SAP CTO Philipp Herzig discusses the company's comprehensive AI strategy, aiming to transform SAP into the "operating system" of the AI era. He highlights SAP's enduring success through technology shifts, emphasizing AI as a business model transition that will automate mundane tasks, elevate strategic thinking, and necessitate a shift towards consumptive and outcome-based pricing. Herzig also explains why traditional LLMs are insufficient for enterprise predictive analytics, leading to SAP's development of RPT1 for tabular data.
Summarized by Podsumo
SAP is re-engineering its entire system with AI, focusing on generative UIs, blending structured/unstructured data in business processes, and building a harmonized data layer to fuel AI.
Philipp Herzig argues that LLMs are not sufficient for accurate predictive analytics in tabular data (e.g., demand forecasting, cash flow prediction). SAP is developing **RPT1 (Relational Pre-trained Transformers)** to address this gap, aiming to democratize high-accuracy predictions at scale.
Key hurdles include **data disaggregation** across complex enterprise landscapes, the **problem of scale** (e.g., 20,000 APIs), and ensuring **security and data privacy** for new AI innovations.
AI will shift SAP's pricing from seat-based to a **consumptive or outcome-based model**, reflecting the value delivered through automated tasks and improved decision-making.
AI will automate mundane tasks in finance, HR, and supply chain, allowing employees to focus on **strategic thinking, scenario planning, and deeper insights**, effectively "up-leveling" every role.
"It's kind of the operating system of a company, essentially, in order to get from everything from order to cash, or source to pay, end-to-end managed for companies around the entire globe."
"AI is only as powerful as the data is."
"LLAMs unstructured world, that's all good, right? But most of the time, if you want to plan forward, right? If you want to make good decisions in a company, you need predictions."