SAP’s Q1 Earnings Drop Tomorrow: AI, Cloud, and Pricing Take Center Stage

SAP will be announcing Q1 2026 results on April 23. Consensus expects revenue of €9.56 billion (approximately $10.4 billion) and earnings per share of €1.64 (approximately $1.79). For SAP customers, the more meaningful signals will come from management’s commentary on cloud momentum, AI adoption, and pricing strategy rather than the headline financials.

A pre-earnings analysis from Morningstar published on April 21 says investors will focus on cloud growth and current cloud backlog trends, with any signs of slowing demand likely to drive market reaction. The outlet maintains a fair value estimate of €265 (approximately $289) for SAP, along with a 5-star rating and a wide economic moat, indicating continued confidence in the company’s long-term position despite near-term uncertainty.

That makes “current cloud backlog” one of the most important metrics on the call. Morningstar says Q1 consensus points to 18% cloud revenue growth and roughly 23% to 24% constant-currency backlog growth, a slight deceleration from 2025. Investors are also watching for any indication of weaker business momentum, particularly given SAP’s exposure to manufacturing customers and broader macroeconomic pressures.

At the same time, SAP is balancing transformation with shareholder expectations. The company has proposed a dividend of €2.50 (approximately $2.93) per share, up 6.4%, continuing its policy of returning capital to shareholders. That creates a dual narrative heading into earnings: maintaining investor confidence while asking the market to absorb the disruption of a major business model shift.

Pricing Model Reset Reflects Structural Change

SAP reportedly is moving away from per-user and subscription-based pricing toward consumption-based AI pricing as agents begin to execute workflows autonomously. CEO Christian Klein said the company will charge based on AI consumption and deploy “forward deployed engineering” teams to build AI applications directly with customers.

The logic is structural. As AI agents take over tasks across finance, supply chain, and procurement, fewer human users are required, weakening the economic foundation of per-seat licensing. Consumption-based pricing should align SAP more closely with cloud infrastructure models, where usage, not access, determines cost.

However, the transition introduces new challenges, particularly around forecasting costs and linking usage-based metrics to technical consumption metrics and business outcomes.

AI Transition, Adoption Extends Beyond Pricing

The pricing shift is part of a broader internal reset. Klein has described the AI transition as comparable in impact to SAP’s earlier move to cloud, warning employees it would be similarly difficult as the company reshapes how it builds products, engages customers, and generates revenue.

That reset includes new organizational structures and delivery models, with SAP expanding consulting-style engagement through dedicated AI teams working directly with customers.

That urgency is also visible in customer adoption data. Per the 2026 DSAG Investment Report, as reported by Ad Hoc News April 12, 77% of productive AI scenarios in SAP customer environments rely on non-SAP solutions, while SAP’s own tools account for just 3%. That gap raises questions about how quickly SAP can convert its AI portfolio into actual enterprise usage.

What This Means for SAPinsiders

SAP’s Q1 call should cover both financial performance and directional clarity. Cloud backlog will indicate whether demand remains stable. AI adoption signals will show whether SAP is closing the gap with third-party tools. And management’s framing of consumption-based pricing will offer one of the clearest signals yet on how SAP expects customers to engage with its AI portfolio in 2026.

Some takeaways for SAP customers:

• AI monetization is shifting procurement dynamics. SAP’s move to consumption-based AI pricing introduces variability into cost structures, introducing variability into cost structures and making forecasting, value realization, and ROI measurement more complex.
• Cloud momentum is tied to macro conditions. Even modest deceleration in cloud backlog growth or signs of elongated sales cycles would indicate that external pressures—particularly in manufacturing-heavy sectors—are influencing ERP investment timelines.
• AI strategy credibility depends on adoption. The gap between SAP’s AI positioning and actual customer usage highlights the importance of execution. Closing that gap will require not just new products, but stronger data foundations and clearer integration into core business processes.