Many AI initiatives falter not due to algorithmic limitations, but because the underlying data is fragmented and inconsistent. Data silos, legacy systems, and a lack of metadata create a "data integrity gap" that prevents AI models from accessing a complete and trustworthy view of information. Enterprises must unify data flows and enforce governance to build a solid data foundation for successful AI implementation. AI
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IMPACT Highlights that successful AI implementation hinges on robust data infrastructure, not just advanced algorithms.
RANK_REASON The article discusses common challenges in data management that hinder AI projects, offering an opinion on the root causes of failure.
