Let's be honest. For years, "AI in claims" felt like something we’d talk about at conferences—a cool, futuristic idea. We saw the demos, we read the white papers, and we all agreed it had potential.
Well, the future is officially here. The models have been built, the tools are ready, and pilot programs have proven that, yes, AI can actually work.
But now we’re hitting the hard part. It turns out that running a successful pilot in a controlled environment is a world away from actually weaving AI into the day-to-day chaos of a real claims operation. Many organizations are finding that their shiny new AI tools end up gathering dust, and adjusters quickly go back to their old ways.
Why? Because the technology was never the real hurdle. The real challenge is people. It’s about changing habits, building trust, and integrating these tools in a way that actually makes an adjuster’s life easier, not harder.
So, how do we get this right? How do we move from a cool science project to a core part of how we work?
The Real Promise of AI: More Than Just a Fancy To-Do List
First, let's get clear on what we're even trying to achieve. The goal isn't just to make things faster. When done right, AI fundamentally changes the job of an adjuster for the better.
I was talking about this with Sarah Scott, who's the Executive Vice President of Product & Corporate Services at CorVel, and she really nailed it. She explained that we're at a "key intersection where piloting AI is becoming the reality of how we work."
The biggest shift she's seeing is the move from being reactive to being predictive.
Think about it. For decades, claims management has been a bit like playing catch-up. An adjuster waits for a medical bill to come in, for a treatment request to be filed, or for a red flag to pop up. They’re constantly reacting to things that have already happened.
AI flips that on its head. Instead of looking backward, we can start looking forward.
"We can now identify risk signals sooner and guide a claim toward an appropriate care pathway," Sarah told me. "For example, we can get an individual in with an orthopedic surgeon faster rather than waiting until treatment becomes urgent."
This is huge. We all know that in workers' comp, delays are the enemy. The longer a claim drags on, the worse the outcome tends to be for everyone—especially the injured worker. By spotting potential roadblocks early, we can intervene before a small issue becomes a massive, costly problem.
The other major benefit? It transforms the conversations our adjusters have.
When AI handles the routine data-sifting and box-checking, it frees up adjusters to do what humans do best: connect with people. But it’s more than just giving them time back. It equips them with insights to lead a better, more informed conversation.
"We’re not just reacting to what’s occurred," Sarah said. "We come into that conversation with informed information about what we anticipate will occur for that individual."
Imagine an adjuster calling an injured worker and saying, "Based on what we're seeing, the next step is likely X, and I've already started looking into the best physical therapists in your area to help with that." That's a completely different conversation. It builds trust. It turns the adjuster from a task-manager into a true advocate.
Where Good Intentions Go to Die: Common AI Pitfalls
Okay, if the upside is so great, why are so many of these projects struggling to get off the ground? It usually comes down to a few common, and very human, mistakes.
1. The "Here's Another Log-In" Problem The single biggest failure I see is a lack of integration. A company buys a cool new AI tool, but it lives on its own, completely separate from the core claims system. Adjusters have to open a new window, remember another password, and manually transfer information.
People will try a new tool for a little while, but as Sarah points out, "they quickly forget about them if they’re not seeing the outcomes and impact they expected." If it’s not embedded directly into the workflow they already use every single day, it will be abandoned. Period.
2. Treating It Like a Point Solution A workers' comp claim is a complex web of interactions—there’s the claim data, the clinical side, provider management, and medical costs. A lot of AI tools only look at one piece of that puzzle. When AI only sees claims data in isolation, it misses the full picture. It can't give you holistic insights that actually drive better outcomes.
3. Creating More Noise, Not Clarity Your adjusters are already swamped. Their caseloads are demanding, and their screens are filled with alerts. The last thing they need is another tool pinging them with low-value notifications. If the AI isn't smart about when it engages, it just becomes more noise. "Introducing ‘something new’ without clear value is often met with resistance," Sarah wisely notes.
4. The "One-Click Wonder" Trap Some systems are designed for passive acceptance. The AI makes a recommendation, and the adjuster can approve it with a single click. While that sounds efficient for low-risk tasks, it’s dangerous for big decisions. It encourages a "rubber-stamp" mentality and erodes the meaningful human oversight that’s essential for complex claims.
Getting It Right: A Practical Guide to Making AI Stick
So, how do we avoid these traps? Based on what works in the real world, here are the keys to a successful implementation.
- Build a Two-Way Street for Feedback. For AI to get smarter and for your team to trust it, they need to be able to give feedback in real-time. If an adjuster disagrees with a recommendation, there should be a simple way for them to say "this isn't right, and here's why" right there in the workflow. It can't be a separate, clunky process.
- Engage at the Moments That Matter. Don't just pepper your team with alerts all day. The AI should pop up at critical decision points. For instance, when it's time to decide on a care pathway, or when a claim's risk profile suddenly changes. That’s when an insight is truly valuable.
- Always, Always Explain the "Why." This is probably the most important one. You can't just have a black box spitting out orders. The system needs to show its work. "You can’t just tell someone, ‘Based on everything you’ve done, now you need to do this.’ You need to tell them why," Sarah insists. When people understand the logic behind a recommendation, they build trust and, frankly, they become better at their jobs.
- Design for Real Engagement. For critical decisions, make the workflow require genuine thought. Instead of a one-click approval, maybe the adjuster has to review the key data points the AI used and type a brief justification. It keeps the human in the loop where it counts.
- Show Them the Results. Your adjusters are skeptical, and they have every right to be. The only way to win them over is with proof. You have to continuously demonstrate the value. Share the data. Show them how claims managed with AI support are closing faster or have better outcomes. "It’s not just an extra task," Sarah says. "There’s actual value being accomplished."
Ultimately, the goal of AI isn't to replace the art of claims adjusting. It's to handle the science, so your adjusters can focus on the art. It’s about elevating them from being reactive task-completers to proactive leaders of the claim.
We’re not all the way there yet, but the path is getting clearer. As Sarah shared with me, she’s hearing from her own teams that they are "achieving that time back in their day and using it to have the conversations that matter."
And at the end of the day, that’s what this is all about. It's about empowering our best people to help that injured worker who just wants to get better and get back to their life. That’s the real bottom line.
