Leading with data, not just managing it.
A senior colleague found a model eighty percent better than anything she'd used all year. Better at what, though? And in your own work, how would you know if the next version quietly lost it? You work through the same chat box daily with almost no view of what the model behind it can do.
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.
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.
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.
Data management has a predictable failure mode: lack of urgency at strategy level, lack of clarity in execution, lack of mandate with the fixers. AI is about to make that dysfunction impossible to survive. The answer is not more governance. It is fewer people in the loop.
In nearly twenty years working with data, I have yet to meet a business stakeholder who woke up wanting a data product. The shift we need is from describing what data is to articulating what data does. "Data-as-a-product" was the right vocabulary for the wrong conversation.
AI is removing the technical barriers that shaped data strategy. When every team can build, the bottleneck shifts from capability to direction. The data leader's most important job isn't enabling more use cases. It's translating business strategy into clear data priorities and saying no to the rest.