SAP's second quarter: The AI takeaways

2026-07-24 · Source: Constellation Research · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Project & Product Management · Depth: Fundamental Awareness, short

Summary

SAP reported better-than-expected second quarter 2026 results, with earnings per share of €1.89 and revenue of €9.88 billion, a 9% increase year-over-year. Cloud revenue grew 22%, with a 2026 outlook of €25.8 billion to €26.2 billion, up 23% to 25% in constant currency. Despite a non-IRFS operating profit hit from Dremio and Prior Labs acquisitions, SAP is driving an "AI transformation" towards an "autonomous enterprise." CEO Christian Klein noted growing AI consumption, with Business Data Cloud in 90% of the 50 largest deals. SAP plans to release approximately 50 AI assistants by Q3 and over 400 Autonomous Suite agents by year-end 2026. The company addresses enterprise AI cost concerns by combining deterministic applications with probabilistic AI, and internally optimizes its own AI token spending. CFO Dominik Asam emphasized a flexible, multi-model AI strategy to avoid lock-in, citing geopolitical uncertainties and sovereign requirements.

Key takeaway

For Directors of AI/ML evaluating enterprise AI strategies, SAP's approach highlights critical considerations. You should prioritize solutions that balance deterministic mission-critical applications with probabilistic AI, actively managing token costs through granular tracking. Emphasize a flexible, multi-vendor large language model strategy to mitigate vendor lock-in and enhance resilience, especially given geopolitical uncertainties and sovereign data requirements. This approach can help your organization navigate AI adoption while controlling expenses and maintaining strategic agility.

Key insights

SAP is aggressively embedding AI into its enterprise offerings and internal operations, prioritizing efficiency and cost control.

Principles

• AI transformation targets autonomous enterprise.
• Manage AI costs through granular control.
• Avoid LLM vendor lock-in for resilience.

Method

SAP is transforming its operating model to build AI at scale, including optimizing token spending via tight, granular controlling.

In practice

• Track AI token consumption at a granular level.
• Combine deterministic apps with probabilistic AI.
• Diversify LLM providers for performance and cost.

Topics

• SAP Q2 2026 Earnings
• Enterprise AI Strategy
• AI Cost Optimization
• Autonomous Enterprise
• Large Language Models
• Cloud Business Growth

Best for: Investor, CTO, VP of Engineering/Data, Director of AI/ML, Executive, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Constellation Research.