Over the past nine months, I’ve had the privilege of sitting down with more than 100 executives to explore AI, its strategic value, tactical implementation, and operational implications. What an experience! Each workshop brought different industries, perspectives, and energy. Yet across all of them, I noticed consistent patterns, moments of genuine insight, and of course a good deal of humor that revealed how leaders are truly approaching AI today. 

One moment I’ll never forget came from a participant who summed up resistance to change with what he called “the three master excuses”: 

1. We’ve always done it like this. 
2. We’ve never done it like this. 
3. Who are you to tell me what to do? 

It made everyone smile and nod. Because whether it’s AI, digital transformation, or any fundamental shift, resistance is part of the journey. 

The emotions in the room 

In every session, I encountered a mix of skepticism, curiosity, and cautious optimism. Rarely indifference. That matters. Executives may not agree on where AI will take them, but they are paying attention. The question is no longer whether AI belongs in the enterprise, it’s how to make it work. 

Three levels of AI maturity 

As I listened and observed, I began to see three broad patterns across organizations. To help me make sense of what I was hearing, I started describing them as belts, borrowing from martial arts: 

• White belts – Organizations that have waited, for various reasons, and are now cautiously getting started. 
  Their focus: Technology choices, skills, and where to begin. They wrestle with build versus buy decisions, vendor selection paralysis, and whether to centralize AI capabilities or distribute them across business units. The primary question is simply: Where and how do we start? 
• Green belts – Organizations that have piloted use cases, often within a single business unit or function. 
  Their challenges run deeper than expected. Failure rates are higher than anticipated. They face organizational resistance on many fronts. Data integration and availablility becomes a bottleneck, siloed systems, inconsistent governance, and the realization that their data architecture wasn’t designed for AI. The question shifts to operational: How do we move from proof of concept to production at scale? 
• Black belts – Their concerns are sophisticated: strengthening data platforms for real-time decisioning, implementing robust ethics frameworks and guardrails, managing costs as compute scales, and building MLOps practices that can support hundreds of models. They’re asking strategic questions: How do we sustain competitive advantage while ensuring responsible AI at scale? 

This simple framework resonated. Many participants quickly placed themselves, and sometimes realized they weren’t where they thought they were. 

Five Insights That Changed the Conversation – aka ‘Aha moments’ 

There were moments in nearly every workshop when I could see something click. A shift in posture. A pause in notetaking. That look when a realization lands and changes how someone sees the problem. Here are the insights that created those moments most often: 

• AI is not ChatGPT. Leaders shouldn’t confuse consumer AI experiences with enterprise deployment. Enterprise AI demands understanding the full spectrum of capabilities, from computer vision to machine learning to intelligent agents, and their limitations. But more fundamentally, it’s about understanding the role of data and corporate know-how. Your organization’s expertise and processes need to be encoded in how you structure and connect information. AI’s value comes from tapping into that institutional knowledge, which means ontologies, semantics, data integration, and governance become strategic, not just technical.  
• AI is not a job killer, it’s a work shaper. AI changes how teams collaborate, which skills become central, and how leadership must adapt. The real transformation isn’t about replacing people. It’s about redistributing and redesigning work. That requires change management as much as technology deployment. 
• Data ownership is not “an IT thing”. Data sits at the heart of every function, finance, operations, customer experience. Without clear business ownership of data quality, lineage, and governance, AI initiatives will fail. CIOs can build the infrastructure, but they cannot own the semantics. 
• You can’t buy transformation. Prepackaged AI solutions are tempting. At best, they bring you to parity with competitors. True competitive advantage comes from use cases rooted in your unique processes, customer relationships, and operational context.  
• Business value before technology. The most important realization, repeated across workshops: We don’t need an AI strategy. We need a business strategy that includes AI. Technology choices follow strategic clarity, not the reverse. 

My own learning 

I took the purposeful decision to build a company around the opportunity of AI. However, the power of any technology lies in its adoption. Running these workshops taught me something invaluable: the complexity leaders deal with right now is enormous. Many industries undergo massive changes, the geopolitical uncertainties pose several threats on the customer and supply side and regulatory landscapes seem out of control. My role is helping leaders place AI within the full spectrum of their strategy, respectfully, with empathy for where each organization stands today. Many digital success stories shared publicly fail at this. They create unrealistic expectations and overlook the complex reality of enterprise transformation. 

Where do you stand? 

The pace of AI adoption varies across organizations. Some move fast; others take careful steps. But across all workshops, I saw one common thread: leaders feeling a deep sense of responsibility, at a time when that is anything but easy.
They seek to align AI with their goals, focusing on its real impact, improving outcomes for people and their business’s future. 

If that sounds familiar, I invite you to reflect: What would it take for AI to serve your goals? 

If you’d like support in finding that path forward, I’d be glad to walk the next steps with you.