Let's be honest for a second. A few years ago, the hype around AI in insurance was off the charts, right? We were all hearing about a future of "touchless claims," where a customer could submit a photo of a dented fender and have a payment in their account before they even got home.
It all sounded amazing. So, we invested. We ran pilot programs. We got excited about the possibilities.
And yet, for so many of us in the property insurance world, that future still feels… well, futuristic. We’ve got these incredible AI tools, but they often feel stuck in first gear. They’re handling a tiny fraction of claims, or they’re only automating one small piece of a much bigger, clunkier workflow.
If this sounds familiar, you're not alone. There's a huge gap between the grand vision of AI and the messy, day-to-day execution of it. The problem isn't the technology itself. The problem is how we're trying to implement it. So, let's talk about how to finally cross that bridge and get our AI initiatives out of the lab and into the real world where they can actually help.
So, What's the Big Holdup with AI?
I've talked to a lot of carriers who are stuck in what I call "pilot purgatory." They have a cool piece of tech that works great in a controlled environment, but the moment they try to scale it up, things start to break down. The results don't match the demo, the team isn't using it, or it just creates more work than it saves.
What’s going on here? It’s usually a disconnect between the 30,000-foot view from the C-suite and the on-the-ground reality for your claims team.
Think of it like this: you just bought a brand-new, top-of-the-line professional kitchen oven. The vision is to serve gourmet, five-course meals. But you’ve installed it in a kitchen with 1970s wiring, you haven’t trained your cooks how to use it, and you’re still using handwritten recipes.
The oven has incredible potential, but it’s completely mismatched with the environment around it. That’s exactly what happens when we try to plug sophisticated AI into our existing claims processes without thinking about the bigger picture.
The Real Roadblocks Holding Us Back
When an AI project stalls, it’s rarely because the algorithm is bad. It's almost always due to one of these three, much less glamorous, roadblocks.
1. The Data Dilemma
AI is basically a prediction machine, and it needs fuel to run. That fuel is data—lots and lots of clean, organized, high-quality data. And what does the data look like at most established carriers? Let's just say it's not always clean and organized.
We have information trapped in siloed legacy systems, adjuster notes that are inconsistent, and decades of claims data in a dozen different formats. Trying to feed this messy data into a sophisticated AI model is the classic "garbage in, garbage out" problem. The AI can’t make sense of the noise, so it gives you unreliable results.
2. Legacy System Limbo
This is the big one. Many of us are still running on core systems that were built before the iPhone even existed. These systems are reliable workhorses, but they weren't designed to talk to modern, cloud-based AI tools.
Trying to bolt a shiny new AI solution onto a 20-year-old mainframe is like trying to put a jet engine on a horse-drawn buggy. You might get it to work, sort of, but it's going to be awkward, inefficient, and probably fall apart under pressure. Without smooth, modern connections (think APIs), your "automated" process ends up requiring a ton of manual data entry and workarounds, which defeats the whole purpose.
3. The Human Factor (It's a Big One)
We can't forget about the people who actually have to use these tools every day. If your adjusters see AI as a threat—a black box that’s coming for their jobs—they will actively or passively resist it.
And can you blame them? If they don't understand how the AI works, don't trust its recommendations, or feel like it’s just making their job harder, they simply won't use it. You can have the best tech in the world, but if you don’t get buy-in and provide proper training for your team, it will sit on a virtual shelf collecting dust.
A Smarter Way to Scale: From Crawling to Running
Okay, so that’s the bad news. The good news is that these problems are solvable. It just requires a shift in our approach, moving from a "big bang" implementation to a more thoughtful, step-by-step process.
Step 1: Stop Trying to Boil the Ocean
The biggest mistake I see is carriers trying to automate the entire claims journey all at once. It’s a recipe for failure. It’s too complex, too expensive, and there are too many things that can go wrong.
Instead, start small. Pick one specific, nagging problem in your workflow.
- Maybe it's automatically reading and categorizing First Notice of Loss emails.
- Perhaps it's using photo analysis to instantly triage low-severity auto claims.
- It could even be something as simple as transcribing recorded statements.
Find a process that is repetitive, high-volume, and relatively simple. Automate that. Get a clear win, prove the value, and build momentum. Once you’ve mastered that one piece, you can move on to the next.
Step 2: Build for Your People, Not Just the Process
This is critical. You need to reframe how you talk about AI internally. It’s not a replacement for your adjusters; it’s a co-pilot.
The goal of AI should be to handle the boring, administrative, and repetitive tasks that bog your adjusters down. This frees them up to focus on the things that require a human touch: showing empathy to a distressed policyholder, negotiating a complex settlement, or using their experience to spot a fraudulent claim.
Better yet, involve your best adjusters in the process from the very beginning. Ask them: "What are the most annoying parts of your day? If you had a magic wand, what would you get rid of?" Their answers are a goldmine. When they help you build the solution, they become its biggest advocates.
Step 3: Connect the Dots
This is the plumbing. It’s not sexy, but without it, nothing flows. Before you even sign a contract for a new AI tool, you have to have a plan for how it will connect to everything else.
How will data from the AI get into your core claims system? How will it trigger a communication to the customer? How will it interface with your payment platform? If these systems can't talk to each other seamlessly, you haven’t built an automated workflow. You’ve just created a new silo.
What Success Actually Looks Like
Let's get one thing straight: for complex property claims, the idea of a fully "lights-out," no-human-involved process is mostly a myth. There are simply too many variables and too much need for human judgment.
Real success with AI in claims isn't about total automation. It's about intelligent augmentation. It’s about making your human team faster, smarter, and more effective.
Success looks like this:
- An AI analyzes thousands of images from a hailstorm and instantly flags the properties with the most severe damage, allowing you to route your best field adjusters there first.
- An AI reviews a contractor's estimate and automatically flags line items that are way outside the regional average, saving your desk adjuster from having to manually check every single one.
- An AI listens to a customer call, detects a frustrated tone, and automatically suggests an escalation to a senior handler to prevent a complaint.
In each case, the AI isn't replacing the human. It's empowering them to do their job better.
Scaling AI in claims is a journey, not a sprint. It’s less of a technology challenge and more of a strategy, data, and people challenge. By starting small, focusing on solving real problems for your team, and making sure all your systems can talk to each other, you can finally start to close that gap between the vision and the reality. You can finally get out of pilot purgatory and start delivering the real value we've all been promised.
