Process first, AI second: What actually works in property claims
Explore why claims modernisation has to start with process, not technology. From cleaner intake and fewer handoffs to practical AI and better data validation, Damon Stafford shares what it takes to get adjusters out of air-traffic-control mode and back to making confident decisions.
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“Clean data, clean inputs, and validation at the beginning of the claim process allow efficiency to snowball to the right objective judgment.”
Damon StaffordCEO of Alpine ItelEpisode 3 transcript
[00:00:00] Dave: Hello and welcome to the Reality Layer podcast. I’m Dave Tobias, Chief Product Officer at Nearmap. Let’s get into today’s episode. Today I’m joined by Damon Stafford, CEO and President of Alpine Intel. Damon has spent his career building and scaling businesses across the insurance ecosystem, including HVACi, StrikeCheck, CCG IQ, and later coming together under the Alpine Intel brand. Today, Alpine Intel works across property, auto liability and workers comp, helping carriers simplify complex claims and investigation workflows. In this episode, we’ll explore what it takes to modernize property claims, where disconnected data and legacy processes still slow decisions down, and how AI and automation can help create faster and more accurate outcomes. Damon, welcome to the podcast. So, Damon, you spent more than a decade building companies around some of the most complex parts of the claims process. What made you first look at this space and think there’s a better way and maybe I can help?
[00:01:06] Damon: Yeah, I didn’t start with a piece of technology or experience and go kind of hunting for a problem. As I got some exposure to the insurance landscape, you know, I saw a lot of highly trained people making, you know, expensive decisions with a lack of facts and, you know, consistency. Policyholders, the carrier, the kind of field technicians at that time and other resources aligned with the carrier, they kind of all had different expectations. And that friction to me seemed to be a good opportunity to solve some business problems. And so I didn’t fall in love with insurance complexity. Kind of more so of like removing the complexity from insurance claims and processes, you know, for a better outcome for the policyholder and the carrier
[00:02:04] Dave: across, you know, your life in the insurance world, you’ve acquired many different types of businesses and put them together. What has that taught you?
[00:02:13] Damon: The type of claim or the subject matter continues to change as we grow and enter new markets. But, you know, the underlying operating problems are remarkably consistent. The information is fragmented, ownership not always clear, lots of time waiting between steps, following up, you know, kind of re. Even re entering data, which seems crazy in 2026, but, you know, I think like the, the biggest or the most obvious is, you know, there’s still a lot of, like, deciding who should make the decision. A lot of handoffs create delays and consistency and so acquisitions kind of reinforce, you know, that the, the company kind of brings smart people and valuable expertise and systems, but a lot of times with the same habits and kind of workarounds that lead to, and different outcomes and so, you know, type of claim changes, but the villains are kind of the same and the missing Facts and unclear ownership and yeah, just too much time waiting to kind of make the right objective decision.
[00:03:24] Dave: Property claims still rely on a lot of processes that have been around for decades. When you look across the claims ecosystem today, what do you see as the biggest opportunity for modernization?
[00:03:34] Damon: The biggest opportunity is still kind of reducing the distance between the loss, the objective facts and ultimately the insurance carrier’s decision on the claim. And if you started intake for an example, you know, if you capture the right evidence early, then you know, that could be like imagery Nearmap, could be, you know, things like photos, weather data, property data.
[00:04:04] Dave: Adjusters have more inputs, more data than they’ve ever had, right? There’s all these, you know, data points coming into them and they’re trying to make decisions. What does that fragmentation cost the carrier in terms of speed, accuracy, consistency?
[00:04:18] Damon: Yeah, I mean there are an endless amount, right. Of technologies and tools and you know, I view it as the, with that kind of chaos, the, that kind of fragmentation, you know, the adjusters. I don’t know, an API with arms and legs, right. They’re translating all of that information and handing it off. And instead of spending their time like applying judgment, they’re looking for documents and reconciling inputs, copying information, trying to determine what information is current. You know, things that don’t necessarily, you know, lead to time to make the right decision. Not to mention the delays and the expense for the, you know, for the carrier. Ultimately, I think an indemnity leakage is, you know, something that is an output of those type processes, you know, unnecessary expense in the claims department. I mean it could even lead to completely overstepping here. Right, but like culture issues, frustration within, within the carrier. So I mean to sum it up, I mean the adjuster should be making the decision, not necessarily like serving as Air Traffic Control for all the documents in a, you know, in a claim or information needed for a plane decision.
[00:05:36] Dave: When you talk about trying to modernize that process, right? You have all this data coming at adjusters and there’s this tendency to just throw new technology on top of an old process. How do you know when the workflow needs to be automated versus just completely redesigned? Where do you start?
[00:05:53] Damon: We’ve had a ton of success over the years starting with the process. First define the process harder than you, you know, than we, you know, we all think every time. But getting complete buy in from the team, which takes collaboration, deliberating amongst, amongst the team. But ultimately you’re looking for alignment, not consensus. And once you have that stable process, then you start the automation work and take it a piece of time, right? Redesign a broken step stage. However it is you view your operational process as an example. Before you know you, you make the entire process faster. And so it spend a lot of time mapping what actually happens. Not necessarily like what the procedure manual or training says, but what’s actually happening. And then you know, once you have those repetitive kind of process and rules engine then you have the right to apply whatever technology tool it is that you’re looking to implement. Pave the path with AI and then call it transformation or so start with the business problems that will allow you to apply technology and all the great AI tools that we have
[00:07:10] Dave: based on that. I think I know the answer to this. But do you think people spend enough time mapping the problem before trying to apply technology to AI or otherwise? Do they really truly sit down or do they just jump to technology?
[00:07:24] Damon: That’s what I see all the time. And we’ve all, we’ve all been there. You know, building the technology around the process and the company and the problem to be solved is the you know, is, is what’s created our success and what I think it is the you know, that’s the definition of you know, success in, in transformation. Buying the technology and then fitting your business around its ability. Sure it works or can, can work, but may not have the impact that you know, that you’re looking for.
[00:08:02] Dave: So we made it a few minutes without saying AI more than a couple times. So now we get to dive into the AI elements of this. You know, lots of excitement. I just came from InsureTech Connect. It was AI everything. Where do you see it genuinely improving the claims process today and do you think the expectations are out ahead of the technology or just we just haven’t gotten there yet.
[00:08:25] Damon: I’m extremely bullish on AI. I mean it’s personally and my entire team and company giving us more energy than ever to create things and get things done. I am skeptical of AI as a noun. We have AI. What’s your AI strategy? The, the, you know, the best, the best cases may be like the most boring, repetitive and measurable kind of tasks there. I mean the, in our world it could be reading data plates for model and serial number things that you know, technology has been around a while but kind of pre populating the next step for review or initial like validation and QA based on best practices really, you know, getting to, if you live in an exception based process, getting to the exception faster and more efficiently. So that, you know, someone with that expertise can jump in and make the decision with, you know, with their mind, not, not, not the technology platform. And yeah, so I think AI is obviously incredible in the world of insurance. There’s a, there are a lot of like, it’s a, you know, kind of a high consequence decision. Like we could automate end to end pretty much everything but objective determination on causation coverage, you know, liability. Pick a category, AI can help you get there. But hopefully with the goal of giving the human, you know, more time to, to think and decide. Because ultimately, you know, we, we own the outcome. You know, we as people within insurance
[00:10:19] Dave: with, you know, the vast different types of companies you guys own and your kind of viewpoint into the claims process. It gives you a front row seat to probably some of the biggest insurance carriers out there and what they’re doing on the claim side. And AI specifically don’t name any names, but where have you seen, within the carrier some of these AI implementations actually work?
[00:10:42] Damon: This is a personal call, not necessarily with, with, but it was with an insurance carrier and I was three or four minutes in before I realized I wasn’t talking to a person. I mean I think some of the, you know, some of the voice telephony, those implementations seem to be like really taking off the, you know, the, the negative is when automation or AI shows up and speed is improved but steps are skipped like evidence is, facts are missed and you know, ultimately you make a bad, bad judgment. And yeah, I think also on intake we’ve seen a lot of, a lot of success in capturing that information quickly with the policyholder in mind first. And that’s getting the right information up front makes everything go faster down the line. And so I think those are the two or three that I think of. But again the negative is if we skip the step of true objective judgment. And so I think that’s the biggest risk.
[00:11:50] Dave: Well, to that point, right. There’s this tension between moving a claim faster and an accurate decision. And it always kind of one seems to come at the cost of another. How can you use some of this data, the AI, the automation, to help carriers rather than just making them trade off like, yep, more speed, but less accurate or less accurate, but more speed. Right. Like how do you, how do you balance that?
[00:12:15] Damon: Those two, they’re not natural enemies. The trade off is when you skip an important step. A lot of the delay comes from an incomplete intake or missing, you know, information. It’s more viable when it means, you know, kind of removing everything that delays getting to the sound decision and so focusing on the, you know, again, back to the, the things that may not be as exciting to talk about in the board meeting, those seem to have the biggest impact.
[00:12:44] Dave: So if you take it from the adjuster’s view back, you know, for them, I don’t think an adjuster is necessarily sitting there saying I want more technology, but it’s ultimately to make a better, faster decision and help to ensure. Right, that’s usually how I’ve seen them kind of approach it. When carriers are looking at evaluating new claims tools and actually helping to make the adjuster’s job better in service to the customer, what are some of the things they should be thinking about? Like how should they test these tools, how should they think about them? Where should they start?
[00:13:21] Damon: Back to the problem to be, problem to be solved with technology. But we’ve seen some implementations and the KPI was, did the, did all of our adjusters log in? And I don’t know, that’s to us, it’s more of like what was in the implementation allowed the claim to get better, the policy order to have a better experience. So starting with the adjuster’s actual work, does the technology tool, does it create efficiency, does it reduce data, does it entry? You know, does it make or provide the ability for the adjuster to make a better decision consistently, more accurately? And so far, I mean, I think whatever those KPIs or those metrics end up being, it’s finding a way for them to be traceable so that you can measure your success with the technology. Am I making the claim better and being able to quantify that
[00:14:24] Dave: when you look five, 10 years ahead, what do you hope will be just completely fundamentally different about property claims? Whether it be for the adjuster, the policyholder, the carrier, it doesn’t really matter. But where do you hope, you know, we end up five, ten years from now, given all the technology that’s around us?
[00:14:45] Damon: From the carrier’s perspective and specifically to claims, I hope that adjusters are spending less time assembling the claim and getting to the decision and more time to exercise and spend time on the best judgment. And fast forward information’s arrived, it’s all been validated, it’s traceable, it’s connected to the, you know, the decision of the claim, the, all the, all the feeds are there and many, many more. The imagery, the property data. And so the future feels less like assembling a, you know, a file, a manila folder, claim file, case file, and more about making the confident decision when it’s time to make a decision because you know that the information you have is valid, objective and that creates confidence.
[00:15:42] Dave: So you talked about the quality of the data coming in and the intake process. We’re starting to hear a lot about the provenance of data and being able to prove that the image is the real image taken at that date. Are you starting to see anything around that? It’s become easier to spoof an image, create an AI version of damage and things like that. Are you starting to see anything on that? Do you think that’s going to be important as we go forward?
[00:16:08] Damon: It’s a lot harder to trust the information that you get. And I’m sure you, you know, we all experience it as leaders with the introduction of like you pick your AI tool of choice, but everything looks great and everything looks professional and everything looks like it’s correct on the photos, imagery and even in even videos. So we’ve seen that they leapfrog the last year and a half or so. And as insurance carriers move more to a self help workflow when the technology exists and you know, insureds are capturing information and the risk becomes higher and higher and so the need for validation there is crucial.
[00:16:54] Dave: Have you seen fraudulent images come through already?
[00:16:58] Damon: We’ve, we flagged them over, we flagged them over time. Saying those quotes probably is the easiest and the biggest culprit. Quotes from contractors on replacement equipment or whatever it may be. We get a little bit of that.
[00:17:18] Dave: It’s interesting to see how this data on one hand looks cool. Your kids can make this really interesting image. But it can also be used to say that your car was damaged when it wasn’t or your fence fell down and it didn’t. Right. You know, the technology is advancing faster than some of the fraud detection tools.
[00:17:38] Damon: I mean a 10 year old now could probably write a very interesting financial thesis on the insurance InsurTech landscape of Alpine or Nearmap in five minutes. And Claude,
[00:17:54] Dave: access to information has changed. Right? But you know, with that access, and I think you touched on this, it’s become easier to make things look real with some real data around it. Even when you know, the conclusion might not make any sense. And so I think that’s the thing we all deal with, right? Whether it be in insurance or just with our own companies. You’re sitting in the seat of, you know, Chief Claims Officer at a carrier right now. Everybody’s telling you to use AI for something. What would you tell that person to go out and start with?
[00:18:30] Damon: I would start with the process of whatever business problem they’re looking to solve. Clean data, clean inputs and validation at the beginning of the claim process allows efficiency to snowball all the way to the, you know, to the, to the right sound, you know, objective judgment when a claim decision is made. And it’s never that easy, right? These are massive organizations with tons of complexity and history. And so it may not be at the beginning, it’s like you can just start over. But finding a stage of the process where technology, AI, automation can create efficiencies and getting a win there that your team believes in, you know, kind of will help build the next, you know, bite of technology implementation. Doing it all at one time is what we all see, right Dave? Or we see it often. It’s great if it works, it’s really expensive if it doesn’t. And I think where we sit today with AI and the ability to create things quickly that solve problems, it’s not as risky to take that first bite and see if it works as opposed to the entire, you know, the, the, the entire platform. And so, you know, that, that’s my, I guess that’d be my recommendation or my suggestion. Start small because by the way, everything’s going to change six months from now anyway.
[00:20:14] Dave: Yeah, well, the technology is shifting faster than some of these POCs can make it to production. Right. And so you have to build your workflows and your engagement around that. Just understanding that’s going to change as you go. And there might be a better way or a better technology or a better process. Right. A couple of days, a couple of weeks, a couple of months from now. So you have some companies that deal with some pretty complex claims. Do you think AI will be helpful in those situations? You know, highly complex, whether it be commercial, residential, forensic investigations or is it already helping you in those businesses today?
[00:20:51] Damon: Simplification or simplifying something is more complex in many cases than taking the traditional route. And so for us, simplifying the claim doesn’t mean we’re pretending that the claim is simple. It means the customer shouldn’t have to manage all of the complexity of the, you know, of the claim and you know, everything from intake all the way to finding the right expert, subject matter expert, to moving things along within the claims process and all the facts and inputs and everything else. And so the, you know, being focused, you know, on sticking to the grind of the process and applying the technology seems to have the best outcome.
[00:21:38] Dave: So Damon, you’ve been in the claims industry now, serving it for, for some time. If you look back, what’s something that you wish you would have known going in that would have made you more successful or maybe helped you execute faster if you would have known that?
[00:21:51] Damon: Thanks for the zinger, Dave. I think that the importance of being incredibly focused on what you, what you do and, and what you do best and nothing else. It’s a very noisy space like a lot of, you know, like a lot of industries. But I think we’re very good at saying focus on and have been over the years what we’re best at and what provides value to our carriers. The reality is that’s kind of all that matters. And so if you’re driving towards that, the success will follow.
[00:22:31] Dave: Damon, thanks for being with us today, sharing a little bit about your experience in claims and technology and appreciate your time.
[00:22:39] Damon: Yeah, thank you, Dave. Always a pleasure.
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Meet the Host
Dave Tobias
Dave is Chief Product Officer at Nearmap, where he leads product strategy across the property intelligence portfolio. He co-founded Betterview, the application now used by carriers across the US for underwriting and risk selection, and joined Nearmap through its acquisition in December 2023. He has spent more than a decade building the tools insurers use to understand property risk.
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Meet the Guest
Damon Stafford
Damon Stafford is CEO and President of Alpine Intel, where he leads the company’s strategy around scalable growth, process automation, proprietary software, and simplifying complex insurance workflows.
Before Alpine Intel, Damon founded HVACi, StrikeCheck, and CCG IQ, later bringing the businesses together under the Alpine Intel brand in 2022. Across his career, he has led the organisation through 12 acquisitions while building businesses focused on property, auto liability, workers’ compensation, and complex claims and investigation workflows.
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