SAP CTO Philipp Herzig: The Greatest Danger of Enterprise AI Is Not Getting It Wrong, But Giving an Answer That "Sounds Right"

Philipp Herzig, Chief Technology Officer of SAP, points out that the most dangerous answers in enterprise AI are not those that are obviously incorrect, but rather those that are phrased correctly and sound authoritative yet are disconnected from actual business realities—so-called "fluent but wrong" responses.

He explains the reason: while enterprise data resides in tables, understanding a specific field requires comprehension of the underlying business processes—including transaction rules, unwritten exceptions, and entity identity (for example, recognizing that a supplier in a procurement system is the same entity as one in a financial system). Simply feeding corporate documents into an AI model is insufficient; models require a readable mapping of business objects, relationships, and rules.

SAP’s solution involves a two-layer structure. The first layer is the Knowledge Graph, which provides models with a machine-readable map of business object relationships. This prevents agents from guessing field meanings, thereby reducing erroneous API calls and retries, and enabling smaller models to handle more tasks. The second layer is Company Memory, which records the context, rationale, authority, and outcome behind approved exceptions so that subsequent users do not have to start from scratch. However, Herzig emphasizes that understanding a rule is not the same as executing it: operations remain within user permissions and approval boundaries. A single successful exception does not become a permanent rule; human process owners must review whether the applicable conditions still hold true.