The buzz around artificial intelligence (AI) is growing louder by the day—and the insurance industry is no exception. From underwriting and claims to customer service and fraud detection, AI is being hailed as a game-changer. But in the rush to adopt this transformative technology, we risk repeating a familiar story.
If you’ve been in the industry long enough, the current AI conversation may feel reminiscent of the Agile revolution that swept through IT departments in the early 2000s. Back then, Agile was the hot topic. Today, AI has taken its place. But the parallels are more than superficial. The missteps many insurers made in their Agile transformations offer cautionary lessons—and opportunities—for how we approach AI implementation now.
Agile: A Cautionary Tale of Incomplete Transformation
Agile methodology was born around the turn of the century out of a desire to overhaul the pre-existing Waterfall methodology known for rigid, documentation-heavy development processes. It promised more alignment with the customer, faster delivery, higher quality, continuous feedback, and more customer-centric outcomes. The Agile Manifesto, published in 2001, was a rallying cry for a better way to develop software—one rooted in iteration, collaboration, and adaptability.
Fast-forward to today: ask any insurer if they’ve adopted Agile, and most will say yes. But peel back the label, and the reality is more nuanced. What many organizations call Agile is, in practice, Hybrid Agile—an undefined blend of Agile and traditional Waterfall practices.
Why Did This Happen?
Because most companies didn’t fully commit. They adopted Agile ceremonies—standups, sprints, backlogs—without embracing the cultural and operational changes required to make Agile truly work. Agile wasn’t just a process shift; it demanded new ways of thinking about leadership, accountability, team structure, and value delivery. But many insurance organizations never invested in the change management necessary to drive that deeper transformation.
The result? Agile became a buzzword. A checkbox. And many of the benefits it promised—greater speed, higher quality, better alignment with customer needs—were never fully realized. Project failures, delayed implementations, and disappointing outcomes became common, despite “Agile” being the official methodology.
Human Resistance to Change
To understand why Agile adoption fell short, we need to acknowledge a simple truth: change is hard. Humans are wired for comfort and predictability. Even when change is necessary—or desirable—it can be deeply uncomfortable. As anyone who’s ever tried (and failed) to stick with a new diet or exercise routine knows, sustaining change is exponentially more difficult than starting it.
Now multiply that resistance across an enterprise. You’re not just asking individuals to change; you’re asking teams, departments, and entire business units to work differently. And if only some adopt the new approach while others resist, the result is friction and dysfunction.
This is why true transformation requires more than just new tools or processes—it requires a deliberate, strategic approach to people and culture. In other words: change management.
Change Management: The Missing Link
Change management isn’t about IT, or even methodology. It’s about people. It’s about psychology, motivation, leadership, and learning. It’s a discipline in its own right—just like finance, operations, or claims.
Agile transformations that succeeded had one thing in common: they were supported by formal change management strategies, often led by dedicated teams that reported directly to the C-suite. These teams didn’t just roll out new software practices; they enabled deep organizational change. They aligned HR, finance, operations, and leadership to a shared vision. They provided training, coaching, communication, and cultural reinforcement. In short, they treated transformation as a human endeavor.
AI: A New Era, Same Story?
Today, we’re on the brink of another transformation—this time driven by AI. And once again, the signs are familiar. AI is everywhere in the headlines. Executives are eager to say their company is “doing AI.” Vendors are marketing AI-powered solutions for every pain point. But in many cases, the implementation is superficial. AI, like Agile, is not a plug-and-play solution. It’s a capability that must be aligned with real business problems. It requires not just technical infrastructure, but organizational readiness. It requires oversight, governance, ethical considerations, and cultural adaptation. And, most critically, it requires humans—skilled, empowered, and change-ready humans. Without that, AI risks becoming yet another buzzword. Another checkbox. Another opportunity squandered.
Learning from the Past to Succeed in the Future
If the insurance industry is to harness the full power of AI, we must treat its implementation not as a technical rollout, but as a business transformation. We must invest in the people side of change—just as we should have with Agile.
That means:
- Appointing change leaders who report into the C-suite and have the authority to drive cross-functional alignment.
- Training managers and teams not just on AI tools, but on new ways of working, decision-making, and collaboration.
- Defining success criteria that go beyond technical deployment to include adoption, impact, and cultural fit.
- Creating space for learning and iteration, understanding that transformation is a journey, not a sprint.
At INFORCE, we’ve seen firsthand how critical the human element is to technology implementation success. Effective systems integration implementations require more than IT know-how; they need seasoned leadership, effective change management, and client-centric people. That’s why we created our INFORCE Academy: to train our team on systems integration and to develop the cultural, technical, and behavioral skills needed for transformation. We specialize in building high-performance teams that can lead and sustain change, from Agile to AI and beyond.
Final Thoughts
As we enter the AI era, the stakes are high. This technology has the potential to redefine how we serve customers, manage risk, and create value. But it won’t succeed on its own.
To unlock its promise, we must lead with intention. Let’s learn from our past. Let’s prioritize people, process, and purpose. Let’s approach AI not just as a tool, but as a transformation. That’s how we’ll turn AI from a buzzword into a breakthrough.
