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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

Philipp HerzigguestSarah Guohost
Apr 23, 202639mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

SAP’s CTO on scaling enterprise AI, agents, and outcomes-based software

  1. SAP positions itself as the “operating system” for enterprises, spanning finance, HR, supply chain, and customer-facing workflows across 400,000 customers.
  2. Herzig argues SAP’s durability comes from standardizing repeatable business needs while continuously re-engineering for major platform shifts (mainframe→client/server→internet→cloud→AI).
  3. SAP’s AI transformation targets three layers at once: generative/proactive UI, agent-driven process execution (“outcome as a service”), and a harmonized semantic data layer to ground AI in enterprise truth.
  4. The hardest enterprise AI problem is not demos but scaling reliable, contextual behavior across huge document corpora, complex master data, and tens of thousands of APIs with strong security guarantees.
  5. Beyond LLMs, SAP is investing in specialized predictive/tabular modeling (RPT-1) to democratize forecasting and decision support that classical ML can do but doesn’t scale organizationally today.

IDEAS WORTH REMEMBERING

5 ideas

Enterprise AI success is an outcome race, not an innovation race.

Herzig frames a growing gap between flashy AI innovation and measurable enterprise outcomes; SAP’s strategy emphasizes reducing effort, time, and cost in real workflows rather than standalone demos.

Scaling matters more than the initial prototype.

RAG and MCP-style integrations look easy on 10 documents or 10 APIs, but SAP customers require personalization, policy correctness, and orchestration across thousands of documents and ~20,000 APIs.

AI is forcing a redesign of the user interface model.

He predicts the end of “UI that teaches humans to click” and a move to generative, proactive, multimodal interfaces that surface issues (e.g., supply chain disruptions) and propose actions.

Agents will shift software from SaaS to “service/outcome as software.”

Instead of rigid end-to-end processes, agents blend structured and unstructured work—handling documents, exceptions, and coordination—while humans supervise higher-level decisions.

Verifiability requires enterprise-grade evals and boundary conditions.

Code agents work because compilation/tests verify outputs; for finance/HR outcomes, teams must encode expected outputs, constraints, privacy/security rules, and ongoing evals to ensure reliability.

WORDS WORTH SAVING

5 quotes

SAP is… kind of the operating system… of a company essentially.

Philipp Herzig

The time is clearly over where you design software… that requires the intelligence to sit in front of the computer.

Philipp Herzig

The biggest challenge… is… teaching the AI to do the right thing at scale.

Philipp Herzig

In the past, we called this process mining, now we call it agent mining.

Philipp Herzig

Our job at SAP is to make the technology disappear.

Philipp Herzig

SAP as enterprise operating systemWhy incumbents endure through tech transitionsThree-layer AI re-engineering: UI, processes, dataEnterprise scale challenges: context, APIs, personalizationEvals, verifiability, and test-driven discipline returning“Agent mining” and decision-trace flywheelsPricing shift: seat-based to consumptive/outcome-based hybrids

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