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SAP: Bringing the ‘Operating System’ of a Company into the AI Era with CTO Philipp Herzig
Brief
Philipp Herzig, CTO of SAP, walks through how the enterprise software leader is bringing its decades‑old platform into the AI era. He frames SAP as the "operating system" for ~400,000 customers—holding critical structured data (GL, invoices, inventory) and running end‑to‑end processes such as order‑to‑cash and source‑to‑pay. Herzig argues SAP endures because customers care about measurable outcomes, and that AI is now the vehicle to help them achieve those outcomes faster and at scale.
Herzig lays out a three‑layer transformation: the user interface (generative UIs that are multimodal and proactive), business processes (agents that blend structured and unstructured work to deliver outcomes rather than just screens), and the data layer (a harmonized semantic model / SAP knowledge graph). He emphasizes practical engineering pain points: scale (hundreds to thousands of documents and context per user, ~20,000 APIs), verifiability, and the need for testable evaluation harnesses ("e‑vals") and agent trace capture ("agent mining") so the system can learn and establish reliable outcomes. Herzig also explains why LLMs are not enough for forecasting and other tabular problems: SAP has spent two years on RPT1 (Relational Pre‑Trained Transformers) to bring transformer‑style pretraining to relational/tabular data so classification, regression and time series can be predicted with less data and more accuracy.
On adoption and business impact, Herzig says early ROI is strongest in unstructured, knowledge‑work domains (consulting, support, sales) where conversational analytics and document processing eliminate manual prep; roles will be "up‑leveled" as agents take mundane tasks. He predicts a commercial shift from seat licenses toward hybrid consumption and outcome models, constrained today by customers' needs for cost predictability and security. Finally, Herzig flags quantum computing and optimization (routing, knapsack, traveling salesman) as longer‑term areas SAP is researching to solve enterprise‑scale combinatorial problems. Throughout the interview the host probes tradeoffs and adoption barriers; Herzig consistently steers answers back to measurable customer outcomes, practical verifiability, and the incremental nature of enterprise transformation.
Why it matters
Philipp Herzig (CTO, SAP) said SAP serves ~400,000 enterprise customers and functions as the "operating system" of a company—running finance, HR, supply chain, manufacturing, logistics, sales, service and procurement end-to-end.
Key details
- Herzig identified three AI re‑engineering priorities for SAP: generative UIs (dynamic, proactive multimodal interfaces), intelligent agents that convert processes into outcomes (service-as-software/outcome-as-a-service), and a harmonized data layer/knowledge graph to fuel AI.
- Herzig reported an internal AI consulting product reduced consultant effort by about 30% (speeding cloud migrations and AI adoption) and called out other early AI wins in travel/expense automation (Concur examples).
- The largest engineering challenge, Herzig said, is scale and verifiability: SAP must handle thousands of documents, context for many countries and roles, and ~20,000 APIs—requiring evaluation harnesses ("e‑vals"), agent traces ("agent mining"), and strong observability to prove correct outcomes.
- SAP published RPT1 (Relational Pre‑Trained Transformers), Herzig said, to tackle tabular/predictive problems—arguing that LLMs alone are insufficient for regression, classification and time‑series forecasting and that RPT1 enables higher‑accuracy predictions with smaller data footprints.
- Herzig expects SAP's pricing to shift from primarily seat‑based licensing toward a hybrid model (consumption + seat) and eventually more outcome‑based contracts as verifiability increases; customers currently demand predictability and security before fully embracing pure consumption.