This white paper examines how traditional data stacks are struggling to support modern analytics and AI initiatives. It highlights challenges such as rigid architectures, slow data pipelines, poor data quality, and limited accessibility for business users. The paper explains how AI-ready data stacks require flexible architectures, automation, and integrated analytics capabilities. It explores how modern platforms enable faster experimentation, improved governance, and operationalized AI use cases. The paper concludes that evolving the data stack is essential for unlocking AI value, improving decision-making speed, and reducing technical debt.

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