Philipp Herzig

Chief Technology Officer of SAP, leading the company's AI strategy and integration.

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Key positions and views

The primary challenge in enterprise AI is not inventing new models but engineering them to perform reliably and correctly at massive scale.

The vast majority of enterprise AI agent tasks will be accomplished via API-based tool calling, with UI automation serving as a necessary but secondary fallback for legacy systems.

A 'right tool for the right job' approach is necessary for AI; LLMs are not suited for predictive tasks on structured data, where classical ML or specialized transformers remain superior.

AI is a catalyst for new paradigms in the software industry, forcing a revival of Test-Driven Development, a shift away from static user interfaces, and a transition to consumption-based business models.

A flexible, partnership-driven architecture is the wisest strategy for a large enterprise platform like SAP, avoiding over-investment in specific AI technologies that are likely to become commoditized.

Podcast consensus on Herzig

Points of consensus

▶Herzig consistently emphasizes that the primary challenge in enterprise AI is not model creation but achieving reliable, correct performance at a massive scale, a key focus for SAP.Apr 2026

▶He views AI as a fundamental catalyst for change, driving shifts in SAP's business model towards consumption-based pricing, altering software development methodologies, and transforming user interfaces.Apr 2026

▶A core part of SAP's AI strategy, as articulated by Herzig, is a pragmatic, partnership-driven approach, maintaining a flexible architecture to integrate with major AI providers rather than over-investing in components that may become commoditized.Apr 2026

▶He asserts that AI agents operating via API-based tool calling will be the dominant method for automating enterprise tasks, with UI automation serving as a fallback for legacy systems.Apr 2026

Points of debate

▶Herzig highlights a growing tension between the rapid 'innovation race' in AI technology and the slower 'outcome race,' where tangible business adoption and value realization are lagging behind.Apr 2026

▶He presents a nuanced view on AI model application, arguing that while Large Language Models are transformative, they are not suited for all enterprise tasks, and classical ML approaches remain superior for predictive analytics on structured data.Apr 2026

▶Herzig points to a conflict in the market between the push for open-source AI innovation and the significant security concerns these tools present for enterprises, citing vulnerabilities that can hinder adoption.Apr 2026

▶He describes a business model friction point where AI naturally leads to consumption-based pricing, but many enterprise customers' need for budget predictability requires SAP to maintain a hybrid model for the time being.Apr 2026

Key themes

▶Pragmatic Enterprise AI ImplementationApr 2026

Herzig's discourse centers on a practical, engineering-led approach to AI. He emphasizes that the hardest problem is not building models, but making them perform reliably at enterprise scale (20,000 APIs), advocating for a flexible, partnership-based architecture to avoid betting on commoditizing tech and stressing the importance of security.

This suggests SAP's strategy is to be a stable, trusted AI integrator for its risk-averse enterprise clients, prioritizing reliability and security over being on the bleeding edge of model development, which could be a strong selling point for large corporations.

▶The Shift to Agent-Based Enterprise SoftwareApr 2026

Herzig envisions a future where AI agents are the primary interface for enterprise software, automating complex workflows. This is supported by SAP's internal use of 'agentic coding' for all developers, the launch of agents in products like Concur, and the development of concepts like 'agent mining' to improve performance.

Investors should monitor SAP's ability to effectively scale these agentic systems across its vast API landscape, as success would create a powerful competitive moat by deeply embedding AI into customers' core business processes.

▶AI as a Catalyst for Business Model TransformationApr 2026

According to Herzig, AI is fundamentally altering SAP's business model, driving a strategic shift away from traditional seat-based licensing towards consumption-based pricing. He acknowledges this is a gradual transition, with a hybrid model currently in place to accommodate enterprise customers who require budget predictability.

This transition to a consumption model introduces both opportunity and risk; while it can capture more value from high-usage clients, it also creates potential revenue volatility and requires SAP to continuously prove its value to prevent customers from optimizing away their usage.

▶Specialized AI for Structured Enterprise DataApr 2026

Herzig argues that general-purpose LLMs are not a panacea for all enterprise needs, particularly for predictive tasks on structured data. He highlights SAP's investment in specialized models like the RPT1 transformer and foundational layers like the SAP Knowledge Graph to bridge the gap between natural language and the company's core strength in structured business data.

SAP's focus on building specialized AI for its core domain could be a key differentiator against more generalized AI platforms, potentially allowing it to deliver more accurate and reliable outcomes for high-value business functions like demand forecasting and financial planning.

Source episodes

Sentiment over time

Not enough data for timeline

Changes over time

1972

Herzig notes that SAP was founded by former IBM employees to create standardized finance software, establishing the company's foundation in core enterprise processes.

Pre-AI Era

As described by Herzig, SAP grew into the market leader for enterprise applications, serving 400,000 customers and building a massive platform with 20,000 APIs, which now presents a significant scaling challenge for AI.

Recent Past

Herzig highlights SAP's development of specialized AI, such as publishing research at NeurIPS on its RPT1 model, a transformer designed specifically for predictive tasks on structured, tabular data.

Present

Herzig reports that SAP is in a phase of broad AI implementation, deploying agentic coding for all its developers, launching AI agents in products like Concur, and seeing rapid growth in tools like 'Joule for Consulting'.

Near Future

Herzig's focus is on the next evolution of enterprise software, which includes developing 'agent mining' to improve AI, moving towards dynamically generated UIs, and managing the company-wide transition to a consumption-based business model driven by AI.

Suggested prompts

How will SAP manage the revenue volatility and customer budget concerns associated with its strategic shift from seat-based to consumption-based pricing for AI? &nearr;Given the scaling challenge of 20,000 APIs, what specific technical and product strategies is SAP employing to make its AI agents reliable at tool selection and execution? &nearr;What is the competitive risk for SAP if generalized foundation models become significantly better at handling structured data, potentially commoditizing specialized models like RPT1? &nearr;How does Herzig's stated focus on the 'outcome race' over the 'innovation race' manifest in SAP's product roadmap, R&D spending, and M&A strategy? &nearr;

Key concepts

AI Agents & Tool Use 1 ep Enterprise AI Strategy 1 ep SAP Products (Joule, Concur) 1 ep Business Model Shift 1 ep Developer Productivity & TDD 1 ep Specialized AI Models (RPT1) 1 ep UI/UX Transformation 1 ep Data & Knowledge Graphs 1 ep AI Security 1 ep

Notable quotes

“The time is clearly over where you design software, where the dump software, that requires the intelligence to sit in front of the computer, right?”

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

“Like there's this Gartner curve, right, where say like there's this AI innovation race and then there's this AI outcome race. Right. Then the gap almost increases. Right. Versus getting narrow.”

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

“Well, the biggest challenge, quite frankly, is, of course, when you look at this, is how do you, it's actually not the AI so much, right? But it's actually teaching the AI to do the right thing at scale, right?”

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

“All developers at SAP are using agentic coding to boost productivity across all programming languages.”

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

Report last updated: Apr 28, 2026

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