▶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
▶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
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