Your enterprise is drowning in unstructured data; documents, reports, meeting recordings, filings, and AI tools that were supposed to help could be quietly making the problem worse. Most companies have shifted their thinking about AI from experimental pilots to expecting real business outcomes. But as they shift, they hit barriers. "We have models, but they're disconnected from our content". "We can't let our employees upload sensitive data into open systems". "We can't let employees upload sensitive data into open systems". "We're duplicating efforts across business units". In this interview, Michael Krantz sits down with Yashoda (Yash) Bhavnani, VP of Product Management for AI at Box, to unpack why enterprise AI keeps falling short, and how to overcome those barriers. Yash breaks down the three core pain points blocking real AI adoption: unstructured data, contextualization, and security & governance, and how the newly launched Box AI Agent and Box AI Studio are built from the ground up to address each one. Topics covered: - The state of enterprise AI adoption today - Why unstructured data is the #1 blocker to effective AI in the enterprise - Why, if AI isn’t solving your unstructured data problem, it might be making it worse. - Why general purpose AI acts like a new hire PhD - The future of AI-powered workflows and automation Subscribe for more content on enterprise AI, productivity, and the future of work.

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