SAP knowledge graph
SAP just told the world that “the knowledge graph won.”
At Sapphire 2026, Christian Klein put the SAP Knowledge Graph at the core of the new Business AI Platform — 452,000 tables, 7.3 million fields, 50 years of ERP engineering made machine-readable.
If you’ve been following me, you can hear my mixture of joy and suspicion.
Joy, because for decades the graph people were the unfashionable ones. Tables were the thing to do. Vectors were the thing to do. Now SAP — the largest enterprise software company on earth — has bet its strategy on the idea that you cannot ground an agent without a structured model of the business.
Suspicion, because what SAP is shipping is not what most people mean by “knowledge graph.” A graph extracted from 50 years of your own product’s schemas is a catalog of SAP. It tells an agent where the purchase orders live. That’s useful (duh), and it’s the easiest possible knowledge graph to build, because the schema was written in 1995 and it’s called the data dictionary.
The hard problem is the graph of your knowledge — and the ontology behind it, in a proper RDF sense. The decisions in PDFs and Slack threads. The link between a contract clause and a delivery risk no ERP table has ever captured. SAP’s graph does not solve this. By design, it cannot. So when the vendor narrative says “knowledge graphs are now foundational infrastructure,” read the fine print. The market is flooded with knowledge graphs that are really just well-labeled schema exports.
Meanwhile, job titles like “ontologist” and “KG expert” are suddenly everywhere. Funny how many experts appear the moment a category goes mainstream. Business as usual in IT, no offense meant.
The interesting work (extracting structure from unstructured knowledge) is still wide open.