Bill Gates recently said he is using generative AI as part of the annual, two-week strategy session that determines how the Gates Foundation spends its next $10 billion. He'll be "taking the strategy notes and actually engaging in a dialogue with ChatGPT, Claude, Copilot, and even having them talk with each other," he said.

I don't have $10 billion that I'm considering how to spend, and you may not, either, but genAI has now improved to the point where it can at least be an aid in setting strategy at almost any level. 

An old boss, Mel Bergstein, used to say: "Strategy is what you don't do." The idea worked for Mel, who founded a consulting firm, Diamond Management & Technology Partners, that pioneered the notion of digital strategy in the mid-1990s. Chunka Mui and I took Mel's adage further in our book, "Billion Dollar Lessons," for which we had 20 researchers spend two years looking at 2,500 major failures to identify the warning signs.

Our recommended solution boiled down to having a "devil's advocate" identify all the underlying assumptions for a strategy, to keep problems from being glossed over as an executive team rushed to realize the CEO's plan. GenAI, used right, can now do a pretty good job of filling that role for anyone.

Let's have a look. 

Gates told the New York Times that, "in a few of the reviews, we’ll actually have the A.I. sit in. In a few cases, we’ll tell it: Hey, only speak if we ask you. And then in a few other cases, we’ll say: Hey, if you hear something you think is wrong or you hear us thinking about, for example, what are these statistics? Please engage. So we’ve gone from it being a joke [when someone suggested using genAI a year ago] to it will be a peer — not making any final decisions, but it will be a peer in deep, complex strategic discussions, making a significant contribution."

I'm not willing to go that far just yet, partly because I'd need to test the domain knowledge that the large language models have about a subtle, heavily regulated business like insurance and partly because genAI has shown itself to be a sycophant. The biggest, overarching pattern of failure that Chunka and I found in the research for "Billion Dollar Lessons" was that people were reluctant to point out obvious problems that could derail initiatives that had the imprimatur of the CEO, and we don't want to replace human sycophants with AI ones. 

But you can describe your strategy to an AI, then pepper it with questions, going deeper each time while demanding evidence for its thinking and insisting it be blunt.

Let's say you're thinking of buying a company. You could start with a general question: What could go wrong? Then you probe. How will our competitors react? What if interest rates increase? The economy goes into recession? The war in Iran escalates? Will we actually be able to cross-sell to our customers? Will the efficiencies from scale be as great as we expect? Who's done something similar and had problems? What were the problems? And on and on and on. 

You can do the same exercise if you're thinking of a new line of business, a software overhaul, adopting AI chatbots — or if you want to explore the dangers of standing pat, of not undertaking a major initiative that is under consideration. 

Major corporations have staff do extensive analysis along the lines I'm suggesting and, in the case of a possible acquisition, draw on their investment banks for advice — but the bias is always toward acting. If the perception is that the CEO backs an idea, the staff strives to make it work, not to kill it. Investment banks only collect their hefty fees if the deal happens, so why would they recommend against it? 

When Chunka and I did a consulting project with a major company that was considering launching autonomous vehicles, we saw elaborate studies it had commissioned from consulting firms that said the robotaxi business was a slam dunk and that the first to enter would win big. That was more than eight years ago. 

It turned out, however, that the executives working on the AV project internally were aware of all the sorts of operational and safety issues that we've all seen play out and that have slowed the robotaxi rollout. Those concerns were brought to the surface in the interviews for our devil's advocate review. 

Warren Buffett used to set aside two, equal pots of money when asking investment banks for advice on possible acquisitions — one for a bank that argued in favor of a deal and one for a bank that argued against it. Whichever carried the day collected its fees, while the other got nothing. You can be sure the "anti" bank worked as hard as possible on its arguments and evidence. 

In my experience, many big ideas should fall apart at the slightest scrutiny. As we wrote in "Billion Dollar Lessons," we thought 47% of the failures we investigated were based on strategies that never should have made it out of the strategy room — and not because those involved were stupid or inexperienced. 

The fifth-biggest cement maker in the world reasoned that it was really in the home products business because so much of its cement was used there. It began also making furnaces and lawn mowers... then went bankrupt and was bought by a competitor. Avon decided that its core competency was its culture of caring — not, you know, its line of cosmetics and its devoted salesforce — so it bought a maker of medical equipment and an operator of retirement homes. It had no idea how to operate the new businesses and quickly sold them at a major loss. In the early days of fax machines, FedEx spent hundreds of millions of dollars to set up a business that would collect a document from you, drive it to the nearest office, fax it to the office nearest your destination, then drive the fax to the recipient. The company somehow missed that Moore's law would soon make the devices so inexpensive that every company would have its own.

It may take time to learn how to use genAI to avoid costly strategic mistakes — and that's even before we start using it as broadly as Gates is planning to. You'll likely also need to introduce to the LLM information specific to your market, including regulatory issues that it likely doesn't have even as these models try to Hoover up everything available on the internet. Insurance is a complicated market.

But I think we're now at the point where genAI can, if managed correctly, start to catch strategic errors. We won't get that 47% figure for obviously bad ideas down to zero, but even heading toward 40%, maybe even 33%, would mark real progress.

Cheers,

Paul

Paul Carroll, Editor-in-chief of Insurance Thought Leadership.

Pricing gets far less attention than claims or underwriting, yet is important. How is the pricing function being modernized in insurance? And how should it be?

Kumarappan Sundaram, Managing Director, PwC US

Pricing gets less attention partly because a lot of the work is infrastructure: platforms, data flows and deployment processes. It is not a front-office workflow that a new tool can visibly transform.

Modernization can be uneven. Personal lines are well on their way. Commercial lines are earlier in the journey and still rely heavily on underwriter judgment and rating engines embedded in policy administration systems.

How pricing should be modernized can largely be a question of sequence. The foundation comes first: decoupling rating from the policy admin system. At the moment, actuaries produce pricing logic, which they hand to IT, which embeds the logic in a rating engine that sits within a policy admin system. So, making a change to the pricing logic means waiting for a new release of the policy admin system. If you decouple, you can make changes to ratings as and when you want.

Once you’ve decoupled, you should build reliable data flows and establish clear governance.

You can then go to the next level of capabilities, like advanced analytics and AI. Let’s say you have a $10 billion book. You’re considering changing a particular rate factor from X to Y, maybe related to driver age. Advanced analytics can help you understand the impact of the rate change on the whole book. The actuaries can then decide whether to make that rate change. Carriers can help deploy and monitor pricing changes quickly and safely.

Paul Carroll

From a technology standpoint, what are the most important shifts in P&C pricing and rating?

Kumarappan Sundaram

Pricing is moving from a periodic project to a continuous capability. With catastrophe volatility and shifting market conditions, treating pricing as an occasional exercise is hard to sustain.

Two shifts stand out. First, dedicated pricing platforms. Rating, models and rules increasingly sit on purpose-built platforms rather than inside the core policy system. Second, modern software discipline. Version control, automated testing and continual monitoring of live models are becoming standard.

Paul Carroll

What is driving carriers to invest in sophisticated pricing platforms now?

Kumarappan Sundaram

Five things.

First, speed-to-market of rate changes. Moving from quarterly or annual releases, gated by IT and policy admin deployment, to something near-continuous is the key value. Decoupling rating logic from the core policy system is also a practical starting point for core modernization, delivering value without an entire core replacement.

Second, loss cost volatility and trend uncertainty. Social inflation, repair and parts costs, tariffs and secondary-peril catastrophe losses make trend assumptions go stale faster. Carriers want faster feedback from emerging claims experience that they can incorporate into price.

Third, segment-level control. Carriers want to grow where they are well priced and hold the line where they are not, which means testing, deploying and monitoring changes at a granular level.

Fourth, regulatory and governance demands. Filing, documentation and explainability requirements keep growing. Audit trails can be far easier to build in than to retrofit.

Fifth, data ecosystem expansion. Telematics, geospatial and property-level data, cat model output and third-party enrichment should flow into rating at quote speed, which can often be the real integration challenge.

A CFO will often ask how the benefit is measured, so programs should define measurement up front.

Paul Carroll

Underwriting and pricing are tightly linked conceptually, but loosely coupled in practice. Why is that, and can they be brought into phase?

Kumarappan Sundaram

The link is strong in theory but weak in practice, mainly for technology and operating-model reasons.

Start with systems and data. Pricing logic sits in a rating engine or pricing platform, while underwriters work in a different workbench based on the data in the submission, so the two rarely share a view of the risk. Then there are different rhythms. Pricing changes on a release cycle, while underwriting decisions are made submission by submission. Overrides and discounts help fill the gap. Finally, the feedback loop is weak. Why underwriters override a price rarely flows back to pricing in a structured way, so a valuable signal is lost.

Modern pricing platforms increasingly aim to close this gap. Bringing them into phase is mainly an integration and governance task. Pricing can be exposed as a service that the underwriting workbench calls in real time, connected to appetite, distribution, retention and claims signals. Overrides can be captured as data, and authority and guardrails can be made explicit. In complex commercial risks, underwriter judgment legitimately plays a larger role, so the aim there is visibility and learning, not automation.

Paul Carroll

How can modern pricing technology help carriers price emerging risks?

Kumarappan Sundaram

Emerging risks, such as cyber, climate-related perils and new auto and energy technologies, may not come with deep loss history. A modern platform helps in four ways.

First, new data sources as proxies. Outside-in cyber scans, geospatial and property-level data, and vehicle and telematics data give carriers signals where claims history is thin. The platform brings them together with internal experience and subject matter specialist judgment, with clear rules for how the weighting shifts as claims data builds.

Second, scenario testing. Teams can test prices against stress scenarios, such as a major catastrophe or a systemic cyber event.

Third, fast, controlled learning. Small, measured changes show what a new risk is worth sooner and with less exposure than a book-wide change.

Fourth, flexible product design. Configurable rating makes it easier to adjust deductibles, sublimits and endorsements as understanding of the risk develops.

The limit can be that technology cannot create data that does not exist. For novel risks, the platform helps carriers learn and adjust quickly, alongside accumulation controls, reinsurance and underwriting oversight.

Paul Carroll

What impact is AI having on pricing and rating, and where should leaders be skeptical?

Kumarappan Sundaram

I would separate what is emerging with clear value from what is still unproven.

The clearest near-term value is GenAI around the pricing process. The more underrated opportunity is in the work surrounding the price: drafting and reconciling rate filings, translating pricing specifications into platform configuration, documenting changes, summarizing competitor filings and preparing data. These steps slow the path to market, and errors are easier to review than in a pricing decision itself.

What is still unproven is AI acting on pricing decisions. The idea of agents that monitor performance, propose rate actions and test them is compelling. But pricing is regulated and auditable, and many carriers are not ready for autonomous rate changes. For now, I expect AI to propose and people to approve, so the quality of that review is the real control.

As AI capabilities become widely available, proprietary data, speed of deployment and governance are likely to matter more as differentiators. Carriers that treat AI as a standalone upgrade, without the platform and operating model underneath it, risk building capability they cannot put into production.

Paul Carroll

What would you advise a carrier to do first?

Kumarappan Sundaram

Measure your cycle time, from the point a pricing change is approved to the point it is live throughout the various channels. Many carriers have never seen that number, and it shows where value is being lost. Then prioritize platform and process changes that shorten it. Put governance in place before scaling AI, and start with one or two lines where faster pricing has a clear, measurable payoff. Resist modernizing everything at once.

Paul Carroll

Thanks. This is all super helpful.

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Kumarappan (Kumar) Sundaram is a Managing Director in PwC’s Property and Casualty Operations and Technology practice and a leader of its Insurance Core Transformation and Pricing/Rating offering. With more than two decades of consulting experience, he partners with US and UK P&C carriers, from large international insurers to small regional ones, across personal, commercial, and specialty lines, with depth in commercial and specialty. He has led numerous large-scale programs modernizing policy, claims, and pricing/rating operations, and works with PwC’s strategy, management, technology, and actuarial teams to deliver integrated solutions, thought leadership, and sales.

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Sponsored by PwC

Collaboration. It's what every leader hopes for in her team. It's the purpose behind much of our communication at work. And it can be a challenge.

There is a collaboration platform in the digital workplace for almost everything. Salesforce. Asana. HubSpot. Figma. Git. ServiceNow. Too many to name them all. Each tracks and assigns work items and enables us to collaborate through a system. The systems sit between us. They enable us to tell one another what to do online. Want something done, open a ticket. Want to feel productive, pound through your task list, and assign "work items" to the next person in the workflow. Fire, then forget.

Most of the time, this works just fine. The efficiency can't be disputed. But ask yourself a couple of questions. Are there times you assign a task in Salesforce and get back a result you didn't expect? Have you ever opened an Asana task and not known what you were being asked to do? Our experience tells us it happens all the time.

Communication scholars and system architects make a distinction between synchronous and asynchronous messages. Synchronous communications occur in real time and involve immediate back and forth between participants. Asynchronous communications allow for time delays. Workflow assignments are asynchronous. They happen independent of an immediate response. Sometimes, in fact, responses don't happen at all. Workflow makes it easy to make an ask of a colleague, assign a task to a subordinate, offer feedback to a collaborator. They also eliminate the synchronicity of a person-to-person "handoff."

Evidence suggests synchronous interaction makes us feel connected at work. It even fosters relationships, which make asynchronous communication on workflow platforms more effective. An intriguing study of software engineers proves it. The engineers worked in two buildings several blocks apart. In some instances, teams worked together in the same building. In others, the team members were spread across the locations. As is common in the software industry, the engineers used a platform to share code, give one another assignments, and provide feedback. Researchers found that teams of engineers working in the same building—that is, colleagues who had frequent synchronous interactions with one another—received 23% more online feedback—an asynchronous interaction—on their code than did those on multi-building teams whose members had less frequent face-to-face contact with one another. "Sitting near workers increases not only in-person but also digital collaboration," the authors write.

The collocated software engineers enjoyed the benefits of social capital: mentorship, coaching, learning, and collaboration. All were achieved through a digital tool that, when coupled with the opportunity for synchronous interaction, worked for and not against social capital. Everyone benefited, especially those newest to the team. Retention among newer joiners was significantly higher among collocated teams than dispersed teams. Young workers and women, the authors tell us, are "substantially more likely to quit the firm when they lose proximity to coworkers." Less feedback. Less connection. Lesser results. It's a lesson the system architects at Amwins, led by Ben Sloop, took to heart when they designed their collaboration platform.

Ben knows his way around a spreadsheet. A boardroom, too. Harvard undergrad. Stanford MBA. A stint as a management consultant at Bain, then a few years in private equity. The perfect pedigree for his current role as president and chief operating officer of Amwins, the nation's largest wholesale insurance broker. But there's more to Ben's success than his stellar resume. Ben told us, "One of the core requirements to gain more responsibility here is having deep, real relationships with our salespeople. I spend a disproportionate amount of my time talking to and staying connected with people in the field. Of all the people that I'll respond to, first is if one of our brokers reaches out to me. That's the top of my inbox."

Amwins' clients are retail insurance brokers and agents looking to find homes for complex, hard-to-place risks—think workers' compensation insurance for circus performers. The insurance carriers who advertise on the Super Bowl don't want the hard stuff. And so, retail brokers work with wholesalers like Amwins to get complex risks placed with one of a large legion of specialty insurance carriers. Each occupies a well-defined niche within the commercial insurance market. Each is expert in a particular type of risk. This creates a classic many-to-many problem. Thousands of customers, thousands of carriers and thousands of Amwins brokers sitting between them.

The challenge is having the right expertise at the right time. "Our brokers have to be specialists," Ben told us. "Our retailers don't want to work with someone that does everything a little bit, because that's kind of what they do. They want to deal with someone that's a deep expert." Enabling his brokers to always find that one expert within their large, decentralized organization became a key part of Amwins' founding mission. It's the hardest part of what they do, Ben told us. "In order to be a deep expert, what that means is that, well, if my client comes to me and they ask me for a construction wrap policy in Scottsdale that I know nothing about, I've got to know where to get them what they need inside our own firm. So, we don't lose that client, right? And so, this created this huge coordination problem."

The company built Amlink to solve it. Amlink is the wholesaler's proprietary relationship management system. It is a workflow platform, but it's different. Ben beamed with a creator's pride as he told us how it works. "I can look in the system and say, well, actually, we have a guy who lives in Scottsdale and does only construction wraps, right? And so, I can reach out to that person who I usually know personally, because we have all sorts of sales events. The point is to build connectivity with an internal coordination mechanism."

We both have seen lots of systems that tried to solve similar problems. All did it the same way. Type in "construction wrap in Scottsdale" and you'd get deal listings, PowerPoint presentations, maybe a white paper or two. Lots of stuff to wade through, maybe some good slides you can reuse, but probably not the answer you need. Amlink is different. It delivers names and contact information and forces conversations to happen. It's effective at both delivering expertise when it's needed and building relationships across the firm. Knowing who to turn to can make or break a deal. Amwins brokers don't just compete on expertise—they compete on connectedness.

Amlink is a unicorn. "I've never seen another system in the industry like this. Everyone says it's a people business. but they tend not to track [data] at the people level," Ben told us. He was clear to put the tech in its proper context: "We are a technology-enabled business. The thoughtful use of technology makes our people better. But we are fundamentally a person-to-person business."

Social capital matters at Amwins. It drives market results. It also creates an environment where people want to stay. "You know, it's funny. I joined 19 years ago, and I still think of myself as one of the newer people," Ben confessed, explaining that Amwins was the kind of place "good people stay for a long time. People typically don't leave just for money. People tend to leave because they just don't feel connected, don't like who they're working with, don't feel compelled by the strategy."

We probably should have introduced you to Ben at the very beginning.

"Social Capital at Work," by John Burrows and Seth Rachlin, will be published by Simon Acumen on Nov. 10. This article is excerpted from their book.

Bill Gross recently announced a free database of AI-generated ideas for innovative companies, based on the 31 methodologies that he's developed over the past three decades and that have powered his Idealab incubator. 

The list of ideas — currently totaling a whopping 1,208,580 — may or may not contain an idea that you'll be able to turn into big bucks, but I think that exploring the database and interacting with the ideas is worth a bit of your time. 

Many of the notions I see for the insurance market are provocative. They can at least suggest where there are holes in insurance offerings today that you should work to fill, even if you don't launch the sort of standalone company that Gross would love to see join his pioneering incubator.

Let's have a look. 

Here is a link to the database. And here is a link to a Fortune article that describes the effort in detail, including Gross's argument that innovations driven by AI will create far more jobs than it will eliminate. The article says Gross's prompts to the AI break down into three categories: 

"Opportunities, which he says are things such as solving a business pain point, removing friction, removing waste, removing danger, and improving design, among others; enablers, such as new technologies, scientific advances, and new materials, processes, or changing cost curves that make solutions more feasible than before; and shifts, such as regulatory changes, as well as behavioral, economic, cultural, or geopolitical movements that make the timing for a particular business seem right. "

Gross also fed his AI "some 17 million source documents that included news stories, research reports, expired patents, public company filings, and threads from online community discussion boards" and came up with his 1.2 million ideas for new businesses.

But are the ideas any good?

I certainly found them thought-provoking. 

For instance, I selected the "fintech/insurtech" option, specified that I didn't want to invest more than $250,000, wanted to get to market within a year and hoped to "make the insurance claims process smoother for customers." The top suggestion, which the AI suggested was a 57% fit for me, was for a "claims trust operating system that... gives policyholders visibility into the evidence shaping settlements and repairs."

I'm not sure that's a business. It would take an awful lot of work to assemble the huge amount of settlement and repair data that would be needed to be truly definitive, but the idea raises an intriguing notion: Somebody will surely try to use an AI to give policyholders a quick handle on what the insurer will likely offer after a car accident, water damage to a home, etc. If the insurance industry provides the initial take, it will be able to lower expectations and control payments. If plaintiff attorneys fill the need, they'll be able to raise expectations, influencing policyholders to be disappointed... and perhaps hire an attorney to contest the offer. 

The competing interests would seem to create an opportunity for a trusted intermediary, but I, at least, have become skeptical about whether it could stay unbiased. I subscribe to the notion laid out by Cory Doctorow in a seminal blog post republished by Wired magazine in 2023 that describes the process of — excuse my French — enshittification. 

As I wrote at the time, Amazon is a great example. It initially built a huge base of customers by doing everything possible to make life simple for them. Once it had the customer base, it courted businesses — and the customer experience started to get gunked up by ads, attempts to keep customers from easily finding the best deals, etc. Once Amazon had built a marketplace where companies had to have a presence, it started milking them as much as possible on behalf of investors — a fee here, a fee there, maybe Amazon would even use its insights into sales data to launch products or businesses that would undercut a company with a hit offering on Amazon's platform. (Amazon has consistently denied doing so but has faced many lawsuits alleging it does.) Just about everybody still uses Amazon, but the experience is very different than it was a decade ago.

Even if I'm right that there isn't a business in a "claims trust operating system," the Idealab database has still pointed to a sore spot that the insurance industry needs to address. Somebody is going to use AI to shape policyholders' thinking about what to expect as a settlement, by providing near-instant estimates. So insurers should at least be thinking about how they show their work, and quickly, to keep plaintiff attorneys from setting unrealistic expectations and accelerating the trend toward more and more litigation. 

So I'd recommend that, whatever your role in the insurance ecosystem, you poke around in the database to see what friction points and opportunities it has surfaced that you might be able to address. There are more ideas than I can count about underwriting, pricing, services from agents and brokers, and on and on. 

If you're like me, your eyes glaze over after half an hour or so of an exercise like this, so maybe make a visit to the database an occasional excursion over the next month, perhaps at the end of the day as you're winding down or before a weekly meeting with colleagues so you can stir up discussions. We all get locked into our mindsets and into our day-to-day routines, and Gross has provided a whole host of prompts that can challenge us to do ever more than we are now.

Cheers,

Paul

One of my takeaways from ITC Vegas this fall was that a lot of vendors are playing defense. A lot of carriers are looking at what AI can do now and asking some version of the same question: Hold on a minute, the world changed, so should we just build everything ourselves?

It's a fair question. The barriers to building have come down a lot in the last year, and the benefits of owning your own technology are real. When a convincing demo takes a couple of engineers instead of a sales cycle, the old reasons to buy start to look pretty thin.

I suppose I'm not the most neutral person to ask. I've spent the last decade building and running Clearcover, an AI-native carrier, and now Dearborn Labs, an AI-native software development firm built specifically for insurance; if anyone was going to tell you to build everything, it would probably be me. But when people ask me whether to buy or build, my answer is...yes. We have components of our system that we bought and didn't build, and we're fine with that. We build more than we buy, though, and more every day.

Is "yes" a dodge? Maybe a little. Mostly it's that buy vs. build was never one decision. Every carrier already runs a mix of both, workflow by workflow. And even the build half of my answer comes with an asterisk.

Where should the line sit?

The choices sit along a spectrum, made one workflow at a time, and lately the mix has been shifting toward build. That makes sense to me, and I'd expect it to keep shifting for as long as building keeps getting cheaper.

Buy what's already solved well. If a problem looks roughly the same at every carrier, and someone has spent years getting good at it, there isn't much to gain from building your own version, and the time and money are better spent elsewhere. But that list gets shorter every month.

Build the workflows where being different is the point, like how you triage a new claim or decide which risks to write. Buying those means working the same way as everyone else who bought the same thing, which probably isn't what you were going for.

Where exactly that line falls depends a lot on the carrier and its lines of business, which is why a single company-wide decision on buy vs. build usually ends up wrong somewhere.

The asterisk

What qualifies as building, because you can put a demo together, is usually the smallest portion of the work. AI can change how a claim moves and which decisions get made without a person ever opening the file, but the systems underneath that workflow rarely change, or at least they rarely change quickly. Not everybody is doing a rip-and-replace of their whole company to launch a new AI workflow. A new claims-intake workflow still has to post to your core system and set reserves the way your actuaries expect, and an underwriting workbench still has to read from policy admin and write its decisions back. None of that shows up in a demo.

The interface is mostly a solved problem now, and that's good news. The hard part is the wiring underneath it. Most of the AI conversations I have with carriers wind up being about traditional software engineering. That's the work of fitting whatever the AI application or agent is into the rest of the company, so the systems supporting a reinvented workflow still work properly.

And the wiring is only half of the asterisk.

The other half is running the thing in production until people actually work that way. Most implementation projects are scoped to end at go-live, which is roughly where this half starts. At Clearcover, adoption of the AI tools across our claims team is 92%+. A number like that gets earned after go-live, inside real claims, over however long it takes for the new way of working to become just how the job gets done.

You might reasonably ask whether all of this is an argument for buying something that comes already integrated. For the work that's solved well, it is. But a bought workflow is the vendor's workflow, designed to fit as many carriers as possible, and in the workflows you'd want to run differently from everyone else, that's exactly the problem.

Build realistically.

Before you build a new workflow, ask three questions of it:

1. Foundation — Was this workflow designed around constraints that no longer exist?
2. Fit — Can it work with the systems you already have, without a rip and replace?
3. Engineering — Do your engineers own the integration layer today, and can you test, govern and audit what runs on it?

The first is about not building on top of a bad foundation. A lot of insurance workflows were shaped by limits that made sense at the time, like a handoff that existed because two systems couldn't talk to each other, or a sampling audit that existed because nobody could read every file. If you automate that workflow as it stands, what you get is the old process running a little faster, and you've spent real money to keep a constraint you no longer have.

The second one is more encouraging. You don't have to fully rip out your systems to improve a workflow, so long as you have the software engineering chops to fit the new workflow into the systems you have.

The third is the one I'd push on hardest, mostly because a demo is very good at hiding it. If the answer is no, I'd want to know that before the demo goes in front of the Board rather than after.

If you're asking me whether to buy or build, I'd say build more than you used to. Just go in knowing that the demo is the easy part, and that the wiring, plus the long stretch of running it until people actually work that way, is the job. So where does your line sit this year, and who's doing the wiring?

Local agents swim in the waters around them, and while a rising tide lifts all boats, that sucking sound before a tsunami hits shore is a clear signal that locality risk is a constant. It's good to be lucky with a rising tide, but luck is not a strategy.

Simulations of the world are a way to describe observed reality and anticipate change, and modeling the changing economic tides of every locality in America (call that a list with 1,900 locations) is a good way to both reflect on what has happened since COVID-19 and to help those agents see what's coming next.

Modeling Communities Like We Model Weather

We simulate weather daily to anticipate storm tracks and estimate damage. We simulate climate change with variation in sea level, flood plains, and riverbanks. We simulate fire spread, hail fall, straight-line wind… and so, too, we should simulate the local economies of 1,900 communities across America.

In a consumer-driven economy, simulating how America spends is like watching a developing hurricane pattern off the shore – is it intensifying or waning, is it heading for land or staying off the coast, what amount of intrusion to interior geographies will a footprint create, and will there be additional risks set in motion as the dynamics unfold, including supply chain and demand patterns that may also be interrupted or surged?

Affordability and Fiscal Health as Key Signals

Just as a student loan may go unpaid if post-graduate incomes are low, the choices households and individuals make on debts to service and debts to default are mirrored at businesses, organizations, and local communities, cities, and municipalities. These local priorities of affordability affect decisions on risk transfer and affect premiums – agents sit amid it all.

(See this article on a data source to predict financial health, going beyond the backward-looking credit reports that insurers use.)

Affordability is a key signal, as is the fiscal health of a locality. When you combine these with the inflows and outflows of households and businesses, any agency can make a plan for the year or two ahead and set in motion a process for continually navigating their locales.

Looking Backward: The Affordability Crisis

People living in houses and driving cars work at businesses and spend money in their communities. Unsurprisingly, changes in the economies of those communities are both observable and predictable. That matters to local agents.

Looking backward, hindsight is usually 20/20 – higher uninsured driver populations occurred where affordability was lowest. More UM/UIM claims drive insurance costs higher, and rates may go up even further. In an economic downswing, rising costs and flat to slow wage increases force more budget stress on consumers. Where job loss escalates stress, things twist up even faster. Not only do consumers decide not to be insured, but they also change other spending habits. More businesses fail when customers can no longer afford them. Collections of tax revenue suffer when high earners migrate away and when business activity slows or falters. Taxes suffer further when sales slump, and property values deteriorate. These can lead to cuts in community services, and spiral from there.

Emerging Patterns: From Data Centers to Remote Work

Independent agents, brokers of all sizes, carriers, wholesalers, specialty, excess, and MGA/MGUs all compete in these predictable localities. And predictions for consumers, businesses, and even the localities are proving their value in understanding and pricing risk.

Some localities see changes that are from emerging patterns of life and risk, like hyperscale data centers. Other changes are more subtle, like a work-from-anywhere job landscape that allows high earners to live wherever they want. Or what happens when AI shifts work tasks in legacy workflows or creates new ways of working?

Boomtowns to ghost towns and everything in between paint the map of America -- parcel-by-parcel, building-by-building, business-by-business, organization-by-organization, job-by-job, and so, too, insurance and risk management dynamics. Some things change very slowly, while others shift abruptly, but the profound effect of "work anywhere" from COVID may be the most significant overarching impact in our generation and the next.

Anticipating Economic Consequences

We anticipate sudden changes to life patterns in a variety of peril-driven scenarios – hurricane, tornado, fire, severe convective storm – as well as in environmental scenarios like sea level change, cyclical patterns like El Niño, and even major disruptions like power outages, transportation interruptions, and cyber interference.

Anticipating economic consequences, especially shifts in contribution to cash flow in community capital assets and liabilities, is another risk in the underlying infrastructure where all the other risks interact. Thriving localities add services and amenities, which attract more growth. Dwindling localities lay off public workers and renegotiate long-term liabilities while trying to improve local revenues with more taxes and fees. Debt offerings and the ability to service debt obligations are then most comparable to Ernest Hemingway's famous line on personal bankruptcy – which happens "gradually, then suddenly."

New Analytics for Local Planning

Analytics now exist for consumers, businesses, and their communities across the 1,900 localities that make up the investable and insurable economies of America.

This observability of a locality, its finances, its threats/opportunities, and its ability to service its debts and obligations is summed across the consumer and business mix of their constituents and forecast against those changing elements to portray current and future years' planning horizons. Local agents can take heed.

Situations with imminent insolvency are expected to make certain choices, while growth-busting upside creates better problems.

Adapting to Local Reality

While agents can always relocate, they are more likely to need to adapt to their local reality. While premium forecasts and business mix are constantly used in distribution planning and customer appetite focus, the pace of change has accelerated inside of the existing process steps. Using a locality stress index along with existing forecasts and affordability measures will make agents smarter in their marketplaces and quicker to react to change.

This means that in troubled areas, expect more business failures, lapses, and non-payment of premiums in a dwindling premium pattern, yet a rise in risk for fraud, theft, and malicious mischief, including a heavier threat of arson. This includes household stress, too, where skyrocketing base rates have pushed daily budgeting stress onto most households for car and homeowner insurance into an affordability squeeze where many drivers are underinsured, overcommitting to large deductibles, or going all the way to lapsing insurance entirely and becoming uninsured drivers.

What does luck look like?

A common quote from successful businesses – "It's better to be lucky, even if you're good."

Never has that been more relevant than today, as changes in local luck have magnified in recent years. Some places have a deep downside – rising taxes, failing businesses, public service degradation, municipal financial stress, drops in quality of life generally, and the ensuing changes in household composition from thriving to just holding on. Some places have all sunshine -- growth in incomes, formation of new jobs, infrastructure improvements, and engaged public services.

To live in an uninvestable locality is the hallmark of being unlucky.

The endpoints are always locality-based. It is where people live, where businesses operate, and where municipal authorities administer. For an agent, the forward look in these markets is flat to down. Fewer businesses for fewer booming households means fewer insurable assets. Even if premiums rise and commissions float with them, the total insurance value opportunity won't be in a boom cycle. The larger concern is a doom cycle.

Investors are aware of the general migration trend outlined in this recent Census article, whereby large metro areas are losing residents, while many smaller metro areas are growing. Details matter, and this is where data and analytics add critical value:

• High earners are migrating away from large metro areas in fiscally troubled states.
• They are migrating to small and mid-sized metro areas: Boise, Idaho; Frisco, Texas.
• High earners are responsible for the majority of personal and property taxes, new business formation, and capital investments.

Scoring the 1,900 Localities

Across the observed 1,900 cities, the municipalities, consumers, and businesses are scored based on their future fiscal viability and likelihood to default across a set of debts (everyday bills, credit cards, car loans, mortgages, business loans, pension commitments, municipal bonds, etc.).

Ranked near the top of the list is St. George, Utah, with a short-term debt ratio of 0.31, which represents total liabilities divided by total assets. St. George is representative of 60 mostly mid-sized "under the radar" cities that have low legacy liabilities and sustainable budgets and are growing their populations with a healthy taxpayer base. This is where you can feel comfortable doing business for the long term. Lucky local agents!

Then there are plenty of typical "middling" cities, with budgets that are not sustainable over the long term due to underfunded pension and benefits liabilities. Steady as she goes for the next few years.

On the other side of the scale – consider a poster child for fiscal mismanagement to the extent it appears on a list titled Uninvestable Cities. These cities are laying off public sector workers today as they struggle with revenue shortfalls. Most of these cities meet the technical definition of insolvency. They appear to have accelerating financial deterioration over the next 24 months, resulting in a highly likely outright fiscal crisis.

Envision trying to do business in a city where high earners (lowest risk, rational decision-makers) are leaving. The businesses are shuttering, as well. New business formation is almost non-existent. The city struggles to fund basic services, while raising taxes. The residual population has lower FICO scores. Crime, including costly fraud, rises.

The Resilience of Independent Agents

Every year another study predicts the demise of independent agents. They are getting too old; they can't scale to digital; they can't compete with changing technology… add in AI.

Yet every year independent agents remain a dominant force in commercial insurance and have never failed to impress in personal lines. Watch how they adapt across 1,900 localities as a way to keep insuring America.

ADDITIONAL THOUGHTS PARKING LOT

What has changed is almost everything all at once.

Blame it on remote work options, but look at how many high-earner households opt to relocate for better taxes, better weather, better schools, better anything.

Blame it on migration, but the businesses and services they need will follow them, while the ones that used to serve them will feel the loss.

Blame it on anything you like, but the undeniable facts are that no one relocates to a state; they move to a specific location. The imbalances from losing localities to gaining localities are seen in shifts in income, housing, business formations/failures, and municipal financial resilience. To assess health, a current approach ties to the capital markets where municipalities trade their debt.

Using a locality lens, it is possible to observe the condition of consumers, businesses, and municipalities. With modern data and analytics, we can track changes over time. Did all three (consumer, business, municipality) improve, stay the same, or deteriorate in the last few years? What is the effect of these local changes, and how fast are changes now occurring? What is in store for the next couple of years?

The US is in the early stages of an economic transformation, by locality, and that means insurance agents have both local threats and opportunities.

Like Detroit filing Chapter 9 bankruptcy in 2013, it is conceivable that other major cities may follow suit. No city is immune to stress in changing local economies, and dozens of large municipalities have a reckoning coming. The tax base within these cities (high-earner consumers and meaningful business tax bases) is departing at rates never seen before.

Hurricane Isaias spun up in the Gulf of Mexico and made landfall near Destin, Fla., as a Category 2 storm on Oct. 9, just two days after becoming the first hurricane of the 2026 Atlantic season, the latest first hurricane in more than a century. Its sudden arrival is a reminder of how quickly a quiet claims environment can change. For property claims teams, the workload extends well beyond the initial wave of reported losses. Damage to hotels, apartment communities, retail centers and industrial properties can lead to large restoration projects and complex invoices that arrive over the weeks and months that follow.

Early modeled estimates of privately insured losses range from $1 billion to $2 billion (Verisk) to $2.5 billion to $4.5 billion (Moody's RMS). While AM Best expects it to be an earnings event for insurers rather than a capital event, the storm created a substantial uptick in work for claims and restoration teams. The aerial imagery firm Vexcel counted about 17,860 damaged structures across Florida, Alabama and Mississippi, including more than 3,100 with major damage. A high-rise condominium resort on Pensacola Beach lost windows across two towers, and an apartment complex in Milton, Fla., lost much of its roof. Damage like that becomes restoration projects, and restoration projects become invoices.

Isaias follows a prolonged stretch of slower claims activity. Industry data puts commercial property claims at 710,000 in 2025, down from 910,000 in 2023. A separate measure, U.S. property claim assignments, fell 12% year over year in the second quarter of 2026 and ran 13% below the five-year average, extending a four-year decline. These are different Verisk datasets, but both point to a slower claims environment. More of the work that remains also arrives in bursts: catastrophe claims made up 43% of second-quarter volume, up from 34% five years earlier.

No claims organization can staff indefinitely for a catastrophe-level workload during a stretch like this. Nor should it. Teams that have adjusted staffing and resources to match lower demand face a particular challenge: claims volume can rebound much faster than review capacity can be rebuilt. When restoration invoices pile up, those leaner teams face a difficult trade-off. Bills can wait for a thorough audit, extending claim cycle times, or move through with less scrutiny, increasing the risk of leakage. Automation offers a way to absorb more work without having to rebuild the entire manual review operation.

The surge also has a long tail. When Hurricane Sally struck the same stretch of coast in 2020, Florida insurers had closed about two-thirds of residential claims six weeks later, but only 13% of commercial property claims. Hurricane Ian made landfall in Florida on Sept. 28, 2022. In the Florida Office of Insurance Regulation's March 4, 2025, snapshot, 96% of the 789,066 reported Ian claims were closed. Yet 6,628 of the 32,850 commercial property claims remained open nearly two and a half years after the storm. Those figures do not establish whether the delays involved coverage, reconstruction, invoice review, or disputes. They do show how long commercial claims work can continue after the initial surge.

The bottleneck moves as the claim progresses

An initial wave of assignments is only the beginning. Adjusters must address coverage, communicate with insureds, inspect damage, review scopes and estimates, and coordinate recovery. Restoration invoices arrive as work progresses, often while those adjusters are still managing both new and existing claims.

Large restoration projects can involve multiple crews, weeks of drying equipment, substantial material charges, and several subcontractors. The resulting invoices can be complex even under normal conditions. When many arrive together, claims teams face two pressures at once: more invoices to review and more work required to review each one thoroughly.

An invoice is not simply a total to approve. Time-and-materials bills can require checking labor hours and classifications, equipment usage and duration, agreed rates, materials, subcontractor charges, markups, and arithmetic. A reviewer may need to compare the invoice with supporting records and the work performed. That takes time, and the work does not become less necessary because the team has fewer people available. Both delayed reviews and unsupported payments carry consequences for the insured, the carrier, and the restoration contractor waiting to be paid.

Adding more reviewers can help, but it takes time to find, train, and coordinate them. It also raises the cost of handling a temporary spike. The more useful question is which repeatable parts of review can be completed consistently without requiring a few more hours of specialist time for every additional invoice.

How automation can absorb a surge

AI-enhanced automation can help teams review restoration invoices faster by extracting and sorting line items and running calculations to identify charges that need a closer look. During a surge, these capabilities support consistent, thorough review.

AI can help turn invoice documents into organized data, while programmed audit rules check calculations and compare charges with agreed rates or benchmarks. The adjuster or specialist still has to consider the work performed, evaluate the findings, and resolve disagreements. Automation reduces the manual checking that precedes those decisions.

Rest-Assured has pioneered the use of AI-enhanced automation in restoration invoice auditing. Property insurers have used the platform to audit nearly $1 billion in property restoration invoices, uncovering documented findings to guide claims professionals through review and resolution.

Measure the effect on the claim

The right measure is not merely how fast an audit comes back. Claims leaders should look at the time from invoice receipt to a documented review, how long exceptions take to resolve, the rate of confirmed findings, the cost per review, and whether payment disagreements are cleared sooner. Those measures show whether faster processing is actually helping the claim move toward resolution.

What to put in place before the invoices arrive

Mitigation invoices begin arriving within weeks of landfall, and larger reconstruction bills follow for months. That gap is the time to prepare. Some insurers are already using automation to take on the time-intensive parts of invoice review, putting them in a better position to handle the next surge. A few decisions made now can keep review from becoming the bottleneck later:

• Confirm the rate agreements, pricing guidelines, and benchmarks reviewers will apply, including how post-storm pricing will be evaluated.
• Set one intake path for restoration invoices and define the supporting documentation each one needs.
• Identify which repeatable review steps, such as line-item extraction, arithmetic and rate checks, can be automated.
• Decide which findings can be resolved routinely and which require an adjuster or specialist.
• Decide in advance where overflow review goes if volume exceeds internal capacity.

Claims teams cannot maintain catastrophe-level staffing through every quiet period. They need a review process that can expand when demand returns. Automating time-intensive invoice checks can help leaner teams absorb a surge without forcing a choice between longer cycle times and greater leakage. Restoration invoice auditing does not have to be a bottleneck, and keeping claims moving does not have to mean letting invoices pass through without scrutiny.

Source notes

• Verisk ClaimSearch Trends Report, 2025 Year-end Analysis, p. 4. Commercial property claim counts are not directly comparable to the separate XactAnalysis assignment measure.
• Verisk Quarterly Property Report, April–June 2026, pp. 3–4.
• Florida Office of Insurance Regulation, Hurricane Ian catastrophe claims data, as of March 4, 2025. Commercial property claims reported: 32,850; open with payment: 5,332; open without payment: 1,296; closed: 80%. Aggregate insurer-reported figures are unaudited and claim statuses can change.
• Isaias timing: CBS News, citing NOAA records, Oct. 9, 2026 (first hurricane of the season on Oct. 7, the latest date in 121 years).
• Isaias insured loss estimates: Verisk, $1 billion to $2 billion onshore (Oct. 13, 2026); Moody's RMS, $2.5 billion to $4.5 billion private market (Oct. 16, 2026). AM Best and Vexcel figures as reported by Insurance Journal, Oct. 15, 2026.
• Florida Office of Insurance Regulation, Hurricane Sally claims data, as of Oct. 26, 2020. Residential property: 57,373 reported, 65.6% closed. Commercial property: 3,649 reported, 13% closed. Figures are unaudited and cover Florida only.

Given its immense and growing capabilities, more and more healthcare organizations are, understandably, looking to implement AI. Adoption is up, with 75% of US health systems using the technology in 2026 – a steep increase from 59% last year. But as AI moves into most corners of American healthcare, the industry must take a step back. AI use for the sake of AI use is not the goal. Rollout of the technology must be targeted, especially as trust in AI is called into question.

This is true across the entire sector, and I've seen it up close across the field I operate in: contested medical review and health insurance disputes. Here, it's vital to insist that AI only be used where it meaningfully addresses a problem and, crucially, that it never makes an important medical decision.

Where AI can help: The No Surprises Act case study

Take the No Surprises Act (NSA) Independent Dispute Resolution (IDR), for example. When the No Surprises Act took effect in 2022, it set off a crescendo of disputes between insurers and providers, which reached 394,140 monthly cases as of July this year. With hundreds or even thousands of pages of documents per case, independent dispute resolution entities (IDREs) have ramped up efforts to handle the sheer volume of evidence. But such enormous numbers remove the adjudication process from the realm of human capacity alone.

That's an area where AI can play an important role, keeping costs and capacity crunches under control. By trawling through documents, pulling out necessary information, and comparing and contrasting evidence from payers and providers, AI can strip away a lot of the administrative burden that takes up so many hours, allowing arbiters and experts to make informed decisions on more cases.

Where AI doesn't belong: Final medical decisions

Equally, though, it also demonstrates a perfect example of where AI simply doesn't belong: it should never make the actual judgment in any insurance dispute. That decision must always remain with a human expert. Just because AI can make the call, and might do it quicker, doesn't mean it should. Taking that step would undermine something even more important: trust.

Rising scrutiny and accountability

Maintaining and building that trust is even more crucial at this moment, because scrutiny of AI's use in IDR is undoubtedly ramping up. In September, the office of New Jersey Rep. Frank Pallone sent letters to six IDREs, asking a series of questions about their operations. One of those questions zoomed in on AI in the IDR process.

This scrutiny is reasonable. These processes carry real implications for insurers, doctors, and hospitals. An IDR decision is a real healthcare decision that determines what insurers pay for procedures, and how much providers receive. In my view, that is enough reason to keep AI out of the direct medical decision-making altogether.

Pallone's letters hint at a vital question many have about all aspects of health AI – one that extends far beyond the healthcare insurance space: is it making decisions about my healthcare without my knowledge?

Growing skepticism across healthcare

Because the skepticism is rising across the board. Federal initiatives such as Medicare's AI-led WISeR program and new patient chatbot initiatives, for example, have come under fire for making key decisions and even prescribing drugs to patients. Irrespective of whether these uses of AI are appropriate, there's no denying that narratives around them are shifting. They are proof that building trust and being targeted with the technology's use at this moment in time is vital.

What patients want: Transparency and control

There is clearly still appetite for AI. Patients themselves are increasingly turning to chatbots for medical advice alongside that of their doctor. What matters is the execution. You can see that in polling by the Pew Research Center, which suggests that an overwhelming 72% of Americans believe it is very or extremely important to know when AI is used in their healthcare – including in administrative decisions, or even note-taking. It's clear that patients see a difference between AI nebulously injected into every aspect of their regular care, and targeted, careful use that adds another component, or extra layer of support, to it.

Building trust through transparency

The answer for all organizations, then – from healthcare providers and healthtech firms to governments – is to make trust a top priority. That means hard-and-fast rules about transparency and accountability. For insurers, it means clearing up what prompts are used to guide generative AI, and, in the context of IDR specifically, where they are relying on human experts to make key judgments about payment offers. In some cases, third-party AI audits might be necessary, but whatever the measures, they must be openly communicated to the public.

Crucially, every organization must be targeted with the technology's use. In every application, the use case needs to be clear from the start. Does AI complete the work better than a doctor, a claims specialist, or an underwriter, or just faster? Speed and volume – AI's strengths – should not necessarily be the priority across all areas of healthcare, especially when they come at the cost of accuracy, reliability, accountability and transparency.

A critical juncture for healthcare AI

This feels like a critical juncture for public trust. Moving forward, we must implement the technology only where it meaningfully solves a problem, and never, ever, to make an important medical decision on its own. Indiscriminate use risks permanently damaging trust and, if we let that happen, we'll lose so many of the meaningful improvements AI has the potential to bring to patients across the world.

A consumer's AI agent can now call an insurer and ask for a quote. The possibility of such calls leaves carriers with two bad options: putting a licensed person on the line with a machine, or risk hanging up on a real person.

Both options are wrong. There's a third.

The AI phone calls are already happening. Reuters reported that people using Meta's Muse, the personal AI agent launched in the U.S. this past September, can have it call businesses to book appointments, check inventory or get quotes from contractors. A Meta employee testing this feature said an insurer kept hanging up on Muse after realizing it was an AI caller.

At InsureTech Connect in Las Vegas, Steve Rullo, who is Guardian Life's chief digital and technology officer, told Insurance Business, "We are already seeing providers calling in for verification of benefits with AI." He described healthcare providers using AI to verify patient benefits, often for an entire list of patients in a single call.

The Debate Is About Websites

Since Muse launched, carriers and marketplaces have been deciding whether to allow it access. Insurify quickly blocked Muse, saying it "risks stripping carrier quotes of critical contextual information." Co-CEO Giorgos Zacharia explained, "A quote without its context is not a fair comparison. It is a number."

Shortly afterward, Kin announced that it would let Muse crawl its site so customers can shop for quotes, and Zebra announced an integration the following day. Employers, a workers' compensation insurer, told Dig-In it doesn't allow third-party AI agents like Muse to access its operational systems or customer data.

Each of those decisions is about access to a website or system. None of them covers phone calls, and blocking Muse online won't stop it from calling. I expect insurers will get a lot more of these calls. Handled the old way, they're a cost. Handled well, every one is a quote in front of a real shopper. That's where the real opportunities are.

The Talking Is About 5% of the Value

The answer is simple: AI for AI, humans for humans. Let AI answer calls from other AI, and let people answer calls from people. This way, a family dealing with a flooded basement won't be stuck on hold while a licensed agent talks to a bot.

Most voice AI answers the phone, then sends the caller through a phone menu or to a person. That's 5% of the value. The other 95% is the work behind the call: pulling up the policy, filing the claim, writing the result back to the core system. If AI can handle a machine caller's request at this level, no person needs to pick up, and licensed people can focus on judgment calls, like denied claims.

Most carriers aren't there yet. According to KPMG International's report Unlocking AI value in insurance, only 29% of respondents use AI agents or automation for end-to-end processes. Rullo said Guardian will keep people involved in its AI processes: "We'll still have a human in the loop." For some risk profiles, he said, Guardian wants alerts on exceptions instead of reviews of every transaction.

People should own the decisions. They shouldn't have to answer every call.

You Can't Always Tell Who Is Calling

Carriers also can't always tell if a caller is a machine or a person. Reuters reported that Meta tested having human contractors make some Muse calls without telling the other party. A Meta vice president said the test without proper disclosures "was a miss," and Reuters said Meta has rolled back that test for now. So a call that seems to come from an AI agent might actually be from a person. Zacharia told Dig-In that Muse got around Insurify's bot detection and didn't identify itself.

A system designed to resolve requests can help both human and AI callers. A system that only routes calls can't.

Policyholders Already Draw the Line

In Liberate's September survey of 1,001 U.S. claimants, 50% said they would prefer a claim paid in 60 seconds with no human contact over talking to a person within 10 minutes and getting paid in three days. Another 41% preferred a person on the phone. Of those who chose the fast payout, 55% still want a human involved in every denial. 

Fast on yes. Human on no. 

Across the whole survey, 61% say a human must review every denial. In the same survey, 86% want to be told when AI touches their claim.

A Hi Marley survey of 550 senior property and casualty decision-makers and 2,000 policyholders in the U.S. and Canada highlights the gap carriers need to close. Eighty-five percent of executives think AI will build customer trust, but only 40% of policyholders agree. Ninety percent of policyholders said it's very or extremely important to know when they're interacting with AI, and two-thirds said their biggest worry is not being able to reach a human when it matters.

Three Rules for Carriers

First, use insurance-specific AI that works within your core systems to answer calls from machines. Sending a bot to a phone menu or a person wastes time and doesn't solve the problem.

Second, keep a licensed person on every denial, no matter what else the AI handles.

Third, be transparent about AI on both sides. Let people know when they're talking to AI, and expect AI callers to do the same.

If carriers follow these rules, they can answer every call, whether it's from a person or an AI. The shopper using a machine gets a response, and the family with a flooded basement gets to talk to a real person. Insurance has always wanted to answer everyone who calls. Now it can.