SAP: Why AI may strengthen, not erode, its moat

·       SAP’s 1Q26 results reflected continued pressure in legacy license and services revenue, but strong cloud revenue and cloud backlog growth suggest the company’s long-term cloud transition remains intact.

·       The current AI transition resembles SAP’s earlier shift from on-premise software to cloud subscriptions, where the company accepted short-term revenue pressure to build a more durable recurring revenue model.

·       The biggest debate around SAP is whether AI weakens traditional SaaS businesses — we think it does the opposite.

·       SAP believes its key competitive advantage lies not just in owning enterprise data, but in understanding the business meaning and relationships behind that data through products such as Knowledge Graph and Business Data Cloud.

SAP is one of the world’s leading enterprise software companies, best known for its ERP systems that help large enterprises manage core business processes such as finance, procurement, supply chain, manufacturing, inventory and human resources. Compared with Salesforce, which is more focused on customer relationship management and front-office workflows, SAP is more deeply embedded in mission-critical back-office operations. Oracle is SAP's closest ERP rival. SAP's edge lies in complex global enterprises that require deep integration across finance, supply chain and operational workflows.

1Q26 results: Cloud momentum continues

SAP's 1Q26 numbers tell a clear story: the cloud transition is accelerating. Current cloud backlog reached EUR 21.9 billion, up 25% at constant currencies — a direct measure of future contracted revenue. Cloud revenue grew 27% at constant currencies, with Cloud ERP Suite growing faster still at 30%. Total revenue grew 12% at constant currencies, and non-IFRS operating profit outpaced that at 24% — a sign that scale is beginning to compound. Legacy revenue lines remained under pressure, but that is the point. SAP is deliberately trading near-term licence income for a larger, stickier cloud business.

Services revenue softened, declining 6%, or 1% at constant currencies. However, management explained that SAP is increasingly using AI migration tools and providing additional customer adoption support to accelerate cloud migration. In other words, SAP is not maximizing every billable consulting hour if doing so slows customer adoption. This creates short-term pressure on services revenue, but it may strengthen long-term customer retention within the SAP ecosystem.

This transition is not new for SAP. SAP has navigated several major technology shifts over the past decades — from client-server architecture to in-memory databases, and later from on-premise software to cloud subscriptions. The cloud transition was particularly disruptive because SAP willingly moved away from a highly profitable model based on upfront software licenses and maintenance fees. During that period, the company faced scepticism over slowing legacy revenue and pressure on profitability, yet continued executing its strategy steadily. Today, SAP has become one of the leading global cloud ERP providers.

This historical context matters because the current AI transition resembles SAP’s earlier cloud transition. The company is again accepting short-term revenue pressure in exchange for building a more durable long-term model. Weakness in license and services revenue should therefore not be interpreted solely as deteriorating demand. It also reflects SAP’s willingness to prioritize long-term cloud and AI adoption over short-term revenue maximization.

Why cloud backlog matters more than license revenue

Cloud backlog is the most important indicator supporting this transition thesis. For SAP, license revenue shows what the company is moving away from; cloud backlog shows what the company is becoming. Current cloud backlog represents contracted cloud revenue not yet recognised — a direct window into future revenue. SAP’s 25% constant-currency growth in current cloud backlog suggests that customers continue committing to SAP’s cloud platform.

The key investment question is therefore not whether traditional license revenue continues to decline. It likely will. The more important question is whether cloud revenue and cloud backlog can continue growing fast enough to replace and eventually exceed the old license model. Based on 1Q26, cloud revenue growth of 27% at constant currencies and Cloud ERP Suite growth of 30% suggest that SAP’s cloud engine remains strong.

Chart 1: SAP current cloud backlog growth, 1Q26 (constant currencies)

The elephant in the room: Is AI a threat to SaaS?

The next major debate for investors is AI. The concern is that AI agents from companies such as OpenAI, Anthropic or Microsoft could reduce the importance of traditional software interfaces. Instead of employees opening SAP screens, navigating menus and manually running reports, users may increasingly interact directly with AI assistants that automatically retrieve data, analyze information and execute workflows.

This could reduce the importance of traditional software interfaces, but it does not necessarily eliminate the need for enterprise systems such as SAP. In enterprise software, the interface is only one layer of value. AI systems still require trusted business data, approval rules, workflow logic, permissions management and transaction execution capability. This is where SAP believes its moat may strengthen rather than weaken.

SAP's defensive moats: Business context and customer trust

SAP's first defensive moat is what management calls semantic business data — in plain terms, not just owning large volumes of enterprise data, but understanding the business relationships and meaning behind it. For example, an inventory number is not simply “100 units of stock.” The system also needs to know whether that inventory has already been reserved for customer orders, whether it affects production planning, whether it can be shipped immediately, and whether it impacts revenue recognition or margin.

SAP has been investing heavily in products such as Knowledge Graph and Business Data Cloud. Knowledge Graph can be understood as a relationship map connecting customers, invoices, inventory, suppliers, pricing, margins and approvals. Business Data Cloud organizes data from both SAP and non-SAP systems into a unified business data layer that AI systems can safely consume. According to SAP executives at the Goldman Sachs European Technology Conference (February 2026), the company's advantage is not simply data ownership, but understanding the contextual business meaning surrounding enterprise data. The Daimler Truck case study below offers a concrete test of this claim.

SAP’s second defensive moat is customer trust. Enterprise software differs significantly from consumer applications because errors can create financial, operational and compliance risks. If a company cannot reconcile its general ledger before financial closing, it requires a trusted enterprise software provider with accountability and support capabilities. This is difficult for experimental AI-native tools or hastily built internal tools that lack enterprise-grade governance to replicate. SAP management specifically argued that mission-critical enterprise workflows require reliability, governance and accountability, which remain key competitive advantages for established enterprise software vendors. We agree, and this is why we treat SAP's moat as deepening rather than eroding in an AI-driven environment.

This is why AI may ultimately strengthen SAP’s moat rather than erode it. AI may reduce the importance of traditional software screens, but it increases the importance of trusted enterprise data, workflow logic and execution systems. In simple terms, AI may change how employees interact with SAP, but it does not necessarily change why enterprises still need SAP.

Daimler Truck North America: A real example of enterprise AI value

Daimler Truck North America provides a concrete example of this logic. Its aftermarket parts business previously relied heavily on manual Excel analysis, fragmented data and human judgment when responding to customer quotations and managing customer churn risk. The business faced intense pricing competition, slow bid preparation and delayed visibility into customer retention risks.

Using SAP Analytics Cloud and SAP HANA, Daimler unified its commercial data — customer behaviour, pricing, inventory, margins and bid history — into a single system. This allowed the company to predict potential customer churn, improve bid pricing recommendations and simulate the impact of pricing changes on demand, inventory and profitability. SAP’s case study described this as AI-driven churn intelligence, bid optimization and dynamic pricing support.

The key takeaway is not simply that KPIs improved. More importantly, the case demonstrates that enterprise AI becomes valuable when it is connected to real operational data. A generic AI model could suggest lowering prices to win more orders. However, SAP-enabled AI can answer a much more commercially valuable question: what price is most likely to win the order while still protecting margins, considering customer history, inventory availability and historical bid outcomes?

This is the core of SAP’s AI moat. Enterprise AI is not only about having the most advanced model. It is about whether the AI can operate within trusted enterprise workflows using real business data.

Margin expansion reflects operating leverage, not just cost cutting

Margin expansion provides another supporting pillar for the investment thesis. SAP’s non-IFRS operating profit grew 24% at constant currencies in 1Q26, faster than total revenue growth. Part of the improvement came from lower share-based compensation expenses, which should not be treated as a purely structural efficiency gain. However, the more important story is operating leverage. Operating leverage refers to a situation where revenue grows faster than costs, allowing profit to expand more quickly than sales.

Chart 2: SAP’s non-IFRS operating margin continues to rise in 1Q26.

SAP’s growing cloud scale and recurring revenue mix are supporting this operating leverage. As Cloud ERP Suite revenue becomes a larger share of the business, revenue visibility improves and the company benefits more from scale efficiencies. SAP also continues investing in internal AI tools to improve development, implementation and operational efficiency over time.

The key risks remain macroeconomic uncertainty, the pace of cloud adoption and the speed of AI monetisation. SAP has warned that escalation in the Middle East could have materially adverse consequences. The company's guidance also assumes relatively stable macro conditions and successful integration of recent acquisitions. Customers also remain cautious about AI spending and may take time before increasing AI-related consumption materially.

Overall, SAP’s 1Q26 results support the long-term transition thesis despite short-term pressure in legacy revenue lines. License and services revenue weakness reflects SAP’s willingness to prioritize cloud and AI adoption over maximizing near-term legacy revenue. At the same time, strong cloud revenue, Cloud ERP Suite growth and current cloud backlog suggest that customers continue migrating into SAP’s cloud ecosystem.

The broader AI debate is also more nuanced than the simple “SaaS is dead” narrative. AI may change how users interact with enterprise software, but it also increases the importance of trusted enterprise data, workflow governance and execution capability. SAP’s long-standing position within mission-critical enterprise workflows may therefore become more valuable in an AI-driven environment rather than less.

Valuation: 67% upside to end-2028

We maintain our BUY rating with a target price of USD 274 by end-2028.

In our February 2025 note, we used 25x PE as a bear-case stress test, assuming a permanent SaaS sector de-rating toward historical software norms. Since then, the sector has de-rated more sharply than we anticipated, and our house view has lowered the fair PE for the broader SaaS sector from 30x to 25x. We therefore now treat 25x PE as our base-case multiple rather than a bear-case floor.

SAP is currently trading at approximately 17x its 2028 forward earnings — a level that appears to price in sustained AI disruption rather than the structural strengthening we believe is underway. Applying 25x PE to 2028 EPS of EUR 9.82 gives a fair value of EUR 245.5, which translates to a target price of USD 274 at a EUR/USD rate of 1.116, implying upside potential of approximately 67% from current levels.

We also include a downside scenario for investors who believe market concerns around AI disruption and SaaS valuation compression persist for longer. At 20x PE, the bear-case target price is USD 219 by end-2028, implying upside of approximately 33.5%. We maintain 25x as our base case because SAP's 1Q26 results do not suggest structural deterioration: cloud revenue, Cloud ERP Suite growth and current cloud backlog all remain strong, and our analysis suggests AI is more likely to increase the value of trusted enterprise workflow systems than to replace them.

Table 1: SAP SE — EPS estimates and fair-value

	2025	2026E	2027E	2028E
EPS (EUR)	6.12	7.22	8.42	9.82
EPS growth	29.66%	18.04%	16.51%	17.00%
PE Ratio	26.83	22.73	19.50	16.72
Upside Potential (Fair PE of 25x)	-	-	-	66.92%
Target Price (USD)	-	-	-	274
Source: Bloomberg Finance L.P., iFAST Estimates
Data as of 14 May 2026

Declaration:

This research report was prepared with the assistance of artificial intelligence (AI) tools. iFAST Financial Pte Ltd does not rely exclusively on AI for content generation; the content of this report – including all investment theses, ratings, price targets and conclusions – has been independently reviewed and verified by the research analyst(s) to ensure accuracy and professional integrity.

For specific disclosure, at the time of publication of this report, IFPL (via its connected and associated entities). The analyst who produced this report holds a position in SAP.