It feels like you can’t have a conversation about the future of insurance without someone bringing up AI. It’s the shiny new tool that’s supposed to change everything, from underwriting and claims to customer service. And in many ways, it’s no longer just hype—it’s happening. AI has officially gone mainstream.
But here’s the thing I keep hearing from folks in the industry, the part that doesn’t always make it into the flashy presentations. While everyone is talking about AI, a whole lot of companies are quietly struggling to get it off the ground.
It’s like they’ve bought a state-of-the-art race car. They’ve polished it, they’ve put it on display in the lobby, and they talk a big game about how fast it can go. But they haven't hired a driver, they're not sure how the engine works, and they’re terrified of taking it out on the actual track. So it just sits there, an expensive symbol of progress that isn't actually going anywhere.
Welcome to "Pilot Purgatory"
There’s a term for this phenomenon that I think is just perfect: "pilot purgatory." It’s that frustrating place where insurers are stuck endlessly testing AI projects without ever actually rolling them out across the company. They run a pilot program, it shows some promise, and… then what?
Instead of hitting the "go" button, they get stuck. They launch another pilot. And then another. They’re caught in a loop of experimentation without implementation.
Why does this happen? Well, it’s not because the technology isn't powerful. It’s because using that power is a lot more complicated than it looks. Moving from a controlled, small-scale test to a full-blown, company-wide system is a massive leap. And it turns out, many insurers are finding they have some serious gaps to fill before they can even think about making that jump.
The People Problem: Who’s Supposed to Run This Thing?
First up is the skills gap. And honestly, this might be the biggest hurdle of all.
You can have the most sophisticated algorithm in the world, but if you don’t have people who understand how to use it, manage it, and explain it, it’s just a bunch of code. We’re not just talking about hiring a few data scientists, either. The problem is much deeper than that.
We’re seeing a huge need for people who can bridge the gap between the tech wizards and the insurance veterans. Think about it:
- AI Translators: You need people who can explain what the AI is doing to underwriters, claims adjusters, and executives in plain English.
- Risk Managers for a New Age: Someone has to be responsible for the risks the AI creates. What if it’s biased? What if it makes a catastrophic error? Who is watching the watcher?
- Ethicists and Governance Gurus: As we rely more on algorithms, we need people dedicated to making sure we’re using them fairly and transparently.
These aren’t roles that existed in most insurance companies ten years ago. And finding people with this unique blend of tech-savvy and deep insurance knowledge is incredibly difficult. So, companies are left with this powerful tool but a team that’s not quite sure how to wield it safely or effectively.
The Risk Riddle: What If the "Black Box" Makes a Mistake?
This leads directly to the next major roadblock: risk and governance. For an industry built on pricing and managing risk, it’s almost ironic how much the risk of AI itself is causing paralysis.
The executives I talk to are genuinely worried. They’re asking the right questions, but the answers aren't always clear. What keeps them up at night?
The "Black Box" Problem
Many advanced AI models are what we call "black boxes." They can analyze massive amounts of data and spit out a decision—like a premium price or a fraud alert—but it can be incredibly difficult to understand how they arrived at that conclusion. For regulators and for our own internal standards, that’s a huge problem. If you can’t explain your decision, you’re on very shaky ground.
The Bias Trap
AI learns from the data we give it. And guess what? Historical data often contains historical biases. If an AI is trained on decades of old underwriting data, it might inadvertently learn to replicate and even amplify biases against certain demographics or locations. This isn't just an ethical nightmare; it's a massive compliance and reputational risk waiting to happen.
The Potential for Big Mistakes, Fast
The beauty of AI is its speed and scale. But that’s also its danger. A human underwriter might make a mistake on one policy. A flawed algorithm could make the same mistake on ten thousand policies in the blink of an eye. The potential for a small error to cascade into a massive financial or legal disaster is very real.
Without clear rules, strong oversight, and a solid governance framework, the perceived risk of deploying AI often outweighs the potential reward. And so, the car stays parked in the garage.
The Final Irony: Who Insures the AI?
And this brings us to what I find to be the most fascinating piece of the puzzle: the coverage gap.
We, the insurance industry, are the experts in risk transfer. Our entire business model is based on creating products that protect people and businesses from new and evolving dangers. So, you’d think we’d be all over creating policies for AI-related risks, right?
Well, not so much.
Insurers are struggling to get their own arms around how to underwrite the risks of AI, both for their own operations and for their clients who are using AI. If a self-driving truck using a new AI causes a multi-car pileup, who’s at fault? The truck owner? The manufacturer? The software developer who wrote the code?
The old models of liability don’t fit neatly onto these new scenarios. As an industry, we’re still figuring out how to price this risk, how to define coverage, and where the responsibility ultimately lies. This uncertainty creates a chilling effect. If we can’t even figure out how to insure the technology, it makes us that much more hesitant to fully embrace it ourselves.
So, where does that leave us? It’s clear AI is here to stay, and the companies that figure this out will have a massive advantage. But getting out of "pilot purgatory" isn't about buying more software. It’s about investing in people, building thoughtful governance, and bravely tackling the tough questions about risk and responsibility. It’s a culture shift, not just a tech upgrade. And that’s a journey that takes a lot more than just a pilot program.
