What happens when the rush toward AI collides with the messy reality of enterprise data that was never designed for it? That’s exactly where this episode with Syntax’s Kevin Dattolico begins.
Before we even hit the record, we swapped stories about music, travel, and a particular farewell concert that set the tone for a conversation that was both grounded and unexpectedly human. But once we got started, the discussion quickly shifted to one of the biggest blind spots I keep hearing about at tech conferences around the world. The AI ambition is far ahead of data readiness.
Kevin leads Syntax across America, working with organizations that rely on SAP, Oracle and complex cloud environments to run their businesses. In our conversation, he explains why many AI initiatives stall or silently reset once they come into contact with real production data. Proofs of concept may seem impressive on their own, but once AI starts interacting with live operational systems, the cracks appear.
Inconsistent data, duplicate records, missing context, and gaps in governance all surface at once. The result is confusion, unpredictable outcomes and a growing realization that the problem is rarely the model itself.
We investigate why ERP data is traditionally trusted, while unstructured data from emails, documents, sensors and logs often tell a completely different story. Kevin explains where the real friction arises when companies try to bring these worlds together, and why assumptions about data quality tend to break down long before the technology does. It’s a refreshingly honest look at what usually goes wrong first, and why leaders are often blindsided, even after years of investing.
One of the strongest themes in this episode is the shift Kevin sees from AI-first thinking to a data-first mentality. That doesn’t mean we should give up on AI spending. It means rebalancing priorities so that these investments actually deliver results that the company can get behind. We talk about what consolidation, cleansing, and transformation look like at an enterprise scale, especially for organizations with decades of technical debt and fragmented systems.
The conversation also takes a thoughtful turn around governance, trust and leadership. Kevin explains how the role of the Chief Data Officer is changing from gatekeeper to enabler, and why modern governance must support speed without sacrificing accountability. Along the way, he’s thinking about the risks of perpetuating weak data foundations, especially in regulated industries where the costs of getting things wrong can be reputational damage or worse.
And then there’s the moment when I was completely blindsided. When I asked Kevin to look back on his career and think about someone who made a difference, his answer led to one of the most moving stories I’ve heard in thousands of interviews. It reminds us that behind every transformation story are people silently shaping the way forward.
If you’re struggling with AI expectations, data realities, or just wondering if everyone else is feeling just as overwhelmed by this shift, this episode will resonate. The challenges Kevin describes are far more common than most leaders admit, and the opportunities for those who get the foundation right are real.
So as AI continues to dominate boardroom conversations, are you confident that your data is ready to support the decisions you ask of them, or is it time to pause and rethink what’s underneath?
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