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.

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? 

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/

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.