Enterprises must rebuild data infrastructure to deploy AI effectively at scale

Enterprise AI adoption is blocked by fragmented, siloed data across legacy systems and disconnected applications, making it difficult for AI systems to generate trustworthy, context-rich outputs. According to Bavesh Patel, senior vice president for Go-to-Market at Databricks, 'the quality of that AI and how effective that AI is, is really dependent on information in your organization.'

AI-ready data requires consolidation into open formats, governance with precision, and accessibility across functions—combining structured and unstructured data while preserving real-time context and enforcing access controls. Enterprise AI differs from consumer AI because business decisions demand high precision in outputs; successful customers require precision to be more than 92%, according to Rajan Padmanabhan, unit technology officer for data analytics and AI at Infosys.

When data infrastructure is properly built, organizations can unlock measurable outcomes, automate complex workflows, and as AI agents evolve from copilots into autonomous operators, move from 'a system of execution or a system of engagement to a system of action,' according to Padmanabhan.