Enterprise AI initiatives are failing at an alarming rate, and Milan Parikh believes he’s identified the root cause. Speaking at Data Summit 2026, Parikh revealed that 73% of companies struggle with AI implementation due to poor data foundation issues rather than algorithmic shortcomings.
The problem lies in decades of accumulated technical debt and fragmented data architectures. Many enterprises built their digital infrastructure reactively, creating siloed systems that cannot support the data quality and accessibility requirements modern AI demands. Parikh emphasized that without addressing these foundational weaknesses, even the most sophisticated AI models will produce unreliable results.
His solution involves a comprehensive data foundation audit followed by strategic rebuilding of data pipelines. Companies must invest in data governance, standardization, and integration before deploying AI at scale. Success requires treating data infrastructure with the same priority as cybersecurity or cloud migration initiatives.
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