There is always going to be another new platform, another integration, and, increasingly, another conversation about AI. But the value of technology isn't in simply having more of it. It's how well it connects and whether insurers get better information when they need it.

At a time when AI is redefining how the insurance industry will function for the next 30 years, a converged environment unifies underwriting, policy administration, billing, claims, and customer engagement on one operating platform, rather than a chain of systems handing data to each other. A claims signal reaches pricing while the claim is still open, informing the next risk assessment in real time. An underwriting decision immediately shapes how service and retention teams treat that customer afterward. Information moves in near real time, following what the business needs rather than the limits of the original system architecture.

The Case for Converged Platforms

Across this platform, AI becomes part of how the workflow runs. An agent reviewing a claim has policy history, the underwriting file, and prior customer interactions available at the same moment, because they live in the same context rather than three different logins. That context lets AI move from flagging an exception to resolving it, and from recommending a next step to taking it, with a human reviewing the outcome afterward. The reasoning behind each action stays visible and auditable, so the people accountable for the outcome can see exactly how the system reached it.

The practical benefits show up quickly. Teams configure new products within a shared environment instead of rebuilding them system by system, so what used to take months of coordinated releases now takes days. Renewal pricing, fraud screening, and service routing draw on the same live data rather than nightly batch exports, so all cross-referenced with each other and reflect the real-time state of the business.

The autonomous AI platform we are describing doesn't mean anyone should abandon the judgment that experienced underwriters and claims handlers bring. A converged environment pairs automation with quality gates. An organization can route a routine renewal straight through and hold a complex commercial risk for human review, using the same data for both. The goal is to make sure the people making the decision are working from the full picture, instead of whatever fragment their siloed, legacy systems offer.

Rethinking Process Design in an AI-First World

It is important that companies should think carefully about the shape of their AI models. There is a natural tendency for people to focus on human-driven, effort-based, bottom-up processes and then apply AI efficiency factors to achieve better productivity results. I believe that approach is fundamentally flawed as it quietly assumes the old process as the correct baseline, when the old process is the very thing that should be questioned.

Companies should work back from the assumption that every function is AI-automated and design that as the starting point, not the improvement point. Then justify, line by line, the human effort that is genuinely needed to be added back: the judgment calls, the client-facing decisions, the regulatory sign-offs, the things an autonomous AI agent demonstrably should not be permitted to do.

The best approach is to question the requirement itself and what value it delivers to the business; wherever possible, delete the step entirely rather than automate it, simplify the real requirements that survive, and only then automate the complete end-to-end process with the minimum human intervention possible. There is no business value in automating a step that should have been removed completely, as this is the most expensive mistake that organizations make.

The clearest sign that this transformation is working comes down to a simple test. Can a service representative see a customer's full history the moment the call connects, without transferring them or putting them on hold while three systems are checked in sequence? Can an underwriter check how similar risks have actually performed against what the rate manual predicts? Can a claims handler settle a straightforward loss the same day it's reported, because the verification, the policy check, and the payment authorization all draw from the same source of truth?

That is the standard worth building toward. For companies where coherence is increasingly a transformational priority, my advice is to build an environment where every part of the business can see what every other part already knows, and where AI has enough context to act on that knowledge instead of merely describing it.

The NAIC's AI Risk Evaluation Supplement is the questionnaire regulators will draw on to examine how insurers govern their AI. It asks whether a model's outputs were tested for accuracy, but only for the models a carrier itself rated high risk. When a customer-facing model gets a coverage question wrong, the policyholder acts on the wrong answer. 

That is the exposure a high inherent risk rating exists to capture. Rate one low instead, and the inquiry ends there. The model keeps answering coverage questions, the answers keep reaching policyholders, and nothing requires the carrier to check whether they were right. Few carriers check on their own.

What a low rating leaves unchecked surfaces later, in a claim file rather than an examination. Misrepresenting policy provisions is a listed unfair trade practice in nearly every state, and a dated coverage statement the policy contradicts is what a misrepresentation claim is built from. The customer who heard "you're covered," paid for the repairs, and then had the claim denied is the one who brings the complaint.

When that complaint arrives, a court will ask how often the model's answers were checked and what the checks found. Errors and omissions underwriters, who now ask about AI at renewal, want the same answers. A carrier that rated its model low will have no file to produce. There is no record of what the model said and no comparison against the forms in effect at the time. 

What it will have is a self-attested risk assessment concluding that the model was low risk. Plaintiffs' counsel look for exactly that pairing: a carrier that never checked, and a document explaining why it did not have to.

How the rating works

The rating is set early, in an inventory. Each carrier lists its AI models, describes what each one does, and assigns each an inherent risk level. Only the models rated high move on, and there the questions turn specific: were the outputs tested for accuracy, how is performance monitored on a continuing basis, and how is the model reviewed against unfair trade practice and claims settlement laws? 

Models rated moderate or low do not reach those questions, and a regulator reading the inventory has discretion to stop at the rating and request nothing further about the model. That is sensible design, since regulators cannot examine everything. It also makes one self-assessed rating the single point of failure for whether a model's accuracy is ever examined, and the carrier is the one holding the pen.

How a customer-facing model goes unexamined

Two judgments decide whether a customer-facing model is ever examined. Does its output reach a consumer, and how much risk does it carry before controls? A carrier can get either one wrong without meaning to.

Destination settles the first question, not who speaks the words. The supplement's background section covers decisions and actions “made or supported by” AI, and a coverage answer that reaches a policyholder is one of them. The pitfall is the supplement's support category, defined as a system that provides information without suggesting a decision or action, which is not counted as having direct consumer impact. A model that drafts a coverage answer is not just providing information. It is supplying the decision the representative delivers, and the support definition excludes exactly that. But because a representative delivers the answer, the model looks on paper like one that only informs an employee, and that resemblance is what puts it in the wrong box. Filing it as support keeps the model out of the count regulators use to decide what to examine, and out of any question about whether its answers were right. Those answers still go out to policyholders, unchecked.

A model that is counted can still be rated low by mistake, and the same representative is usually why. Most carriers put two guardrails around a model's answers. The representative is expected to check it before it goes out, and the platform runs its own internal check against policy language. Both are controls, and because they are built into the process, crediting them when rating the model is an easy mistake to make. The supplement asks for inherent risk, the risk the model carries before any control is applied, and the word "inherent" is easy to read past. Under the supplement's own terms, the representative and the platform check belong in the residual rating, after controls. A carrier that credits them in the inherent rating has rated its model lower than the supplement intends, and taken it out of the testing questions in the process. Both judgments are already recorded, one line per model, in the carrier's own inventory.

Why the cheaper rating costs more

Rating a model low is the cheaper answer. It closes a line on the inventory, raises no question the carrier has to answer, and creates no obligation to find anything out. Rating it high creates recurring work: finding out whether the model's coverage answers were right and continuing to do so while the model runs. The pressure runs one direction, and nothing in the supplement pushes back. The saving lasts a quarter. The gap it leaves is permanent. That work cannot be backfilled later, because the evidence exists only while the conversations are happening, and controls do not close the gap. A representative who catches a wrong answer fixes that conversation and leaves no record of it, so even a carrier with an excellent review process cannot separate an isolated error from a model-wide one. It also cannot see drift. Models do not hold still after launch. They get updated, the policy forms they draw on are amended, and the questions customers ask shift. A test run before deployment describes the model that was deployed, not the one answering calls this quarter. What a low rating costs is a year of conversations nobody measured, and nobody can go back and measure.

I cannot tell you how often AI models give wrong coverage answers, and neither can anyone else, because almost nobody is measuring. The draft is open for comment through Sept. 29, with a vote on a later version expected in November. The questions are being written now, and the evidence they ask for can only be gathered before they are asked. Rating a model low does not change what it told a policyholder. It only changes whether anyone was looking.

The general assumption about innovation is that it's top-down. Someone invents something complicated and expensive. As it achieves scale, the innovation becomes simpler and less expensive, making it accessible to broader audiences. Rinse and repeat until the innovation becomes global.

In insurance, that trend has typically meant an innovation in the U.S. or Western Europe that finds its way over time into developing economies.

But there's also an interesting trend known as "reverse innovation" that I've watched play out for some 15 years. If you have a multimillion-dollar medical device, you can take cost out, time and again (and again and again), and still not make it cheap enough to be economic in a poor, rural community. So some smart folks in India working within the severe cost constraints of a poor area invent a version of the medical device — and sometimes that device works well enough that it can reverse the normal flow, migrating from developing areas into the major economies.

To learn more about what reverse innovation is happening — or could be happening — in insurance, I turned to George Kesselman, the founder of InsurTech Asia. He grew up in Canada and went to school there, before spending the last nearly two decades working in Hong Kong and now Singapore, so he has a thoroughly global perspective.

His short answer: Pay very close attention to China.

His longer answer follows.

I met George through a sister organization, the International Insurance Society, for which he has been hosting a working group on innovation that I'm part of. (George will present the findings from the IIS's global survey on insurers' priorities, as well as insights from our working group, at the IIS' annual gathering, the Global Insurance Forum, being held in London at Lloyd's on Nov. 15 and 16.) Here is what he said about innovation in Asia, edited slightly for length and clarity:

KESSELMAN: This year, I had the pleasure of doing a coast-to-coast tour over two weeks in the U.S. and Canada, spending a few days in New York, Toronto, Vancouver, and San Francisco. My read is that innovation is really quite different in North America than what's happening outside. 

The last wave of innovation, from roughly 2014 to 2023—let's call it a 10-year span—was very much an Asia-first wave. It was all this digital insurance that started in China, then Southeast Asia, with a lot of new models when it comes to embedded, small insurance. Then I think the innovation went a little bit more to Europe. In North America, it hasn't really taken off in the same way.

This time, the wave is kind of inverted. With all the AI innovation, all the investments, it's North America first, then Europe. Asia is seeing bits and bobs but not nearly as much as the other two regions.

My theory is that the scale is massively different in the regions, and the problems are also quite different as a result. Asia is very much about the growth of insurance. Insurance wasn't really popular here because the region was a lot less developed. The last 10 to 15 years have seen tremendous economic growth, so innovation and insurance started to come in. Insurers couldn’t scale the same face-to-face distribution, so they found different ways, including digital distribution.

But the scale still remains drastically different. When I was in North America and Canada, everybody talked in terms of billions of dollars. In Asia, people talk about millions, maybe sometimes tens of millions. 

CARROLL: What kinds of embedded insurance innovations emerged in parts of Asia that have filtered out to other parts of the world?

KESSELMAN: In e-commerce, we saw quite a bit of innovation in warranties and the return shipping space. Now we're seeing innovations around trying a new product and getting coverage if you don't like it—you experience an upset stomach after eating something, or face delayed delivery. These are very region-specific and emblematic of the logistics and distribution challenges in Asia.

Travel is another ecosystem that saw significant innovation. Mobility is the third. Then fintech-related insurance is probably the fourth. Each has its unique characteristics. In fintech, for example, we've seen a lot around cyber incidents—if your wallet gets hacked, you get paid—or bill payment protection, where if you get sick, the bill gets paid on your behalf. These are embedded and bundled propositions that are very continuous to the core offering.

These innovations started here because digital leapfrogged in a lot of countries where the infrastructure didn't exist before and really accelerated adoption. Last week I talked to one of the big e-commerce players here, and they estimate about a billion dollars in these micro embedded insurance propositions being conducted yearly now. The growth is definitely there, and the trend has reached scale across the region, though it's still relatively small compared with an average MGA in the States.

As for how this is spreading, some of it is now landing in Europe. Warranty products, lens insurance, hearing aids—these are probably more specific to Europe because the needs and market development are different. In the mobility space—scooters, car insurance, and ride-sharing—I'm hearing that's happening there, as well.

CARROLL: I assume China has played a big role.

KESSELMAN: Embedded and digital insurance really took off from China. The big players were Ping An and ZhongAn—both kind of protection-focused players—and both of them really aggressively invested in innovation. China probably remains three or four years ahead of the rest of the region.

From China, innovation propagated to Southeast Asia. I think India is now also quite a significant market for it, but China was very clearly the first because of the combination of digital payments penetration being super high there, the development of super apps that put everybody—all the distribution—in the same place, and then e-commerce and all the logistics. Everything developed at the same time pretty rapidly.

CARROLL: What are some examples in Asia that other parts of the world could learn from in terms of insurance innovation?

KESSELMAN: Ping An and ZhongAn remain interesting examples. They have been investing very actively in AI, and I think they're still very much focused on distribution and servicing, and developing propositions that are very targeted.

In India, there are some insurers that are starting to do some really interesting stuff. Acko is one that comes to mind in the mobility space that has been quite active. The rest of the region has pockets of innovation, but because the countries are generally smaller in scale, it's very hard for insurers to really grow very rapidly. You know, you start in Singapore, then you need to expand into a neighboring country, and then you need to go and redo the whole growth journey again.

CARROLL: What sorts of interesting startup ideas are you seeing at Insuretech Asia?

KESSELMAN: There have been a couple of waves of startups. The first was really about trying to disrupt distribution. That wave has kind of passed now.

As part of the second wave, we have a couple of really interesting startups. Two are unicorns. Bolttech is one. They focus on distribution and have a couple of different complementary models. They have a very big presence in the U.S. and a global presence, but their headquarters are in Singapore.

Then we have InsureMO, which is like an infrastructure for insurance distribution. They have very big operations in India and Southeast Asia, and now they have growing operations in Europe, the U.S., and Latin America, as well, but they’re also based in Singapore.

CARROLL: What makes those companies different?

KESSELMAN: I think it’s exactly the fact that they started in Southeast Asia, so they needed to do everything very cheaply and fast even though the platforms that are here are very demanding in terms of the operational support that’s needed, in terms of technology. Being incubated here, I think, gave them that natural foundation of really building a scale-up in a demanding environment. They tell me that when they go to the States and talk to insurers there, insurers are very surprised that they can do things for a fraction of the price and at double the speed.

CARROLL: What has surprised you about how AI is being adopted—or not being adopted?

KESSELMAN: The part that surprises me is that it is still very much a technology conversation rather than a business conversation. To give you an example: Recently a couple of insurers in the region came here, and they have flagship programs, but they're all kind of stuck. They don't really know how to solve the problem of creating business value with AI.

This is the same theme we saw in the IIS survey.

Everybody kind of saw this shiny object and had experience with really interesting outputs of this technology, but there's a disconnect between the solution that exists and the problems that insurance companies need to solve. I also think there's a disconnect between the leadership level of the organizations and middle management organizations, which is not as bought into this whole idea of "let's bring AI everywhere, let's augment every job with it."

CARROLL: That's such a common problem—bosses demand change, and underlings nod their heads… but they also find ways to keep doing things the way they always have. Peter Drucker once told me that people don’t change. They just die off, and the next generation then does things differently. But we were talking 25 years ago. I hope we’ve learned a bit since then.

This has been great, but any final thoughts?

KESSELMAN: There are interesting developments in health insurance, again starting with China. Everything is getting so integrated. Hospital data is getting a lot more integrated with insurance data, and it's getting linked up in the hospitals to make it much more proactive—managing health, managing well-being. So rather than putting strain on hospitals, with insurance being really at the back of it, the loop is starting to get closed.

The rest of the world will eventually need to tackle that integration issue as populations age and healthcare systems are probably redesigned. Insurance right now is still very much on the receiving end.

That integration in health insurance is probably one of the most interesting things happening in this part of the world, and, again, I’d use China as the reference market. 

CARROLL: Thanks, George. This is super helpful.

Cheers,

Paul

$117 billion: the insured average annual loss (AAL) estimated for natural catastrophe events in the United States, according to Verisk's 2026 Global Modeled Catastrophe Losses Report. This figure represents roughly two-thirds of the $171 billion global AAL benchmark. The AAL is not a forecast for next year; it is the level of loss the insurance industry should be prepared to absorb over the long term. North America remains the dominant contributor, accounting for $124 billion of the global total.

A benchmark built for today's risk environment

Global insured losses have topped $100 billion for each of the past six years. Since the report's first edition in 2012, estimated global modeled insured average annual losses have increased from $59 billion (in 2011 USD) to $171 billion (in 2025 USD). The increase reflects several factors, including exposure growth, expanded model coverage, advances in catastrophe science, and continuing refinement of the industry's view of risk. The 2026 AAL benchmark also reflects the latest generation of model updates, so it captures current science rather than relying on the assumptions of the last decade.

Why the average is only part of the story

The insured AAL is a long-run expectation, not a prediction. Especially in the U.S., frequent perils such as severe thunderstorms, wildfires, inland floods, and winter storms drive the average, while severe perils, including hurricanes and earthquakes, drive the extremes. Losses in any single year can land far from the mean, which is why the full range of modeled outcomes matters more than any one number.

The difference between average and extreme outcomes illustrates why insurers focus on more than the mean. Against the $171 billion global AAL, the same models estimate a 100-year industry loss approaching $477 billion and a 250-year loss exceeding $600 billion. Catastrophe models are designed to look beyond historical experience and quantify plausible events the industry has not yet experienced.

A single year of lower loss activity doesn't mean lower risk

Catastrophe losses were below average in 2025, but a below-average year reflects a favorable draw, not a change in the risk landscape. Despite the absence of a landfalling hurricane in the continental United States, global insured catastrophe losses still exceeded $100 billion. Wildfires and severe thunderstorms demonstrated how the accumulation of frequency-peril losses can generate significant industry losses even without a headline catastrophe event. The first half of 2026 further illustrated the point. The U.S. p&c industry's combined ratio improved to 92.7 from 96.5 a year earlier, and its estimated net underwriting gain nearly tripled to $31.7 billion. Those results show where the industry landed on the curve this year, not a shift in the underlying risk. The $117 billion expectation for the U.S. doesn't shrink because the first half of the year was kind.

A favorable year can improve financial results, but it does not alter the underlying distribution of catastrophe risk that insurers must manage.

Better to be prepared than surprised

For risk leaders, preparation starts with the AAL, the anchor for catastrophe risk appetite and capital planning and the number with the deepest data behind it. Catastrophe models rely on increasingly detailed exposure information, including building characteristics, reconstruction costs, and claims experience. The quality and currency of that data influence how insurers evaluate risk appetite, reinsurance needs, reserving assumptions, and capital allocation decisions. As exposures evolve and catastrophe science advances, maintaining an accurate view of risk becomes increasingly important.

Three considerations stand out in an environment where catastrophe losses regularly exceed historical expectations. First, evaluate risk appetite against the full range of modeled outcomes rather than recent loss experience alone; the tail of the distribution is often what places the greatest strain on balance sheets. Second, consider how reinsurance programs respond to the perils that drive extreme loss scenarios, not just the events dominating recent headlines. Third, reassess capital assumptions when exposure profiles or catastrophe models change materially, rather than relying solely on periodic review cycles. The purpose of catastrophe modeling is to look beyond what happened this year and quantify what could happen across thousands of plausible future scenarios. The industry's challenge is not preparing for the year it just experienced, but for the years it has not yet seen.

Sources

2026 Global Modeled Catastrophe Losses Report: https://www.verisk.com/resources/campaigns/modeling-insured-catastrophe-losses-a-global-perspective/

1H 2026 underwriting results: https://www.verisk.com/company/newsroom/

When a representation letter lands on a workers' comp claim, an experienced adjuster does roughly the same things every time. They acknowledge the letter, reach out to the claimant's attorney, adjust reserves now that an attorney is involved, and update the claim summary. Ask 10 adjusters for the sequence and you'll get the same list in slightly different orders, with a step or two of local flavor.

The same holds when a return-to-work note comes in, full duty or with restrictions, or a demand package. It even holds for silence: a claimant who hasn't heard from anyone in three weeks is its own trigger, with its own response. These sequences repeat from file to file, and mostly from shop to shop. An adjuster with 15 years of experience has run some of them a thousand times, and could run them half asleep.

Workflow orchestration gets defined in a lot of ways. If we keep it really simple, it comes down to this: when something needs to happen on a claim, the right response follows.

Done well, the routine response happens on its own, and the exceptions land with a person quickly. The way to get there is a system that learns the actions taken on past claims, when they were taken and what they led to, and then executes them automatically. In other words, it's like a new hire on the claims floor, one who happens to have read every claim the company ever closed and can be trusted with the motions everyone already agrees on. The calls that matter stay with whoever owns the file. And like any new hire, the system gets brought along in phases. None of this was practical before, because most of a claim file is prose. But language models can finally read it.

Where to Start

The place to start is simple, getting the work itself into view. What's happening on each file and what the adjuster is doing about it, gathered in one place instead of spread across the diary, the inboxes, and the core system. Today the diary is the closest thing to that picture. A file comes up because 60 days passed, not because anything happened on it, and the first half hour goes to finding out whether anything did.

An overview like that is worth building before a single routine runs on its own. It shows what changed overnight and what's waiting on a decision, and it starts the adjuster's morning at the right files. It also means the state of a file no longer lives only in the adjuster's head, so nothing stalls when someone is out for two weeks, and a handoff doesn't start from zero. And it gives a claims operation something it has never really had: a clear picture of its own work. Everything else, the suggestions and eventually the actions, builds on that clarity, because work can only be handed to a system once it can be seen. The phases are how that picture gets built.

Phase One: Analyze

Every one of these sequences starts from a trigger. Sometimes the trigger is an event, like a letter arriving or a claimant going quiet. Sometimes it's a scheduled action: a state deadline on the first payment, or a follow-up that was promised and hasn't happened yet. The first phase is about proving those triggers get caught. The system doesn't act on anything yet. It reads everything that arrives and reports what it sees: what changed on the file since yesterday, and which of those changes should set a sequence in motion.

The same reading, pointed backward, is where the insights start. Once the files are readable, questions that used to require a file review project can be answered directly from the data: how much subrogation actually went by unnoticed last year, or how long attorney letters sat before anyone opened them. Each of those numbers is a count of missed triggers, and together they make the case for orchestration from the operation's own files.

A system that only watches already earns its keep. Nothing important sits unread anymore, and leadership questions stop being answered by sampling and gut feel. It's also the phase today's models are ready for. Reading a claim file and noticing what matters is exactly the kind of work language models have become reliably good at, and the cost of a miss is an alert that didn't fire, which is no worse than the status quo. Every week of watching also builds a record of what the system noticed and what the adjuster then did about it. That record is what justifies the next phase.

Phase Two: Suggest

In the second phase, the system starts making recommendations. The acknowledgment letter shows up drafted. The claim summary is updated and waiting. The reserve review appears as a proposed task with the reasoning attached, and the wage statement request is ready the day a claim turns lost time. So is the status update to the injured worker, who mostly just wants to know that someone is paying attention. Plenty gets corrected before it goes out, and some suggestions get ignored, which is useful information in itself.

Reviewing this output is real work. If the system surfaces 40 suggestions a day to an adjuster who already has 30 diary entries due, nothing has been automated; a backlog has been created with extra steps. The review process has to be staffed and sized like any other part of the operation, and it has to be worth the adjuster's time. The corrections matter most. Every fix an adjuster makes to a suggestion is a lesson, and a system that keeps those lessons gets better every week it runs, while a system that throws them away will be making the same mistakes a year from now. The way out of this phase is consistency, motion by motion: suggestions going through untouched often enough that the approval becomes a formality.

Phase Three: Act Where Possible

The last phase is the one vendor demos lead with, and the one that should come last. Some motions start running on their own, but only the ones with a record behind them, and that record is task by task. Sending an acknowledgment letter unsupervised is a very different proposition from touching a reserve unsupervised, and phase two produces the evidence for that call: how often adjusters approved each kind of suggestion unchanged, and where the overrides cluster. Settlement authority for a new adjuster grows in the same way, with the track record. Autonomy should expand at the pace the record justifies.

The AI models will keep getting better. That doesn't change the design. It just changes how quickly a claims operation moves through the phases.

Annual investment in data centers is projected to double from around US$500bn in 2024 to more than US$1trn as early as 2027. The investment opportunity extends far beyond server halls to electricity generation, grid infrastructure, cooling, networking, and semiconductors.

According to Allianz Research, the US and China are expected to account for around 62% of new global capacity additions through 2030, but the next wave of investment is becoming increasingly global. In Europe, Germany, the UK and Ireland remain major markets, but faster expansion is expected in Spain, Finland and Denmark, where power availability and permitting conditions can be more favorable. Across Asia Pacific, excluding China, installed capacity is projected to increase from around 9GW today to more than 28GW by 2030, with Malaysia expected to grow more than tenfold.

The sector's biggest constraints are increasingly physical rather than financial. Competitive advantage is increasingly determined by access to electricity, grid connections, permitting, specialized equipment and skilled labor. In the US alone, the construction industry faces a shortage of around 439,000 skilled workers, while an estimated 349,000 additional workers may be needed in 2026.

Climate resilience is increasingly a strategic consideration rather than an operational afterthought. Around 79% of global data center capacity is already located in areas exposed to heightened natural catastrophe risk, while 54% is exposed to chronic heat and drought stress. Some of the fastest-growing AI infrastructure markets are also among the most climate-exposed, including Northern Virginia, US, Johor in Malaysia, and Marseille, France. Acute flood, wildfire and wind exposure is highest in the Americas, affecting 86% of capacity, while chronic heat and drought stress is greatest in Asia Pacific, where 89% of capacity is exposed.

Insurance is evolving alongside the sector. As data centers assume a more critical role in infrastructure, comprehensive insurance cover has become a prerequisite for financing many large-scale AI infrastructure projects. Construction costs for a single AI campus can exceed US$20bn, with insured values rising substantially once high-performance computing equipment is installed. The global data center insurance market is projected to grow from around US$11bn today to more than US$24bn by 2030, reflecting rapid capacity expansion, rising insured values and increasing operational complexity.

Demand is expected to extend beyond traditional property cover towards integrated solutions spanning construction, engineering, property, business interruption, cyber and liability, while also creating new opportunities in areas such as energy resilience, operational continuity, and technology risk.

Allianz Commercial analysis of insurance industry data center-related claims shows that fire is the leading driver of loss severity, accounting for well over 50% of around €700mn (US$800mn) worth of losses. Natural catastrophe activity ranks second, followed by willful acts, which include crime and cyber incidents, followed by power failure. Water damage is the most frequent cause of data center claims, followed by willful acts, fire, and equipment breakdown. Business interruption is the primary driver of claims severity by line of insurance, highlighting the significant financial impact of operational downtime.

The data center risk profile is changing as facilities become larger, more complex, and increasingly connected. Hyperscale and colocation of campuses can bring together multiple tenants, construction works, servers, supporting utilities and on-site infrastructure in one physical or operational space. A single event can therefore trigger claims across property, construction, business interruption, liability, cyber, and financial lines. Real-life claims case studies show that in hyperscale facilities, damage to external cooling systems, hot works-related fire damage, and a delay in start-up caused by power disturbances have each resulted in losses in the US$50mn to US$100mn range.

To read the full report, please visit: The data center construction boom: risks and claims trends

For years, the insurance industry has talked about real-time underwriting as the goal: pull in current data, assess the risk, calculate a price, and deliver a quote almost instantly. But that definition works better for a personal auto policy than it does for a complex multimillion-dollar commercial risk. If insurers want to make real-time underwriting a reality across more of the industry, the definition needs to be broader. The goal doesn't have to be an instant decision, but rather using agentic AI and the most current information available to make better decisions, faster.

That distinction matters. Fully automated, straight-through processing can work when risks are relatively standard. But underwriting doesn't need to be automated from beginning to end to become more real-time. For complex risks, the opportunity is to automate certain areas, bring more current information into the decision, and give underwriters what they need to act faster.

Real-Time Underwriting Looks Different Across the Industry

While real-time underwriting will not look the same across every line of business, the ultimate goal can be the same: accurately assessing risk and acting on that information as quickly as possible. For standardized risks like personal auto, homeowners policies, and small commercial BOP, the result may be agentic AI solutions performing straight-through processing with human oversight. For large commercial risks, where experienced underwriters need to evaluate complex exposures, full automation may not be practical or desirable, but AI tools can expedite a number of manual processes.

Across those different types of risk, three principles can move underwriting closer to real time.

Automate the processes that can be automated. Automation should accelerate the underwriting process without removing the judgment that complex risks require. Agentic AI tools can ingest and structure submission data, identify missing information, check the risk against appetite, and summarize key information. Underwriters then review the recommendations, exceptions, and complex risk factors. By automating the work surrounding the decision, the underwriter can spend more time on the decision itself.

Incorporate more current information into risk decisions. Insurance has traditionally relied heavily on historical information. Loss histories, prior claims, and other historical data remain essential, but the past is not always an adequate predictor of what happens next.

Insurers now have access to an expanding range of external data that can provide a more current view of risk. Telematics, for example, can provide information about actual driving behavior. Climate-related risks can change the relevance of historical property loss data. A property with limited losses over the previous two decades isn't necessarily exposed to the same level of risk today. The challenge is turning that information into something underwriters can actually use by incorporating it into the models and workflows that drive decisions.

Continuously monitor risk after a policy is written. Risk doesn't stand still once a policy is bound. Events can happen during a policy term, including new claims and changes in financial conditions, which can alter the insurers' view of the risk. Rather than waiting until renewal to understand what has changed, insurers can continuously monitor meaningful developments across individual accounts and their broader portfolios.

That doesn't mean every new signal triggers changes in pricing or coverage. Instead, continuous risk assessment gives insurers a more current understanding of the risks already on their books and better information when it is time to make the next underwriting decision, including at renewal.

Putting the Real-Time Mindset Into Practice

None of this requires insurers to rebuild underwriting overnight. Three practical steps can begin moving the organization toward faster, more responsive decision-making.

1. Incorporate new data into the models that drive decisions. There's an expanding universe of external information. Beyond telematics, connected home devices and geospatial imagery provide more comprehensive information on property risk. Unstructured sources such as news and social media can provide valuable insight into litigation and financial conditions, for larger commercial risks. But more data does not automatically produce better underwriting. Carriers can determine which data meaningfully improves risk assessment and then update rating models, rules engines and underwriting workflows so those signals can influence decisions.

   All of the rating engines don't need to be overhauled at once. Insurers can start with a specific line of business or risk factor, identify an external data source that can improve the assessment of that risk and test how the new information changes pricing and underwriting outcomes. Rating logic should also be flexible enough to consume new data through APIs or other integrations rather than requiring extensive system changes each time a new source is added.

2. Orchestrate data across the enterprise. Insurers often already possess valuable information that underwriters cannot easily access. Systems such as policy administration, claims, and billing may operate separately, leaving relevant information fragmented across the organization. A significant claim could occur for a commercial account, and an underwriter might not discover it until renewal.

   This is not necessarily a data problem, but an access and orchestration problem. Connecting those workflows can make underwriting more responsive. Insurers should work toward creating a connected data layer that allows information to flow between systems and establishes consistent definitions and formats so it can be used across the organization. APIs and industry data standards can help insurers connect legacy and modern platforms without waiting for a wholesale core replacement.

3. Automate the work surrounding the decision. Even when the underwriting decision itself requires human judgment, much of the work surrounding it may not. Submission triage is a good example. Rather than having an underwriter immediately begin reviewing every incoming submission, agentic AI powered solutions can first assess whether an account meets basic criteria, identify missing information, and summarize what's been provided. If information is missing, the solution can notify the broker immediately instead of discovering the gap later.

In addition to triaging, agentic AI and automation tools can increasingly handle other manual processes including data ingestion and summarization leaving underwriters to focus on the risks and decisions that actually require their expertise.

Real-time underwriting does not have to mean instant underwriting. It means making underwriting more responsive at every stage, using agentic AI solutions to automate where it makes sense, bringing current information into the decision, and maintaining a more current view of risk after the policy is written. For some risks, that may mean straight through processing. For others, the underwriter is the decisionmaker throughout. But the opportunity across all lines is to give underwriters enhanced information sooner so they can make better decisions.

The life insurance industry faces a significant opportunity: consumer interest remains strong, but many customers struggle to understand how life insurance fits their current needs, what it costs, and how to maximize its value. As expectations evolve, insurers must move beyond traditional product-led approaches and create experiences that are relevant, personalized, and easy to navigate. 

The insurers outperforming the market have demonstrated that customer centricity is more than a business philosophy. It is a growth strategy. By reimagining how they engage consumers, empower advisors, and leverage data and AI, these organizations are strengthening customer relationships, increasing loyalty, and delivering measurable business results. 

Capgemini's latest World Life Insurance Report examines how leading insurers are closing the value perception gap and transforming consumer interest into long-term growth. The report uncovers the strategies, capabilities, and operating models that distinguish best-in-class insurers from their peers, offering a practical blueprint for organizations looking to improve engagement, reduce policy lapses, and drive sustainable performance. 

Discover how life insurers can create more relevant customer experiences, deliver trusted guidance at critical life moments, and build intelligent foundations that support personalized engagement at scale. 

Key takeaways 

• Understand why consumers value life insurance but often question its relevance and affordability.
• Learn how leading insurers increase engagement through personalized, life-stage-based interactions.
• Discover how AI-powered advisors and modern distribution models enhance customer experiences.
• Explore strategies for unifying customer data to enable proactive, intelligent engagement.
• See how customer-centric insurers achieve higher growth and lower policy lapse rates. 

Ready to learn more? 

We have built a lot of technology around insurance distribution. We have systems for advertising, quoting, underwriting, CRM, lead management, automation, servicing, and now AI. But there is a part of the experience before most of those systems begin that I think deserves more attention.

Someone has to move from "I need insurance help" to "Here is an organized request that another person can actually understand and act on." Today, we often treat those as though they are the same moment.

A shopper fills out a form or expresses interest, and a record begins moving through the system. But the shopper may still not understand who will receive the information, what happens next, or what kind of contact to expect. The insurance professional receiving the request may still have to figure out why the person is shopping, whether the request fits, what has already been explained, and what information is still missing.

That creates work on both sides, and it can make an already confusing insurance-shopping experience feel even more fragmented. The shopper may have to repeat herself. The professional may have to reconstruct context that should have been clear earlier. And the system can still call the handoff successful because information moved from one place to another.

The Middle Layer

I think the industry needs to pay more attention to this middle layer. Not as another marketing funnel. Not as a way to move people faster. As the point where shopping intent becomes an organized insurance request that both sides can understand.

For the shopper, that means clearer expectations. Who may receive the request? What happens next? Is a licensed insurance professional likely to review it? What kind of contact should the shopper expect? The goal is not zero contact. It is clearer, more controlled contact.

For the insurance professional, it means better context. Why is the person shopping? What has already been provided? What still needs to be clarified? Is the request something this professional can reasonably help with? A request should be organized enough that the receiving professional can make a meaningful decision instead of starting from zero.

That is different from simply collecting more fields. More information does not always create more understanding. A long form can still produce a weak request if the person completing it does not know what will happen next or if the professional receiving it still has to figure out the reason behind the request.

Where Consumer Control Matters

This is also where consumer control matters. Insurance shopping is often repetitive, high-pressure, and difficult for the shopper to control. People worry about spam calls, who gets their information, whether they are being sent everywhere, and whether someone licensed is actually going to help them. Those concerns are not separate from the request process. They are part of it.

A better request layer should preserve enough shopper authorization and context that the next step feels understandable rather than surprising. The shopper should know what she is choosing. The professional should know enough to decide whether to engage. And the handoff should carry forward the information needed to continue the experience instead of restarting it.

AI can help here, but AI should not become the story. It can organize information, identify missing details, summarize what the shopper has already explained, and improve request readiness. Those are useful capabilities. But the technology still has to support a clear human process.

If the shopper does not understand what is happening, or the professional does not have enough useful context, making the handoff faster does not necessarily make the experience better. We may simply automate confusion.

What Marketing Doesn't Measure

I came to this problem after spending years around digital acquisition and customer behavior. In marketing, we get very good at measuring clicks, forms, and conversions. Insurance made me look at those actions differently because there is a person inside every one of them, and the action is often only the beginning of what that person actually needs.

That is the problem I am working on through Ensurance, a U.S. insurance-shopping company beginning with auto insurance. We are building around the idea that a shopper should be able to start one organized request with more clarity and control, while participating licensed professionals receive better context around requests they may choose to engage with.

Behind that, we are developing CATE, our Controlled Access Trust Engine, as the controlled-access and request-intelligence layer. Its role is to help structure requests, improve readiness, support controlled access, and create more useful opportunity context while preserving shopper authorization. CATE is being built, and planned AI capabilities are still under development.

The larger point is not about one company. As insurance becomes more automated, the quality of what enters those systems matters more. The industry does not need another way to turn shoppers into records faster. It needs a clearer way to turn insurance-shopping intent into an organized request that both sides understand.

That is the missing layer I think deserves more attention.

The insurance industry's persistent talent crunch goes far deeper than a hiring gap – it's also becoming a knowledge gap, as the accumulated judgment of a workforce built over decades retires faster than it can be replaced.

By the end of 2026, the U.S. Bureau of Labor Statistics estimates roughly 400,000 insurance professionals will have retired in the last half-decade alone. Today's average insurance employee is in their mid-40s to mid-50s, depending on the line of business. One in four underwriters is already over 50, and less than a quarter of our workforce is under 35.

Turnover has climbed from a historical 8-9% to 12-15% industry-wide. Even with a nearly unprecedented 14 months of workforce reductions, as insurance economist Dr. Robert Hartwig has noted, the sector's 3.3% unemployment rate is still far below the national average of 4.4%.

And that's just the demographic aspect to the story. The bigger issues are what insurers lose when long-tenured experts retire - and what we're doing right now to fill the talent pipeline with future innovators and leaders.

The Industry's Best-Kept Secret Was Its On-Ramp

Ask almost any insurance executive how they got their start, and you'll hear some version of the same story. They largely majored in something other than RMI (risk management/insurance) as undergrads. They came to insurance in an entry-level position – a junior-level claims processing role, a customer service line, or an underwriting support seat.

The pay was good but not great, and the title wasn't impressive. But this was accepted as part of the bargain for landing a job that taught them how the business actually worked in a field that offered tremendous stability and growth opportunities.

They learned how a policy is priced by experiencing the underwriting and pricing process in real-time.

They learned how a claim goes sideways by handling complex claims.

Over years, that ground-level exposure became accumulated knowledge, which in turn became true expertise and leadership.

This story isn't merely an exercise in nostalgia; it's the operating model that built our industry's immense knowledge base.

Insurance knowledge has never been something learned just from a textbook. It's acquired by doing the scut-work long enough to understand why the rules exist. This is the foundation for valuable context that later empowers people to spot the exception, catch the fraud, or make the call a model won't – or can't.

It also happens to be one of the industry's most reliable recruiting advantages.

Nevertheless, insurance has struggled for years with a perception problem. Nearly one-third of the global population is Gen Z (birth years 1995-2012, per Pew Research), yet 79% of respondents in this age cohort say they've never considered working in insurance because it looks boring or overly corporate, according to a 2025 Cake & Arrow survey.

Beyond this, insurance is marketed in such a way that it's either an abstraction (or worse, the bill their parents complain about bitterly). The best we can hope for is that kids who are not old enough to drive are able to embrace and internalize geckos, "Mayhem," and "Bibberty" as past generations learned to recognize Mickey Mouse or Joe Camel in their pre-literate phases.

The insurance career on-ramp – the promise that you can enter the field without an advanced finance degree and build a real career in an essential, thriving industry that's focused on the future – is one of insurance's strongest attractions as a career destination. Dismantling this at precisely the moment we need it most would be a catastrophic mistake.

Where AI Actually Helps, and Where It Quietly Doesn't

This is not an argument against AI. Applied strategically, AI eliminates repetitive, low-value work (data entry, document review, first-pass triage, routine correspondence) that used to take hours or days. Agentic AI is proving useful for executing entire multi-step processes, like claims intake or policy servicing, without a person managing every stage. This efficiency creates a higher standard of service and customer satisfaction.

But there's a vast difference between using AI to remove drudgery and AI inadvertently undermining insurance's "farm system," the roles in which fledgling insurance experts build knowledge and wisdom. Companies eliminating entry-level claims or underwriting support positions to fatten margins as AI scales to handle the volume aren't merely cutting costs. They're turning off the talent pipeline that credentialed underwriters and licensed adjusters flow through.

Industry hiring forecasts make the stakes plain: projections show underwriting and claims roles shrinking even as demand grows for developers, actuaries, InfoSec specialists, and data scientists. But those burgeoning roles don't carry the state licensing and credentialing requirements that underwriting and claims work does – which means the very entry-level positions insurers are cutting are the ones that produce the credentialed underwriters and licensed adjusters that insurance businesses (and in many states the law), require.

This creates more than a talent or experience shortfall: it potentially opens a compliance gap, since carriers need licensed, credentialed professionals to legally price and adjust risk. Underwriters typically spend years earning their Chartered Property Casualty Underwriter (CPCU) or Certified Insurance Counselor (CIC) designations, building the on-the-job experience that shapes how carriers price and structure risk.

Claims adjusters face even greater educational and regulatory hurdles: 34 states require independent adjusters to hold a license, and many additional states require staff (in-house) adjusters to be licensed as well, meaning a person cannot legally handle a claim in a majority of the US without passing state exams and meeting continuing education requirements.

Training alone does not clear that bar. Eliminating entry-level roles in which claims workers accumulate the experience and knowledge needed to earn these official designations doesn't merely remove valuable institutional memory; it endangers insurers' future ability to compete in increasingly crowded markets.

Are We Eating Our Seed Corn?

FINRA's 2026 oversight report isolated AI hallucinations as a specific compliance risk, noting the failure isn't that systems misunderstand questions – it's that they don't recognize when they don't know the answers, replacing probability with, well, "plausibility" – answers that "look" right to the untrained eye.

Some carriers have started layering additional review AI agents into the process specifically to catch these errors before they reach a human operator, and early research shows this step can cut hallucination rates meaningfully. But someone still has to know what a correct answer looks like well enough to catch what even these systems miss.

Today, those people are the ones who came up through the ranks. Tomorrow, absent this entry-level talent pool, the industry might lack adequate numbers of human experts in the loop to verify and correct AI outputs just as our industry leans harder into the technology.

If insurers de-emphasize developing talent internally and try to fill gaps by hiring experienced insurance workers from other companies, they'll be competing in a tightening market in the least cost-effective way possible.

The USBLS projects roughly 21,500 claims job openings per year over the next decade. Experienced underwriters and adjusters don't grow on trees – and the industry is shedding them faster than it's producing them.

A carrier that starves its own farm system hasn't opted out of the problem. It's locked itself into paying retail for talent that competitors are still growing at wholesale, ensuring a future of longer hiring cycles and higher salary floors.

How Insurance Leaders Can Act Today to Safeguard Their Business for the Long Haul

• Protect roles that build judgment while you automate tasks around them. View entry-level positions for what they teach, not just what they cost, and redesign them so AI absorbs the repetitive tasks while people spend more time on work that builds understanding.
• Use AI to raise the ceiling on early-career work, not lower the floor. Give newer employees tools that let them take on higher-value problems sooner, with human and AI oversight built in, rather than tools that perform the work itself for them and leave nothing to learn.
• Keep a trained human expert in the loop on every agentic workflow that matters. Regulators are already moving this direction. More than 20 states have adopted the NAIC Model Bulletin on AI Systems as of mid-2026, and examiners expect documented human oversight on high-risk decisions.
• Treat internal development as a cost strategy, not just a culture initiative. Every role built internally is one you don't have to buy at a premium in tight talent markets.
• Build succession plans ahead of the exit wave, not after it. Know which roles are at risk this year and who could be ready to step up with the right support before those seats become external searches.

AI is helping insurers work faster and more efficiently than ever. Today's leaders are responsible for recognizing when and where we should solidify and expand people's knowledge, rather than reflexively eliminating roles that have traditionally added value across the insurance lifecycle.

Sources

U.S. Bureau of Labor Statistics data, via PropertyCasualty360, "Insurance industry talent shortage is imminent," June 2025

Insurance Thought Leadership, "Insurance Industry Faces Critical Talent Shortage," May 2026

Insight Global, "Retiring Underwriters Are Creating An Insurance Talent Shortage"

Sonant, "Insurance Staffing Shortage 2026: Crisis Data & AI Solutions," April 2026

Sonant, "Insurance Agency Talent Shortage: Solutions for 2026 & Beyond"

PYMNTS, "Insurance Industry May Be Unprepared for Agentic AI Risks," 2026

Notch, "AI Hallucinations in Insurance: Risks & Fixes"

actuary.info, "Adversarial Self-Critique Rewrites AI Underwriting Governance," June 2026

Cake & Arrow, "Why Gen Z Is Ambivalent About Working in Insurance," Oct. 2025

NAIC, "State Licensing Handbook, Chapter 18: Adjusters"; ADbanker, "Insurance Adjuster Licensing Requirements by State," 2025-2026

Kore1, "Insurtech Hiring Trends 2026" (kore1.com/insurtech-hiring-2026) https://www.pewresearch.org/short-reads/2019/01/17/where-millennials-end-and-generation-z-begins/