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.
A senior colleague found the best model she'd used all year. A day later, an export directive switched it off for everyone. She went back to Opus and kept working. But what happens when the model that vanishes isn't helping a person, and is instead running a process the business can't switch off?
The most capable AI model on the market just disappeared overnight, and the conversation is all about power. But how powerful the model is matters far less than whether you can tell good output from bad. And that gap doesn't close with better models. It widens.
AI is being sold as a uniformly transformative force. The reality is more nuanced. The most impressive gains come from one domain, driven by years of focused engineering. Here's what that means for your AI strategy and the questions every leader should be asking.
A project needs product data from another part of the organization. The data exists, but the definitions don't match. So the project team fixes it: just enough, just for this use case. Each fix makes perfect sense in isolation. From an organizational perspective, it's penny-wise, pound-foolish.
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?