University’s Dynamic Sustainability Lab.

A few weeks ago, this LinkedIn post about homeowner insurance, along with its comments, triggered my nerdy attitude to crunch numbers. So, below you find the results of my deep dive.

Now, let's talk about the economics of homeowner insurance in the U.S market.

The source of the figure used in that post is the “Analyses of U.S. Homeowners Insurance Markets, 2018-2022: Climate-Related Risks and Other Factors” published in January 2025. The comments range from the incumbents being inefficient and ripe for disruption, to homeowner insurance having complexities in underwriting and claims that structurally limit the efficiency that can be achieved, to it being all about acquisition costs. 

Let’s start with a look at the profitability of this line of business over the past decade, based on the NAIC data. At the industry level, the home insurance business has barely made any money. The underwriting profits have been on average at -1.6%, meaning that claims and expenses have been higher than the premiums collected. Thanks to investment gains, the sector has, on average, made 0.7% in profits. 

Source: Naic data

The sector has suffered in the past few years, and it returned to technical equilibrium with a 99.7% COR only in 2024 .

Source: Elaboration on IEE NAIC data

Obviously, these figures aggregated at the market level are the result of heterogeneous performances among the carriers in the market. As shown in the figure below representing market share and loss ratio of the top 25 homeowners insurance writers, there are carriers achieving a significantly better loss ratio – so profitability - than the market.

Source: Naic data

Now, let’s look at the composition of the almost 40% of the premiums not paid as claims. The costs to operate the business (loss adjustment expenses and the general expenses) account for about 15 points on the combined ratio.

Source: Naic Data

Comparing the home insurance business line with the other top 10 business lines (representing more than 90% of the total P&C premiums), we can see that its cost profile is similar to personal auto and significantly less costly than the commercial lines.

Source: Naic data 

At least relative to other insurance business lines, homeowners insurance doesn't appear inefficient. However, let’s take a closer look at the details of the costs for some of the top carriers. Here are the 2023 expenses, as reported in the insurance expense exhibit, by two of the top 10 U.S. homeowners writers (both have been more profitable than the market average both in 2023 and in most recent years). 

Elaborations on 2023 Statutory Statements

Excluding commissions and taxes, the operating costs of the homeowner business for these two payers are about 17-18 points on the combined ratio.

Nowadays, at any insurtech conference, you hear the announcement of some AI agents able to increase the efficiency of insurance processes and run the business with fewer people. Assuming a cost allocation split on the homeowner business similar to the one these companies have at the group level, only about half of these 17-18 points are linked to personnel.

Even an impact, net of AI costs, of 20% efficiency will not result in a two-point reduction on the combined ratio.

Source: elaborations on Naic data

The left side of this bill is the biggest opportunity. Avoiding claims from happening, and mitigating situations that have already occurred, is the big insurtech opportunity in homeowners insurance. Connect and protect is the name of this opportunity.

Connect & Protect is the biggest insurtech opportunity in homeowners insurance

At the recent Insurance Innovator USA in Nashville, a leading insurer has shown the split of the claims in their portfolio by peril.

Applying this split to the average loss ratio of the market for 2018-2024 yields 15 points on the combined ratio that can be addressed with fire protection solutions, and 21 points that can be addressed with water escape prevention solutions.

Whisker Lab and Ondo, both members of the IoT Insurance Observatory, are at the forefront of this connect-and-protect transformation of the homeowners insurance industry.

Whisker Lab has already partnered with more than 30 U.S. homeowners insurance carriers to offer its Ting device – a solution for preventing electrically generated fires – to their policyholders, and is already protecting a portfolio of more than one million connected homes.

Ondo is instead focused on preventing and mitigating water damage. My exchange (below) with their CEO, Craig Foster, over a recent weekend illustrates the results achieved so far and his vision about how insurtech can fix homeowners insurance.  

Matteo: Ondo's mission is to reduce the expected losses on a homeowners insurance portfolio. Could you share the risk prevention impact you have been able to bring to your partners and the ROI of this Insurtech approach?

Craig: Absolutely. At Ondo, we quantify ROI in two core ways:

1. Documented Claims Savings — When LeakBot plumbers visit a home based on a leak alert, 15% of the time they find and fix a leak that is actively damaging or potentially damaging to the home.   In each of those instances our plumbers create a Claims Mitigation Report which include pictures and moisture scans giving insurer partners a complete picture of the leak scenario. This gives our insurer partners the ability to quickly estimate the loss avoided, giving an immediate and tangible read on savings potential and ROI. For every dollar an insurer partner spends on LeakBot they see two to four dollars in loss savings.
2. Control Group Analysis — As programs scale, partners compare performance against a statistically matched control group. This allows for a rigorous actuarial view of the impact on claim frequency and severity.

This dual approach is well-established across our partner base and has been refined over years of implementation.

While most partners keep exact ROI data confidential for competitive reasons, Swedish insurer Lansforsakringar publicly reported a 45% reduction in escape-of-water claim costs. In aggregated analyses across multiple markets and cohorts, we've seen savings of up to 70%, driven by both fewer claims and materially lower severity. LeakBot tends to intercept the largest claims before they spiral - such as catching leaks spraying into crawl spaces before structural damage occurs. One US partner recently resolved such a case for under $3,000 - where the same loss could have easily exceeded $30,000 had it gone undetected.

Our ROI performance has proven consistent across geographies. Older partners like Hiscox (UK) and Topdanmark (DK) continue to scale based on long-term savings. In the US, we’ve signed nine insurer partners recently. Those who launched early - such as Nationwide, PURE, Mutual of Enumclaw and Selective - have already expanded their programs from a single pilot state into a total of 23 US states after seeing strong early impact. That ROI from the analysis of Claims Mitigation Reports is what continues to drive adoption and long-term renewal.

Matteo: I recall the event in London about nine years ago, when LeakBot was presented to the insurance community. Connect and Protect has undertaken a long journey in our sector since then, and we are finally starting to see significant momentum. What are the three most relevant changes you have seen collaborating with large insurance incumbents?

Craig: We’ve definitely seen a major shift in how insurers view connected home technology. Here are three of the biggest changes:

1. From “Will This Ever Scale?” to “What’s the Right Tool for the Job?” Nine years ago, many insurers were still debating whether IoT could ever move beyond pilot stage. Today, the question is no longer if, but how best to deploy it. We’re seeing real strategic commitment - especially as solutions like Ting (fire risk) move toward 2 million homes in the US. In water, we’ve emerged as the go-to prevention partner, and our discussions now focus on the right methods to get as many homes protected as possible.
2. From Claims Savings to Tangible Customer Experience Initially, partnerships focused purely on the actuarial ROI. That remains key - but insurers now also value the customer experience impact. LeakBot turns an intangible product into a proactive service, with an NPS consistently above 80. Policyholders love it - and insurers see improved retention and brand loyalty. For many partners, that CX story becomes as important as the claims data.
3. From Plug-and-Play to Deep Integration In the early days, insurers opted for zero-integration turnkey rollouts. Today, the most forward-thinking carriers are building full-stack platforms that integrate with our APIs from day one. A standout is Nationwide in the US, who built a proprietary smart home backend that allows seamless integration with solutions like ours. This level of IT and data maturity unlocks greater scalability, efficiency, and personalization.

Matteo: What is your vision for the future of insuring homes? How will it look in 2035?

Craig: By 2035, home insurance will evolve into a cognitive home protection service - not just a policy, but an intelligent system actively working to prevent losses in real time. Powered by ambient computing, ubiquitous connectivity, and edge AI, the home will become both self-monitoring and insurer-integrated.

1. 1. Insurance Will Live Inside the Cognitive Home The connected home will become the cognitive home - a space where devices like LeakBot quietly monitor risks, interpret signals, and take action without the homeowner needing to intervene. This is AI-powered ambient computing in practice: invisible, automatic protection woven into the fabric of daily life.
2. Insurers Will Become Real-Time Decision Engines With ubiquitous connectivity and richer data from IoT devices, insurers will pair these insights with AI to make smarter decisions - on renewals, claims, pricing, and service triggers. The most advanced insurers will effectively become cognitive risk mitigation machines - constantly adapting and optimizing in real time to help their customers avoid loss, not just recover from it.
3. Claims Will Be Pre-empted, Not Just Paid Edge AI enables instant decisions directly on the device, cutting latency and enabling proactive service. A leak doesn’t become a claim - it becomes a service call. Risk is neutralized early, affordably, and invisibly. As these systems mature, we’ll see a steep drop in claim severity - and a new standard for what home protection means.

At Ondo, our vision is to be the leading global provider of claims prevention technology for home insurance. We expect LeakBot to become the default standard for mitigating water damage claims — first in the United States, then globally. As the insurance industry shifts from reactive to cognitive, we’re building the core infrastructure to power that future.

We talk a lot about smartphones and even smart homes, but let's not sleep on smart cities. They are getting more wired all the time, in ways that don't just provide convenience — such as alerting drivers to open parking spots — but that make people safer and reduce insurance claims.

The two latest examples that caught my eye are the deployment of sensors on Chicago streets that can detect flooding and a General Motors patent application for a way to use its cars to sense road condition. The Chicago sensors will relay warnings to property owners and to authorities. The GM data will flow to drivers to let them avoid trouble and to local governments that can fix the roads. 

But there's a lot more besides, and progress will likely accelerate from here.

The Chicago and GM stories demonstrate both the promise of innovations in cities and what pieces still need to fall into place so those innovations can be deployed widely and deliver major benefits.

In Chicago, 50 sensors will be deployed over the next 18 months on bridges, above roads and in sewers. Wireless and powered by solar, the cylindrical sensors will use sonar to measure the depth of water beneath them. If levels are rising at a threatening pace, the sensors will create instant flood maps for city authorities, who will alert property owners in affected areas. 

Chicago is a big place, so a lot more than 50 sensors will eventually need to be deployed. Bugs will also surely need to be worked out of the technology. Costs will need to come down — each sensor currently costs about $1,500.

But all those issues feel manageable, based on the cost and performance curves that are normal for this type of technology. Sensors for water leaks in homes, for instance, began as elaborate devices, shrank to about the size of hockey pucks, and now, according to an announcement from Hartford Steam Boiler, can be as thin as four credit cards stacked on top of each other.   

As it happens, as I was about to publish this commentary, the New York Times published a piece this morning about a system that is similar to Chicago's, that is further along and that underscores the need for the final, key piece: getting the word out, and rapidly. 

Spurred on by damage from Hurricane Ida four years ago, New York City has installed 250 sonar-based sensors whose cost is just $300 apiece and plans to double the number of sensors by 2027. The Times reports a big improvement in understanding flash flooding in real time — previously, authorities only learned of problems based on emergency calls, social media posts and news reports.

But the notification system is passive. You have to monitor a city website to see where flooding may be happening. If you're alert, likely because you've suffered damage in the past, you're fine. If not, you're as vulnerable as ever. 

The leaders of FloodNet, the sensor network New York uses, say they're piloting a system that can alert people via email, which will mark a huge improvement. An even bigger one will be when FloodNet and similar sensor operators can ping the cellphones both of those who've signed up for flooding alerts and of any others in an area, such as drivers, who might be affected.

The GM system has much further to go than the flooding sensors but could also deliver major safety benefits if GM can, in fact, gather useful information about road conditions from sensors that track the traction that a car's tires get and the movement in its suspension system. I grew up driving in Pittsburgh, where potholes seemed to show up everywhere during the spring thaw, and would have loved to know to be prepared to skirt a big one just ahead. A whole lot of drivers suffered damage to their cars and put in insurance claims that could have been avoided if a system like GM's were in place.

GM will face the same notification issue that the flood sensors do. It's one thing to have one car detect an anomaly. It's quite another to collect data from enough cars to be sure there's a problem with a road surface, and even more difficult to communicate that finding to a driver in real time. 

I'm optimistic that the connection challenges can be addressed relatively quickly because of a system called Sidewalk that Amazon has introduced that is an inexpensive backbone for communication with sensors. 

One of the challenges for "connected cars" has been that they typically communicate with their hosts via cellular networks, requiring lots of relatively expensive bandwidth. Sidewalk, by contrast, is low-bandwidth and low-cost. 

It operates via a mesh concept: A sensor doesn't need to send a signal so strong that it will reach a cell tower miles away. The signal just needs to be strong enough to reach any other device that is in the mesh network and within half a mile. That next device can then forward the data to any other device and so on until it reaches a major node that can send the data to its final destination. 

A mesh network can get overloaded if lots of data needs to be sent, but something such as a water-depth sensor is just sending a single number, perhaps every few seconds. The system Hartford Steam Boiler announced is, for instance, transmitting its data via Sidewalk. 

As Sidewalk and potentially similar communication backbones are developed, sensor networks will no longer need to worry about how to transmit their data. They just have to get it to a Sidewalk/etc. node. Similar standards will develop on the back end, handling the notifications to those who want them. 

So we can let our imaginations run wild. What else, beyond warnings about flooding and bad roads, should be sensed in a city and relayed to interested parties in real time?

If you step back a bit, you can see that smart cities have made real progress in recent years and decades. Some of that progress is mostly convenience. Traffic lights are synchronized so you generally don't have to stop on a main road if you're driving at the speed limit. Signs or phone alerts tell you just when that bus or subway will arrive. Your phone lets you know of traffic jams and can reroute you. Sensors in the pavement and in streetlights can monitor parking spaces and let you know when one is empty. 

Some of that convenience leads to safety. Knowing that there is an accident ahead of you makes you less likely to plow into something. Getting people into parking spaces faster reduces traffic in cities — a remarkable amount of which is people looking for spots — and decreases the number of accidents. 

And with the flood sensors and, perhaps, GM's sensing of road conditions, we're seeing even more opportunities for safety. 

What's next?

Let's have at it.

Cheers,

Paul

Artificial intelligence has moved past the proof-of-concept phase. Businesses are integrating AI into operations at a record pace, from customer service and logistics to medical diagnostics and HR decision-making. But as the benefits of AI grow, so do the risks, and most companies have not adequately addressed who will bear the legal and financial consequences when things go wrong.

The problem isn't the potential for harm alone. It's that the liability landscape for AI is undefined, shifting and increasingly litigious. When an algorithm produces biased results or a chatbot dispenses incorrect medical advice, it's not always clear who should be held responsible: the business deploying the tool or the developer behind the code. For companies that own or rely heavily on AI, especially those with captive insurance companies, now is the time to scrutinize these risks and evaluate how captives can help fill a widening gap in risk management.

AI failures already have consequences — and lawsuits

The assumption that AI risks are futuristic or theoretical no longer holds. In 2024, a federal judge allowed a class action to proceed against Workday, a major provider of AI-driven hiring software, after a job applicant claimed the platform rejected him based on age, race, and disability. The suit, backed by the EEOC, raises thorny legal questions: Workday argues it merely provides tools that employers configure and control, while plaintiffs claim the algorithm itself is biased and unlawful. 

The case highlights the growing legal gray zone around AI accountability, where it's increasingly difficult to determine whether the fault lies with the vendor, the user, or the machine. In another case, an Australian mayor threatened to sue OpenAI after ChatGPT incorrectly named him as a convicted criminal in a fabricated bribery case. The mayor wasn't a public figure in the U.S., and the false output had real reputational consequences.

These incidents are no longer rare. In 2023, the New York Times sued OpenAI and Microsoft for copyright infringement, claiming their models used protected journalism content without permission or compensation. The lawsuit reflects a growing concern in creative and publishing industries: generative AI systems are often trained on datasets that contain copyrighted material. When those systems are then commercialized by third parties or used to generate derivative content, the resulting liability may extend to businesses that integrate those tools.

More recently, the Equal Employment Opportunity Commission issued guidance targeting the use of AI in hiring decisions, citing a spike in complaints tied to algorithmic bias. The guidance emphasized that employers, not vendors, would typically bear responsibility under civil rights laws, even when the discriminatory impact stems from third-party software.

These examples reveal a pattern. AI is being used to make decisions that carry legal weight, and the consequences of failure (reputational, financial and regulatory) often fall on the business deploying the system, not just the one that created it.

A legal and regulatory framework is forming

The global regulatory environment is evolving quickly. In March 2024, the European Union formally adopted the EU AI Act, the first comprehensive legal framework for artificial intelligence. The law classifies AI systems into four risk categories--unacceptable, high, limited and minimal--and imposes stringent obligations on businesses using high-risk systems. These include transparency, human oversight and data governance requirements. Noncompliance (related to high-risk AI systems) can lead to fines of up to 7% of a company's global annual revenue.

While the U.S. lacks a national AI law, states are moving ahead with sector-specific rules. California's proposed Safe and Secure Innovation for Frontier Artificial Intelligence Models Act would require companies to test for dangerous capabilities in large language models and report results to state authorities. New York's Algorithmic Accountability Act aims to address bias in automated decision tools. Several federal agencies, including the FTC and the Department of Justice, have also made it clear that existing laws, from consumer protection to antitrust, will apply to AI use cases.

In Deloitte's Q3 2024 global survey of more than 2,700 senior executives, 36% cited regulatory compliance as one of the top barriers to deploying generative AI. Yet less than half said they were actively monitoring regulatory requirements or conducting internal audits of their AI tools. The gap between risk awareness and preparedness is widening, and businesses with captives are in a unique position to act.

The role of captives in addressing AI liability

Captive insurance companies are not a replacement for commercial insurance, but they provide an essential complement, particularly for complex, fast-evolving risks that the traditional market is hesitant to underwrite. AI liability falls squarely into that category.

For example, a captive can help finance the defense costs and potential settlements tied to AI-generated errors that fall outside the scope of cyber or general liability policies. This might include content liability for marketing materials created using generative AI, or discrimination claims stemming from algorithmic hiring tools. In some jurisdictions, captives may even fund regulatory response costs or administrative fines where allowed.

Captives can also provide coverage when a third-party AI vendor fails to perform as promised and indemnification clauses prove insufficient. In such cases, a captive can reimburse the parent company for business interruption or revenue losses that stem from the vendor's failure: a growing risk as more companies integrate third-party AI into core workflows.

Because captives are owned by the businesses they insure, they offer flexibility to craft tailored policies that reflect the company's actual AI usage, internal controls and risk tolerance. This is particularly valuable given how little precedent exists in AI litigation. As case law develops, businesses with captives can adjust coverage terms in near real time, without waiting for the commercial market to adapt.

Building AI into captive strategy

To incorporate AI risk effectively, captive owners must begin with a clear-eyed assessment of their own exposure. This requires collaboration across legal, compliance, IT, risk management and business units to identify where AI is in use, what decisions it influences and what harm could result if those decisions are flawed.

This analysis should include:

• Inventorying all internal and third-party AI systems
• Mapping potential points of failure and legal exposure
• Quantifying financial impact from regulatory enforcement, litigation or reputational damage
• Evaluating existing insurance coverage for exclusions or gaps
• Modeling worst-case outcomes using internal data or external benchmarks

Once this assessment is complete, captive owners can work with actuaries and captive managers to design appropriate coverage. This may include standalone AI liability policies or endorsements to existing coverages within the captive. It may also involve setting aside reserves to address emerging risks not yet fully insurable under traditional models.

Risk financing alone is not enough. Captives should also be part of a broader governance strategy that includes AI-specific policies, employee training, vendor vetting and compliance protocols. This aligns with the direction regulators are taking, particularly in the EU, where documentation, explainability and human oversight are mandated for many high-risk systems.

Boards are paying attention

AI is no longer just a back-office issue. In 2024, public companies and shareholders sharply increased their focus on artificial intelligence, especially on board-level oversight and shareholder proposals. According to the Harvard Law article AI in Focus in 2025: Boards and Shareholders Set Their Sights on AI, the percentage of companies providing some disclosure of board oversight grew by more than 84% year over year and more than 150% since 2022. This trend spans all industries. Meanwhile, shareholder proposals related to AI more than quadrupled compared with 2023, mostly calling for greater analysis and transparency around AI's impact.

This intensifying scrutiny signals a clear mandate for risk managers and captive owners to deliver solutions. Captives offer companies a flexible tool to fund, control and adapt their responses to the rapidly evolving AI risk landscape and regulatory environment.

Conclusion

AI is changing how businesses operate, but also how they are exposed. As regulatory frameworks tighten and litigation accelerates, businesses must prepare for the reality that AI-related liability is no longer speculative. Captive insurance companies offer a powerful tool to manage that exposure, not by replacing traditional coverage, but by addressing what lies outside its bounds.

For companies that rely on AI, the question is no longer whether liability will emerge–it's whether they are positioned to handle it. Captives provide a path forward, giving businesses the ability to design, fund and control risk management strategies that evolve as fast as the technology they are built to protect.

Our fast-changing world has become increasingly connected. Risks such as geopolitical instability and climate change continue to escalate on the world stage. With both risk dependencies and risk drivers surfacing and evolving more and more rapidly, so much is changing so quickly that by the time this article is published it may well be (slightly) outdated.

Yet capital modelling techniques have barely changed. Current models are unreliable, under-used and time-consuming. The insurance industry needs tools that are dynamic and built for cross-business and cross-functional collaboration.

Capital models have a clear advantage over long-term projections, as they are reassessed at least every year and so reflect the latest changes in risk drivers. A key advantage for the modeling of climate risks is that the uncertainty is much reduced compared with that resulting from long-term projections of climate trajectory.

How the insurance gap affects climate losses

It is worth first highlighting the fundamental difference in metrics considered when looking at climate losses. Total losses or ground-up losses consider the total cost of a catastrophe, while insurers consider the insured value -- for example, the cost they are contractually liable to. The difference between the two (the 'insurance gap') effectively measures the difference between the real costs incurred versus the costs covered by the insurance industry.

For climate losses, this means total losses could increase while the insured costs may decrease, in particular following the ramping up of climate mitigation measures, such as flood and fire defenses protecting inhabited areas, or carriers' stopping the writing of lower tranches or pulling out of a region altogether. How the insurance industry chooses to tackle the insurance gap issue will affect the level of climate losses insurers will see coming through.

Current approaches unfit for purpose

Insurers frequently adopt a bottom-up and granular approach. The wider aspect and connectedness of global events is generally considered to some extent when calibrating dependencies. However, this requires tedious monitoring and updating of parameters that are extremely difficult to estimate. Traditional approaches also tend to give considerable weight to past data, which is not necessarily the best basis to form an opinion on risks that are evolving fast.

Cyber is a good example of such a risk. While it is not a new risk, data remains too sparse for robust modeling, and the various types of cyber attacks continue to rise in volume and severity, with new defenses constantly being designed and built to try to keep pace with these threats. Furthermore, insurers need to ask themselves whether the dependency of cyber risk with other global risks, such as climate change and geopolitical conflicts and terrorism, is being appropriately reflected in their current models.

More generally, chief risk officers and other senior stakeholders responsible for the capital model should be asking their teams:

• Can they rely on the output from their capital model? Is this complex risk landscape reasonably reflected in a transparent, dynamic way to enable challenge and communication?
• Are they able to confirm that the view of risk implemented in their capital model is consistent with that established in other parts of the business (e.g. pricing and exposure management)?
• Are the traditional approaches they are using providing value given the extensive work required for setting parameters?
• How much compounded uncertainty – stemming from limitations and uncertainty at granular risk levels that are then aggregated – is too much? Does this uncertainty make the model outputs unreliable and useless?

Identifying risks and drivers

Traditionally, the risks assessments conducted by companies have been focused on their own risks, with little consideration for global socioeconomic factors. The past few years have shown us that this approach is no longer suitable and that risks should be assessed more holistically.

The changing dynamic of the risk landscape does not necessarily translate into a new, distinct risk category but often results in additional losses simultaneously affecting several risk components. Creating a map of all the risks and their drivers affecting the company should be a key step before trying to model this complex risk environment.

The advantage of short-term modeling for long-term risks

Modeling long-term risks over a short time horizon can seem challenging. Climate change risks (typically described as comprising physical, transition and liability risks) are not only long-term but also highly uncertain in how they will develop over time.

Climate change risks are also affected by geopolitical instability, supply chain issues and inflation, as well as national government policies and the emerging threats and benefits from AI. Therefore, it is not the isolated, long-term impact of climate change that we should be focusing on for capital modelling purposes but its combined, short-term impact with other risks. And short-term modeling undeniably provides greater flexibility.

The evolution of physical risks from climate change is already baked-in for decades. For example, our actions now won't affect catastrophes in the short term. The year-on-year impact on physical risks (resulting from human activity from long ago) remains limited, albeit trends are emerging when looking at the past decade. "Secondary" catastrophe risks are a good example, having become key catastrophe risks, as demonstrated by recent flood and severe convective storm losses.

However, current catastrophe models remain suitable provided they are regularly reassessed to confirm they capture all plausible scenarios, with in- and out-of-model adjustments applied to ensure this.

All of the above needs to be considered alongside an evolving insurance gap and other global socioeconomic variables - these can change relatively quickly and are difficult to capture in long-term scenarios, although less challenging over the next year or so.

Liability risks are difficult to model, as the uncertainty around whether, when and to what extent climate litigation claims will emerge is incredibly high. However, it is important to distinguish between estimated average losses over the next 12 months, maximum losses (which, according to contracts' terms and conditions, will also contain important exclusions such as criminal behaviors and those drive by personal profit) and the uncertainty around average losses. Given the slow pace of court judgments and the money big companies can spend on lawyers to defend them, it seems highly unlikely that we will see an unsustainable level of losses emerging very quickly. Therefore, it is reasonable to assume that yearly reassessed allowances in the capital model for "known unknowns" would be sufficient to absorb losses that have emerged over the year beyond the estimated average losses. However, liability risks are also affected by other global, socioeconomic factors.

A holistic view of climate risks to capital models

As highlighted in the Bank of England paper, "Report on climate related risks and the regulatory capital frameworks," climate risks require a forward-looking approach. Past data would not appropriately reflect future trends and impacts from climate-related risks.

Although scenarios are already used to validate models, their use as a calibration approach remains limited, as this requires moving away from traditional models and a big leap into innovative approaches.

A natural answer to this would be to consider a "catalogue" of scenarios that provide a good representation of plausible events, with each scenario being decomposed into its risk drivers (not unlike how event catalogues are constructed in catastrophe models).

For this reason, we have been working on an approach in which current-era dependency models can be expressed in terms of explicit, dynamic, and possibly systemic risk drivers that would model the combined impact of climate risks and other global risks in a transparent and holistic manner. Each risk would be decomposed into its (systemic) risk drivers and a variable specific to this risk representing the non-systemic aspect. Then risks or portfolios can be combined and aggregated using relatively simple mathematic formulae and distributions.

This approach could also be extended to all the risks the company is exposed to, as well as to the ultimate time horizon basis, in which case Monte Carlo simulations and their inherent uncertainty would not even be required. This would also help moving from discrete time or single-time-step models into a continuous time setting for "always-up-to-date" risk analysis. The key advantage of a continuous time model is that coverages are only modeled in between their exact start and end dates.

This would turn a time-consuming, under-used model hindered by compounded uncertainty into a dynamic risk, pricing and portfolio management tool. It would also replace onerous, isolated, parameter guesstimation with a collaboration across business functions that provides a consistent view of risk with reduced effort.

In the world of insurance, brand strength is a formidable asset. However, even the strongest brands must adapt to meet the evolving needs of consumers and the digital-first world.

The insurance market is at a pivotal juncture. The next generation of insurance isn't just about offering policies; it's about delivering an exceptional customer experience, optimizing operational efficiency, and demonstrating agility. While a strong brand provides a solid foundation, resting on past successes is not an option in today's fast-moving, tech-driven environment. 

Opportunities for Established Insurers in a Digital-First Landscape

Today's policyholders expect more than just financial products—they demand seamless digital experiences, quick and transparent claims processing, and personalized interactions. This shift requires insurers to rethink traditional models and integrate modern solutions.

A strong brand is only as powerful as the experience it delivers. Insurers must prioritize exceptional customer experiences across every touchpoint, particularly during the claims process, which often serves as the ultimate test of a brand's promise. By focusing on digital-first experiences, insurers can ensure that interactions are not only efficient but also empathetic and tailored to meet evolving expectations.

Established insurers, with their longstanding reputation for trust and stability, have a unique opportunity to lead this evolution. By leveraging their brand strength, they can introduce digital transformation initiatives that not only improve internal processes but also enhance the overall customer journey. The transition to digital claims processing, for instance, offers a chance to replace paper-based, manual workflows with automated, intuitive systems that resonate with both digital-native generations and more mature policyholders.

Evolving With Broader Product Offerings and Market Reach

The insurance market is becoming increasingly diverse. As carriers introduce broader product offerings—from hybrid life and long-term care policies to new investment and annuity options—they must also adapt their operational frameworks to support these innovations. 

For insurers looking to expand their market reach, particularly into underserved segments, operational efficiency is crucial. A strong brand can open doors, but delivering on that promise requires back-end systems that are as sophisticated as the products themselves. This is where a robust claims administration platform can provide a competitive edge, enabling carriers to scale efficiently while maintaining high service standards.

Maintaining Agility in a Changing Regulatory Environment

Regulatory requirements in the insurance industry are ever-changing. Whether adapting to new compliance standards, managing data privacy, or ensuring accuracy in claims processing, established insurers must remain agile. Strong brands often come with a legacy of traditional processes, and adapting these to meet modern regulatory expectations can be challenging.

The integration of automation and data analytics within claims systems offers a path forward. By automating compliance checks and streamlining document verification, insurers can reduce regulatory risks and maintain transparency with both policyholders and governing bodies.

Embracing Ecosystem Planning for Sustainable Growth

As insurers seek to broaden their reach and future-proof their operations, building a strategic ecosystem is crucial. This involves forging partnerships with technology providers, financial service firms, and even healthcare and long-term care organizations to create a holistic approach to customer service and operational efficiency.

An effective ecosystem allows carriers to integrate new technologies seamlessly, offer broader and more tailored product portfolios, and respond quickly to market changes. By building a robust network of partners, insurers can diversify their offerings and strengthen their position as leaders in the insurance sector.

Unlocking Opportunities Through Claims Experience

A well-orchestrated claims process does more than resolve claims quickly—it plays a strategic role in asset retention and business generation. When beneficiaries experience a smooth, empathetic claims journey, they are more likely to view the insurer as a trusted financial partner. This opens the door for insurers to engage beneficiaries during pivotal life transitions, potentially converting them into new clients.

For example, a streamlined digital claims experience combined with personalized outreach can guide beneficiaries through financial planning opportunities, including annuities, life insurance, or investment products. Insurers that engage during the claims process can turn an administrative necessity into a powerful sales and retention channel.

Bringing Brand Promise to Life Through Claims Innovation

Ultimately, the claims process is where the brand promise is truly tested. It is often the most critical touchpoint in the customer lifecycle, and a positive claims experience can turn policyholders into lifelong advocates. Conversely, a slow, opaque, or cumbersome process can erode even the strongest brand equity.

Modernizing the claims process through digital tools and automation is not just about efficiency—it's about delivering a compassionate and transparent experience that reflects the brand's values. For insurers that have built their reputation on trust and reliability, enhancing the claims process is a natural extension of their brand ethos.

Positioning for Growth: Where Technology Meets Tradition

While technology plays a vital role in evolving claims and servicing processes, it is the thoughtful integration of this technology into established business models that drives success. Strong brands do not need to overhaul their entire infrastructure to modernize. Instead, they can take strategic, incremental steps that offer immediate benefits while preparing for long-term transformation.

At Benekiva, we blend tradition with innovation through human-centered automation—what we call Humanomation—to help insurers optimize claims processes without losing the personal touch that their brands are known for.

The insurance industry is poised for significant change, and those who act now will lead the market forward. Strong brands have the advantage, but sustaining that advantage requires a commitment to evolving with the times, embracing technology, and keeping the customer at the heart of every decision.

General opinions about AI are split. Even among the most knowledgeable, 31% think AI does more harm than good. Just 22% see its benefits outweighing its risks. People are wary about AI's potential risks, from spreading false information to stealing jobs.

This goes for the insurance industry, as well. Even though 57% of insurance organizations have identified AI as the most important technology over the next three years, there's still plenty of hesitance when it comes to the more complex and high-stakes areas of the business, such as policy review and interpretation.

These concerns are often rooted in a misunderstanding of how AI operates in the context of the insurance workflow; for complex cases, humans should lead the way with support from AI. It's up to leaders to make this part of the picture clear within their organizations. Here are three ways to do that.

Tout AI as a Copilot

One of the biggest myths about AI in insurance is that the technology is meant to replace people. Much of AI, after all, is marketed on its ability to increase productivity and efficiency, which are often codewords for reducing headcount.

But the goal of AI in policy review and interpretation is not to replace your highly experienced and knowledgeable experts. AI is instead a copilot that adds a second set of eyes to the process.

A good AI copilot, for example, might allow your people to upload a policy, ask a question, and instantly get a precise, citation-backed answer. The user could then click straight to the source text to check the AI's work.

Your experts are still in control and still responsible for the finished product. AI is there simply as another tool to accelerate their work. Now they can spend their time thinking strategically instead of combing through endless text.

In its copilot capacity, AI might also inspire your policy experts to look into issues they might not have considered otherwise. Those new ideas will require additional human research and refinement, of course. But just planting that seed is AI demonstrating its value to amplify and accelerate their work.

These examples are not cases of AI stealing jobs. Human reasoning and verification and interpretation remain integral to the process. This is simply technology helping to make complex tasks a little more manageable.

Manage Expectations Around AI

Policy review and interpretation work requires stringent accuracy, which is another area where doubters question the viability of AI. Analyzing, synthesizing, and reasoning upon the information in a 300-page policy is complex work. And AI isn't perfect at it right now. But the nuanced nature of policy review isn't an argument against using AI. It's simply another endorsement for treating AI as an aide rather than a full-time standalone solution.

Think of it this way: The right AI for this context is not one that makes complex decisions. It's one that can analyze and summarize hundreds of pages of documents, identify the relevant portions, and surface them for a human to review, then make a decision.

In this case, AI is helping to make the initial work go much faster. Even with a few oversights that may need citations and corrections by humans in a follow-up review, that added upfront speed is well worth the investment.

AI will get closer to 100% accuracy as the technology continues to develop and advance in the coming years. But even short of that goal in its current form, it can greatly benefit insurance carriers in their policy review work.

To Drive AI Adoption, Start by Raising Awareness

Defining AI's role and acknowledging its limitations are two great ways to help build trust around the technology. But leaders also need to foster an environment of experimentation and innovation in their broader effort.

For example, seeing is believing for many AI skeptics. So show them what it can do. When employees are encouraged to sample AI, they're more likely to grasp its time-saving possibilities and its ability to generate fresh ideas. Now they can picture how AI will affect their workflow, making them more likely to embrace it – and less likely to worry about losing their jobs.

Getting more comfortable with AI will also likely open their eyes to how the technology can help them better serve customers. From policy to claims, AI can provide fresh insights and new ways of leveraging data. That helps the entire organization work at a higher level, which is something everyone can get behind.

Make the Case: Modernization and Trust Aren't Mutually Exclusive

AI is like any other new technology. Some people will be inclined to focus largely on its exciting possibilities. Others will mostly see its faults and threats. Satisfying both groups means highlighting AI's potential to work alongside humans to unlock greater creativity and productivity.

For insurance providers, highlighting that message means focusing clearly on the role that AI can and should play. It's not a doomsday solution that aims to replace the human expertise so vital to policy review and interpretation. Rather, it is a tool that can speed up and enrich that important work.

Because in the end, people are and always will be the key to success.