•     We maintain a BUY rating on SAP, supported by accelerating cloud demand and a credible AI-driven growth narrative.

•      Accelerating current cloud backlog (CCB) growth strengthens revenue visibility and indicates that generative AI is supporting, rather than disrupting, customer commitment to SAP.

•      AI's inclusion in over 90% of SAP's 50 largest transactions this quarter shows it is becoming a core component of the company's biggest cloud contracts.

•      Early procurement and finance deployments demonstrate a credible path from SAP’s data and workflow advantages to deeper customer adoption.

•      Successful profit recovery would complete the investment case and support 38% potential upside to our end-2028 target price.

A mixed quarter, but orders took centre stage

SAP’s second-quarter results sent two different signals. Current cloud backlog (CCB) grew 26% year-on-year at constant currencies, around 2 percentage points above market expectations and up from 25% in the first quarter. Cloud revenue reached EUR 6.281 billion, also slightly ahead of expectations.

Profitability was weaker. Adjusted EPS of EUR 1.59 missed the consensus estimate of EUR 1.76, while the non-IFRS operating margin of 27.8% fell short of the expected 29.3%.

The market focused on the more forward-looking order data. SAP’s shares rose 6% on the day of the results. As of 17 August, the share price had rebounded around 40% from its 23 July low, although it remained approximately 11% lower year to date.

The CCB beat itself was modest. More importantly, growth accelerated when the market had expected it to slow. This suggests that macroeconomic uncertainty and changes brought about by generative AI have not weakened demand for SAP’s cloud contracts.

For investors, the result provides greater confidence in SAP’s growth outlook. However, it does not resolve concerns about profitability.

Table 1: SAP’s 2Q financial highlights

Metric	2Q26 Actual	Consensus	Beat / (Miss)
CCB growth (cc)	26%	24%	Beat
Cloud revenue	EUR 6.281bn	EUR 6.262bn	Beat
Non-IFRS operating margin	27.80%	29.30%	Miss
Adj. EPS	EUR 1.59	EUR 1.76	Miss

Current cloud backlog is the stronger signal here

CCB represents the cloud revenue SAP expects to recognise over the next 12 months from existing contracts. It therefore provides a more forward-looking view of order trends and corporate IT budgets than revenue recorded in a single quarter.

Management still expects CCB growth to moderate during the rest of the year. It cited macroeconomic and geopolitical uncertainty, delays to some contracts in the Middle East, and the unpredictable timing of large transactions.

This caution is understandable. In the previous financial year, management’s expectation of only a slight slowdown in CCB growth proved too optimistic. Fourth-quarter CCB growth, announced in January 2026, came in at 25%, below the market’s 26% forecast. SAP’s shares fell 11% that day.

This quarter’s signals were more encouraging. CCB growth accelerated, and the sales pipeline and project coverage were stronger than previously expected following May's Sapphire conference — SAP's flagship customer event, where it launched its Autonomous Enterprise strategy. Deals were also not taking longer to close than usual, a sign that macro uncertainty isn't yet dragging out sales cycles.

Management's cautious outlook therefore looks less like a genuine signal of weaker demand, and more like a safety margin — guidance conservative enough to absorb a delayed Middle East deal or a large contract slipping into a later quarter, without missing the number they've committed to.

The next test is whether the backlog remains resilient and converts into cloud revenue.

Chart 1: SAP — Current cloud backlog growth (constant currency, YoY %, 2Q25–2Q26)

Source: Bloomberg Finance L.P., iFAST Estimates

Data as of 30 June 2026

AI is strengthening SAP’s commercial position

We previously argued that AI could strengthen — rather than weaken — SAP's competitive moat: its structural advantage over rivals, built on owning the trusted data and workflows large enterprises run on. As AI is embedded more deeply into business processes, it depends increasingly on trusted enterprise data, clear permission controls, and integrated workflows — the exact foundation SAP already has inside its customers' core systems.

The second-quarter results provide further support for this thesis. More than 90% of SAP's 50 largest transactions included both AI and SAP Business Data Cloud as key components. This indicates that AI and data capabilities are becoming important components of SAP’s largest cloud contracts, strengthening the overall value of its platform.

The acceleration in current cloud backlog provides another encouraging signal. Generative AI has not reduced customers’ willingness to commit to SAP’s cloud products. Instead, SAP appears to be using AI to support cloud migration, encourage upgrades to more advanced packages, and expand customer adoption across its product portfolio.

For now, much of the financial benefit is likely to be captured through these broader cloud contracts rather than through separately priced AI products. SAP has not disclosed how much incremental revenue AI generates within individual transactions. Nevertheless, its growing presence in major contracts suggests that AI is already contributing to SAP’s commercial relevance and longer-term growth potential.

The next opportunity is deeper workflow adoption

Winning contracts is an important first step. The larger opportunity lies in applying AI across the finance, procurement, and supply-chain processes that SAP already supports.

CFO Dominik Asam told reporters after the results that most AI usage, measured in tokens, remains concentrated in relatively straightforward applications such as coding assistants and chatbots. Core business workflows are more difficult to automate because they involve multiple connected steps and require high standards of accuracy and compliance.

These accuracy and compliance requirements could reinforce SAP's competitive advantage. Effective automation depends not only on the AI model, but also on access to reliable enterprise data, clearly defined permissions, and a detailed understanding of how business processes operate. These are areas where SAP’s existing position in customers’ core systems is particularly relevant.

SAP now needs to expand the number and scale of real-world deployments. Progress in this area would show that its advantage extends beyond selling AI-enabled products to helping customers use AI effectively across complex business processes.

Early deployments support the workflow opportunity

SAP's AI wins this quarter weren't concentrated in one industry. Named customers choosing SAP's AI and data solutions included Amadeus and Booking.com (travel), PwC (professional services), Vale (mining), Oki Electric Industry (electronics), and University Hospital Zurich (healthcare), among others. Most of these are named wins without disclosed outcomes, so they show variety, not proof. The real question is whether any of them go beyond the simple use cases — chatbots, coding tools — that Asam said are where most AI use still sits today.

Two deployments this quarter provide an early answer. The examples at Lemvigh-Müller and Amadeus show SAP's AI already moving beyond chatbots and into procurement and financial workflows.

Danish steel and technical equipment wholesaler Lemvigh-Müller deployed SAP AI agents in its procurement process through NTT DATA, a global IT services firm and SAP implementation partner that is part of Japan’s NTT Group. The agents read supplier emails and PDF documents, match the information against purchase orders in SAP, and send exceptions or unmatched items for human review.

According to management, the system achieved a touchless-processing rate of more than 90% and an order-matching accuracy rate of approximately 98%. By combining document recognition, data reconciliation, and exception handling, the solution performs several connected tasks within a core procurement process where accuracy is critical.

Amadeus provides another concrete example. Its bank statements often lack the information needed to match customer payments with outstanding invoices. Finance employees previously had to retrieve the missing details manually from customer emails and credit-card files before matching them with accounts-receivable records in SAP.

The AI agent developed by Amadeus and SAP can read these unstructured documents, validate the relevant data, and automatically create payment advice.

These deployments show that SAP AI can already automate complex, multi-step tasks within procurement and financial processes — not just the simpler use cases CFO Asam described as the industry's current norm. Neither case is fully automated: human review remains necessary for exceptions, but complete automation is not required for the technology to deliver operational value.

Lemvigh-Müller and Amadeus are only two names out of the wider list above — it isn't yet clear whether this same depth of automation is happening at the other customers, or whether most of them are still at the chatbot stage Asam described. However, the two cases demonstrate a credible route from SAP's existing strengths in enterprise data and workflows to practical AI applications. Broader deployment would deepen customer reliance on SAP's platform, support cloud adoption, and strengthen its longer-term growth opportunity.

Profitability is the immediate test

SAP’s cloud orders remain resilient, but the second-quarter margin shortfall raises a separate question: how quickly can its AI investments translate into stronger profit growth?

The weaker margin reflected higher spending on AI testing environments, product launches, marketing, specialised talent, AI model usage (measured in tokens, the unit AI systems are billed by), and research and development. The acquisition of Reltio also diluted profitability.

Some of this spending should fade — the testing and launch costs look temporary. But money spent on hiring AI talent, running AI models, and recent acquisitions could stay high for longer. So this margin miss isn't necessarily a one-time blip.

Here's why that matters. Strong order growth already answered one worry: customers aren't turning away from SAP because of AI. But if margins keep disappointing while orders stay strong, the worry shifts to a different question — is SAP's AI spending actually paying off?

Management says profit growth will bounce back to a healthy rate in the second half. But that's a promise for the whole six months, not for any single quarter — so one good or bad quarter on its own won't prove whether they're right. Third-quarter results are the next checkpoint. Even a weak quarter wouldn't break that promise outright, but it would leave the bigger question — is this spending worth it — unanswered for longer.

Key risks

Middle East and EMEA exposure: SAP derives a substantial share of revenue from EMEA, and management flagged on the 2Q call that “the situation in the Middle East remains fluid.” Continued disruption could delay large-deal signings and slow backlog conversion into revenue.

Margin recovery is not yet proven: full-year guidance implies operating-profit growth returning to a mid-teens rate in the second half, but management has only committed to the full-half trajectory, not to any single quarter’s margin. A further shortfall in 3Q would weigh on both earnings and sentiment.

Business-model transition risk: SAP is moving toward more consumption- and outcome-based pricing for AI, which could introduce volatility into reported growth and margin trends while the transition plays out.

Competitive and AI-investment risk: competition at the AI-agent layer is intensifying as major LLM providers scale capabilities and pricing aggressively. This could push SAP toward heavier ongoing AI investment and further M&A, pressuring margins beyond what is currently guided.

Investment view and valuation

We maintain a BUY rating on SAP (NYSE: SAP), with a target price of USD 287 by end-2028.

We remain cautiously positive on SAP. Strong cloud backlog and the inclusion of AI and SAP Business Data Cloud in more than 90% of its 50 largest transactions support the commercial side of the AI thesis.

The Lemvigh-Müller and Amadeus cases also provide early evidence that SAP AI is entering procurement and financial workflows. However, broader adoption and standalone AI monetisation remain unproven.

SAP must still show that customers can use its AI reliably and at scale—and that the required investment can generate sustainable returns.

Our 2026 and 2027 earnings forecasts have been reduced, while our 2028 forecast has been raised. This reflects the current investment case: near-term costs are weighing on earnings, while strong backlog improves longer-term revenue visibility.

Based on forecast 2028 EPS of EUR9.93 and a fair P/E multiple of 25 times, the implied target price is USD 287, representing potential upside of around 38%.

Even under a more conservative 20x multiple, if margin recovery falls short of guidance, the implied target is still USD 230 — upside from current levels. The case for owning SAP doesn't depend on everything going right.

For investors, the near-term priorities are clear. SAP’s order performance supports confidence in demand and its longer-term AI positioning. The next stage of the investment case depends on whether the backlog converts into cloud revenue, AI adoption expands across core workflows, and operating-profit growth recovers as guided.

Table 2: Projections for SAP SE (NYSE: SAP)

	2025	2026E	2027E	2028E
EPS (EUR)	6.12	7.15	8.38	9.93
EPS growth	29.66%	16.85%	17.20%	18.48%
PE Ratio	29.34	25.11	21.43	18.08
Upside Potential (Fair PE of 25x)				38.24%
Target Price (USD)				287
Source: Bloomberg Finance L.P., iFAST Estimates
Data as of 17 August 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.