Enterprise AI hinges on high-accuracy outputs, requiring better data context, unified architectures and rigorous measurement frameworks, say Bavesh Patel from Databricks and Rajan Padmanabhan of Infosys. Despite AI’s consumer allure, enterprise leaders face a lesser-known but more critical challenge: data infrastructure that is unified, governed, and fit for purpose.

The gap between AI ambition and readiness becomes one of the defining challenges in digital transformation. For AI to deliver value, data must be consolidated into open formats, governed with precision, and made accessible across functions. Without this foundation, businesses risk ‘terrible AI’, as Patel bluntly states. That means moving beyond siloed SaaS platforms toward a unified, open data architecture capable of combining structured and unstructured data.

The value focus is critical for enterprise AI, says Padmanabhan, especially as enterprises seek precision in the outputs driving business decisions. Leading companies tie AI deployment directly to business metrics, using governance frameworks to determine what delivers results.

The future lies in turning fragmented information into a strategic asset capable of powering smarter decisions and new ways of operating. As AI agents evolve from copilots into autonomous operators managing workflows and transactions, the organizations that win will be those that build the right foundation now.