SAP: Bringing the ‘Operating System’ of a Company into the AI Era with CTO Philipp Herzig

2026-04-23 · Source: No Priors: AI, Machine Learning, Tech, & Startups · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Advanced, extended

Summary

SAP CTO Philip Herzig discusses the company's comprehensive AI strategy, emphasizing its role in re-engineering enterprise software across three levels: UI transformation, business process automation via agents, and data layer harmonization. SAP, a market leader serving 400,000 enterprise customers in finance, HR, supply chain, and more, is deeply embedding AI to deliver outcomes and reduce customer effort by up to 30%. Herzig highlights the shift from traditional UIs to "generative UI" and the use of AI agents to blend structured and unstructured data, moving towards an "outcome as a service" model. A key technical challenge is scaling AI to handle 20,000 APIs and vast, disaggregated customer data while ensuring security and verifiability. SAP is also pioneering RPT1, a relational pre-trained transformer, to democratize predictive analytics for tabular data, addressing a gap where large language models are less effective.

Key takeaway

For Directors of AI/ML or AI Architects navigating enterprise transformations, recognize that AI adoption hinges on addressing data fragmentation, scalability, and security. Your teams should prioritize building robust data foundations and evaluation frameworks for AI agents, moving beyond basic chatbot implementations to deliver verifiable business outcomes. Focus on re-engineering core processes with AI to achieve significant cost and time reductions, preparing for a future where software shifts towards outcome-based consumption models.

Key insights

SAP is re-engineering its enterprise software with AI across UI, processes, and data to deliver measurable customer outcomes at scale.

Principles

• AI is a business model transition, not just technology.
• Focus on customer outcomes, not just innovation.
• AI is only as powerful as the data it uses.

Method

SAP's AI strategy involves generative UIs, AI agents for process automation, and harmonized data layers. It emphasizes "agent mining" to capture tribal knowledge and refine processes, creating a data flywheel for continuous improvement.

In practice

• Implement agentic coding for developer productivity.
• Prioritize data harmonization for AI readiness.
• Develop robust evaluation (evals) for AI agent reliability.

Topics

• SAP AI Strategy
• Generative UI
• AI Agents
• Data Layer Transformation
• Predictive Analytics

Best for: Director of AI/ML, AI Architect, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by No Priors: AI, Machine Learning, Tech, & Startups.