Salesforce charts layered data architecture for enterprise AI

Enterprise organizations are shifting from cloud-native IT stacks to AI-native ones, and that transition is reshaping how they think about data architecture. The move reflects a fundamental change in what enterprises expect from data systems: no longer just speed and scale, but the ability to deliver trustworthy, explainable answers in a conversational interface. Knowledge graphs have emerged as the connective tissue binding fragmented data sources together—lakehouses, operational databases, customer profile stores—into a coherent system of intelligence.

Baker's framing reveals why this shift is non-trivial. The problem is not that enterprises lack data; it is that their data lives scattered across dozens of copies in incompatible systems, with no shared semantic understanding of what it means. A metadata layer can bridge that gap by translating business language into database columns, and graph technology can track relationships and context across those translations. But without careful governance—semantic policies that legal and security teams can understand and enforce—even the best AI reasoning engine will produce unreliable answers. Most organizations, Baker suggests, are still struggling to design these governance layers at the semantic level rather than hiding them inside individual databases.