Data governance and quality identified as critical foundations for enterprise AI

The deployment of enterprise AI is increasingly dependent on the quality and governance of underlying data. While traditional reporting often includes a human validation layer to catch errors, AI systems can produce confident but incorrect outputs at scale if they are built on inconsistent definitions, undocumented pipelines, or contradictory documents.

At the Inc42 CTO Summit in Bengaluru, executives from PhonePe, OceanBase, and Fibe discussed the necessity of establishing strong data foundations. PhonePe’s head of engineering, Kisalay Ranjan, noted that the company sets rules for individual data elements across transactional databases and data warehouses to ensure high quality and compliance. He emphasized that implementing these controls after automation has already begun is significantly more difficult.

Key requirements for reliable AI applications include data lineage—the ability to trace a model’s output back to its source—and clear access controls to prevent the exposure of sensitive information. As organizations move from pilots to large-scale deployment, the focus of data governance is shifting from mere regulatory compliance to ensuring the operational accuracy of AI outputs.