Show Notes
Artificial intelligence may be dominating boardroom agendas, but many enterprises are discovering that the biggest obstacle to meaningful adoption is the state of their data. While consumer-facing AI tools have dazzled users with speed and ease, enterprise leaders are discovering that deploying AI at scale requires something far less glamorous but far more consequential: data infrastructure that is unified, governed, and fit for purpose.
That gap between AI ambition and enterprise readiness is becoming one of the defining challenges of this next phase of digital transformation. As Bavesh Patel, senior vice president of Databricks, puts it, “the quality of that AI and how effective that AI is, is really dependent on information in your organization.” Yet in many companies, that information remains fragmented across legacy systems, siloed applications, and disconnected formats, making it nearly impossible for AI systems to generate trustworthy, context-rich outputs.
“Really, the big competitive differentiator for most organizations is their own data and then their third-party data that they can add to it,” says Patel.
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