Closing the Gap Between Data Strategy and AI Delivery

AI programs often stall because existing data strategies were designed for analytics, not production AI. The article identifies gaps in data quality, unstructured-data readiness, legacy architecture, governance and ownership that emerge when AI moves beyond prototypes. It argues for use-case-specific data requirements, reusable retrieval and integration components, and production controls designed from the outset. Technology leaders should evaluate AI readiness through reliability, governance, reuse and scalability—not model accuracy alone.