Weekly essays with actionable insights on what truly matters in data and AI and why they matter for your organization's long-term success. I write to clarify my own thinking, articulating ideas forces deeper understanding and broader perspective. My goal is for these reflections to offer practical value as you navigate your own data and AI journey.
Most organizations can't answer a simple question: what are you actually optimizing for? With agentic AI, leaving it unanswered has consequences that are faster, bigger, and harder to reverse than anyone anticipated.
Most AI thought leadership falls into two camps: hype or prediction. But there's a more relevant frame: the economics of viability. What's currently too expensive to do in your industry that becomes a real option when AI changes the cost equation? And what does that unlock?
The data professionals who consistently deliver outcomes aren't the ones with the strongest technical skills. They're the ones who understand how value moves through the organization. Most data problems are adaptive challenges that organizations keep treating with technical fixes.
A few years ago, I watched a product recommender start behaving oddly. The model was fine. The pipelines were fine. What had changed was something nobody on the data science team knew about. That moment showed me something I now see everywhere: data visibility is AI's most overlooked bottleneck.
Someone asks: 'What data do we already have?' Within minutes, the room is problem-solving. Everyone wants to deliver. So the team works with what's available. And from that moment, the most expensive decision in the project has already been made.
Most governance frameworks grew by accumulation. The 'cut in half' question forces you to explain why each piece exists. What you'll find is which parts have a clear rationale and which parts exist purely because they seemed responsible at the time.