SAP: Bringing the ‘Operating System’ of a Company into the AI Era with CTO Philipp Herzig
Episode
45 min
Read time
2 min
Topics
Remote Work, Investing, Startups
AI-Generated Summary
Key Takeaways
- ✓Enterprise AI adoption gap: The gap between AI innovation and actual enterprise outcomes is widening, not narrowing. Companies with fragmented data from M&A activity or siloed purchasing decisions face the steepest barriers. Organizations that completed data harmonization work over the past decade now adopt AI capabilities measurably faster and achieve return on investment sooner than those starting from scratch.
- ✓Scale is the hidden engineering problem: Building a RAG chatbot on 10 documents is trivial; scaling to 20,000 APIs creates context bloat and disambiguation failures. SAP's Joule assistant must cross-reference employee location, tax jurisdiction, and payroll data to answer a single travel policy question correctly — a problem that only surfaces when moving from proof-of-concept to production deployment.
- ✓LLMs cannot replace predictive models for tabular data: Demand forecasting, cash flow prediction, and payment delay classification require classical regression and classification approaches like XGBoost. SAP's two-year NeurIPS-published research produced Relational Pretrained Transformers (RPT-1), a transformer architecture redesigned for structured tabular data that enables high-accuracy predictions from small datasets without dedicated data scientists.
- ✓Agent mining replaces process mining: As AI agents handle business processes, they surface undocumented "tribal knowledge" — decisions made via phone calls or Slack that never enter systems of record. Capturing agent decision traces allows companies to identify process anomalies, elevate local best practices to global standard operating procedures, and build a data flywheel that continuously improves agent accuracy over time.
- ✓Pricing is shifting from seats to consumption to outcomes: SAP's current model is a hybrid — primarily seat-based with consumption elements — because enterprise customers still require cost predictability. The trajectory moves toward outcome-based licensing similar to Sierra's model, but only as verifiability of agent outputs improves. Enterprises resisting pure consumption pricing cite fear of runaway costs, not skepticism about AI value.
What It Covers
SAP CTO Philipp Herzig outlines how the 400,000-customer enterprise software platform is rebuilding its architecture around AI agents, why large language models fail at predictive analytics, and why AI represents a business model transition from seat-based licensing toward consumption and outcome-based pricing models.
Key Questions Answered
- •Enterprise AI adoption gap: The gap between AI innovation and actual enterprise outcomes is widening, not narrowing. Companies with fragmented data from M&A activity or siloed purchasing decisions face the steepest barriers. Organizations that completed data harmonization work over the past decade now adopt AI capabilities measurably faster and achieve return on investment sooner than those starting from scratch.
- •Scale is the hidden engineering problem: Building a RAG chatbot on 10 documents is trivial; scaling to 20,000 APIs creates context bloat and disambiguation failures. SAP's Joule assistant must cross-reference employee location, tax jurisdiction, and payroll data to answer a single travel policy question correctly — a problem that only surfaces when moving from proof-of-concept to production deployment.
- •LLMs cannot replace predictive models for tabular data: Demand forecasting, cash flow prediction, and payment delay classification require classical regression and classification approaches like XGBoost. SAP's two-year NeurIPS-published research produced Relational Pretrained Transformers (RPT-1), a transformer architecture redesigned for structured tabular data that enables high-accuracy predictions from small datasets without dedicated data scientists.
- •Agent mining replaces process mining: As AI agents handle business processes, they surface undocumented "tribal knowledge" — decisions made via phone calls or Slack that never enter systems of record. Capturing agent decision traces allows companies to identify process anomalies, elevate local best practices to global standard operating procedures, and build a data flywheel that continuously improves agent accuracy over time.
- •Pricing is shifting from seats to consumption to outcomes: SAP's current model is a hybrid — primarily seat-based with consumption elements — because enterprise customers still require cost predictability. The trajectory moves toward outcome-based licensing similar to Sierra's model, but only as verifiability of agent outputs improves. Enterprises resisting pure consumption pricing cite fear of runaway costs, not skepticism about AI value.
Notable Moment
Herzig draws a direct parallel between test-driven development from the early 2000s and modern AI agent deployment — arguing that the discipline developers ignored for decades is now mandatory, because agents require precisely defined boundary conditions and evaluation harnesses to produce verifiable, maintainable enterprise-grade outputs.
Episode Transcript
Hey, listeners. Welcome back to No Pires. Today, I'm here with Filip Herdzik, the CTO of SAP, the enterprise juggernaut. We talk about their AI strategy, why SAP has endured and thrived through several technology transitions, why entrepreneurs are underestimating the challenges of scale, why AI is a business model transition, not just a technology transition, why he thinks that LMs are not enough for predictive analytics and even about the traveling salesman problem in the real world and the Strait Of Hermos. Welcome, Philip. Philip, thanks so much for being with us. Yeah. It's a pleasure to be here. Thank you. Everybody knows the name SAP, but I do think that for, lots of engineers or people who aren't close to the system in a larger enterprise, they don't really know, like, the breadth and function of the platform. Like, can you just describe, what you guys do for customers? Oh, absolutely. I mean, look, SAP is the market leader, right, in enterprise of software applications and platforms. Right? I have some 400,000 enterprise customers. And usually, I just running their finance, HR, and supply chain, manufacturing, execution, logistics, warehouse management, and then, of course, everything on the customer side, sales, services, commerce, procurement, you name it. Right? End to end. Right? Like SAP, we always say we have the broadest portfolio in terms of, end to end, running the business end to end. This is where SAP started with. Right? Giving real time insight. And, usually, I really describe this as it's not just software in itself. It's kind of the operating system, right, of a company, essentially, in order to, get, you know, from from everything from order to cash or source to pay, right, end to end managed for companies around the and around the entire globe. Mhmm. I definitely wanna talk about AI, LLMs, some of those stuff that you guys are doing internally and then around, predictive models as well. But just because the the macro backdrop is on everyone's mind, both from a technology and an economic perspective Oh, sure. I wanna talk about, like, SAP's position in the market a little bit. SAP has has stood the test of time through multiple technology and market cycles. I, as a early stage venture capitalist, I'm I'm kind of on the other side of this where the narrative is like, well, when you have Internet and cloud and mobile and AI and social, like, you have, an opportunity for new players. What what do you like, SAP, you know, even today is the, I believe the largest, like, market cap enterprise software vendor versus sort of the the last generation of the new guard, like the sales forces of the world. How has that happened? Like, how did you how did you do it, and what does make what makes it so durable? Well, what makes it so durable, right, at the end of the day I mean, if you think about this and …
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Books, tools, and gear mentioned in this episode
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Tools
- JouleBy guest
by SAP
“SAP's Joule assistant must cross-reference employee location, tax jurisdiction, and payroll data to answer a single travel policy question correctly”
Products
other
by SAP
“SAP's two-year NeurIPS-published research produced Relational Pretrained Transformers (RPT-1), a transformer architecture redesigned for structured tabular data”
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