Transforming Insurance with AI: People, Processes and Culture at the Core

The AI conversation insurance is actually having

To understand where insurance is headed with AI, Edinger says it helps to zoom out. The real inflection point wasn’t generative AI — it was insurtech.

“For decades, insurance operated in its own ecosystem,” he explains. “Over the last decade, insurtech became a real force because the insurance industry historically operated in its own silo — legacy systems that still worked, strong profitability and little pressure to change.”

Insurtech disrupted that equilibrium, forcing carriers to think differently about innovation and integration — whether new ideas came from inside or outside the organization. That groundwork matters. Early "AI" in insurance was largely rules-based decisioning. Today’s shift is different. Learning AI now evolves processes and adapts over time, changing how work actually gets done inside carriers.

“That’s what makes this moment fundamentally different,” Edinger says. “Today, we actually have learning AI that evolves processes and technology inside carriers.” Now we’re not just automating tasks. We’re changing the way decisions flow through the organization.

Where AI is working today, and why the advantage is narrowing

Most carriers can point to real AI success stories in claims administration. When millions of claims move through highly regulated environments, often governed by 50 different state rule sets, even small efficiency gains deliver outsized ROI.

AI excels at sorting complexity: prioritizing claims, flagging regulatory nuances and reducing administrative drag. As a result, many carriers are already seeing value, often through out-of-the-box solutions. But that advantage won’t last.

“As soon as everyone reaches similar efficiency levels, the delta shrinks,” Edinger notes. “The question quickly becomes: Where does my company truly differentiate — what’s our secret sauce — and how do I apply AI to amplify that?”

With AI, everyone expects efficiency, but differentiation is where the true payoff is.

The cultural mistake that kills AI adoption before it starts

One of the biggest threats to AI success has nothing to do with technology: fear-based messaging. Headlines predicting massive job losses may grab attention, but they create paralysis internally. 

“If you’re sitting in a contact center and you hear that 80% of jobs are going away, that’s terrifying,” Edinger says. “And it blocks execution.”

The reality is more nuanced. Most organizations experience natural attrition of 10% to 15% annually. Over three years, that alone reshapes the workforce — without layoffs. AI adoption typically combines attrition with reskilling, not wholesale elimination. The problem isn’t change; it’s how leaders talk about it.

Effective messaging anchors employees to what they already know: their role, their purpose and how their work contributes, while showing how tools will evolve around them. This is critical during periods of dramatic change.

“The work evolves,” Edinger says. “But the purpose remains.”

Process mapping: What makes AI work

Many organizations now have more data than ever, but their processes haven’t kept pace.

“If you don’t clearly understand your current process, it’s very hard to change it,” Edinger explains. "That’s where many AI initiatives stall. Technology gets layered on top of undocumented workflows, creating confusion instead of clarity. Process mapping does more than improve operations — it lowers fear. When people can see what’s changing, what’s staying and where AI supports them, adoption improves dramatically. That’s where Experience Design International focuses its work. We’re not technologists, though we understand technology deeply. Our focus is on marrying strong technology with clearly understood processes and thoughtful change management."

People should feel like they’re winning, too. As the company grows, ROI improves, and that success flows back to employees through compensation, opportunity and stability.