Paul Carroll

Denise, we were talking the other day about the fundamental changes occurring in insurance, and you had quite a list. Could you start us off by walking us through some of those?

Denise Garth

The industry is changing a lot, and it's not just technology — it's everything. Risk is changing, customer demographics and expectations are changing, where people are living is changing. 

One of the biggest things we're seeing is the growing protection gap. The cost of insurance has increased significantly due to climate and weather events, rising claims costs, and the legal challenges the industry faces. It is unsustainable for customers, forcing them to make difficult decisions such as not buying insurance, switching for a lower cost, increasing deductibles, and more. It is a tipping point of change.   

We see a new era for insurance — one that's really built around intelligence to enable adaptability.

The way forward is going to require both an operating model and a technology foundation redesign and redefinition. We've been talking about transformation for the last 10 to 20 years, and in most cases, it was about ripping out the technology and putting in something new over the existing operating model. Now we must rethink the operating model: how we want and need to do business to remain relevant.

In today's world, products are evolving. You still need auto, but there are so many variations of it now — autonomous vehicles, people doing things with Uber and the gig economy. There's a whole different set of product types needed to support those, and that goes across all products, whether it's P&C or L&A&H. 

We have to do business in a way that fits this future, not the past.

Our operating models have been crafted over decades around a myriad of constraints, business assumptions, and challenges from the past. They've evolved by layering in technologies, manual work, point solutions — and we now face what I call a "spaghetti infrastructure" that has created a really inefficient, unprofitable, and employee-constrained operation. It's added a level of complexity on top of an already complex business. 

Instead of just replacing technology with the next modern core solution, we have to think about what it is we compete on. That's where technology really begins to come into play — not just cloud-native technology and robust core systems, but now AI, both in terms of technology infrastructure and business architecture that can redefine the operating model and business processes. 

In a webinar I just did, I shared that 82% indicate they want to do something with AI, but very few are actually doing it, or they're doing it in a piecemeal way. AI needs to be more than just an add-on technology. It has to be embedded into and redefine how we do business, so you can constantly optimize what you're doing. That redefines the overall business value of cloud and AI-native core that the market begins to see and realize in business outcomes.

I predicted that by 2030, we could see a 20-point reduction in expense ratios — and it's starting to happen as you see publicly traded insurers talk about what they're doing with AI. That is going to completely change the competitive landscape. 

Paul Carroll

For me, the big thing I see companies potentially missing — because I've seen them miss it in other waves of technology over the past several decades — is the need for the agility you mention.

Gen AI is going to allow the sort of breakthrough that Amazon produced in the first wave of the internet. It didn’t just do the old things better; Amazon reinvented retail. If insurers lock themselves into developing a better form of what they've done before, they're going to miss out on a lot of opportunities.

From a technology standpoint, how do you enable the agility that insurers need?

Manish Shah

Before diving into the solution, I want to make sure we also look at the broader, common theme underlying these problems. A lot of people blame the insurance industry for not having modern systems, for not knowing their customers, for not having the right products or pricing. But if you really dig deep, the biggest issue facing the insurance industry — the one causing all those other problems — is that it simply cannot keep up with how fast the world is changing. Insurance is out of phase.

Customer expectations are significantly different and changing almost daily. There’s a huge change in risks and in how those risk profiles are developing. And the technological advancements happening today are leaps and bounds faster than what insurance companies' general culture allows them to absorb.

They're not unaware of the problem. The issue is how fast they can adopt new technology, how fast they can change their culture and get to changes in products, better pricing, better distribution, and so forth.. 

Our view is that it's not just about using technology or solving a niche problem. It's about making your mission-critical systems nimbler and relying on a partner and ecosystem framework rather than a traditional command-and-control framework. 

Not every innovation has to be built in-house from the ground up. The real value companies can leverage is to test the technological innovations that companies like ours bring to them in a meaningful way — roll them out to customers, learn from them, test them, understand user behavior, and refine them.

That's why our approach is not simply about selling technology or a core system. It's about having intelligence built into every workflow, every process, every customer interaction — so you can get meaningful feedback from customers that allows you to evolve faster than the rest.

It's not a technology discussion — it's a speed discussion. How fast can I validate my ideas? That, clearly, is the biggest impediment in the industry.

Most people are still grossly underestimating what AI can and will do to every single business. Insurance is not an exception. Regulations will shield you only for so long, but when it comes to customer service, operational efficiency, improved profitability, faster turnaround, claims resolution, and better underwriting — AI, and more importantly, agentic AI, is going to play a huge role in every single one of those areas.

Whether people embrace it or resist it, in the next 18 to 24 months, a hybrid workforce — built with humans and AI agents working together — is going to be common. We're literally talking about leveraging artificial intelligence not as a tool but as an entity that works alongside humans. And that means the human workforce is going to have a very different role. They won't be writing the first draft — they'll be validating it. That is a huge cultural shift.

If organizations don't start engaging with this thought process early and experimenting with it now, they'll eventually be pressured to do it in a hurry. And if you try to implement this in a rush, even if you can get the technology in place, you cannot simultaneously implement the cultural shift that needs to accompany it. Doing it sooner is critically important.

Denise Garth

We talk about the "capacity gap." The capacity to have the right type of people running the business inside an insurance company is under significant strain — particularly given that a large percentage of the workforce is expected to retire by 2030. Estimates put those losses at 40% to 50%. You're going to lose your underwriters, your claims adjusters, your billing professionals — people who know your legacy systems, let alone people who understand your products and your business.

That's exactly where the hybrid workforce comes into play. Not only can it help you do more with the resources you have, but it can also educate and train new people in a consistent way — creating real value, consistency, and quality for those coming in and trying to learn this business. It gives them the confidence to do the work and learn along the way. That's a major factor in all of this that a lot of insurers haven't fully faced up to yet.

Paul Carroll

Peter Drucker used to say that culture eats strategy for breakfast. And when you look at AI — or just the new technology environment, in general — if you approach it as a destination, something you're going to do once, you're going to fail. 

It has to be a cultural shift, something you work on this week, next week, next month, and the month after that. 

Denise Garth

It really comes down to leadership, because you're going to have to redefine the organization and people's roles — jobs are going to look very different. 

Paul Carroll

How does software need to evolve to support a hybrid workforce of both humans and AI agents?

Manish Shah

Today’s software was designed to be used 100% by humans. And human users have a little bit different constraints than AI users. For example, humans can't process too much information at once. We need multipage forms in a user interface, relational databases, more structured data — things like that. AI agents don't have those same constraints. Software today must be designed for both people and AI agents to do the work they’re best suited for. 

Toward the latter part of the year, we plan to release a brand-new user interface, suited for each type of user. Providing seamless handoffs between them is also a key part of that design consideration. 

The current core system user design is simply not going to be adequate for where the world is moving. The industry has come a long way in the last 20 to 25 years in modernizing, but the fundamental pain points are still there — how long it takes to implement modern software, the cost, how long it takes to maintain it, the total cost of ownership. 

Just like Claude has created a significant dent — in a lot of people's minds and in the markets — with the idea that "I can build the software," we think the same kind of shift is possible for enterprise implementation. Sure, that came with a lot more enthusiasm than realism at first, but I think it will get there.

Why can't AI implement our software? Why does an implementation take three years? Our goal is to build a Claude-like AI capability that interacts directly with business users and translates that into system configurations — allowing our customers to actually move forward.

Paul Carroll

Thanks, Denise and Manish. 

About Denise Garth

Chief Strategy Officer at Majesco, Denise Garth drives thought leadership and innovation strategy for insurers worldwide. She’s a global voice on digital transformation, customer experience, and the future of intelligent insurance ecosystems, shaping how carriers modernize and reimagine their business models.

About Manish Shah

President and Chief Product Officer at Majesco, Manish leads global product innovation across intelligent core systems, AI-powered platforms, and digital ecosystems. A visionary technologist, he’s known for helping insurers accelerate modernization while staying true to human-centric design and trust.

For decades, insurance has relied on a model that assumes time is on its side. Risk could be assessed, priced, and adjusted in cycles. Products evolved gradually, and systems were built for control rather than speed. That model is now under pressure in ways it was never designed to handle.

The issue is not that insurers lack insight. Most organizations have more data than ever before, along with increasingly sophisticated models to interpret it. The problem is far more practical: they cannot act on that insight fast enough. Pricing updates remain tied to fixed cycles, model changes take time to deploy, and by the time adjustments are implemented, the underlying risk has already shifted.

Inside insurance organizations, this tension is well understood. There is no shortage of awareness or intent. The frustration comes from the gap between what teams know needs to happen and what they can execute. Pricing changes sit in queues, model updates wait for deployment windows, and while those changes move through the system, the underlying risk continues to move.

The gap between the speed of risk and the speed of response is no longer just inefficiency. It's showing up in loss ratios, missed growth opportunities, and an increasing inability to compete on speed.

A model that cannot keep up

Insurance was not designed for continuous change. Pricing is still adjusted at defined intervals, underwriting models are updated periodically, and product changes move through systems that assume a relatively stable environment.

Risk no longer behaves that way. Exposure can shift materially between pricing reviews. New data arrives continuously, often from sources that did not exist even a few years ago. By the time updates are implemented, the assumptions they were based on are frequently out of date.

Most insurers recognize this dynamic. The challenge is not diagnosing the problem, but overcoming the structural constraints that prevent them from responding in real time. Legacy systems, internal processes, and the way decision-making is organized all introduce delay, even when the business is trying to move faster.

The result is a fundamental mismatch between how risk evolves and how insurance operates.

When technology slows you down

Much of the industry conversation around innovation focuses on adopting new technologies. But for many insurers, the more immediate issue is the technology already in place.

Core systems continue to underpin underwriting, pricing, and product configuration, yet were built for a different era. They prioritize stability and control, which made sense when change was incremental, but they are far less suited to an environment where conditions shift constantly.

This creates a form of operational inertia. Even relatively straightforward changes can trigger complex processes, requiring coordination across multiple teams and systems. As a result, external changes move faster than internal responses. Updates queue behind IT backlogs, implementation timelines stretch, and opportunities to respond to emerging risks are missed.

It's not a lack of capability that holds insurers back. It's the difficulty of translating that capability into action within the constraints of the existing operating model.

The AI gap is an execution gap

The same pattern is playing out with AI and advanced analytics. The potential is widely understood, and in many cases, already proven. More precise pricing, improved risk selection, and better customer engagement are all achievable outcomes.

What remains unresolved is how to operationalize those capabilities at scale.

In many organizations, AI is still being deployed as a series of point solutions rather than integrated into the core of decision-making. Data remains fragmented, insights are generated in isolation, and the process of moving from analysis to action is slower than it needs to be. This is not a failure of ambition but one of integration.

Without an operating model that can absorb and act on these capabilities continuously, AI risks adding another layer of complexity rather than delivering meaningful transformation. The gap between what is technically possible and what is practically achievable continues to grow.

Innovation that arrives too late

One of the clearest consequences of this dynamic is the speed of product innovation. Emerging risks require new forms of coverage, more flexible pricing, and the ability to adapt offerings as conditions change. Yet bringing new products to market remains a slow, resource-intensive process. By the time a product is launched, the risk it was designed to address may already have evolved.

In effect, insurers are often pricing yesterday's risk in today's market.

This lag has direct commercial implications. It limits the ability to seize new opportunities, exposes reliance on outdated assumptions, and makes it harder to compete in areas where speed and adaptability are becoming critical.

More than an efficiency problem

It's tempting to frame these challenges as operational inefficiencies. At its core, this is a question of missed opportunity. Every delay in responding to changing risk conditions shows up somewhere. In pricing that no longer reflects exposure. In products that reach the market too late. In capital deployed against assumptions that are already outdated.

Over time, this erodes both profitability and competitiveness. It also has wider implications for the role insurance plays in the economy. When insurers cannot respond quickly enough to evolving risk, it becomes harder to price and transfer that risk effectively, which in turn affects how capital is deployed.

A breaking point for the operating model

The insurance industry has adapted to change many times before, but the current moment is different in both speed and scale. What the industry is facing is not a series of isolated challenges, but a structural shift in how risk behaves. The operating model that has supported insurance for decades is reaching its limits.

Closing the gap between the speed of risk and the speed of response will require more than incremental improvement. It will require a fundamentally different approach, one that allows insurers to move from periodic decision-making to continuous, real-time action.

The industry is not short on data, insight, or ambition. What it lacks is the ability to translate those strengths into action at the pace the market now demands. That is why this moment feels different. This is not simply another innovation "phase," it's the point at which the traditional operating model breaks.

Has a family member ever given you a gift you can't bear, yet can't refuse, and it simply becomes part of the decor? It might be a decanter so impractical that it's ornamental, but it has to be brought out every time they come over; or a portrait that asks fundamental questions about the nature of your relationship, yet over time you no longer register that it's there. 

Operational challenges can be like this; unwanted gifts that become clutter, obstacles that are easy to rationalize. As they accumulate, they require incremental effort to navigate and leach efficiency. Yet when we approach a familiar operational challenge from inside the organization, we risk framing the challenge so narrowly that we're boxed in with too few options available. We refer to this as approaching challenges through a lens of our experience - and it can become part of the problem, rather than a means of solving for it.

Seeing a problem through the lens of our experience describes a way of seeing that includes all our knowledge of the history of the problem. All the attempts to resolve it, the failures, the frustrations; it's the voice that says, "We've tried that before and it didn't work." Returning to our furniture metaphor, it's not dissimilar to saying, "We can't move that painting. We took it down once, and my brother got upset." The lens of experience is effective at keeping you on the same track but it's less likely to help change direction. Evaluating a persistent operational challenge through a lens of expertise is vastly more effective.

Approaching a familiar challenge through a lens of expertise means stepping outside of the challenge, viewing it more objectively, and applying our knowledge to that problem. This is the secret sauce of consulting, the classic "outside-in perspective," yet it's possible to strengthen this capability within your own organization. The key is understanding how changing the structure of a problem helps to create new ways of seeing it. By carefully evaluating a problem and adjusting its constraints, experienced operators can see a familiar challenge with a broader perspective, and then bring their hard-won expertise to bear.

I worked with an insurance property repair firm whose leaders shifted their focus from a lens of experience to a lens of expertise with spectacular results. They were part of an insurer's repair vendor panel and found themselves competing across a broad range of repair categories, tackling jobs that ranged from minor fence repairs and garage doors through to major reconstructive work for insureds. Smaller jobs only required general handymen - low cost, low risk, and the pool of available contractors was broad - whereas the larger jobs required more skilled trades and more oversight - higher cost, higher risk, and a narrower pool of trades. Larger firms on the panel could absorb the occasional job that went off the rails, but this firm was small enough that even one or two jobs that went over budget hit profits hard. That was the model. Until this firm opted to re-imagine and renegotiate their panel membership.

The repair firm reimagined their business in two stages: first, they negotiated with the carrier to remain on the panel as a "small repairer." They would only accept smaller repair work but take higher volumes. This was feasible because the pool of trades was large and - given the nature of largely weather-related property damage - jobs were often geographically co-located. One trade could attend multiple sites in a day, which allowed for bundling and improved efficiency. In exchange, the repairer would offer a reduced rate because they weren't subsidizing larger jobs.

Second, they re-designed their operations from within by re-structuring their project management approach. They turned the entire model upside-down, from how they hired trades and retained them to how they would project manage each job. Each repair was broken into its discrete segments (plastering, painting, electrical, and so on) and were arranged such that the right trade attended at the right time - a virtual production line. Trades tapped in and out on their cellphone app, which gave the business visibility of their activity, plus allowed for them to estimate the time required for each job - a feedback loop that informed project, pricing, and contract-hiring forecasts.

The results were significant. The carrier ultimately integrated the model directly into its property claims flow, allowing customers to move from first notice of loss to completed repairs with a speed that hadn't previously been possible. Customer satisfaction ratings exceeded 90%. The firm had transformed itself not by responding to competitive pressure, but by isolating the fundamental conditions of their business and restructuring them to reveal entirely new ways of operating.

Your operations function may be more or less complicated than this example, but there's likely at least a handful of persistent challenges you'd love to unpick. Start by examining the assumptions and constraints that shape how you interpret the problem. Change those, and new solutions will follow.

The insurance industry can take pride in the fact that innovation can't happen without it. Until innovators and their insurers figure out how to defray the risk from driverless cars, commercial space flight, etc., they can't go to market. But innovation also can't happen without lawyers. While we non-lawyers complain about how they slow things down, innovations can't scale until the legal system develops a framework for adjudicating the inevitable problems. 

Generative AI is moving into its early legal phase, according to a report from Gartner Group. The report predicts that by the end of the year there will be more than 2,000 legal claims worldwide related to "death by AI," as mistakes by the software or by those implementing it may be the root cause of fatalities. 

The implications will be most immediate for health insurers but will be felt soon enough in just about every corner of the insurance industry, especially where AI is being used to try to anticipate and prevent losses.

Let's have a look.

Gartner frames the "death by AI" issue as a broad one for companies in all industries, suggesting that general counsels need to be aware of the risks and need to work with insurers to purchase coverage. Gartner predicts that by 2030 there will be a 60% increased in corporate spending on security and governance related to AI. From that standpoint, AI looks like a big, new opportunity for insurers.

I'm more concerned about the potential surprises that may be waiting for insurers. 

Those insuring medical practices, for instance, may be caught by surprise if the caretakers turn tasks over to AI that then go awry. Human doctors are still very much in the loop at the moment, but there's a real push toward instituting a combination of telemedicine and automated AI advice, especially to reach people who live in remote areas or other "healthcare deserts." So decisions will real consequences may start moving quickly into the AI. 

The theory is great. You outfit people with wearables that monitor their health, alerting doctors of any warning signs. You coach people on eating, sleeping, exercise and so on. Doctors are reachable by Zoom for consultation and diagnosis. 

But what happens when the AI misses the signs of an impending stroke? What happens when it misdiagnoses a diabetic? 

A columnist in the Washington Post recently wrote about an experiment in Utah that raises all of these questions. It's a very responsible test, limited to having AI refill prescriptions, and could have major benefits. The columnist, an MD and former health commissioner in Baltimore, writes: 

"Right now, getting a prescription refilled can be challenging. Many patients call a doctor’s office and struggle to reach the right person or are told it’s not possible without an in-person visit, which requires time and travel. Some end up putting off that visit and go without medications, which can be dangerous for those with chronic diseases such as hypertension, diabetes and cardiovascular issues."

But she also quotes a professor at Harvard Medical School who says that, "while some drugs might appear to be low-risk on paper, prescribing them is often complicated and patient-specific. He noted that many drugs require ongoing monitoring, including regular lab tests, attention to side effects and careful and nuanced discussions with patients. 'It’s not clear that AI is fully able to replicate that,' he said."

And I believe that people -- including those on juries -- hold machines to higher standards than they do humans. Humans can make errors in the heat of the moment. We know we aren't perfect. But software is written by very smart people who aren't under instant time pressure and are vetted by large, responsible organizations (with deep pockets). So AI can't just be good. It has to be perfect.

The potential for legal surprises won't just relate to "death by AI," either. There will also be "injury by AI," at a far greater rate. (While more than 40,000 people die in car accidents in the U.S. each year, for instance, some 2.5 million are injured.) 

And the claims won't just hit healthcare providers that may have misdiagnosed or mistreated someone. I worry about the companies that use AI to detect dangerous situations in workplaces. What happens when they miss one and someone is hurt or killed? What happens when sensors don't detect the electrical problem in a home that leads to a fire, or the leak that's about to become a flood? When the forward-looking dashcam doesn't spot the deer that has jumped into the road? 

As I've written, consumer advocates are already blaming the big, bad algorithm for any decisions they don't like on underwriting and claims. Those legal issues are about to broaden, especially for those promising prevention via AI.

We'll get through this. The legal framework will gradually develop, and we'll learn what the rules are going to be. But we need to brace ourselves for complications like the coming wave of "death by AI" claims.

Cheers,

Paul

Insurance has always been about navigating uncertainty, but the kind of volatility we face today is different. In just a few years, underwriters have had to absorb the impacts of a pandemic, new conflicts, evolving sanctions, and persistent inflation, all while global trade routes and partnerships grow less predictable.

The difficult truth is that many major risks can't simply be avoided. Crude oil still passes through the Strait of Hormuz. Agricultural goods still move through contested territories. The job for insurers is not to reroute around these risks but to reprice them as conditions change.

That shift is forcing a fundamental rethink of how the industry perceives exposure, how it uses data, and how quickly it can make decisions.

When Stability Assumptions Break Down

Most analytical and AI models are built on an assumption of stability. They work best when trade patterns, political conditions, and market behavior stay within the limits of historical norms. But that isn't how the world works anymore.

In a structurally unstable environment, it's not that insurers lack sophisticated tools. The problem is that the information those tools rely on is changing faster than the models can adjust. A sanctions update, a sudden military escalation, or a disruption in shipping routes can alter risk conditions overnight.

When that happens, the gap between model predictions and real-world conditions widens, leaving insurers uncertain about when and how to act.

The True Constraint: Decision Velocity

The biggest limitation facing insurers today is not computing power or model design. It's decision velocity: the ability to act at the speed of change.

Underwriters constantly face a tradeoff. They can make quick decisions based on incomplete information or slower, more informed ones that come too late to matter. That tension is especially visible in specialty markets like marine or trade credit, where exposure conditions shift daily.

To stay ahead, insurers need to move from fixed risk assessments to continuously updated ones that integrate internal and external signals in near real time.

Building Trusted Context at Scale

Improving decision velocity starts with better data, but it doesn't end there. The real challenge is turning large amounts of fragmented data into a foundation of trusted, connected context.

Consider a marine insurer covering shipments through the Red Sea. By pulling in vessel tracking data, shipping advisories, satellite imagery, and even local security updates, that insurer can build a live picture of exposure as conditions evolve.

The same applies to other lines of business. Trade credit insurers can monitor political developments, sanctions dynamics, and partner credit signals to anticipate defaults. Property and business interruption insurers can track supply chain issues or regional cost surges to better understand how claims severity might shift.

When these insights are connected, decision-making becomes faster, sharper, and more confident.

From Static Underwriting to Continuous Risk Assessment

Traditional underwriting cycles were built around periodic reviews: evaluate, bind, and revisit at renewal. In a world where risk conditions evolve daily, that cadence no longer fits. The industry's next step is continuous risk assessment. With a connected data ecosystem, insurers can refresh exposure views constantly, manage forms, endorsements, and pricing as new intelligence arrives, and align capacity decisions with live market conditions.

This approach doesn't replace actuarial discipline; it enhances it with context. The result is underwriting that keeps pace with the environment it's meant to protect against.

Seeing, Trusting, and Acting Faster

The future of insurance will belong to organizations that can see more, trust their data, and act faster than disruption can spread. Speed, in this case, does not mean cutting corners. It means using connected, contextual insight to make sound decisions at the right moment.

In a fragmented, fast-changing world, the winners won't necessarily have the most complex models. They will have the clearest view of reality. Because when everything is connected, the real constraint isn't intelligence. It's decision velocity.

The conversations I'm having with senior people across the industry at the moment have a familiar shape. Someone describes a problem — payment delays, a reconciliation that won't close, a carrier partner asking hard questions about fund visibility — and then, almost in the same breath, they say some version of: "we've known about this for a while."

That's the part that interests me. Not the problem itself, but the fact that it's been known about. Because what that tells you is that the industry has been carrying a form of debt — not financial debt, but operational debt. Deferred investment in the infrastructure that actually moves money, reconciles accounts, and connects claims teams with treasury. It's been accumulating quietly for years, and the conditions that made it easy to ignore are changing.

The buffer is getting thinner

For most of the past decade, there was enough slack in the system to absorb a degree of operational inefficiency. When investment returns are strong and pricing cycles are favorable, slow reconciliation and fragmented fund management don't really show up as problems. They show up as mild annoyances, something for the back office to sort out eventually.

That buffer is narrowing. AM Best has flagged that margin pressure is likely to build through 2026 as rate moderation continues and loss severity persists, particularly in casualty lines. In that environment, those inefficiencies stop being invisible. Finance teams spending hours on manual reconciliation aren't doing liquidity planning. Treasury teams managing reactive funding calls aren't optimizing how capital is deployed. Those are real costs. In a tighter market, they start affecting results.

The timing matters. If you've been telling yourself that the infrastructure investment can wait, the window for waiting is getting smaller.

What the data actually shows

Earlier this year, we surveyed more than 200 senior insurance professionals across claims, finance, and treasury in the US and UK. Some of what came back was striking. Not because it surprised me, but because of how consistently people described the same problems.

Nearly eight in 10 identified internal process inefficiencies as a key barrier to timely claims payments. Two-thirds said accessing readily available funds was a genuine challenge, and that figure rose to 74% in the US. Only one-third of finance leaders said they had clear visibility into delegated claims funds. And just 1% described collaboration between their claims and finance teams as highly effective.

That last number is the one that stays with me. 1%. These are teams that are jointly responsible for payment execution, reconciliation, and financial oversight — and they're essentially operating in separate worlds. That's not a technology problem. It's a structural one, and it's been allowed to persist because the consequences haven't been visible enough to force a change.

Operational risk doesn't stop at your own front door

One thing that often gets missed in these conversations is that insurance is a network business. A carrier can have its own house in order and still be exposed through the weakest link in its chain. If a TPA, broker, or delegated authority is running on outdated processes — quarterly reconciliations, reactive cash calls, no real-time fund visibility — that's the carrier's problem too. It shows up in payment delays, reconciliation errors, and regulatory exposure.

We see this clearly in our own work. Some of the most sophisticated carriers we speak to have invested significantly in their own operations, only to find that the friction sits with a partner they didn't think to scrutinize. In a delegated model especially, you're only ever as good as the operational standards of the people you've trusted to act on your behalf.

The compliance dimension is hardening

There's also a regulatory dimension to this that I think gets underweighted, and the signals from both sides of the Atlantic are worth paying attention to.

In the UK, following a super complaint in late 2025, the FCA announced it will conduct formal reviews of claims handling, servicing and consumer understanding across the general insurance market in 2026. That's not a consultation paper or a future proposal. This is active scrutiny, already underway, focused specifically on how claims are managed and paid.

In the US, California's new claims laws that came into effect on Jan. 1 this year require insurers to accelerate payouts to wildfire survivors, part of a broader legislative package designed to make payment timeliness a hard obligation rather than a best-practice aspiration. These aren't isolated developments. They reflect a direction of travel that is consistent across markets: regulators are increasingly treating payment operations as a conduct and governance issue, not just an efficiency one.

The practical consequence for insurers is that the back-office processes which were once invisible to regulators are becoming visible compliance signals. Carriers that lack real-time visibility into claims funds, or that rely on manual reconciliation across distributed structures, are carrying more regulatory exposure than they may realize. Fixing the operational gap and fixing the compliance gap are, increasingly, the same exercise.

The investment logic has changed

For a long time, the case for investing in operational infrastructure was framed around efficiency, doing things faster and cheaper. That case was always true, but it wasn't always urgent enough to compete with other priorities.

The framing has shifted. Real-time fund visibility, accurate reconciliation, and controlled disbursement aren't just operational improvements anymore. They're signals to your carrier partners, your regulators, and your claimants that you are a capable and trustworthy counterparty. In a market where margins are compressing and scrutiny is increasing, that signal is worth more than it used to be.

The industry has the tools to make this shift. What it needs now is the recognition that the good years, which helped absorb the cost of operational inertia, may not be coming back in quite the same form. The debt is coming due. The question is whether you address it on your own terms, or wait for the market to force your hand.

Key Takeaways

• Legacy system fragmentation remains the primary barrier to ROI rather than the AI technology itself.
• Agentic systems replace rigid "if-then" logic with dynamic reasoning to navigate complex underwriting and claims.
• Poor data quality in autonomous loops creates a feedback cycle of bad decisions and financial liability.
• Scaling requires an escalation tier where humans verify AI confidence scores to maintain fiduciary responsibility.
• Insurers without real-time API connectivity risk total disintermediation as brokers and aggregators shift to AI-native ecosystems.

The insurance industry is currently captivated by the promise of agentic AI. Unlike the static "if-then" logic of traditional RPA, agentic systems reason, use tools, and pursue goals. They promise a world of touchless claims, autonomous underwriting, and a fraud defense that evolves in real-time.

For insurers operating under sustained combined ratio pressure, volatile catastrophe (CAT) exposure, and shrinking distribution margins, this shift is strategic. Agentic AI appears to offer operating leverage at scale, compressing expense ratios while improving loss performance and portfolio steering.

Yet, as pilot programs move toward production, a frustrating pattern is emerging: enterprise architecture was built for human-centered silos, not autonomous orchestration. Most global carriers still operate across regionally fragmented cores and vendor-locked policy administration systems (PAS) designed for human-mediated workflows and batch reconciliation.

However, these environments were never built for autonomous orchestration across underwriting, claims, and reinsurance. Without architectural modernization, deploying agentic AI onto these brittle foundations does more than just stall ROI. It introduces new forms of operational and regulatory fragility.

We are moving beyond digital transformation. The real inflection point for insurers is agentic readiness.

The Shift from Rules to Reasoning

Traditional insurance automation is deterministic. A rule engine flags claims above a monetary threshold. A rating engine recalculates the premium based on predefined variables. A referral workflow escalates risks outside delegated authority. These systems are efficient within narrow guardrails, but brittle when context shifts.

Agentic AI changes the operating model. Consider a complex auto claim following a severe weather event. An agentic system can validate storm intensity data, correlate telematics feeds, benchmark repair estimates against regional inflation trends, evaluate prior FNOL behavior, and dynamically recommend reserve adjustments aligned to actuarial development patterns.

In commercial lines, it can ingest broker submissions, extract exposure data from the schedule of values, analyze five-year loss runs, interpret manuscript endorsements, and draft underwriting rationale aligned to delegated authority and treaty structures. The misconception is that these capabilities can be layered onto legacy cores.

In reality, most multinational insurers operate across heterogeneous policy administration systems spanning geographies, lines of business, and regulatory regimes. Human underwriters, adjusters, and operations analysts still bridge gaps between claims, billing, reinsurance, and finance. When an autonomous agent attempts cross-system orchestration, it encounters API limitations, latency constraints, inconsistent data lineage, and fragmented identity management.

Data Quality Debt is the Silent Destabilizer

In the context of agentic AI, data quality is a solvency risk. When an agentic system is given the autonomy to adjust reserves or initiate endorsements, "dirty" data, such as inconsistent loss history or fragmented policy records, becomes a feedback loop of bad decisions.

An agentic-ready carrier requires modular, API first architectures where rating events, reserve movements, underwriting referrals, catastrophe exposure updates, and reinsurance recoverables are observable within unified event streams. Agents must learn against actual loss emergence and settlement outcomes — not synthetic feedback loops detached from financial reality.

The Necessity of Human-in-the-Loop Governance

A frequent concern among regulators and C-suite executives is the loss of control. How do we ensure that a non-human identity doesn't errantly deny a valid claim or misprice a catastrophic risk? The answer lies in replacing vague oversight with structured role-based governance.

The architecture must support both Underwriter-in-the-loop (UITL) and Adjuster-in-the-loop (AITL) controls. These are integrated UI/UX components where the AI presents its reasoning, its confidence score, and the specific data points it used to reach a conclusion.

This is particularly vital in specialized lines like Directors and Officers or Cyber insurance, where the risk landscape shifts faster than any model can retrain. By designing architecture that treats the human as an escalation tier rather than a manual processor, insurers can scale without abandoning fiduciary responsibility.

Defensibility in the Age of Autonomy

When an AI agent takes an action such as denying a claim or adjusting a premium, insurers must provide a defensible audit trail that stands up to regulators and reinsurers. Traditional logs that show updated system records are no longer sufficient. We need immutable agent action logs.

This technical requirement involves documenting what tools were queried, what version of the model was used, and what specific data inputs were retrieved at that exact millisecond. In healthcare and life insurance, where compliance is non-negotiable, this level of transparency is the difference between a successful deployment and a multimillion-dollar fine. If you cannot reconstruct the logic of an autonomous decision six months after the fact, that decision is a liability.

Distribution Disruption: Agentic AI Beyond the Core

The disruption is not confined to internal operations. AI-native insurance apps embedded within conversational platforms are reshaping distribution economics. When quoting, comparison and policy binding move into AI ecosystems, insurers with brittle core systems will struggle to expose pricing, underwriting rules, and policy data through secure, real-time APIs. Agentic readiness is both an operational capability and a distribution survival requirement.

In personal lines, AI-enabled aggregators can dynamically compare pricing and coverage language across carriers in seconds. In commercial lines, digital brokers are beginning to pre-qualify submissions using AI copilots before they ever reach an underwriter. Insurers that cannot expose pricing, appetite, capacity constraints, and policy data through secure, scalable APIs risk being disintermediated.

Agentic readiness is therefore not just an operational capability. It is a distribution survival requirement. Architectural modernization determines whether an insurer participates in AI native ecosystems or becomes invisible within them.

Rethinking Accountability and Compliance

The biggest compliance risks emerge when accountability for AI-led decisions is poorly defined. If an agentic system in a personal risk management workflow makes a discriminatory pricing error, who is responsible? The data provider? The model developer? The enterprise architect who enabled the integration?

To mitigate this, we must shift our view of enterprise risk management (ERM). We are entering an era where agent identities must be managed with the same rigor as human employees. This means assigning specific permissions, spending limits, and kill switches to autonomous agents. In areas like disaster recovery and planning, agentic AI can be a massive asset, but only if the guardrails are hardcoded into the architecture, not just the policy manual.

The Path Forward from Silos to Orchestration

The payoff for solving these architectural challenges is measurable and profound. Insurers who move beyond the pilot purgatory of agentic AI see higher straight-through processing (STP) rates, lower leakage, and significantly faster cycle times. But more importantly, they build a resilient foundation that is ready for whatever the next generation of intelligence brings.

The transition from a process-centric organization to an agentic-ready one is a necessity for survival in a high-frequency, high-data-volume environment. We must stop asking if the AI is ready for insurance and start asking if our insurance architecture is ready for AI. The future of the industry belongs to those who treat their enterprise architecture not as a collection of legacy systems, but as a living, breathing nervous system capable of supporting autonomous thought.

My favorite anecdote from last week's ClimateTech Connect was a little gem of high tech meeting low tech: a sophisticated network of sensors and a woman with a rake that, together, are protecting hundreds of homes from flooding.

A panelist at the conference on mitigating the risks from climate change said flash flooding had washed away some 300 homes in a small town in the U.K. As the insurance industry helped it rebuild, the town took advantage of improvements in technology and installed sensors that monitor upstream water levels. When they reach potentially dangerous levels, an alarm sounds in the mayor's office. A clerk then grabs her rake and walks down the street, where she clears the debris that collects in a culvert, ensuring that any flood waters will quickly run off.

Few problems have such simple, happy solutions, of course, but the conference still offered some hopeful signs in a world seemingly buried under warnings of impending doom. The mere fact that hundreds of senior people from a whole variety of vantage points — big banks, home builders, municipalities, etc., as well as insurance companies — spent two days in Washington, D.C., strikes me as a good sign.

I'll share a few highlights, in the hope they provide food for thought.

A former fire chief said my second-favorite thing at the conference. I almost hesitate to share, because, in retrospect, what he said is obvious. But it had never occurred to me, and, in my defense, I hadn't heard anyone else say it despite having spent years wrestling with how to get people to understand that everyone  in a community is in the fight together when it comes to wildfire risk.

I knew that reducing the risk to my house reduced the risk to yours, and vice versa, but I didn't think strategically enough — and the former fire fighter helped me out. He said it doesn't help a community much to have a scattershot approach to hardening homes against fire. He said communities have to be systematic. That means focusing on the homes at the edge of the community closest to the wildlands that might catch fire, while worrying far less about the homes that are well inside the boundary. 

That sort of approach not only makes sense but seems more manageable. It reduces the amount of money that is needed to protect a community and takes some of the onus off individual homeowners to alter their landscaping, put mesh over vents to keep embers from getting into a home, etc. A homeowners association could undertake the hardening work on the key homes on behalf of the whole community. 

Or a community could follow the lead of Amy Berry, CEO of the nonprofit Tahoe Fund. She has raised $30 million of private capital to leverage $200 million of public funds for more than 220 projects, including five in the Tahoe area that take the sort of approach advocated by the ex-fire chief. The fund uses public resources to identify homes that could be "superspreaders," then knocks on their doors and offers to help harden their homes. (These projects are near and dear to my heart, given that I used to live just down the road from three of these projects. When I mentioned the name of the town that had our favorite pizza place, she said its name instantly.)

More broadly, the conference embodied the sort of broad conversation, reaching well beyond the insurance industry, that needs to happen. Francis Bouchard, a managing director at Marsh, has hit that theme hard at ITL, including in an interview I did with him last fall and in a webinar I conducted with him and Nancy Watkins, a principal at Milliman, in December. At ClimateTech Connect, Francis continued the theme with a fireside chat with Illya Azaroff, president of the American Institute of Architects. He represents 110,000 architects and described all he's doing to try to get them to design for resilience from the get-go. JP Morgan, which has announced a massive financing initiative related to climate change, was represented by Sarah Kapnick, its global head of climate advisory. The climate chief for Massachusetts was there, too. 

So, there was a broad array of important, interested parties even before you got to the insurance ecosystem, well represented by Nationwide, Travelers, Munich Re, etc., including a host of intriguing technology startups. There were lots of foreign accents, too, which suggests that we're getting the sort of cross-fertilization of ideas that really hard problems require. 

The only real disappointment was that the federal government didn't show, other than to describe what data sets might be available and useful, but that lack of presence was hardly a surprise, given the current administration's stance on climate change and promotion of fossil fuels.

Denise Garth, chief strategy officer at Majesco, told a story that epitomized for me just how hard we're going to have to keep pushing. A storm with huge hail hit her home in Omaha, doing $140,000 of damage, including requiring a new roof. Her insurer, a top-five carrier, promptly cut her a check, but its agent missed an opportunity of the sort we just can't miss if we're going to make the world more resilient. 

It was only when Denise started dealing with a roofing contractor that she learned that rubber roofs were available that looked like tile, shake or whatever she wanted. In the future, hail would just bounce off. She had a rubber roof installed and actually got a 20% discount from her carrier as a result. But somebody — actually, lots of somebodies — needs to do a much better job of educating agents and encouraging them to counsel customers. 

After attending last week's conference, I'm encouraged about the progress we're making on resilience, but we have a long way to go.

Cheers,

Paul

The insurance sector has a well-documented mismatch between its AI ambition and operational readiness. While 82% of insurance companies believe AI will define the industry's future, only 14% have fully integrated it into their financial operations, and 52% describe their data governance frameworks as early-stage or still developing. The distance between those numbers reflects how most firms are approaching AI as a strategy to announce rather than an operational capability to build.

All data cited in this article is from AutoRek's 2026 Insurance Operations and Financial Transformation Report, based on 250 interviews with insurance and healthcare insurance managers across the U.S. and U.K. The three most commonly cited barriers were legacy system integration challenges (42%), fragmented data environments (39%) and a shortage of in-house AI expertise (40%). None of these are new problems, but the cost of carrying them forward has grown significantly.

Data fragmentation is the core problem

The average insurer managed 17 data sources feeding premium processes alone. Each source represents a different format, a separate update frequency and another potential point of failure in the reconciliation chain. AI deployed across such an environment does not streamline operations; instead, it amplifies the inconsistencies already embedded within those systems.

This is why firms that have made measurable progress on AI integration share a starting point. They first standardized their data architecture before layering on automation capabilities. They also built workflow and governance frameworks that are auditable and measurable rather than theoretical. Reconciliation was typically automated first, creating a reliable and consistent data environment that makes AI-driven workflows viable later in the process.

M&As create back-office operational complexities

Industry consolidation is accelerating, and the operational burden is falling on already strained infrastructure. 54% of insurers said incompatible systems and data architectures were their biggest post-merger integration challenge. For firms managing over a dozen data sources before a deal closes, an acquisition means introducing additional complexity before the existing complexity is resolved.

The carriers who are able to realize sustainable value from the merger treat data harmonization as pre-merger work. Integration planning begins at the architecture level rather than after the deal closes, ensuring that new systems are absorbed into a standardized environment instead of being added to an already fragmented one.

Settlement cycles measure operational health

44% of insurers faced settlement periods exceeding 60 days. Transaction volumes are projected to grow 28.7% over the next two years.

Settlement cycle length is the clearest indicator of how well data moves between systems and how much manual intervention is required to close transactions. Firms with shorter settlement cycles have typically completed foundational infrastructure work, including implementing automated reconciliation, reducing the number of data sources and establishing governance frameworks. The correlation between operational discipline and AI readiness was consistent across the research.

The data show a clear path forward

Despite the persistent barriers, the research shows clear intent to act with 50% of firms prioritizing AI and machine learning, 42% focusing on automation of back- and middle-office functions and 51% citing regulatory requirements were the primary driver of modernization decisions.

Insurance firms seeing results from those investments have sequenced them deliberately. They have taken a structured approach, starting with governance frameworks, followed by data standardization, then building automation on top before introducing AI. That sequencing matters because AI running on fragmented, manually managed data will produce similarly fragmented and manually intensive results, only at greater speed and cost.

The operational reality from inside the carrier

I spent 12 years within the carriers including MetLife, HSBC Life, Aviva, AIG and Generali before moving into insurtech. The constraints highlighted in this research were recognizable from the inside. The organizations that made the most progress treated back-office infrastructure as a strategic investment rather than an operational cost and made data quality an asset and a prerequisite for adopting new technology.

With 6% of insurers reporting no AI usage in financial operations at all, the performance gap between firms that have modernized and those that have not is widening. As transaction volumes grow and consolidation continues, that gap will complicate the path forward for firms that have deferred the infrastructure work. The decisions insurers make about data infrastructure in 2026 will determine how much value they ultimately capture from their AI investments.

For decades, commercial insurance has operated on a largely reactive model. Insurers assess risk using historical data, price policies at the start of the cycle, and respond financially after losses occur. While this approach has ensured stability, it is increasingly misaligned with today's dynamic risk environment.

Industries such as logistics, transportation, and construction now operate under continuously evolving conditions, where risk exposure changes in real time. In this context, static underwriting and retrospective claims management create critical blind spots, limiting both visibility and control. The widening gap between how risk is priced and how it behaves is placing growing pressure on traditional insurance models.

At the same time, advances in telematics and connected technologies are redefining what insurers can observe and influence. Real-time behavioral and operational data is enabling a shift toward continuous, intervention-driven risk management.

Understanding the Emergence of Continuous Insurance

Continuous insurance represents a structural shift in how risk is assessed and managed. Instead of periodic evaluations, insurers can now maintain a real-time view of exposure through continuous data streams.

Telematics plays a central role in this transformation. By capturing detailed data on asset usage, environmental conditions, and human behavior, telematics systems provide a level of insight that was previously unattainable. This allows insurers to move beyond static assumptions toward dynamic, evidence-based risk assessment.

As a result, insurance is evolving from a transactional model into a continuing process—where risk is continuously monitored, interpreted, and influenced. Intervention is no longer reactive; it is increasingly preventive.

Telematics as the Backbone of Real-Time Risk Visibility

The growing adoption of telematics insurance is not simply enhancing existing models but redefining their foundation. What makes telematics transformative is its ability to convert operational activity into measurable and actionable risk signals.

In commercial auto insurance, for instance, telematics systems capture driving patterns such as acceleration, braking behavior, route selection, and exposure to high-risk environments. This creates a continuous feedback loop where risk is not inferred from past incidents but observed directly as it unfolds.

More importantly, this data does not remain static. Through advanced analytics, it is translated into risk intelligence that can inform immediate decision-making. Insurers can identify emerging patterns, anticipate potential incidents, and enable timely interventions that reduce the likelihood of loss.

This shift from data collection to real-time intelligence marks a critical step in the evolution toward continuous insurance.

The Transition from Periodic Underwriting to Continuing Risk Evaluation

Traditional underwriting operates within defined timeframes, often relying on annual policy cycles. While effective in stable environments, this approach struggles to capture the variability of modern risk landscapes.

Continuous insurance introduces a more adaptive model where underwriting becomes a continuing process. Real-time inputs from telematics systems allow insurers to reassess exposure continuously rather than at fixed intervals.

This has several implications. Risk pricing becomes more closely aligned with actual behavior and conditions, reducing the gap between expected and realized outcomes. Emerging risks can be identified earlier, enabling corrective actions before they escalate into claims. Over time, this leads to more accurate underwriting and improved portfolio performance.

The shift is not merely operational but conceptual. Risk is no longer treated as a fixed attribute but as a dynamic variable that requires constant evaluation.

Redefining the Role of the Insurer in a Continuous Model

As insurance becomes more data-driven and continuous, the role of the insurer is undergoing a fundamental transformation. The traditional function of compensating losses after they occur is being complemented by a more proactive role in preventing those losses altogether.

Telematics insurance enables insurers to engage directly with policyholders in managing risk. By providing real-time insights and behavioral feedback, insurers can influence decision-making at the point where risk is created. This represents a shift from financial protection to operational partnership.

In this emerging model, insurers are not external entities responding to events but integrated participants in their clients' risk environments. Their value lies increasingly in their ability to reduce uncertainty rather than simply absorb it.

Operational Impact of Telematics in Commercial Fleet Environments

The operational impact of telematics insurance is most clearly visible in commercial fleet environments, where real-time data has become integral to both risk management and performance optimization. By continuously capturing and analyzing driver behavior and vehicle usage, telematics enables insurers and fleet operators to move beyond retrospective assessments and actively manage risk as it develops.

This shift introduces a dynamic feedback loop in which data-driven insights inform immediate actions, improving both safety outcomes and operational efficiency. Over time, this not only reduces claims but also enhances overall fleet performance, creating a more aligned and resilient risk ecosystem.

Key operational outcomes include:

• Continuous visibility into driver behavior, including speeding, harsh braking, and route risk exposure
• Early identification of high-risk patterns, enabling timely corrective interventions
• Improved driver accountability through continuing monitoring and performance feedback
• Reduction in accident frequency, supporting better loss ratios and underwriting performance
• Enhanced fleet efficiency through optimized routing, fuel management, and predictive maintenance

Strategic Realignment in a Telematics-Driven Insurance Landscape

The rise of telematics insurance is not only transforming operations but also driving a broader strategic realignment within the insurance industry. As real-time data becomes central to risk assessment, insurers are being compelled to rethink how they compete, collaborate, and create value.

In this evolving landscape, the ability to access, interpret, and act on data is emerging as a critical differentiator. At the same time, insurers must navigate increasingly complex ecosystems where data flows across multiple stakeholders, raising important questions about ownership, control, and long-term positioning.

This transformation is both technological and organizational, requiring insurers to build new capabilities while shifting toward a more proactive and partnership-oriented model.

Key strategic implications include:

• Real-time data emerging as a core driver of underwriting accuracy and competitive differentiation
• Increased importance of data ownership and control in shaping long-term market positioning
• Greater reliance on partnerships with telematics providers, platform operators, and OEMs
• Expansion of insurer capabilities in advanced analytics, real-time processing, and digital infrastructure
• Evolution of business models toward continuous engagement rather than periodic interaction
• Cultural shift from reactive claims management to proactive risk prevention and client collaboration

A Structural Shift Toward Embedded and Preventive Insurance

The movement toward continuous insurance reflects a broader transformation in how risk is conceptualized. Insurance is gradually becoming embedded within the operational fabric of businesses, supported by real-time data and continuous feedback loops.

Telematics insurance will remain central to this evolution, enabling insurers to maintain visibility and influence at every stage of the risk lifecycle. As adoption increases, the distinction between risk assessment and risk management will continue to blur.

Over time, this will lead to a model where prevention becomes the primary objective and claims become less frequent by design.

Conclusion

The transition from risk transfer to risk intervention represents a defining shift in commercial insurance. Telematics insurance is at the core of this transformation, enabling continuous visibility, predictive insight, and proactive engagement.

Insurers that successfully adapt to this model will move beyond their traditional role and become integral partners in managing and reducing risk. In an increasingly complex and fast-moving environment, the ability to intervene before loss occurs will determine long-term relevance and competitive advantage.