Despite significant investment in AI, many projects stall because the underlying data is fragmented, outdated, or inaccurate. Large enterprises often run hundreds of systems, dispersing data across various silos like customer records, financials, and operations. This patchwork results in inconsistent AI outputs, raising risks of misleading decisions and impeding adoption.
Industry leaders like Mastercard and L’Oréal demonstrate the importance of integrating and cleaning data into trusted, connected foundations for better decision-making and customer experiences. The shift toward relying on AI agents and copilots for workflow automation and insights demands data that is accessible in real time and structured for AI consumption.
The old rules of data management no longer apply in what has been termed the Age of Intelligence. Organizations that fail to unify their data risk falling behind, while those focusing on a strong data foundation will unlock AI’s true potential, emphasizing that the competitive advantage lies in data quality over the choice of AI models.
👉 Pročitaj original: CIO Magazine
