SAP: Bringing the ‘Operating System’ of a Company into the AI Era with CTO Philipp Herzig
Philipp Herzig (guest), Sarah Guo (host)
In this episode of No Priors, featuring Philipp Herzig and Sarah Guo, SAP: Bringing the ‘Operating System’ of a Company into the AI Era with CTO Philipp Herzig explores sAP’s CTO on scaling enterprise AI, agents, and outcomes-based software SAP positions itself as the “operating system” for enterprises, spanning finance, HR, supply chain, and customer-facing workflows across 400,000 customers.
SAP’s CTO on scaling enterprise AI, agents, and outcomes-based software
SAP positions itself as the “operating system” for enterprises, spanning finance, HR, supply chain, and customer-facing workflows across 400,000 customers.
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).
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.
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.
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.
Key Takeaways
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.
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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.
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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. ...
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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.
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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.
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“Agent mining” can turn human-in-the-loop friction into a data flywheel.
By capturing decision traces and tribal knowledge (often outside systems of record), organizations can detect anomalies, standardize best practices, and generate new labeled data for evaluation and improvement.
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LLMs won’t replace predictive analytics; tabular models must be reinvented for scale.
Forecasting demand, cash flow, and payment behavior needs strong regression/classification on structured data; SAP’s RPT-1 aims to make these predictions easier to build and deploy without armies of data scientists.
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Notable 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
Questions Answered in This Episode
On “generative UI,” what concrete UX patterns replace today’s transaction screens for finance/HR—chat, dashboards, guided flows, or mixed modes?
SAP positions itself as the “operating system” for enterprises, spanning finance, HR, supply chain, and customer-facing workflows across 400,000 customers.
Get the full analysis with uListen AI
How does SAP plan to manage tool-selection and disambiguation at scale (e.g., ‘order’ meaning purchase vs. sales vs. maintenance) across 20,000 APIs?
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).
Get the full analysis with uListen AI
What does an enterprise-grade eval suite look like for an SAP finance agent—what are the core test cases, failure modes, and acceptance thresholds?
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.
Get the full analysis with uListen AI
In “agent mining,” who owns the captured tribal knowledge (IT, process owners, security), and how do you prevent it from encoding bad or non-compliant practices?
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.
Get the full analysis with uListen AI
Where do you expect computer-use agents (UI automation) to be unavoidable, and what guardrails are required before letting them operate in production systems?
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.
Get the full analysis with uListen AI
Transcript Preview
There's a lot of exciting opportunities and new things you can build that we only dreamed of in the last, I don't know, twenty years at least since I'm a developer.
Mm-hmm.
We are serving some of the largest customers. They have a lot of heritage, they have a lot of complex landscape. They can reduce like thirty percent of their efforts, right, to get to the outcome faster, which of course then directly reduces the cost. The time is clearly over where you design software that requires the intelligence to sit in front of the computer. If you look at classical software, what did you do? You designed a user interface to teach a human how to get their task done by clicking through the UI, essentially. This is over.
[upbeat music] Hey, listeners. Welcome back to No Priors. Today, I'm here with Philipp Herzig, the CTO of SAP, the enterprise juggernaut. We talk about their AI strategy, why SAP has endured and thrived through several technology transitions, why en-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 Hormuz. Welcome, Philipp. Philipp, 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, uh, 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, uh, 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? We have some four hundred thousand enterprise customers, and usually I desc-- running their finance, and HR, and you know, supply chain, manufacturing, execution, logistics, warehouse management, and then of course, everything on the customer side, sales, services, commerce, procurement, you name it, right? Um, end-to-end, right? Like SAP, we always say we have the broadest portfolio in terms of, uh, end-to-end, running the business end-to-end. This is where SAP started with, right? The re-- 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, uh, of a company essentially, uh, in order to, um, get, you know, from, from everything from order to cash, or source to pay, right, end-to-end managed for companies around the, around the entire globe.
Mm-hmm. Um, I definitely wanna talk about AI, uh, LLM, some of the stuff that you guys-
Sure
... are doing internally, and then around, um, predictive models as well. Uh, but just because the, the macro backdrop is o-on everyone's mind, both from a technology and an economic perspective-
Oh, sure
... um, I wanna talk about like SAP's position in the market a little bit. SAP has, uh, 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, um, uh, an opportunity for new players, um-
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