No Priors

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

More than fifty years ago, the modern idea of the standard enterprise software was birthed at SAP. Now, after managing companies through technological shifts from the mainframe to mobile, SAP is at the forefront of closing the AI adoption gap for their customers. SAP Chief Technology Officer Philipp Herzig joins Sarah Guo to talk about how SAP has remained a durable end-to-end “operating system” for its more than 400,000 customers from finance to supply chain. Philipp argues that the AI transition in businesses should focus on customer outcomes, UI changes, business processes, and the data layer. He also explains the challenges in enterprise AI adoption, including security, scaling, and data fragmentation, as well as the importance of evals and verifiability. They also discuss SAP’s suite of AI products, limitations of predictive tabular models, how SAP is shifting its pricing models in the AI era, and Philipp’s interest in quantum computing optimization. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @pheartig | @SAP Chapters: 00:00 – Cold Open 00:42 – Philipp Herzig Introduction 01:18 – What SAP Does 02:51 – Why SAP Endures 06:53 – CTO Priorities and AI Push 12:14 – Scaling AI in Enterprise 17:06 – Verifiability and Agent Mining 20:42 – Tool Calling vs. Computer Use 22:11 – Domains Where Agents Deliver Value 24:58 – Limitations of Predictive Tabular Models 29:07 – Barriers to Enterprise Adoption 31:54 – How AI Will ‘Uplevels’ Work 34:03 – How AI Changes SAP’s Pricing Model 36:41 – What Makes a Winner in the AI Era 38:53 – Day in the Life of a CTO 40:08 – Customer Challenges 42:36 – Business Problem of Quantum Computing 46:21 – Conclusion

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Apr 23, 2026·39m·Watch on YouTube ↗

📖 CHAPTERS

1. 1
   0:00 – 0:42

   Cold open: AI as the new interface and 30% efficiency gains

   A quick teaser frames SAP’s AI thesis: companies can materially reduce effort and cost by embedding AI into enterprise workflows. Philipp argues the era of software that requires “intelligence in front of the screen” is ending as UIs and processes become more autonomous.

2. 2
   0:42 – 1:18

   Meet SAP’s CTO Philipp Herzig and the episode roadmap

   Sarah introduces Philipp and sets expectations: SAP’s AI strategy, adoption challenges at scale, predictive analytics beyond LLMs, and how business models shift with agentic software. The framing emphasizes that AI is not only a technology transition but a business model transition.

3. 3
   1:18 – 6:53

   SAP as the ‘operating system’ for enterprises

   Philipp explains SAP’s breadth across core business functions—finance, HR, supply chain, manufacturing, procurement, and customer-facing operations. He positions SAP as an end-to-end system of record enabling “order-to-cash” and “source-to-pay” at global scale.

4. 4
   6:53 – 12:14

   CTO priorities: going ‘all-in’ on AI across product and engineering

   Philipp describes SAP’s internal adoption (agentic coding) and customer-facing AI products focused on measurable outcomes. He highlights Joule for Consulting as a high-ROI example that accelerates migrations and modernization in complex SAP landscapes.

5. 5
   12:14 – 17:06

   The real engineering challenge: scaling AI from demos to enterprise reality

   Philipp argues the hardest part isn’t building an LLM demo—it’s getting correct, context-aware behavior at massive scale. As document counts, policies, and integrations grow, systems must personalize responses, manage context limits, and orchestrate thousands of tools and APIs reliably.

6. 6
   17:06 – 20:42

   Verifiability, evals, and ‘agent mining’ as a data flywheel

   To make agents trustworthy, SAP leans on verification and evaluation strategies similar to what made coding agents effective: you can test outcomes. Philipp describes capturing decision traces—“agent mining”—to turn tribal knowledge into data, detect anomalies, and continuously improve SOPs and agent behavior.

7. 7
   20:42 – 22:11

   Tool calling vs. computer-use agents: what belongs where

   Philipp expects most enterprise automation to rely on tool calling via structured integrations rather than UI-driving ‘computer use,’ due to reliability and operational preferences. However, computer-use approaches remain valuable when APIs are missing or legacy systems persist.

8. 8
   22:11 – 24:58

   Where agents deliver value first: unstructured work and data-to-insight workflows

   Early wins cluster around unstructured domains—service/support, sales, consulting, document-heavy knowledge work—where LLMs excel. As orchestration improves, SAP also targets ‘chat with your data’ experiences that generate analyses on demand and can be pinned into lightweight, evolving dashboards.

9. 9
   24:58 – 29:07

   Why LLMs aren’t enough for predictive/tabular analytics (and SAP’s RPT approach)

   Philipp argues enterprise decision-making needs robust prediction (demand, cash flow, payment behavior), which classic LLM token-generation is not optimized for. He describes SAP’s research on relational/tabular transformers (RPT) to reduce reliance on bespoke model-building and scale predictive ML across many countries and contexts.

10. 10
    29:07

    Enterprise adoption barriers and the shift to consumption/outcome-based pricing

    Adoption is constrained by fragmented data landscapes, integration difficulty at scale, and enterprise-grade security requirements. On monetization, Philipp expects a gradual move from seat-based licensing to hybrid and more consumptive models, with outcome-based pricing becoming more feasible as trust and verifiability improve.

11. 11

    Why SAP has endured through multiple technology transitions

    SAP’s durability is framed as an outcome-driven story: customers consistently want reliable business outcomes, even as underlying tech shifts (mainframe → client-server → internet → mobile → AI). Standard software scaled better than repeated bespoke implementations, and that economic logic still holds.

12. 12

    Three-layer re-engineering: UI, processes, and data foundation

    SAP’s AI transformation is presented as a parallel to the cloud shift: you don’t just “host” existing software differently—you redesign how it works. Philipp breaks the change into three levels: generative UI, agent-augmented business processes, and a harmonized data layer to power reliable AI.