For me, the most prescient issue in business today is this: companies must stop treating AI as a set of experiments and start embedding it into the very fabric of their operations. My work focuses on helping leadership teams transition from pilots to measurable outcomes, tied to revenue, product impact, and customer experience.

I’ve lived through successive waves of transformation—from 2G through 5G and into the Internet of Things. Each wave reshaped how we connect, but none moved with the velocity or breadth of AI. Unlike earlier technologies that disrupted one sector at a time, AI is reshaping nearly every function simultaneously—design, marketing, finance, operations, even legal.

Still, no transformation succeeds without trust. In AI, trust hinges on transparency—how data is treated, how system decisions are explained, how governance is embedded. Trust is not abstract; it’s a design requirement. Confidence indicators, provenance of recommendations, and visible governance structures aren’t optional. They are the mechanisms by which adoption takes root. As regulations fragment across states and nations, mature organizations will distinguish themselves not only by compliance but by weaving governance into workflows from the outset.