Enterprises chasing AI hype often hit a wall when their data lives in fragmented legacy systems. Databricks senior vice president Bavesh Patel argues that AI quality hinges on unified, governed information. Without open formats and real‑time context, models produce unreliable outputs, which he calls “terrible AI.” The gap between boardroom ambition and data readiness now defines the next wave of digital transformation across industries worldwide.

Infosys unit technology officer Rajan Padmanabhan stresses that precision‑focused AI demands both structured and unstructured enterprise data, plus strict access controls. He notes successful customers achieve over 92% accuracy, treating AI as a business metric rather than a sandbox project. Governance frameworks help decide which models deliver measurable value and which should be retired quickly for long‑term competitiveness.

Both speakers agree the remedy is an open, unified data architecture that consolidates internal and third‑party sources. By reconfiguring pipelines for AI‑ready data, real‑time freshness and rigorous governance, companies can unlock automation, new revenue streams, and cross‑functional insights. Organizations that invest in this foundation now will avoid the costly trap of deploying AI on shaky data foundations in the evolving AI economy.