Let’s be honest, AI is the topic of conversation in every insurance boardroom right now. And for good reason. The promises are huge: lightning-fast claims processing, smarter underwriting, hyper-personalized customer service. It all sounds fantastic, like a magic wand for efficiency and profitability.
I’ve sat in on these presentations. The charts are slick, the projections are optimistic, and the tech team is buzzing with excitement. It’s easy to get swept up in the momentum and give a hearty "yes" to the next big AI initiative. But I want you to pause for a second.
Before you sign off on that multi-million dollar project, we need to talk. Because beneath the shiny surface of algorithms and automation, there are some fundamental questions that, in my experience, boards often miss. And missing them can turn a promising investment into a regulatory nightmare or a brand-damaging mess. Think of this as a friendly chat, a quick gut-check before you dive in.
First off, what’s this AI actually eating?
This might sound like a funny question, but it’s the most important one you can ask. Every AI model is "trained" on data. It learns by looking at thousands, or even millions, of examples. The quality of that data is everything.
Think of it like building a skyscraper. You wouldn't dream of starting construction without first making sure you have a rock-solid foundation, right? If you build on sand or swampy ground, it doesn't matter how brilliant your architecture is—the whole thing is going to come crashing down.
In the world of AI, your data is the foundation.
So, when the team presents their AI model for underwriting, you need to ask:
- Where did this data come from? Is it our own historical claims and policy data? Did we buy it from a third party? Is it a mix?
- How clean is it? Is it full of errors, duplicates, or outdated information?
- Most importantly, is it biased? Let’s face it, our historical data in insurance can reflect old, outdated ways of thinking. If we train an AI on data from a time when we unknowingly discriminated against certain neighborhoods or groups of people, guess what? The AI will learn to be a very efficient, very fast discriminator.
"Garbage in, garbage out" isn't just a catchy phrase; it's the fundamental law of artificial intelligence. Approving an AI project without rigorously interrogating the data foundation is like signing off on that skyscraper without ever looking at the geological survey. It’s a massive, avoidable risk.
Okay, but can we explain why it did that?
Imagine this scenario. Your new AI-powered claims system automatically denies a claim. The customer, rightfully upset, calls and asks for an explanation. What do you say? If your answer is, "Uh... the computer said so," you have a very big problem.
This is what we call the "black box" issue. Many complex AI models can be incredibly accurate, but their internal logic is so complicated that even the data scientists who built them can't fully explain how they reached a specific conclusion.
This is a non-starter in a regulated industry like insurance. We have to be able to explain our decisions, especially adverse ones. Regulators demand it, and frankly, our customers deserve it.
So, the second critical question for the board is about documentation and explainability. It’s not enough for the AI to be right; we have to be able to show our work.
When a project is proposed, ask the team:
- If this model flags a claim as fraudulent, what specific factors led to that conclusion?
- If it adjusts a premium at renewal, can we provide the homeowner with a clear, step-by-step reason for the change?
- Have we built a system that logs why a decision was made, not just what the decision was?
Pushing for "explainable AI" (or XAI, as the tech folks call it) isn't about slowing down innovation. It's about building trust—with regulators, with your customers, and even within your own organization. No underwriter is going to trust a tool they don't understand.
And where exactly does this thing fit in our workflow?
The final piece of the puzzle is figuring out where the AI actually lives in your day-to-day operations. This isn't just a technical detail; it fundamentally changes the risk profile of the project.
Is the AI meant to be a helpful co-pilot, or is it the autopilot flying the entire plane? Let me explain.
AI as a Co-Pilot
In this model, the AI is a decision-support tool. It might analyze a complex commercial property submission and highlight key risk factors for a human underwriter. It could review a pile of documents for a claim and summarize the key points for the adjuster. The AI provides insights, but the final, critical decision is still made by an experienced professional. This "human-in-the-loop" approach is a fantastic way to gain efficiency while keeping a crucial layer of expert oversight.
AI as an Autopilot
Here, the AI is making the decisions autonomously. Think of a system that automatically approves or denies small, straightforward auto claims without any human touch. This can be incredibly efficient for high-volume, low-complexity tasks. But the risk is also much, much higher. What if a bug in the code leads to thousands of incorrect denials in a single afternoon?
When a new AI deployment is on the table, the board needs to be crystal clear on this point. Ask:
- Is this tool making recommendations or is it making final decisions?
- At what point does a human get involved? Is there an easy way to override the machine?
- If it's fully automated, what are the guardrails? What's our plan for when it inevitably makes a mistake?
There's no single right answer here. Some processes are perfect for full automation, while others will always need a human touch. But approving a project without explicitly defining its role in the pipeline is like giving someone the keys to a race car without knowing if they plan to drive it around a track or down a crowded city street. You need to understand the context to understand the risk.
So, the next time you're in that boardroom and the AI presentation begins, listen for the answers to these three questions. It’s not about being a skeptic; it’s about being a steward. AI is an incredibly powerful tool, but like any tool, it’s only as good as the planning and foresight of the people who wield it. And that, ultimately, starts with you.
