Rebuilding the data stack for AI

Rebuilding the data stack for AI

From Business Lab by MIT Technology Review Insights

April 27, 2026 · 48 min · Episode 106

About this episode

The episode discusses the challenges enterprises face in adopting AI due to fragmented data infrastructure.

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.

People in this episode

Guest: Bavesh Patel

Topics covered

  • data infrastructure
  • artificial intelligence
  • digital transformation
  • enterprise readiness
  • data quality

Keywords

  • AI adoption
  • data stack
  • enterprise data
  • digital transformation
  • data governance

Mentioned in this episode

Organizations: Databricks, MIT Technology Review Insights

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