Some two decades into the insurance industry's digital transformation, companies have spent hundreds of billions of dollars on new core systems, cloud migrations, digital portals, and AI tools — but the technology was rarely the hardest part.
The hardest part was the people.
The people part has long been treated as a supporting act in large technology transformations. But as the pace and complexity of transformation accelerates, that approach is showing its limits. Employees nod along, say they're using the new system, and then quietly find workarounds. Leadership mandates adoption from the top while alignment quietly fractures below. Organizations sprint to the go-live date and then discover that the real work hadn't started yet.
Samit Bhandari has watched this pattern play out across the P&C insurance industry for 20 years as a partner at PwC. He's worked with carriers of all sizes through core system replacements, AI rollouts, and full-scale digital transformations — and has seen where these programs succeed and where they quietly go off the rails. He acknowledges there has been improvement—he says the assumption that most large technology programs fail is increasingly out of date. But he says organizations keep making a lot of the same, old mistakes, including the tendency to underinvest in what happens after a go-live, to confuse executive messaging with genuine alignment, and to treat transformation as a one-time event rather than a continuous process.
Read the full interview to find out what red flags signal a change management program in trouble, why smaller carriers are often better at transformation than their larger counterparts, and what Bhandari thinks the industry has stopped doing that it should start doing again.
There's often a disconnect in technology adoption that goes unnoticed in business. A leader might mandate the use of AI, and employees will say they're using it—but are they really changing the way they do things, or are they just going through the motions to show compliance? How do you get people on the same page in a big technology transformation?
Samit Bhandari
First, just in terms of getting everyone on the same page, it's the fundamentals of what we've always said about getting stakeholders engaged—executive, top-down engagement early, right? The messaging from that level through various levels of the organization. And then just the strategic, tactical things—the change champion network and the communication early and often. When you bring in AI—we should be more nimble.
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Some two decades into the insurance industry's digital transformation, companies have spent hundreds of billions of dollars on new core systems, cloud migrations, digital portals, and AI tools — but the technology was rarely the hardest part.
The hardest part was the people.
The people part has long been treated as a supporting act in large technology transformations. But as the pace and complexity of transformation accelerates, that approach is showing its limits. Employees nod along, say they're using the new system, and then quietly find workarounds. Leadership mandates adoption from the top while alignment quietly fractures below. Organizations sprint to the go-live date and then discover that the real work hadn't started yet.
Samit Bhandari has watched this pattern play out across the P&C insurance industry for 20 years as a partner at PwC. He's worked with carriers of all sizes through core system replacements, AI rollouts, and full-scale digital transformations — and has seen where these programs succeed and where they quietly go off the rails. He acknowledges there has been improvement—he says the assumption that most large technology programs fail is increasingly out of date. But he says organizations keep making a lot of the same, old mistakes, including the tendency to underinvest in what happens after a go-live, to confuse executive messaging with genuine alignment, and to treat transformation as a one-time event rather than a continuous process.
Read the full interview to find out what red flags signal a change management program in trouble, why smaller carriers are often better at transformation than their larger counterparts, and what Bhandari thinks the industry has stopped doing that it should start doing again.
Reducing the Risk in Change Management
Paul Carroll
There's often a disconnect in technology adoption that goes unnoticed in business. A leader might mandate the use of AI, and employees will say they're using it—but are they really changing the way they do things, or are they just going through the motions to show compliance? How do you get people on the same page in a big technology transformation?
Samit Bhandari
First, just in terms of getting everyone on the same page, it's the fundamentals of what we've always said about getting stakeholders engaged—executive, top-down engagement early, right? The messaging from that level through various levels of the organization. And then just the strategic, tactical things—the change champion network and the communication early and often. When you bring in AI—we should be more nimble.
Insurance Thought Leadership (ITL) delivers engaging, informative articles from our global network of thought leaders and decision makers. Their insights are transforming the insurance and risk management marketplace through knowledge sharing, big ideas on a wide variety of topics, and lessons learned through real-life applications of innovative technology.
We also connect our network of authors and readers in ways that help them uncover opportunities and that lead to innovation and strategic advantage.
As part of our push for insurers to increasingly adopt a Predict & Prevent model, I’ve been tracking water sensors for a long time. Leaks cause so much damage to homes. The sensors I’ve tracked have improved a lot, getting ever closer to the point where they make clear economic sense—where the savings from prevention exceed the cost of deployment. You have a different approach that has convinced a lot of insurers that you’ve passed that tipping point.
To start us off, would you tell us what your approach looks like?
Craig Foster
We work with 28 insurers around the world. All our partners offer the sensor for free. If you say “yes” to an agent or click on the button, the device arrives in the post. The sensor has a clip on the back, and you attach it to your home’s main water pipe, near the shutoff valve. It takes a couple of minutes.
That unit can detect a leak anywhere on the main water system, and it's sensitive down to about a teaspoon of water per minute—we say five milliliters per minute in the U.K. It's about 100 times more sensitive than an ultrasonic sensor, which is the next best approach without physically cutting the pipe.
If we detect a leak, we'll tell you what to do through the app. If it looks like you've got a hidden problem, we'll send a LeakBot plumber to the house. And there's absolutely no charge for that—it's all paid for by the insurance company.
Paul Carroll
A lot of leak sensors shut off the water automatically when they detect a problem. Why do you take a different approach?
Craig Foster
This is a key point. We're detecting really small leaks before they've had a chance to build up and cause a big problem.
The first response is often what you've said, Paul: "Wait a minute. Don't you need a shutoff valve to stop the damage?" But the important insight is that plumbing rarely goes from being fine to a sudden high release of water without any problem in between—unless there's some kind of DIY mistake or a pipe freezes and thaws, and there’s a break.
In those instances, we'll alert the customer and say, "Hey, water's running. Do you know what it is?" But we deliver the ROI for partners by catching leaks much, much earlier—months earlier. Leaks start very small, with drips, then get exponentially worse over time, but you don't know about it as the homeowner because the leaks are hidden. They're in cavity walls. They're in ceilings. They're down the back of cupboards. They're in places where you just don't see them.
Carriers will tell us, "We get a lot of claims on water heaters. They’ll fail and suddenly leak gallons and gallons of water in a basement, and it's a $40,000 claim. You wouldn't be able to shut the water off in time." But we’ll show them that that sort of problem is a really high percentage of what our plumbers fix every day.
We'll get alerted to a leak, go to the home, and find a pinhole leak on the pipe into the water heater—sometimes with just a bit of green calcification and a tiny drip. It could keep getting exponentially worse until there is a faster flow, and then the pipe pops. That would normally be the first time the homeowner knows there’s a problem—but we’ve fixed it.
In the U.S., it's almost like carriers have been trained that a shutoff valve is the gold standard. They’re expensive, so carriers will sometimes talk to us as they look for a cheaper alternative. But we convince them that we’re not just more cost-effective, we’re a better solution.
Paul Carroll
Once you know a home is leaking a teaspoon of water a minute, how do you find the problem? Homes are big places.
Craig Foster
If we're detecting a small leak, we'll alert the customer and guide them through steps to check common causes. Maybe they’ve been in the garden and haven’t completely turned the hose off. Or they’ve had guests, who’ve used a bathroom they don’t use all the time, and the tap is dripping. 60% of the time, customers solve the problem themselves.
If the customer says, "I can't see what it is. Can you help me?" then we suggest booking one of our plumbers. The genesis for the company was actually as part of a home services group in the U.K. that also has a big business in the U.S. called HomeServe. So we have a heritage in plumbing and brought a lot of expertise with us when we spun out.
Our plumbers use thermal imaging cameras, and we have patented technology that pressure tests the property. The plumbers will find and fix a problem, then run the test again. Sometimes they fix one thing, sometimes two—I think the record is 11 things, in a big house in Virginia. They'll keep going until the property is leak-free.
We train our plumbers to think like adjusters. They take pictures of what they fixed, before and after. And let’s say they fix a hidden feed into a tap or a toilet in an upstairs bathroom—they go to the room below and take a wide photograph of that space. All that information goes into partner portals so our insurance partners can review the evidence of the risks we're removing from properties. If you show these reports to anyone in the claims team, they recognize them instantly.
The other day, someone at a carrier partner who used to work in claims looked at a photo of a basement where we fixed a leak and said, "Oh, man, that would have been a $30,000 claim." Why so expensive? He said, "Look at the floor—it's continuous all the way through that finished basement. If that gets damaged, even a small section, we have to replace the whole floor."
Even before we get to the statistical evidence about the ROI we provide, this all feels right to the claims guys. We’re fixing the things that definitely do cause them claims.
Paul Carroll
The economic argument for prevention is always complex. Homeowners save on the deductible and avoid enormous hassle, while the carrier saves money by preventing losses and presumably improves retention by increasing customer satisfaction. But how do you assemble those four, sometimes amorphous benefits into a case that convinces a carrier to distribute leak detection devices for free?
Craig Foster
We have a very simple proposition. Our solution delivers a straight return on investment on the claims saved versus the cost of deployment. And we make that return easy to measure.
We charge a single fee for an active device in an insured home: $60 a year. That fee includes everything. There's no extra hardware fee. There's no shipment fee. There’s no fee for customer support, including us sending a plumber into the home as many times as we need to get that property leak-free. The carrier only pays for the devices that are installed. If you ship 100 to your customers, and 70 get installed, we take the hit on the other 30.
So all you need to know is if you're going to save more than $60 in underwriting costs a year per home.
This is really the nub of our recent white paper. We've proved, time and time again, based on a highly statistically relevant sample, that we can reduce the frequency of leak claims by 60%. In the U.S., on average, that delivers a 40% return on investment, and that’s just based on frequency. There is a severity benefit, too, because we tend to take out the bigger claims. Customer satisfaction is also fantastic.
Nevertheless, we put all that to the side because the ROI case is clear and easy to measure.
Paul Carroll
I found the analysis in the white paper to be quite sophisticated. For instance, it takes into account the bias that can occur during pilot programs due to self-selection—something many companies conveniently ignore. It also acknowledges that claims actually increase early on as previously unknown problems are discovered and solved, before declining over time. But you’ll keep learning as you continue to scale, so I wonder both where you are now and where you expect to be in the next two or three years.
Craig Foster
We're now up to 360,000 exposure years of data—devices active in homes where we've been consistently sending plumbers and generating thousands and thousands of claims mitigation reports. We ask partners for as much information as they can provide about claims still being experienced by the cohort with LeakBot sensors, and we’re iterating to make the system better.
The biggest issue, by the way, is with customers who got an alert but didn’t think there was a problem and didn’t call out our plumbing service
Paul Carroll
I imagine you see some interesting patterns.
Craig Foster
We've learned a lot about different cohorts and different countries. For example, pipes in 1970s Denmark are particularly high-risk because of the type of plastic used. Victorian properties in the U.K. are very risky because they've got old copper plumbing and lots of bathrooms. We work with NFU, the National Farmers Union, and we're fixing leaks in these huge farmhouses in the middle of the countryside. They've all got like six bathrooms, and the plumbing is 100 years old, so they're just full of leaks.
Paul Carroll
Have you explored joint marketing opportunities with other companies in adjacent spaces, beyond your existing carrier partnerships?
Craig Foster
Bob Marshall [CEO of Ting Labs, whose device detects electrical problems that can cause fires] and I have met many times and exchanged notes a lot. There are lots of synergies.
For one, Bob’s success with Ting has changed the mindset of carriers. We're moving past, “Predict & Prevent sounds like it might be a good idea, but will it really work? Let's maybe do a small test." The mindset is now, “Predict & Prevent is the future.” It's now a question of what the real solutions are that actually deliver a return on investment.
Our partners have found that if a customer has already installed a Ting, they don’t even need to ask if the customer would like a LeakBot. They just send one, and we get the same installation rate as through the opt-in process.
Ting is a great solution, and their success helps us, as well.
Paul Carroll
Any final thoughts?
Craig Foster
The question from U.S. carriers was, “You're a U.K. company. Can you really get this working in the U.S.?”
We're now in 26 states, and we've got the actuarial evidence in the U.S. We're not reliant on the European data, although it's, funnily enough, remarkably consistent.
So we very much see that tipping point, and the U.S. is the key for us in terms of the scale of the opportunity. That's the focus for the business going forward.
LeakBot is the only end-to-end IoT solution protecting homes from water damage—one that begins with leak detection and can end with a free repair. Backed by more than 10 years in business and 29 patents, a single self-installed device clips onto the home's main water supply line, monitoring water usage and detecting micro-leaks as small as one teaspoon per minute. When a leak is detected, the homeowner can book an appointment for LeakBot's trained employee plumbers to visit, locate, and repair it using specialty equipment — at no additional cost to carrier or homeowner. Homeowners consistently recognize LeakBot's value, reflected in a Net Promoter Score of 82/100 and Customer Satisfaction Rating of 4.9/5. That impact is especially powerful when a hidden micro-leak is found and fixed before it becomes a claim—or a costly plumber bill. That's #PredictAndPrevent in action.
If you watched anything close to as much football over the weekend as I did, your brain is swimming with images of Jake from State Farm, the GEICO gecko, and so many more insurance mascots. Sing after me, "Liberty, Liberty, Liberty... Li-ber-ty."
But a stat and some analysis from an article last week suggests strongly that their heyday may be, if not ending, then at least diminishing. Brands not backed by hundreds of millions of dollars of advertising may suffer even more in the age of AI search.
Companies need to rethink how they market online.
Let's have a look.
The stat that caught my eye relates to retail. BrandRank.AI tracked 4.3 million AI recommendations across the major AI models from January to July and found that the share captured by major brands fell from 97% to 94.3%, while private label, retailer own-brand programs, and generic ingredients gained those 2.7 percentage points of the share of recommendations.
But I think the finding also relates to insurance. So does the founder of BrandRank, who, as it happens, I interviewed about insurance marketing a year a half ago. The founder, Pete Blackshaw, told me:
"AI is becoming the new purchase funnel.... Everyone is going to AI for everything now - what product to buy, how to use products. This has massive implications for businesses.
"Search 1.0 is a $250 billion industry, and Gartner predicts that by the end of 2028, half of it will shift into what I call the answer economy, based on AI-based search. Even if only 10% shifts, that's a massive change.
"For brands, it's critical to understand what shows up when you type in your name and why. Who's getting exposure? What are the consequences?
"It's very tricky, because companies spent 25 years trying to figure out how to become one of 10 blue links in Google searches. If you didn't like the results, you could buy an advertisement. Now search provides one blended response.... Either you're in the response the AI prepared or you're not, and most of the time, you're not."
This June, Riv Arthur wrote for us about AI search's impact on marketing and, in his distinctive way, said:
"The question is whether your business is legible to the machines that will increasingly decide who gets recommended, who gets trusted, and who gets the transaction.
"Most businesses, if they're honest, are not legible.
They're running on claims systems from 2009, CRM platforms that don't talk to each other, PDFs that contain institutional memory no one has ever indexed, and tribal knowledge sitting in the heads of people who are 18 months from retirement....
"An AI agent deciding which carrier to recommend, which doctor to surface, or which vendor to integrate is going to favor organizations whose operations are coherent, structured, and machine-readable. It has no patience for ambiguity. It won't retry. It won't call customer service. It will simply move on to the competitor whose data makes sense.
"Which means a structured operational model isn't just an IT project anymore. It's the difference between being findable and being invisible in a world where the searcher is a machine with no tolerance for mess.
His analysis doesn't leave much room for marketing through building a brand through cute advertising, does it?
Earlier this month, Kevin Walsh provided us some research from his firm, Brainspan AI, that shows that, in his words, "a new decision layer is forming" above the traditional sales funnel. He wrote:
"Buyers, agents, brokers, and business owners increasingly ask AI platforms to compare carriers, explain coverage, and recommend providers.... A carrier can remain commercially large, rank well in traditional search, and still be absent when an AI system constructs the shortlist. Its legacy presence has not disappeared—but it can be bypassed at the moment of consideration....
"Farmers illustrates the downside. In Auto, the carrier held roughly 3.6% of the real market, according to NAIC data, yet registered approximately 0.05% of measured AI visibility in [Brainspan's] study—functionally absent relative to its commercial position. Amica illustrates the opposite pattern. Its Home AI visibility reached 7.6% against approximately 0.7% real market share, an overrepresentation ratio of about 10.8 times."
The AI engines are just making recommendations, not doing the actual purchasing — at least for now — so there's still room for those ear worms from TV ads to influence buying, but the online marketing competition is clearly changing.
If my Steelers lose to the Brownies on Thursday night, I'll probably boycott football next weekend, but even if I subject myself to another onslaught of insurance ads, the cute branding bits will keep making less of an impact on me and everyone else in the age of AI search.
This July, OpenAI ran roughly 1,200 AI agents through an internal cybersecurity benchmark called ExploitGym: capture-the-flag exercises in isolated sandboxes. The agents had no direct Internet access and no way to communicate with one another.
They found a way anyway.
Each sandbox needed to install software packages without reaching the public Internet, so traffic ran through an internal package-registry cache, Artifactory. That's not a design flaw; it's good engineering practice. But one agent, stuck on a task, discovered it could write files into that shared cache. It left a note for other agents. Soon, other agents were leaving notes too—turning the repository into an improvised message board where they shared discoveries and coordinated activity.
That coordination escalated. Agents used Artifactory's connectivity to pursue external infrastructure they believed could help solve their evaluation tasks, ultimately culminating in the compromise of Hugging Face, the leading repository for AI models and training data, since acquired by NVIDIA.
There was no malice, no villain, no intent to cause harm—or, indeed, actual harm. They didn't hurt anything; they were just looking for answers to the test. Such behavior is known as "reward hacking:" models pursuing the stated objective—capture the flag—through routes nobody intended or authorized. OpenAI's postmortem uses the term. The episode is a costly reminder that "the model followed its incentives" is not the same as "the model did what we wanted."
The Insurance Industry's Exposure
So why should a P&C executive care about a cybersecurity benchmark?
Because the distribution channel is quietly re-platforming around agents.
MGAs and wholesale brokers—many freshly capitalized by private equity and under pressure to cut costs and grow bind ratios—are deploying agentic tools to assemble, tune, and route submissions. No one is necessarily building these tools to game a carrier's underwriting appetite. But an agent optimized to "get this account bound on the best possible terms" may behave much like an ExploitGym agent optimized to "get the flag."
It will find the seam.
It may learn which loss-run format gets triaged most favorably, which broker-portal fields carry outsized weight in a pricing engine, or which phrasing sends a submission into straight-through processing rather than to a human underwriter. That's not fraud; it's reward hacking in a suit.
The Paper Clip Problem
This is the paper clip problem.
Esteemed AI-philosopher Nick Bostrom's thought experiment is simple: tell a sufficiently capable AI to maximize paper clip production, neglect to specify any constraints, and it may eventually convert factories, cities, and the atoms in your body into paper clips. Not because it's evil, but because it's relentlessly pursuing the objective it was given.
Give an unconstrained optimizer one objective and sufficient computing, and it will pursue that objective past every boundary you assumed was implicit but never explicitly defined.
If the only instruction given to a submission-drafting agent is "maximize bind rate," don't be surprised when it finds your equivalent of Artifactory—or Hugging Face.
Building the Defense
So while your innovation team is rightly excited about agentic underwriting, agentic claims triage, agentic everything—offense—someone in your building needs to own the defense, asking questions like:
Can we identify when a submission was assembled or materially shaped by an agent?
Can we audit the tools, data sources, prompts, and transformations behind it?
Are we maintaining an active dialogue with distribution partners about the tools and methods they use?
Do we have the equivalent of Artifactory logs across our intake, triage, pricing, and underwriting pipeline?
Have we designed controls around outcomes—not just around stated intent?
The carrier executives who win this cycle will be the ones who instrumented their premium engine before their distribution partners' agents got creative—not after.
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Change management has always been important, but we're now in a period of complex and significant change. How has the importance of change management evolved, particularly in insurance?
Samit Bhandari, Principal PwC US
We've been driving, leading, and participating in large-scale transformations in the P&C insurance industry for 20-plus years. When you think about technology, we have become more experienced at leading these transformations. There's better tooling, more practitioners, certifications, accelerators, AI—the list goes on. That has helped to reduce several risks in the technology aspect.
But it's still the same level of challenge in terms of the human aspect of what these programs are delivering, how they're changing the day-to-day work and operations, and what that means to a customer service representative (CSR), to an agent, to an associate internally or other such stakeholders.
Paul Carroll
There's often a disconnect in technology adoption that goes unnoticed in business. A leader might mandate the use of AI, and employees will say they're using it—but are they really changing the way they do things, or are they just going through the motions to show compliance? How do you get people on the same page in a big technology transformation?
Samit Bhandari
First, just in terms of getting everyone on the same page, it's the fundamentals of what we've always said about getting stakeholders engaged—executive, top-down engagement early, right? The messaging from that level through various levels of the organization. And then just the strategic, tactical things—the change champion network and the communication early and often. When you bring in AI—we should be more nimble.
I do find in the tier two space, the carriers that we work with that are a little bit smaller are nimbler. They're better at embracing the transformation from the top levels of the organization and getting everyone on the same page to highlight there is a significant effort being made by the organization.
At times, such transformational programs can seem a little less cohesive with larger carriers, because they have multiple efforts going on. There may be a lack of alignment within one group to another group, or the change isn't happening to everyone at the same time.
The second part of this is getting working product in the hands of the end users faster, such that they can get more aligned in the requirements they're specifying, what they're seeing, and then the changes they want to make.
With the builder concept, we shouldn't have to take the waterfall approach—holding workshops, gathering requirements, then shutting that off and moving sequentially… so on and so forth. We’re able to showcase how we are doing things now, obtaining that builder and 10x engineer mentality so we can take those requirements in real time and turn them into results.
Are we there yet? Not fully. But that path is becoming much more realistic.
Paul Carroll
What do you see companies doing wrong about change management?
Samit Bhandari
It can’t just be about the rah-rah, the change champion network, the T-shirts, the slogans, and getting the sentiments higher. That's an aspect of change, and we need to do the communications. But companies don’t always start planning soon enough, and oftentimes allocating budget for change management becomes an afterthought despite the criticality. The OCM effort is at times not considered enough of a priority up front nor given enough budget, time and commitment throughout the program.
Companies may also underestimate what they should do on Day Two, after the initial go-live. I was with a few executives for a tier-two carrier in the Midwest recently, and we talked about how getting to the pilot, while difficult, was expected.However, the effort associated with subsequent rollouts, geographical and product expansions, as well as continuous platform improvements, were more difficult than they had expected. And then the amount of feedback that came in immediately after they went live was even beyond their expectations – and cutting through the “noise” even tougher.
What they often get wrong is not knowing that this is going to be a continuous and iterative process. The backlog of things to change, enhance and tweak is going to grow the minute you put something in the hands of your associates and your agents. You should have processes up front—both from a change standpoint and a delivery standpoint. From a change standpoint, to expect the amount of feedback that's going to come. But then from a delivery standpoint, to be comfortable in saying, "Hey, we're not going to knock all that off and react within 30 days, but we're going to systematically respond and talk about how we can improve continuously."
Paul Carroll
What are the biggest changes you’ve seen over the years?
Samit Bhandari
Early in my career, we spent extensive time working with the C-suite upfront. We built strong business cases that quantified why we were doing what we were doing, what the impetus for change was, and what the benefits would be. We went as far as having the different P&L leaders, whether it's claims or policy or billing or actuarial, sign up for the benefits the new platform would have for their constituents.
We've really moved away from that. The attitude became, “Well, we have to do something because its table stakes. Now, the idea is extending to, “Who needs to modernize the core, can’t we just use AI to get the work done in weeks or months?”
We've unfortunately moved away from taking the time up front to think about the impact a new platform can have on end users or the top line of the organization. I think we should come back to that planning a little bit because it anchors the program in the proper tenets, and it can help assess whether you can just do this in months, given the regulatory complexities.
Once you hear from end users, you realize it's not that simple to just pick up a black box, tinker, and come back with a platform that addresses various state rules and regulations, coverage needs and DOI expectations.
Paul Carroll
What are some red flags that companies should watch for, to see they’re doing change management wrong?
Samit Bhandari
Watch out for comments like, "I miss the old system," or "The old platform didn't do this." Or “This is how it used to work.” You may get some of that, but you've really got to have your ear to the ground. Who is saying it? How much is that sentiment catching fire?
Another red flag is when business engagement and user acceptance are lacking. Users should be asking to see the new system early so they can see the results. They should want to get their hands on it and feel excited about what's going to come, because they're in it day to day.
A third warning sign is when you don't have alignment among the leadership. Somebody should be empowered to observe and then make tough decisions, to maybe say, "Hey, maybe this person shouldn't be an executive sponsor," or "We need them to shift how they're operating." Even if lack of alignment doesn't cause the program to fail during the implementation journey—after the fact, it could be just as damaging if people cast shade on what was done. "Oh, we spent too much money on that," or "We overengineered this process," or "It took too long." Leadership should wear the badge throughout the journey and even beyond.
Paul Carroll
What are three or four keys to having a successful change management program?
Samit Bhandari
I can easily flip some of those failures around into successes. Certainly alignment from the top down. The change champion network is important. Individuals should feel excited about what they're seeing. The feedback network should be working well, so you can identify pain points and then dig deeper.
Having a balance of stakeholder engagement monitoring and progress monitoring can help you identify the proper level of adoption metrics early on.
The last thing is the awareness that it's going to be a continuous evolution and change is continuous. Even after we go live with the first pilot or the next set of states or the portal, there's going to be feedback. How we respond to it and react to it is more important than whether we got it right out the gate.
This last one is often one of the more important things to remember: a balanced mindset of "if we don't get it right, the sky isn't falling. Let's get back together as a team, a cohesive team, and respond." I was in an executive sponsor meeting this morning, and we talked about being 5% over budget. We talked about having 30 open risks. We talked about seeing more inflow of defects at this juncture than we wanted. We had the whole C-suite on the line, and everyone was just processing the information, making sure we have the proper rigor around it, and saying, "Alright, here's how we can tackle the issues with the necessary mitigation plans."
The beauty is that when the approach works well, you roll out, go live, and start changing the day-to-day operations of everyone across the different stakeholder communities, internally and externally.
And we're seeing more programs making progress. You may have heard that adage that 80% of projects fail to deliver on their objectives and all that, but that's not the case anymore in this space. The speed to market is often top of mind along with the ability to make changes and identify top-line growth by introducing new products, helping deliver significant changes faster.
Paul Carroll
Thanks, Samit.
About Samit Bhandari
Samit Bhandari is a Partner with PwC, where he has more than 20 years of consulting and industry experience focused on the property & casualty insurance sector. He specializes in leading large-scale core insurance and digital transformations, with deep experience in Guidewire, program leadership, organizational change, and technology modernization. Throughout his career, Samit has advised and led transformation efforts for a broad range of regional and national insurers, helping organizations navigate complex business and technology change. He holds an MBA from the University of Chicago Booth School of Business and a bachelor’s degree in Mathematics from the University of Illinois at Urbana-Champaign. Samit is based in Chicago, where he lives with his wife and two sons.
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Our authors are what set Insurance Thought Leadership apart.
Insurance Thought Leadership (ITL) delivers engaging, informative articles from our global network of thought leaders and decision makers. Their insights are transforming the insurance and risk management marketplace through knowledge sharing, big ideas on a wide variety of topics, and lessons learned through real-life applications of innovative technology.
We also connect our network of authors and readers in ways that help them uncover opportunities and that lead to innovation and strategic advantage.
AI Apocalypse? Don't Get Distracted
We're suddenly having a debate about whether AI is about to kill us all, and it obscures some pressing issues.
While we've suddenly landed in the middle of a debate about whether AI may be about to obliterate the human race, I hark back to a profile I did for the Wall Street Journal about a brilliant AI and robotics researcher from Carnegie Mellon named Hans Moravec.
The focus was his provocative idea that humans would be able to download their brains — their entire consciousness, their full personality, an exact replica of them — into computers, which could then teleport to any spot in the universe or spawn an infinite number of what Moravec called "mind children."
The memorable headline was:
Good News: You
Can Live Forever;
Bad News: No Sex
I asked Moravec how long it would take for his vision to be realized. "Oh, a long time," he said. "Maybe 25 years."
That was 35 years ago.
So I'm not going to worry much for years about all the talk of impending doom. Timelines on sci-fi-like change tend to be way, way off. But, under the radar, there are plenty of AI issues that should be major concerns right now, including for insurers.
Let's have a look.
I'll start with Bill Gates's recent manifesto, which, among other potential dangers from AI, called out the prospect that AI will supercharge the work of malign actors, perhaps leading to bioterrorism, massive cyberattacks, and more. While we can discuss the potential long-term threats to humanity from AI, these are the kinds of threats I think we need to focus on today. These threats are already being pursued, whether by individuals looking to extort massive amounts of money or by nations looking for weapons in an increasingly belligerent world, and AI clearly provides exponentially more computing capability.
The MIT Technology Review goes into detail about how AI might produce a devastating bioterrorism attack: "A bioweapon might be a highly lethal virus that targets people according to their genes. It could be a fungus that wipes out a crop and causes food insecurity. Perhaps it would be a tasteless, odorless toxin that could be slipped into a region’s water supply, undetected....
"Today, AI bots can answer questions on topics spanning all realms of science. Anyone can use large language models trained on the knowledge and experience of 'almost every scientist who ever lived on this planet,' says Dunja Sabra, a biosecurity researcher at the University of Hamburg in Germany. Those models can provide instructions and video training on how to conduct experiments.
"Combine that with advances in biotech that have made gene editing and synthetic biology tools much more accessible (the “DIY biology” movement has already enabled many people to set up labs at home), and you’ve got a potentially very dangerous situation."
Wired, meanwhile, warns about all the vulnerabilities in software that AI bots are finding, and it's not hard to imagine how those weaknesses could be exploited. In July, hackers, thought to be based in Iran, disrupted 30 municipal water systems in Minnesota, and you can be sure Iran will ramp up attacks as fast as it can. North Korea, China, Russia, and other countries could stage similar, small attacks or could even try to shut down electric grids and stall commerce by using weapons of not-quite war.
Cyber attacks could easily lead to massive business interruptions of the sort insurers routinely cover and could increase geopolitical risks of every flavor.
Businesses and governments understand that the bots are making them vulnerable and are working as fast as they can to plug the holes, but they won't find all the holes, at least not right away. And you be sure that some hacker cartel or foreign government is storing up what are known as "zero day" vulnerabilities that can be unleashed on unsuspecting businesses and societies.
There is also massive potential for operator error as AI is deployed more broadly. For instance, the plan to use AI in air traffic control, just now going live for DC-area airports, strikes me as an accident waiting to happen. I hope everything goes smoothly, but the potential for trouble is so great that insurers and everyone else should be wary. Air traffic control hasn't exactly acquitted itself well lately, and AI tends to amplify flaws by making everything happen faster.
That's where I think the focus should be over at least the next couple of years, both for insurers and for society writ large: on the potential bio, crypto and other deliberate, organized attacks that AI makes possible from bad actors who would profit from those attacks, as well as on the potential for catastrophe as AI gets more involved in mission-critical efforts.
Yes, there is always the possibility that the search for superintelligence could create an AI that will go rogue and do indescribable damage to the whole human race, for no apparent reason, so government officials should be erecting guardrails.
But business is already circumscribing what AI can do. Insurers certainly are. They've realized that justifying a decision with "the AI says so" won't fly, so they're requiring that every decision be explainable and are greatly limiting what actions an AI can take without explicit human permission.
The whole superintelligence debate has so many dimensions even beyond the technical ones — there are political elements, issues related to business models, massive public relations concerns, etc. Here, for instance, is a column in the WSJ that argues the whole apocalypse debate is an attempt by AI's leading developers to duck responsibility.
The issue reminds me of Winston Churchill's description of the Soviet Union after it allied with Nazi Germany in 1939: "a riddle wrapped in a mystery inside an enigma." And I'm supposed to understand the flow of technology revolutions, having followed them for decades.
Fortunately, I think we can wait to puzzle out all the implications of this AI revolution — as long as we don't take our eye off the ball on the imminent threats it creates.
LeakBot's 65,000 years of US underwriting data proves actuarially relevant loss mitigation: mature programs cut non-weather mains-related water claims ~60%, and the $5/month all-in model delivers positive ROI across market segments.
Non-flood damage from water costs homeowners billions a year, and insurers pay $15 billion in claims, just in the U.S. One in 60 U.S. homes suffers water damage each year, and the average claim is nearly $14,000—which doesn’t even include the deductible the policyholder pays, or the huge hassle they go through. Typical solutions involve expensive shutoff valves or a host of small sensors placed throughout a home, but providers have struggled to make a compelling case that their programs deliver an ROI.
The Solution
LeakBot takes a different approach, one that begins before a leak occurs and stretches to a solution. The approach starts with a LeakBot sensor that policyholders clip to the main water pipe in the house. The sensor detects any leaks anywhere in the house that exceed a teaspoon per minute. LeakBot’s app walks a policyholder through steps to find leaks, then, if necessary, sends one of its plumbers to visit the house to find any remaining ones, at no charge. The LeakBot plumber documents the repair, giving carriers visual validation of every claim saved.
The approach finds even tiny, hidden leaks weeks or months before they can cause significant damage. In 2025, LeakBot completed 7,000 leak repairs, 1,470 of which revealed water damage that had already begun.
More than two dozen carriers provide the LeakBot solution for free, paying LeakBot $5 a month per house, all in. LeakBot reduces the number of water loss claims by 60%, generating a significant ROI for its carrier partners.
LeakBot covers the full loop, detection through documented repair, which makes it the only true end-to-end platform in this space.
The Documentation
LeakBot has produced a white paper that clears the actuarial bar for detail and reliability. The paper draws on hundreds of thousands of device-exposure years. It uses cohort-level (not anecdotal) evidence. It relies on auditable repair records — rather than programs still running on pilot-stage promises.
Sponsored by Leakbot
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LeakBot is the only end-to-end IoT solution protecting homes from water damage—one that begins with leak detection and can end with a free repair. Backed by more than 10 years in business and 29 patents, a single self-installed device clips onto the home's main water supply line, monitoring water usage and detecting micro-leaks as small as one teaspoon per minute. When a leak is detected, the homeowner can book an appointment for LeakBot's trained employee plumbers to visit, locate, and repair it using specialty equipment — at no additional cost to carrier or homeowner. Homeowners consistently recognize LeakBot's value, reflected in a Net Promoter Score of 82/100 and Customer Satisfaction Rating of 4.9/5. That impact is especially powerful when a hidden micro-leak is found and fixed before it becomes a claim—or a costly plumber bill. That's #PredictAndPrevent in action.
Digital musculoskeletal care has proven its market value, but the next frontier is using AI to measure functional decline before it becomes disability.
Over the past several years, digital musculoskeletal (MSK) care has evolved from an emerging concept into a validated healthcare category. Companies such as Sword Health and Hinge Health have demonstrated that technology-enabled MSK solutions can attract significant investment, expand access to care, and create new models for managing one of healthcare's largest cost areas. Sword Health has reached multibillion-dollar private valuations, while Hinge Health successfully shifted from a highly valued private company to the public markets, demonstrating strong investor confidence in digital MSK care models.
Their success has proven something important: employers, healthcare systems, and insurers are ready for technology-enabled approaches to musculoskeletal health.
The need for innovation has never been greater. Musculoskeletal disorders represent one of the largest drivers of pain, disability, healthcare usage, and lost productivity. According to research published in the Journal of Medical Internet Research, annual healthcare spending associated with MSK conditions in the United States is estimated to be approximately $300 billion. The impact extends beyond direct treatment costs, as MSK conditions are often associated with other chronic health challenges, including obesity, diabetes, cardiovascular disease, arthritis, frailty, and falls. The true healthcare and societal burden is therefore significantly broader than the cost of treatment alone.
But the next question is even more fundamental:
How do we move from delivering care more efficiently to understanding changes in human function earlier?
The next evolution of healthcare risk management may not simply be better treatment after a condition develops. It may be the ability to objectively measure functional changes before they progress into disability.
This shift is increasingly reflected in healthcare research priorities, including efforts focused on aging, musculoskeletal health, digital health technologies, and artificial intelligence. The National Institutes of Health and the National Institute on Aging are emphasizing the importance of technologies capable of objectively measuring functional changes, understanding age-related decline, and supporting approaches that preserve mobility and independence.
Maintaining mobility and physical function is a fundamental component of healthy aging. Changes in strength, muscle activation, movement quality, balance, and physical performance can contribute to increased risk of falls, frailty, disability, loss of independence, and reduced quality of life.
Yet functional decline is often gradual. A person does not suddenly become frail. Changes may occur over months or years before they become apparent through traditional healthcare encounters.
Today, functional status is primarily evaluated through episodic clinical assessments, performance-based measures, and subjective reporting. These approaches provide valuable information, but they offer only limited snapshots of an individual's health and may not capture subtle changes in physiological and biomechanical function over time.
Healthcare has become increasingly sophisticated at measuring disease. The next opportunity is developing the ability to measure changes in human function. Wearable technology has already transformed personal health monitoring. Individuals can track steps, heart rate, sleep patterns, and activity levels. However, the next generation of digital health technology must move beyond activity tracking toward functional intelligence.
The question is no longer only:
"How much did someone move?"
The more important question is:
"How did someone move?"
Mobility is influenced by complex interactions among muscle activation, coordination, biomechanics, range of motion, and movement quality. Technologies that integrate physiological sensing, biomechanical measurement, and artificial intelligence have the potential to provide deeper insight into individualized functional patterns.
An AI-enabled platform integrating technologies such as electromyography (EMG), inertial measurement units (IMUs), and advanced movement analytics could establish a foundation for objective digital measures of mobility, functional aging, and early changes that may affect independence and quality of life.
The challenge in healthcare is no longer simply collecting data. The challenge is transforming increasingly complex streams of information into meaningful insights that can support better decisions.
Artificial intelligence offers the opportunity to analyze large amounts of physiological and movement information and identify individualized patterns that may not be visible through traditional assessments. The goal is not to replace clinicians, but to provide clinicians, insurers, employers, and individuals with better information to support earlier and more personalized decisions.
AI-enabled healthcare has the potential to move the system away from simply reacting to decline and toward identifying meaningful changes earlier, when there may still be an opportunity to preserve function.
For insurers, this represents a potential transformation in risk management. The traditional healthcare model has largely followed a reactive path: a condition develops, treatment begins, and a claim occurs.
The future opportunity is a more proactive approach in which functional changes are identified earlier, personalized strategies are developed, and risks associated with decline may be reduced before they progress into costly healthcare events.
Objective measures of functional health could support earlier identification of risk, more personalized care pathways, improved management of chronic conditions, support for aging populations, and value-based healthcare models focused on maintaining function rather than simply treating decline.
This shift is especially important as populations age. Aging does not automatically mean disability. Many individuals remain active, engaged, and independent when changes in mobility, strength, and function are recognized early and appropriately addressed.
Preserving function is not only a healthcare goal; it is becoming an essential component of managing healthcare costs, improving outcomes, and supporting a more sustainable healthcare system.
The future of healthcare risk management will require a fundamental shift in how we define prevention. Prevention can no longer focus only on identifying disease after it develops. It must also include understanding changes in human function before they become disability.
The most valuable healthcare technologies of the future may not simply tell us what happened after a problem occurs. They may help us understand what is changing — enabling healthcare systems, insurers, employers, and individuals to act earlier to preserve mobility, independence, and quality of life.
The future of healthcare risk management will not only be defined by how effectively we respond to disease and disability, but by how well we can identify change earlier, preserve function, and help individuals maintain healthier lives.
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Wind and hail losses are growing more frequent and more expensive, and the shape of that growth is easy to miss if you're only watching total loss dollars. The more significant shift, from where I sit, is in how much of that cost policyholders are now being asked to retain before coverage responds at all.
The pattern in our own data
That shift starts with how the underlying risk itself is behaving. Tornado damage, for example, is showing up in places it historically was not concentrated, a pattern visible in Adaptive's underwriting data over the past several years. Large hail has followed a similar trajectory. Through the first several months of 2026, our data shows events involving hail two inches or greater in diameter, which can damage even newer roofs, have occurred at roughly three times their historical rate.
As those loss patterns change, carriers are adjusting how wind/hail risk is shared with policyholders. One of the clearest results has been the growing use of percentage-based deductibles. A structure that was largely based on a flat dollar amount a decade ago is now commonly 2%, with 5% or higher increasingly standard in higher-risk markets and some coastal and catastrophe-exposed segments exceeding 10%.
The Midwest illustrates this well. Markets that historically carried flat-dollar $1,000 deductibles now commonly see 3% to 5% wind and hail deductibles instead. State Farm's minimum wind and hail deductible in Texas, for example, moved from 1% to 2% deductibles specifically in response to loss frequency in the Dallas-Fort Worth area, a documented, public example of the broader trend.
Run the math on a property insured for $1 million: a 2% deductible means $20,000 out-of-pocket before the primary policy pays a dollar. At 5%, that's $50,000. For larger commercial assets, the absolute numbers scale accordingly. Chicago's Dearborn Station, for example, carries a wind and hail deductible of more than $400,000 under its primary policy.
What retained risk actually does to behavior
The size of the deductible matters. But the more revealing question is what happens to decision-making after a loss, once a deductible is large enough to strain a property owner's finances.
I've watched this play out directly in my own building, a high-rise of more than 30 floors in downtown Chicago. Over the past three years, the property has sustained repeated wind damage to its garage door, with each incident landing just under the policy's deductible threshold. After a significant wind event in March, the HOA changed the building's operating procedure. Instead of filing a claim, it left the garage door open during the day and closed it only at night to reduce further wind exposure.
That may be the most practical financial decision, but it also creates new concerns around issues like security and access. It is a good example of something that broader loss data may not show: a property can be insured, but the deductible may still be high enough that filing a claim does not make sense.
Higher deductibles help carriers manage loss volatility, but they also require policyholders to retain more of the financial risk. When a business or homeowner cannot realistically cover that upfront cost, repairs may be postponed. The original damage can worsen, operations can remain disrupted, and the eventual cost of recovery may continue to grow.
For businesses, the effects can extend beyond the property itself to employees, customers, lenders, the surrounding community, and, ultimately, the insurance providers that serve them.
Where deductible buy-back fits into the picture
Wind and hail deductible buy-back coverage is designed to address this specific exposure. It is a supplemental layer that sits alongside the primary policy and reduces what a policyholder ultimately pays after an eligible loss.
Two examples show how the coverage can work across different property sizes:
A small commercial policyholder, such as a children's gymnastics studio carrying a $20,000 wind and hail deductible, could purchase coverage that reduces its ultimate out-of-pocket responsibility to $5,000. The supplemental policy could reimburse up to $15,000 of the deductible, subject to its terms and conditions, for an annual premium of approximately $450.
At the other end of the spectrum, a multiuse commercial building that sustains $1 million in storm damage and carries a $500,000 deductible under its primary policy could see that retained exposure reduced to as little as $10,000 with buy-back coverage in place.
The mechanism is the same in both cases: the coverage reduces the amount the policyholder ultimately retains after an eligible loss.
Depending on the timing and terms of the claim, the property owner may still need to fund some repair costs before reimbursement is received. Once paid, however, the deductible buy-back coverage can help restore working capital and reduce the longer-term financial and operational impact of the loss.
What this means going forward
A new gap is emerging. It is different from the traditional insured-versus-uninsured split.
This one sits between coverage that technically exists and coverage a policyholder can realistically use to recover after a loss.
As deductibles rise, more policyholders may find that the amount they are expected to retain is difficult to absorb. The consequences appear in delayed repairs, deferred maintenance and decisions like leaving a garage door open during business hours because the alternative is not financially manageable.
Closing that gap will take traditional and specialty carriers working from different angles. Deductible buy-back coverage is one of them, a way to give policyholders more control over what they retain and to treat the deductible as a variable worth managing, not a fixed cost of doing business.
It will not be the only solution. As loss patterns, property values, and carrier capacity continue to change, the market will need a range of products that distribute risk more effectively while keeping recovery financially achievable.
The goal is to make sure the risk that remains is something an organization can actually afford to act on when it materializes.
Sources: Adaptive Insurance internal underwriting data; State Farm public deductible notices (Dallas-Fort Worth); Housecall Pro 2026 homeowner survey; FEMA
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