AI initiatives frequently fail to expand because businesses struggle with silos of untrusted data, leading to limited project scopes. The inability to trust AI outputs results in companies restricting their AI to controlled, small environments, impeding innovation and broader adoption. A fundamental requirement for successful AI projects is high-quality, ready data that is cleansed and unified, facilitating effective, real-time operations and analytics.
Defining AI-ready data encompasses secure and trustworthy information about critical business entities, ensuring that records are not duplicated and information is accurate across systems. Modern data unification platforms, like Reltio, play a pivotal role in overcoming legacy challenges by cleaning and enriching enterprise data, providing the foundation for scalable AI solutions. The current competitive landscape demands not just the technology but also a robust system of context to enable AI to function effectively, emphasizing that speed and data readiness are essential for leveraging AI capabilities. Companies that fail to provide quality data are likely to lag behind in the rapidly evolving AI-driven market.
👉 Pročitaj original: CIO Magazine
