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? 

I don't know about you, but my bingo card for generative AI did not include its wiping out the jobs of Kenyans who had for years been writing essays for cheating American college students. Yet here we are, according to an article in the New York Times last week. 

That story, plus other surprises from the early years of generative AI, should remind us that our ideas about how the AI revolution will play out should be held lightly. We need to be able to drop ideas immediately when they turn out not to be true and pursue new opportunities as soon as they present themselves.

Let's have a look. 

The New York Times article says Kenya had already seen one wave of job losses to AI. Thousands had been employed transcribing recordings of meetings, but such transcriptions became an early task for AI and began to disappear in 2017. Still, ChatGPT created a shock when it debuted in late 2022. At the peak, some 40,000 people in Nairobi were being paid to do homework for others, but almost all those jobs have diminished in scope or disappeared. Why would a student pay someone to write a paper that AI would generate for free?

The article says that "what jobs remain in essay writing are mainly for 'humanizers,' who edit papers written by A.I. to avoid detection from the cheating software used by many universities."

We've seen plenty of other surprises because the backlash against AI caught Big Tech unawares. Any company associated with the hyperscale data centers being used for AI now faces massive pressure because of their water and electricity needs and because lots of people just don't want to live or work anywhere near them. Flock Safety had achieved an $8 billion valuation as it installed cameras around the U.S. to help police track cars used in crimes but has recently stirred up horror about the possibility of a surveillance state.

At a smaller scale, we've seen an Air Canada chatbot go rogue and offer a discounted fare that the airline had to honor. Zillow set up an AI to monitor home prices and purchase undervalued properties, only to find, some $500 million in losses later, that the project was a flop. IBM and McDonald's set up an AI to handle drive-thru orders but canceled the project after customers posted funny, and highly embarrassing, videos of mistakes on orders — such as adding hundreds of dollars in chicken nuggets or bacon toppings on ice cream.

It's easy to imagine where other, important surprises might arise, too. AI will surely have some effect on where people live, because it will change the dynamics of remote work. AI already seems to be having some effect on car sales, because data centers are contributing to increasing electricity prices, which make electric vehicles less enticing. 

In the much-quoted framework from the late U.S. Secretary of Defense Donald Rumsfeld, there are lots of unknowns out there about AI. We can't know much about the unknown unknowns. But we can certainly prepare ourselves for the known unknowns, and I'd argue that all the surprises that have hit us so far — job disappearances, including in Kenya; technology glitches; backlash to AI — fall into the "known unknown" category. (I'd further argue that known unknowns were Rumsfeld's undoing. He should have known that loads of problems could arise after the U.S. occupied Iraq and should have prepared far more for contingencies, rather than assume that Iraqis would place flowers in the barrels of U.S. tanks.)

The history of technology is full of bad ideas — and of pivots toward transformative ones. Alexander Graham Bell initially thought the telephone would be used to broadcast into people's homes — you'd pick up your phone and listen to, perhaps, a symphony orchestra. (Bell also suggested that the standard greeting should be, "Ahoy!" Hmmm....) YouTube began as a video dating site. Slack was a chat channel for a multiplayer game that got left by the wayside. PayPal started as security software for the late, great Palm Pilot, which had a digital payment feature. And so on.

My favorite pivot actually comes from well outside the tech world — from chewing gum. A young man who moved to Chicago in 1891 sold his father's line of soap and offered small amounts of baking soda for free as an incentive to buyers. The baking soda turned out to be more popular than the soap, so he began selling baking soda. This time, he offered some free chewing gum as the incentive. That gum became so popular that, 130 years later, I have a pack of William Wrigley's spearmint gum on my desk.

So my conclusion from the job losses in Kenya (and elsewhere), and from the many other surprises we've seen, is that mistaken ideas about AI are fine — as long we hold them lightly and can move on from them quickly. Think big, because the opportunities with AI are enormous, but learn really fast. 

Cheers,

Paul

The insurance industry has been arguing all summer over whether AI liability can be insured. Most carriers have decided - at least for now - that it can't. They are narrowing policy language on the GL, cyber, and product liability forms, adding AI exclusions, trying not to repeat the "silent cyber" problem from last decade. A smaller group of MGAs, Lloyd's coverholders, and reinsurer-backed programs has started writing affirmative AI liability coverage. Their approaches have little in common: performance warranties, adversarial testing, governance reviews, litigation-based models or just AI endorsements bolted onto existing cyber policies.

AI liability claims are still sparse, but the risk exposures are concrete: an autonomous agent that approves $50,000 in payments it shouldn't have, or an AI hiring tool that screens out protected classes. The potential buyers: any company deploying AI where errors hit third parties. Healthcare, finance, legal, HR screening.

These are different perils, different policy forms, different loss dynamics, and that's part of what makes measurement so hard. We're in the early innings of AI insurance. Some skeptics say you can't price it at all. I wouldn't go that far. But what are we actually trying to insure, and on what basis

Who's Writing AI Liability Today

The market is small enough that you can name the entire first cohort.

Munich Re launched the first dedicated AI insurance products, aiSure, as a performance warranty: if the model drifts below defined accuracy thresholds or produces discriminatory outputs, the policy pays. They've been at this since 2018 and remain one of the few reinsurers with a dedicated AI liability product in market.

At Lloyd's, three coverholders have launched AI liability products. Armilla AI offers standalone coverage, with underwriting informed by over 500 AI system evaluations. AIUC takes a different path: it certifies an AI model first, then adds insurance. Their AIUC-1 framework puts systems through thousands of adversarial simulations. The insurance is written on Beazley paper, with ElevenLabs, an AI voice generation company, as their first public customer. Testudo builds its underwriting off AI litigation data, though the dataset is still thin.

Corgi started offering an AI and algorithmic liability endorsement on top of D&O, E&O, and cyber. Cowbell added AI-specific underwriting factors to its cyber risk-rating framework in July. That's probably the near-term path for most cyber MGAs: AI bolted onto cyber, not a standalone line.

And then there's the rest of the market. Technology companies can buy standard Tech E&O from Vouch, Hartford, or Hiscox, with an AI endorsement added. No AI-specific risk assessment, no evaluation of model behavior. Premium is based on revenue, headcount, vertical, prior claims, the same way you'd price a SaaS platform or payroll tool.

A handful of players are experimenting with AI-specific underwriting. A much larger market isn't measuring AI risks at all. Total dedicated premium for AI liability: immaterial.

From Signal to Pricing

AI liability is one of the hardest emerging risks to insure.

Parametric insurance works because the trigger is a pre-agreed, observable, independently verifiable number: a NOAA weather station, or a cat model from Moody's RMS.

AI evaluation scores don't work that way. A hallucination rate changes with the test set, the evaluator, the model version, and the business context. Armilla and AIUC evaluate AI models, but when the evaluator is also the insurer, the data isn't independent anymore, by definition.

What underwriters need is a validated chain: an observable AI signal that correlates with loss frequency, that maps to expected severity, that can be modeled across a portfolio. I haven't seen a public demonstration of this chain from signal to pricing.

Gallagher Re laid this out in their June 2026 report "Anthropic's Fourth Way": current AI evaluation methods "were not designed for underwriting and are not fit for that purpose." Benchmarks measure how models perform on controlled tests. But losses happen in deployment, not in testing. Their conclusion:

"If a model cannot be tested, insurers end up pricing uncertainty rather than risk."

Without better evaluation, that leads to two failure paths: AI losses absorbed silently into existing policies until carriers exclude them, or standalone products launched without foundations that collapse after early losses.

Without independent model evaluation, the market defaults to underwriting governance: deployment approval processes, human oversight. Most AI underwriting is done that way today, but I'm not convinced that is enough. Cyber insurance tried governance-based underwriting for years, remember? Applicants checked "yes" on the MFA question, and half the time they didn't have it deployed properly. It took a catalyst, the ransomware wave of 2020/21, to force the industry to verify independently what applicants were telling them.

Where the Cyber Comparison Breaks Down

BitSight and SecurityScorecard built outside-in security scores for cyber, based on a client's open ports, SSL certificates, and malware infections. The same approach doesn't work for AI risk. A publicly visible chatbot might reveal something about prompt-injection resilience. It reveals nothing about the agent's authority, data flows, approval thresholds, or what happens when it makes a wrong decision. No external attack surface to scan.

There is a trust problem underneath all of this. Policyholders resist giving insurers access to internal data. They worry it might get used against them in a coverage dispute. And insurers have their own reasons not to look. I learned this at a cyber MGA: if you discover a vulnerability in a client's network through an internal vulnerability scan and don't act on it, you're exposed to E&O claims. Better not to know. For AI, it gets worse, as the vulnerabilities are harder to define, and there's no patch to deploy.

The Missing Layer

What the nascent AI insurance market needs is an independent measurement layer, something that takes technical AI evidence and turns it into data an underwriter can price from.

AI governance platforms like Holistic AI and Credo AI score AI systems on bias and robustness. They were built for compliance teams, not underwriters, and tell you whether a system meets a regulatory standard. They don't tell you the likelihood and cost of a liability loss.

Neither side can build this layer alone. The insured won't share data they fear could be used against them. The insurer faces the E&O problem I described. This layer needs to sit between them, the way a credit rating agency sits between borrower and lender.

AI insurance will need at least one credible, independent source of underwriting-grade evidence. Whoever builds that becomes infrastructure.

What would you trust as underwriting evidence for an AI agent: pre-deployment testing, runtime telemetry, an independent rating, a contractual warranty, or some combination?

The grassroots approach to AI adoption has probably gone as far as it’s going to go.

Point solutions are impressive—but rarely scale. In-house pilots generate strong early results, only for performance to deteriorate for lack of consistent data.

As for copilots, people generally appreciate tools that draft emails, summarize documents, or eliminate busywork. But enthusiasm tends to wane when the tool stops assisting and starts deciding. Handing approval authority to a model can feel indistinguishable from handing over your badge and desk.

That's not irrational. Nobody organizes a grassroots campaign to accelerate their own obsolescence.

The next big push in enterprise AI, when it comes—and I suspect it will come sooner rather than later—will come from above: top down, through the capital structure.

Trillions of dollars are being invested in the infrastructure required to power the AI economy. The institutions providing that capital aren't investing on faith. They have underwritten a thesis: that vast investments in computing, data centers, power generation, networking and models will produce vast economic returns.

For that thesis to work, somebody has to create the demand.

Consider who is financing the AI buildout. BlackRock, Blackstone, Apollo, KKR, Goldman Sachs and other institutions that manage or deploy trillions of dollars are helping finance hundreds of billions of dollars in AI infrastructure. Many of those same institutions are among the largest shareholders of America's public companies, including insurers.

At Travelers, for example, Vanguard, BlackRock, State Street and Fidelity together account for roughly one-third of the company's shares. The financial system is simultaneously financing the supply of AI infrastructure and owning the enterprises whose adoption of that infrastructure will be necessary to justify the investment.

There's an almost poetic financial loop here worth noting: Blackstone's AI infrastructure investments are partly financed with insurance capital. Blackstone now manages $290 billion in insurance capital, while its infrastructure and data-center strategy explicitly connects digital infrastructure with its insurance-capital platform.

All of this means the capital funding AI infrastructure and the capital owning the companies expected to use it increasingly overlap.

Put more plainly: the same capital on your shareholder register may also be financing the infrastructure that needs you to become an AI customer.

The pressure will run downhill. Capital markets will lean on boards. Boards will lean on CEOs. CEOs will hang targets on business units. And eventually the questions will get very specific: What percentage of your workforce is AI-enabled? What's the productivity delta? Where's the ROI? What are your competitors doing with AI that you aren't? What is stopping you from moving faster?

Delivering solid business results is, today, enough. In the near future, how those results are delivered—through what combination of human effort, automation, and AI—will increasingly factor into executive evaluation.

Insurance leaders can prepare now. That does not mean recklessly deploying AI everywhere. It means creating the conditions under which AI can produce durable, defensible business results.

Governance is part of that infrastructure. Done badly, governance becomes an institutional “no.” Done well, it becomes a mechanism for saying “maybe” or “yes,” depending on the use case, the risk, and the evidence.

There is also substantial work between an AI pilot and a legitimate business result. Enterprise information must become usable by machines that can reason and act on it. That means building ontologies, pipelines, object models, knowledge graphs and relational data stores; connecting systems never designed to work together; and establishing the controls that make machine decisions auditable.

None of that is especially glamorous. All of it takes time.

Which is why waiting for questions from investors, analysts, and boards may be the wrong strategy. By the time those questions arrive, you do not want to be explaining why you haven't started. You want to be showing what you're building, what it does, and what it's worth.

The AI infrastructure industry needs demand. Build your own AI infrastructure to create demand for theirs. Build before they ask.

There is an uncomfortable question confronting the property and casualty insurance industry:

Are we experiencing a temporary backlash, or is insurance entering a new era of fundamental distrust?

Insurance has never been an easy product to love. Consumers pay premiums for something they hope they never need, governed by contracts that can be difficult to understand and fully appreciated only when a loss occurs. The relationship can change dramatically in a single claim. But something feels different today.

Distrust is broader, louder and increasingly connected to public frustration over rising costs, corporate power, artificial intelligence, data collection, climate risk and the perceived behavior of large institutions.

For an industry whose fundamental product is a promise—we will be there when something goes wrong—trust is paramount.

A warning we wrote about before

In December 2024, Stephen Applebaum and I wrote “Broken Trust, Insurance Industry Included.” Our premise was straightforward: the public reaction following the killing of UnitedHealthcare CEO Brian Thompson was an alarm bell for the entire insurance industry, not simply the health insurance sector. We pointed to rising premiums, coverage withdrawals, privacy concerns, widening protection gaps and growing skepticism toward technology as evidence of a broader erosion of confidence.

Nearly two years later, that warning appears increasingly relevant.

The issue is no longer simply whether consumers trust their insurance company. It is whether the public increasingly distrusts the insurance system itself.

The symbol of a much larger problem

The December 2024 killing of Brian Thompson was horrifying. Yet the public reaction to the alleged perpetrator, Luigi Mangione, revealed something the insurance industry should not dismiss as simply an isolated social phenomenon.

Polling following the killing showed unusually strong sympathy for Mangione among younger Americans. The reaction was not really about one individual. It reflected anger toward a system that many people believe has become too powerful, too complicated and too disconnected from the people it serves.

UnitedHealthcare operates primarily in health insurance, not P&C. But to the public, “insurance company” can be a much more important category than the distinctions between health, auto, homeowners or commercial insurance.

That should concern every P&C executive.

Then came the claims controversies

The industry’s claim function has increasingly become the center of the conversation.

In 2025, a U.S. Senate hearing examined insurance claims practices following natural disasters, with executives from Allstate and State Farm appearing alongside policyholders, adjusters and consumer advocates. Allegations that claim evaluations had been manipulated or unfairly reduced received significant attention, although the insurers disputed them.

In June 2026, Oklahoma Attorney General Gentner Drummond filed another lawsuit against State Farm alleging that the insurer’s “Hail Focus Initiative” used undisclosed standards and other practices to reduce payments on hail and wind claims. State Farm disputes the allegations.

Just this week, Los Angeles County announced a lawsuit against State Farm over its handling of claims following the January 2025 Southern California wildfires, alleging delays, underestimation of losses and other improper claims practices. State Farm has disputed the allegations and pointed to billions of dollars in wildfire claims it has paid.

These cases have not established that the insurers acted improperly. Lawsuits contain allegations, not findings of fact. But public trust is rarely determined by the final disposition of a lawsuit – many of which are dismissed, in favor of the insurer or most often amicably settled. Either way, the narrative gets there first.

Profitability makes the narrative more difficult

The industry’s financial performance adds another complication.

P&C insurers have experienced a substantial improvement in profitability as premium growth, stronger underwriting results and investment income have combined to produce record-setting earnings.

Strong insurer profitability is not inherently evidence of consumer mistreatment, yet broad-brushing narratives paint a negative picture to make a point.

Insurance is a capital-intensive business. Insurers need adequate returns to support capital, absorb catastrophe losses, pay claims, invest in technology and remain capable of writing business through difficult cycles.

But consumers don’t necessarily see that complexity. Instead, the narrative can become:

My premium went up. My deductible went up. My claim was denied. In all cases, the insurer made more money.

Whether that conclusion is actuarially accurate is almost beside the point. It is emotionally powerful. And when consumers are already frustrated by inflation and the cost of housing, automobiles and repairs, insurance profitability becomes an easy target.

The “closed without payment” problem

A recent Wall Street Journal analysis of auto insurance claims illustrates the challenge.

The Journal reported that the percentage of certain auto liability and medical claims closed without payment had increased materially over the past decade. Analysis of NAIC data showed that 45% of such claims were closed without payment in 2025, compared with 35% in 2016. That is an important statistic.

But it is also an example of how a technically accurate statistic can create a broader impression that may not tell the whole story.

“Closed without payment” (CWP) does not equate to “wrongfully denied.” Claims can close without payment for numerous reasons, including coverage issues, duplicate claims, fraud, liability determinations or other factors. In fact, CWP rates are a singular gauge for purposes such as monitoring claim productivity and should not be isolated from average claim payments, reserve accuracy, re-open rates and other metrics to assess payments. Certainly not to accurately judge claim settlement fairness.

The industry’s response made an important distinction: the increase identified by the Journal was concentrated in liability and medical claims, while physical-damage claims were being paid at essentially the same rate as a decade ago.

A technically correct defense can still fail as a trust strategy.

If consumers hear “nearly half of claims aren’t being paid,” a subsequent explanation about claim categories, coverage triggers and statistical methodology may never overcome the initial impression.

The industry needs to communicate in a way that makes the underlying economics and claims experience understandable—not simply defensible.

Insurance has always had a trust problem

None of this is entirely new.

Insurance has several structural characteristics that make trust difficult. It is intangible, complex and often mandatory. The customer pays first and receives value later—sometimes years later. And when the customer most needs the product, the insurer must determine whether and how much it will pay, which can easily create friction.

A policyholder sees a damaged roof. An insurer sees a contract, causation, exclusions, depreciation, replacement cost, engineering evidence, estimating methodology, fraud indicators and applicable regulation. Both may believe they are acting reasonably. But one side has generally spent decades learning how the system works. That asymmetry creates distrust.

Four forces amplifying the problem

Affordability: Consumers have experienced dramatic increases in home and auto premiums in many markets. Those increases have legitimate drivers—repair costs, medical costs, litigation, catastrophe losses, reinsurance, inflation and changing risk. But consumers experience one part of the equation: the bill.

Claims: The claim is the industry’s moment of truth. Every difficult claim creates a potential advocate—or detractor. Social media can now turn an individual dispute into a national story almost instantly.

AI and data: AI can improve claims accuracy, detect fraud, accelerate settlement and reduce administrative expense. But from a consumer’s perspective, AI can also sound like: a computer decided not to pay me. The broader public debate over AI, privacy, data centers and technology companies suggests this skepticism will grow.

Distrust of institutions: Insurance is not operating in isolation. Banks, pharmaceutical companies, technology companies, healthcare organizations and other large institutions are experiencing versions of the same credibility challenge. Insurance is particularly vulnerable because its product is fundamentally built on trust.

The industry’s response

There are reasons for optimism. Some insurers recognize that restoring trust requires more than advertising.

Farmers has introduced a Coverage Review initiative designed to help consumers better understand what their policies do and don’t cover. Importantly, the service is available even to consumers insured elsewhere. This attacks the trust problem upstream by not waiting for a claim to explain the policy. Time will tell if this is simply clever marketing or possibly a new way to encourage discussions that consumers tend to avoid.

State Farm has also taken steps to return value to customers, including a $5 billion cash-back dividend for qualifying auto customers, while reducing auto rates in several markets. Progressive has returned excess profits to eligible Florida personal auto policyholders, while USAA has also reduced Florida auto rates and returned value to its members.

Rate increases are easing in several markets after several years of extraordinary increases. These actions matter. But trust is not restored by one dividend, one advertising campaign or one rate decrease. It is restored through repeated evidence that the organization behaves consistently with the promise it makes.

The ecosystem has a role to play

This is not solely a carrier problem.

Solution providers, claims technology companies, TPAs, adjusters, brokers, agents, consultants and analysts all influence the customer’s perception of insurance. Every automated decision and claims estimate. Every AI recommendation and vendor interaction. And each confusing communication. They all become part of the insurance brand.

That means the industry’s technology agenda cannot simply be about doing things faster and cheaper.

It also has to be about doing things in a way customers perceive as fair, understandable and trustworthy.

Instead of asking only, Can AI make this decision?

We should ask, Can the customer understand why this decision was made?

Instead of asking, Can we automate this claims process?

We should ask, Where does a human being add trust and judgment?

And instead of asking only, Can we reduce claims expense?

We should ask, Can we reduce expense without damaging the customer’s perception of fairness?

The uncomfortable opportunity

The industry should not respond to today’s distrust by simply defending itself more aggressively.

Some criticism is unfair, and some statistics are presented without sufficient context. Some claims disputes are considerably more complicated than the headlines suggest. And insurers are confronting genuine challenges from catastrophe risk, inflation, litigation, repair costs and capital requirements.

All of that is true. But another truth is equally important:

People don’t trust institutions simply because the institution can prove it is technically correct.

Trust comes from transparency, consistency, empathy and evidence. The industry has an opportunity to use the same technology driving transformation to address the trust problem. AI can make insurance more automated—but also more transparent.

Data can make underwriting more sophisticated—but also help consumers understand their risk.

Claims analytics can reduce leakage—but also identify where customers experience unnecessary friction.

And stronger profitability can provide the capital to invest in better products, better experiences and better claims operations.

The real question

Insurance does not need to become universally loved.

It needs to remain credible.

When someone buys homeowners insurance, auto insurance or commercial coverage, they are not really buying a policy document. They are buying confidence that when something goes wrong, someone will stand behind the promise.

That promise is the product.

The insurance industry has a trust deficit. The question is whether carriers and the broader ecosystem will treat that deficit as a public-relations problem—or recognize it as a structural business problem, a technology problem, a claims problem and ultimately a product problem.

The industry has spent enormous amounts of money making insurance more sophisticated. Perhaps the next investment should be making it easier to believe.

The policy isn’t the product. Trust is.

Paul Carroll

You’ve long been my go-to guy on the business implications of artificial intelligence. To start us off, how would you set the table for insurance agents and brokers in terms of how they should think about how AI will play out?

John Sviokla 

Well, first, I think it's important to reassert that salesmanship is going to continue to be needed—probably even needed more. How salespeople serve, what segments they serve, and so forth will be different, but the role remains.

The people who go too far toward automation are going to lose. The people who don't automate enough are going to lose. There's going to be a new balance point.

AI will not change the fact that a very small number of salespeople sell most of the stuff. It's not going to democratize selling, I don't think. In fact, the distance between the top people and the average is going to increase, not decrease. 

Technology is an amplifier. Getting a bigger amplifier does not make you a better guitar player. But if you're a really good guitar player, you'll likely have a bigger audience. Right?

Paul Carroll

You and I have talked about a few areas where agents and brokers should be adopting AI. Would you walk us through those? 

John Sviokla

The critical things a salesperson needs to understand in the early going are account planning, product and service knowledge, and sales skill. AI can help in each of those areas.

It can help you in account planning in terms of finding people and profiling them. It can help you with sales skill through simulation. You can actually tell an AI, "I'm going to sell this person. Here's their background. Go find out about them. Here are my products. Help me understand what's right for them." Then you can actually simulate the sales process. AI is a flight simulator for selling if you do it right.

The third thing is product and service knowledge, and AI is miraculous in this area. Even the general models know a ton. But certainly, if you have any kind of custom setup, it's trivially easy for you to load up all your product information, compliance forms, all that other stuff in a local database. 

AI can be a big cost driver because of the turnover rate in distribution channels that have high turnover rates. LIMRA says, for instance, that as many as 80% of new life insurance agents quit in the first 12 to 18 months. You sell to your relatives, and then you're out of the business.

Even if an agency doesn’t get better at keeping people, you’ll materially reduce your training costs, and you’ll be giving new agents tools and training that should make them more successful sooner. Will you go from 80% churn to 65%? No, but will AI take it from 80% to 75%? Yeah.

Most insurance companies and general agencies stink at onboarding. They think they're good because they spend money, but they aren’t. In an hour with the proper documents, I bet I could create a robot that would be much better than your best trainer on understanding product features, product regulations, and product documentation.

The other thing is that training is once-and-done, even though the half-life of the knowledge that I've been given at the beginning is short. Well, AI is there all the time. You can always ask, “Hey, I'm going to go pitch Paul. Tell me again about how to think about this product. What are the regulatory things again? Does this product work in this IRA, and all that?” That's just not happening right now. Insurance companies and general agents can't afford to keep staff around to answer those questions, to do the genius bar. AI is a genius bar for everybody all the time.

For account management, you look for people in your area, and you put names into the AI. When I pitch top management, there’s enough information out on the internet that I can actually do informal psychological profiles of the individuals and then of the whole team. I ask what points in my presentation are most important for each person, then ask the AI to simulate our interaction. Right now, I’m doing that with text, but in time I’ll be loading pictures of the team and will be talking with them. 

Paul Carroll

Going back to our work together in the early internet days, we saw how it takes a while for people to figure out how to adopt something that's radically new. What tips would you offer agents and brokers on AI? Should they take a top-down approach, bottom-up, or both?

John Sviokla

It's absolutely both. But you have to understand something first—and I'm going to make a blanket proviso about something I know is more complicated than the way I'm expressing it. 

AI is not a technology. AI is a capability: a capability that you have to grow, that you cannot buy.

You buy the beginning of it, but it's like buying seeds or buying bulbs. You've got to grow them. The people need to learn, the machine needs to learn, and then that group together—the people and machines—needs to learn.

The proper way to think about it is what we call our RISE adoption model, which is the old S-curve adoption model with some tweaks. 

The R part is research and education, and that's a pure investment. The proper measures of that stage—do you succeed or not—are two: Do people use it, and do they feel confident while they're using it? You bring people in and train them up.

The I is islands of automation. You invest in a portfolio of innovations. That cost should be net zero because you lose a little money here and make a little there as you build a capability.

Then you go to scaling, the S part. You manage that the same way as any other initiative. Here's the time frame. Here are the inputs. Here are the outputs. Here's the accountability. Here are the goalposts. All that stuff. 

This is where lots of organizations fall off, for three reasons. One, a lot of executives basically want you to do the scaling work off the side of your desk. "Hey, Paul, that looked great. Why don't you scale it?" No, to scale, I have to deal with data, process, security, scale, updates—all that stuff. 

If you want people to reinvent things, organizations need to give people a way to raise their hand and note that they don’t have the necessary time and resources. Most don’t. That’s the second reason for failure.

The third reason—this is the most complicated—is that organizations in general no longer have the people who actually know how to do process redesign, who can do real R&D, and who know how to buy small. Very, very few large companies know how to buy small. How do you work with small vendors? Nobody knows how to do that.

Finally, the E is emergent intelligence. This is where AI capability becomes self-sustaining and strategically differentiating. AI models improve autonomously. Agentic systems coordinate across the enterprise without constant human direction. Hybrid human-AI decision-making becomes the operating norm. 

Paul Carroll 

What should someone reading this do tomorrow morning to get started?

John Sviokla

First, assess where you are on the RISE framework.

If you're at islands of innovation, what's the process you're going to use to figure out what you're going to scale? If you're at scaling, do you have the three competencies I talked about in enough supply to succeed? 

The measurements of success don’t have to be that complex, by the way. Once you’re scaling, you should see revenue per employee increasing. When you get to emergent intelligence, the revenue per employee should be increasing at an increasing rate.

Paul Carroll

This is great, John. For those who want to learn more, here is a link to your big AI conference this month in Boston, the 4th Annual GAI World 2026. Want to give us the 25-second pitch?

John Sviokla

Thanks. The confererence is about helping business leaders in financial services and healthcare understand where they are, what they need to do next, and how they scale for economic value. The first two days are speakers and some hands-on activity. For those who want to stick around on the third day, we go deep on how to use Claude in your personal and professional life. All of GAI World 2026 takes place Sept. 28-30 at the Hynes Convention Center in Boston.  We would love for members of the ITL audience to come and learn from practitioners who are scaling and getting value.

Paul Carroll

Super. Thanks, John.

About John Sviokla

Dr. Sviokla is the chairman and co-founder of GAI Insights. Throughout his career, he has explored the practical implications of leading technologies. He is widely published and was a partner at PwC, vice chairman of Diamond Technology Partners, and a professor at Harvard Business School, where he pioneered AI research and AI courses. Dr. Sviokla has his doctorate, master's, and BA from Harvard University. He was named an executive fellow at Harvard Business School to develop cases for the MBA and executive ed programs and is a Forbes contributor.

Salesforce and Anthropic announced Claudeforce in late August. Here’s the pitch, stripped of the press-release glaze: Claude’s reasoning now sits inside Salesforce’s workflows, and Salesforce’s data now sits inside Claude. Ask who last spoke with a client, and you get a clean answer as fast as you can type. What used to take an hour clicking through six screens now takes one sentence. 

Great for users. But for executives, that’s not the story.

The story is what happens when you ask a harder question: “What does our churn risk look like over the next 90 days, and who’s at risk?” The system shrugs. That shrug is the whole ballgame.

For 30 years, insurance technology strategy has had one move: consolidate. First we digitized—paper became PDFs, phone calls became email. Then we platformed, on systems like Guidewire, Duck Creek, and Majesco, on the theory that if everyone were on the same platform, the enterprise would finally speak one language. Both eras shared a quiet assumption: the human is the worker, and the computer is the tool that helps the human do the job faster.

Claudeforce is the first real crack in that assumption. It forces executive committees to answer a question that used to be hypothetical: Are we still building technology for people to use, or are we starting to build an operating environment where machines increasingly do the work?

At root, that’s a technology problem with a technology answer. Palantir CTO Shyam Sankar put it well: “If you try to fix the system, you’re going to lose. The goal is to win without fixing systems.” Translation for insurance: stop trying to force every legacy system into one warm data lake. Build the semantic layer instead—the ontologies, object models, permissions, and APIs that let an agent understand what your systems mean, not just what they contain. You don’t need one database. You need a distributed operating environment that machines can read and act on.

That has obvious capital allocation implications. The win isn’t consolidating data for humans to navigate; it’s making distributed data intelligible for machines to act on. Unified systems still matter where economics or regulation demand it. But unified experience no longer requires unified data—it requires a semantic layer good enough that an agent can move across your fragmented systems the way a good analyst does. The center of gravity of your technology investment plan shifts.

It also has unavoidable cultural implications. Building a machine-readable layer over how work gets done is, whether you say it out loud or not, an investment in machines doing the work. The human-to-machine ratio stops being an automation metric buried in an ops review and becomes the operating model itself.

The transition from workers having computers to workers being computers requires leadership and a spine, not just a rollout plan.

I’d position the transition as great news—which I sincerely think it is. The jobs on the chopping block are mostly the digital rockpile: the manual lookups, the re-keying, the 12-screen scavenger hunts that human beings hate and that quietly corrupt information as it moves through the enterprise. Nobody’s proudest work is chasing down which system has the current address on file.

Look at Amazon—the app, the checkout, the returns flow, the whole digital experience. When’s the last time you talked to a human to get something resolved there? Not the co-branded credit card; that’s a bank. The actual Amazon experience. For most of us, the honest answer is never, and we don’t miss it. That’s not an absence of humans (Amazon employs 1.6 million people). Plenty of people built, and keep improving, the system that makes it work. It’s an absence of rockpile jobs. Nobody at Amazon is manually checking your order status or re-keying a return into a second system, because the digital experience was built end-to-end for machines to run, with no human standing in the loop as middleware or connective tissue. The humans create the value upstream; the system delivers it downstream, on its own.

That’s the shift Claudeforce points to for insurance: not fewer systems, but the right systems—semantic infrastructure that lets machines navigate the mess so humans don’t have to. Your next technology dollar shouldn’t buy another layer of software for people to learn, but the machine-readable layer that makes the existing stack feel smaller. More technology, yes—but the kind that finally makes it feel like less.

Sabine VanderLinden, a keen observer of venture capital in insurance, recently wrote a startling sentence: "50% of every insurtech dollar invested in the first six months of 2026 went to companies that will never sell you a policy."

That number is a far cry from what proponents expected when the insurtech wave began a decade-plus ago. At that point, predictions were rife that some Big Tech company such as Google or Amazon would do a cannonball into insurance and change the game entirely or at least that some startup would figure out a way to leapfrog incumbent carriers and make them play catch-up. 

But VanderLinden's analysis provides a guidepost about where investment in insurtechs is today and where I think it's going.

Let's have a look. 

Artificial intelligence still takes up the vast majority of the headspace for most incumbents as they try to innovate. They're spending enormous effort to look for efficiencies in processing claims, in underwriting, in sales, and so on. They're also experimenting with ways to set up autonomous agents and to coordinate their actions while staying within crucial guardrails.

VanderLinden's analysis found significant funding for AI-based insurance startups, too, but they were just the third biggest category in the first half of the year. First was: "risk data. Satellites, sensors, and driving behavior.... This is happening because proprietary risk data has become the scarcest asset in the value chain. Models are abundant. Compute is abundant. Ground truth is not. The ventures that own a persistent, hard-to-replicate view of physical risk are commanding late-stage checks."

She highlights ICEYE, which raised a $500 million Series F "to expand its radar satellite constellation for natural catastrophe monitoring," and Cambridge Mobile Telematics, which raised $350 million for its insights into driving behavior. She also mentions mea platform ($50M), Fulcrum ($25M) and Axle ($17.5M).

Her observation certainly dovetails with what I'm seeing. I've long argued that the surest insurtech winners would be what we called "arms dealers" during the early internet days. Just as Sun Microsystems made bank by selling servers to startups, whether they thrived or, more likely, crashed and burned, companies that developed important, proprietary data sources were always likely to thrive. 

And there have been impressive advances in risk data, as evidenced by any number of articles we've published recently at ITL. This interview I did with Eagleview lays out a vision for how aerial views of properties will enable continual monitoring of property risks. This piece, from Nearmap, describes how the condition of roofs and other aspects of properties can be tracked long before a claim surfaces. This describes advances in "hyperlocal" weather intelligence. This explains how catastrophe modeling is moving beyond static pictures of disasters and toward images that show how floods, wildfires, etc. develop over time. We've also published on new ways to track maintenance records of commercial properties to better understand the likelihood of a claim, to monitor for the next pandemic, and so on. 

VanderLinden says the second biggest category of venture investment was in digital-first insurers and MGAs and offers a key insight: "Not one of them is a generalist." She writes:

"Alan raised $116M for digital health in France. Corgi closed a $106M Series B insuring technology companies. Counterpart took $50M for small-business liability, Shepherd $42M for construction, Lassie $75M for pet, Zego $28M for gig drivers. Stoïk and Mitigata both raised for cyber, on two different continents.... The funded insurance provider of 2026 is specific, defensible, and priced for its niche."

Third was AI: "$216M went to AI-led claims, underwriting, and operations automation.... These ventures do not compete with insurers. They sell digital labor to them. Claims handling, prior authorizations, underwriting triage, document verification: the workflows where a human-agent ratio can shift fastest and the savings land on the P&L within quarters, not years."

The insurtechs that went after incumbents head-on -- notably Lemonade, Hippo and Root -- are still around and seem to have stabilized after years of struggle but are way down from their peaks in early 2021. Lemonade shares are off some 65%, Root is down 85%, and Hippo has fallen 90% even as the S&P 500 has nearly doubled. So I continue to believe that the sorts of "arms merchants" VanderLinden describes are the future of insurtech.

Cheers,

Paul

P.S. If you'll permit me a proud papa moment....

My older daughter made her debut in the Wall Street Journal over the weekend. She trekked the 500-mile Camino de Santiago in Spain this spring and wrote an essay [free link] about the trip that has generated some 500 comments and emails and spent some time on the "most popular" list. It's a splendid piece. I'm delighted for her.

I also love that the Carroll family is now on its third generation at what we joke is the family business. My father spent a year at the WSJ. My younger brother and I combined for 59 years. Now Shannon....

The average consumer views insurance as a reactive product. A pipe bursts. A fire starts. They file a claim, and their carrier helps them to recover and rebuild.

That captures the core of the insurance value proposition, but it overlooks a key element: the role insurers play in preventing losses before they happen. This is critically important. Few homeowners realize there are simple, effective steps they can take to protect their homes from these losses.

As customer expectations evolve and preventable losses continue to impact homeowners, insurers across the industry are beginning to rethink their role — not simply as organizations that pay covered claims but as organizations that help customers avoid losses altogether.

Recognizing this shift, we’ve expanded our portfolio of risk management tools and resources — including two smart sensors, Ting (electrical fire prevention) and LeakBot (leak detection) — to help policyholders[1] identify hidden risks and intervene before losses occur. 

“Helping customers recover after a loss will always be at the heart of what we do,” says Corey Vigliucci, AVP of sales and underwriting for Westfield Personal Lines. “But if we can help prevent that loss from happening in the first place, that’s an even better outcome. That’s why we’re investing in practical solutions that help homeowners and farm owners identify hidden risks before they become losses. It’s another way we’re helping protect what matters most.[2]”

Here’s what that looks like in action.

Ting: Preventing electrical fires

Every 10 minutes, a family in the United States is impacted by an electrical fire. The average electrical fire claim costs approximately $215,000, according to Triple-I, and that’s only the financial damage. The emotional devastation homeowners face when losing their home to a fire is immeasurable.

To help homeowners mitigate the risk of devastating electrical fires, we worked with Ting Labs to offer Ting, its electrical fire prevention system, to all its eligible personal lines and farm insurance policyholders.

Ting detects hidden electrical hazards before they escalate into fires. The system combines a smart sensor, a mobile app, an advanced signal analysis and a fire safety team that works with homeowners in real time to help identify and mitigate risks.

Simple by design, the smart sensor plugs into any standard outlet and uses advanced technology to identify electrical arcing, faulty wiring, failing outlets and other hidden hazards that homeowners might never detect.

On average, Ting sends fire hazard alerts to approximately one in 60 homeowners each year. About one in 27 of those alerts would have resulted in a fire if the hazard had remained undetected.

The value extends beyond homeowners. These tools also help equip agents to have more proactive risk management conversations with customers.

“This investment also creates meaningful value for our agents by equipping them to have more proactive risk management conversations with customers,” said Dave Ruppel, AVP of sales and underwriting for Westfield Agribusiness. “By identifying potential issues before a loss occurs, agencies can reinforce their role as trusted advisors, deepen customer relationships and help improve long-term customer retention.”

Matthew Boyert, CEO and founder of Boyert Insurance Group, experienced the benefits of Ting firsthand. One day, while meeting with a client, Boyert received a Ting alert on his phone that read, “Fire detected!” 

“I rushed home with my heart beating at 100 miles an hour,” he recalls. He immediately called the Ting support team and learned the alert was for a potential fire hazard.

When Boyert arrived home, a Ting representative helped him isolate the issue, which turned out to be an electrical outlet with loose wiring that was actively arcing.

“If I didn’t have that device, I would not have known there was arcing that could have caused a catastrophic fire,” Boyert reflects. “We could have lost our house, our memories, everything.”

Stories like this resonate with homeowners and help reinforce the importance of proactive risk management.

Matthew Mangus, president of Miller’s Insurance Agency, has seen what happens when electrical fire hazards go undetected. In the past two years, he has seen two clients lose their homes in fires caused by electrical failures.

“I’ve met with clients two days after their homes burned down,” he reflects. “Seeing their mindset as they figure out what to do next is heartbreaking.”

Since launching its Ting offering in May 2024, we’ve enrolled more than 21,000 Westfield customers in the program. During that time, we’ve identified nearly 200 potential “saves” across both electrical and utility fire hazards, including panel failures, faulty outlets and wiring issues.

LeakBot: Tackling hidden water losses

Although fire presents one of the most disastrous risks, water damage is among the most common and costly. Non-weather water damage, such as plumbing failures, appliance leaks or burst pipes, is the second leading cause of homeowners insurance claims in the U.S., accounting for approximately 23%-28% of all claims, according to Consumer Affairs.

Triple-I reports the average non-weather water damage claim is approximately $15,400, with hidden leaks behind walls or beneath floors often triggering the worst losses.

That’s where LeakBot, a smart water leak detection solution, comes in. The technology helps homeowners identify non-weather-related leaks, often before any visible signs of damage emerge. Earlier this year, we began offering LeakBot to eligible policyholders in Ohio, Indiana and Pennsylvania. In just a short time, more than 5,000 homeowners have enrolled.

When a leak is detected, homeowners gain access to a support team and specially trained plumbers who help diagnose and resolve the problem before it escalates into a major claim.

The technology’s simplicity is part of its appeal. LeakBot installs in minutes by clipping onto a home’s main water pipe, with no tools or plumbing expertise needed. Once installed, it quietly monitors the home in the background.

For agents, that simplicity is a major advantage. It makes it easier to introduce homeowners to risk prevention and demonstrate added value beyond the policy itself.

According to Consumer Affairs, fewer than 20% of homeowners take steps to avoid leaks, such as plumbing inspections or installing leak detection systems, despite approximately 65% of water damage incidents being considered preventable. By offering a simple tool like LeakBot, agents are well positioned to help change those statistics.

“I’m telling my Westfield customers about LeakBot, and there’s a lot of interest,” says Boyert. “There are not many carriers that offer one, let alone two, preventive risk management devices free of charge to their insureds. Kudos to Westfield for that.”

The agent opportunity: Moving beyond the policy

For independent agents, preemptive risk management tools like Ting and LeakBot transform the conversation from transactional to advisory, creating more frequent, meaningful touchpoints with customers, and reshape how they deliver value.

Data plays an important role in making those risks tangible. When customers understand how common and costly these losses are, prevention becomes easier to appreciate.

“Insurance is an intangible product. You’re basically selling a promise,” says Boyert. “Tools like this give us something tangible that we can offer to clients to give them additional peace of mind.”

Agencies can strengthen relationships and build trust over time by introducing solutions that actively help protect customers. By equipping agencies with carrier-backed risk management solutions, insurers are enabling agents to go beyond transactional interactions and position themselves as long-term advisors.

“It’s a great retention tool,” says Mangus. “As an agency owner, I’m always interested in two elements. Can this help our clients, and will it help with retention? With both, that’s a win-win.”

Customers who embrace risk protection often become more loyal to both the agency and the carrier. “The customers who recognize the value these solutions provide — and understand that Westfield introduced them to the concept — often become more loyal to both Westfield and, by extension, Miller’s Insurance Agency,” Mangus explains.

By equipping agents with practical risk-prevention tools and resources, we’re helping redefine the role agents play — from policy providers to trusted risk advisors. As personal lines continue to evolve, carriers that help customers prevent losses, not just recover from them, will help define the next generation of insurance value.

[1] Customers must have an eligible homeowners, WesPak®, WesPak Estate®, or farmowners policy with an owner-occupied dwelling to claim Ting. LeakBot devices are only available for homeowners policyholders in Ohio, Indiana and Pennsylvania.

[1] Please see footnote 1.

About the author:

Casey Burke is Director of Standard Lines Marketing at Westfield, where he helps shape marketing strategies that support customers, independent agents and the evolving needs of the insurance marketplace. He brings more than 20 years of marketing and sales experience, including more than a decade in the insurance industry. Throughout his career, Casey has focused on connecting customer insights, business strategy and practical solutions to drive growth, strengthen relationships and deliver meaningful value