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No Priors · April 23, 2026 · 45m

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

Philipp Herzig, Sarah Guo
SpotifyApple Podcasts

Philipp Herzig, CTO of SAP, discusses how the 50-year-old enterprise software giant is navigating the AI transition for its 400,000+ customers. The conversation covers SAP's strategy for closing the AI adoption gap, the critical importance of data quality and verifiability in enterprise AI, the shift from predictive models to agentic systems, and how AI is forcing SAP to rethink its pricing model from per-user licenses to outcome-based approaches.

This summary was generated from show notes and public descriptions, not from a full transcript review. Details may contain inaccuracies.

Highlights

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Enterprise AI Requires Verifiable, Measurable Outcomes—Not Just Model Performance17:06
Enterprise AI adoption fails when companies optimize for model metrics rather than business outcomes; success requires verifiability, traceability, and measurement of real customer impact.
ClaudeChatGPTGemini
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Data Fragmentation Is the Largest Barrier to Enterprise AI Adoption29:07
Enterprise companies struggle with AI not because models aren't good enough, but because their data lives in silos—finance systems, HR systems, supply chain systems—with no unified access or governance.
ClaudeChatGPTGemini
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Agentic Systems (Tool Calling) Will Outpace Static Predictive Models in Enterprise20:42
Enterprise AI is shifting from static predictive models ("predict the next quarter's revenue") to agentic systems that call business tools, take actions, and adapt based on real-time data.
ClaudeChatGPTGemini
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AI Will Force Enterprise Software to Shift from Per-User Licensing to Outcome-Based Pricing34:03
SAP's traditional model charged customers per user or per transaction; AI-driven automation will collapse transaction volumes and user counts, forcing a shift to pricing based on business outcomes or value delivered.
ClaudeChatGPTGemini

Editorial

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Enterprise AI Adoption Requires UI Redesign and Business Process Reengineering, Not Just Better Models06:53
Technical improvements to AI models matter less than redesigning how users interact with systems and reimagining the business processes those systems support.
ClaudeChatGPTGemini

Misc

✧SAP has survived five major technology shifts (mainframe → client-server → web → mobile → cloud) by remaining the 'operating system' for enterprise business processes
✧Enterprise AI adoption is hampered not by model capability but by data fragmentation, security concerns, and lack of verifiability
✧Herzig emphasizes that AI success in business depends on measuring customer outcomes, not just model performance
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