Enterprise AI has moved from pilot projects to daily operations, with copilots and predictive agents now common in finance, supply chain, HR and customer service. A recent survey predicts half of firms will run AI in at least three functions by the end of 2025. Executives, however, report that raw model speed offers little value without business‑level context, warns Irfan Khan, president of SAP Data & Analytics.

To supply that missing meaning, companies are abandoning pure data aggregation in favor of a data fabric that stitches together clouds, applications and operational systems while preserving semantics. Knowledge graphs and catalog‑driven metadata become the glue, letting AI agents query enterprise facts in natural language. Only 9% of firms feel fully prepared for such integration, highlighting a maturity gap.

The fabric acts as the primary interface for autonomous AI, aligning decisions across finance, procurement and customer ops so agents share a common business view. When context remains intact, AI can prioritize strategic accounts, respect contractual limits and choose supply‑chain tradeoffs that move the organization forward. Enterprises that adopt this layered approach report higher data trust and faster, more reliable automation.