Executives Bavesh Patel of Databricks and Rajan Padmanabhan of Infosys highlight that fragmented, siloed data remains a major barrier to effective enterprise AI adoption.
They advocate consolidating structured and unstructured data into open, governed formats, enabling measurable business value, automation, and new business models through AI-ready data architectures.
The discussion emphasizes that robust governance, value measurement, and AI literacy are essential for scaling AI, with successful enterprises leveraging unified data to unlock efficiency, innovation, and competitive advantage.
Is a single unified data platform the only path to AI success, or are decentralized alternatives more practical?
What separates the few companies succeeding with AI from the 92% that have failed to operationalize it meaningfully?
How can companies govern rampant 'shadow AI' use without stifling the very innovation they seek to foster?
Since 70% of AI project failures are organizational, what is the first step to solving the human side of adoption?
Beyond productivity gains, what new revenue streams has agentic AI realistically created for traditional industries so far?
How will a new 'Agent Operating System' prevent teams of AI agents from causing chaotic business outcomes?