SAP is strategically driving customers toward cloud adoption by integrating AI solutions into its cloud offerings. The rise of cutting-edge open-source models like Deepseek has bolstered SAP’s AI capabilities. By hosting these models on as part of their cloud offering SAP is making cloud migration even more appealing.  

However, these advancements have also opened the door for organizations to deploy powerful AI models on-premise, giving them greater control over their data and technology stack. This shift has introduced a new tension within the SAP ecosystem, as businesses weight the benefits of cloud-based AI against the autonomy of on-premise solutions.

So, what do these AI developments mean for organizations already using or considering SAP solutions?

1. The Rise of Local AI: Why Deepseek Matters / Deepseek’s Breakthrough and Its Implications for SAP Clients

Recent headlines have spotlighted a major development in AI: the emergence of Deepseek, a Chinese startup that has demonstrated enterprise-level AI capabilities through open-source innovation. SAP leadership has taken note, with CFO Dominic Asam calling Deepseek’s advancements “good news” for SAP.

One of Deepseek’s most significant contributions is proving that local AI deployments are no longer just theoretical—they are increasingly viable. Traditionally, large language models (LLMs) have been run through public cloud services due to high computational requirements. However, Deepseek’s latest releases suggest that cost efficiencies and technical breakthroughs are making it possible to operate advanced AI models on local infrastructure.

1.1 Why This Matters for SAP Clients

• Data Privacy and Compliance: Many SAP customers—especially in Europe—face stringent data sovereignty regulations. On-premise AI deployments provide an alternative to public cloud models, allowing businesses to maintain full control over their data while ensuring compliance with industry and regional regulations.

• Customization and Security: Running AI in-house enables deeper customization for industry-specific use cases while offering enhanced security for sensitive information. Organizations can fine-tune models to align with their unique business processes without relying on external providers.

• Owning Your Data—A Competitive Advantage: AI-driven organizations generate valuable datasets through user interactions with LLMs. By keeping AI models in-house, businesses can build and retain proprietary knowledge bases, refining their models over time. As the cost of AI training continues to decline, companies that accumulate years’ worth of internal AI interactions will have a significant strategic advantage—unlocking opportunities for highly customized AI applications tailored to their specific needs.

2. SAP’s Strategy: Move to the Cloud

Despite the rise of local AI deployments, SAP remains firmly committed to driving cloud adoption. The cloud offers clear advantages, such as offloading infrastructure management and maintenance to SAP, but it also shifts control over data from enterprises to cloud providers.

SAP is not a primary developer of large language models (LLMs), but it recognizes AI as a crucial part of its future. AI services play a strategic role in SAP’s push to migrate customers to the cloud. By embedding AI capabilities into its cloud products, SAP creates strong incentives for businesses to remain within its ecosystem. Solutions like SAP Joule and ChatGPT API integrations, branded under “Business AI,” are exclusive to SAP’s cloud, making migration increasingly attractive. This approach appears to be paying off—SAP reported a 25% increase in operating profit for 2024, largely driven by cloud-based revenue.

2.1 SAP’s AI-Driven Cloud Strategy

SAP’s CEO, Christian Klein, has positioned “Business AI” as a core pillar of growth for the company. This term encompasses SAP’s AI service offerings, including hosted LLMs, chatbots, document processing, and more. The company’s focus on cloud-based AI aligns with broader trends in enterprise software:

• Cloud as the Growth Engine: SAP’s cloud and software revenues continue to see double-digit growth, fueled in part by AI adoption. Rather than competing with foundational AI developers, SAP is positioning itself as an AI hub—offering AI integrations while relying on external providers for model development.

• Reliability, Relevance, and Responsibility: SAP emphasizes that its AI solutions must meet high standards of reliability and ethical responsibility. This focus is crucial for enterprise clients who need stable, transparent AI solutions that won’t disrupt mission-critical business processes.

2.2 The Business Model: AI as a Cloud Growth Driver

SAP is leveraging its strengths—ERP systems, backend infrastructure, and enterprise data models—to integrate best-in-class AI solutions rather than developing foundational models itself. This strategy allows SAP to act as a one-stop shop for enterprise AI, capitalizing on its existing customer base to cross-sell AI services.

While AI adoption is still in its early stages, cost remains a key barrier. However, as the cost of running AI models like Deepseek declines, more businesses will explore specialized AI applications. The industry is still searching for a killer use case in enterprise software—whether it’s advanced automation, predictive maintenance, or real-time financial forecasting—that will deliver transformative business value. SAP’s bet is that cloud-based AI will be the foundation of this transformation, and for now, its cloud strategy is driving significant financial returns.

3. Outstanding issues with AI and on Premise AI: Infrastructure, UI, and the Elusive Killer Use Case

Despite the excitement around AI, significant hurdles remain—particularly for on-premise deployments. While cost efficiencies and open-source advancements have made local AI models more viable, several critical challenges could slow adoption:

3.1 Complex Infrastructure and Expertise Gaps

Even as on-premise AI becomes more accessible, deploying and maintaining large models requires significant technical expertise. Running LLMs locally isn’t as simple as installing software—it involves managing server architecture, data pipelines, GPU clusters, and continuous model optimization.

Currently, the knowledge required to reliably operate and monitor on-premise AI is still scarce, making implementation a challenge for many businesses. However, as the industry matures and best practices emerge, expertise in local AI deployments is expected to grow, lowering the barrier to entry over time.

3.2 SAP’s User Interface (UI) Challenges

SAP has long been known for its powerful backend capabilities—but not for user-friendly interfaces. Historically, SAP’s UX has been a pain point for users, and the same concern applies to AI-driven features.

For AI adoption to succeed, seamless user experience is critical. If SAP cannot integrate AI in an intuitive, accessible way, businesses may struggle to see the value. Without significant UI improvements, even the most powerful AI tools could go underutilized.

3.3 The Search for a True Game-Changer

Right now, many enterprise AI applications offer only incremental improvements—such as autocomplete suggestions, document summarization, or basic chatbot interactions. While useful, these features aren’t transformative.

The real question is: What is the “killer use case” for AI in enterprise software?

For AI to truly revolutionize business operations, it must go beyond text generation and deliver high-impact, business-changing solutions—such as:

• Fully automated supply chain orchestration that adapts in real-time to disruptions.

• Predictive analytics that minimize downtime and optimize financial performance.

• Intelligent process automation that dramatically reduces operational costs.

Until AI delivers clear, quantifiable value beyond convenience, enthusiasm alone won’t be enough to drive mass adoption. Businesses need more than just AI-enhanced workflows—they need AI-powered transformation.

4. What Does This Mean for Your Business?

When it comes to AI, hype often outpaces reality. However, the developments surrounding Deepseek’s model and SAP’s own AI push underscore a few key points for any SAP customer or prospective adopter:

1. Focus on Use Cases: Before diving headfirst into AI, identify the concrete problems you want to solve. Do you need predictive analytics for inventory management? Automated invoice processing? Customer sentiment analysis? Align your AI investment with clear business goals to ensure meaningful ROI.

2. On-Premise vs. Cloud Considerations: Evaluate which deployment model best suits your organization’s needs. If you have stringent data privacy requirements, local AI might be viable—especially as hardware and software solutions become more cost-effective. On the other hand, if you prioritize flexibility and scalability, cloud-based AI may still be the better fit.

3. Keep an Eye on SAP Developments: SAP is not primarily building its own LLMs, but rather partnering or licensing where it makes sense. Stay informed about updates to SAP’s “Business AI,” as well as improvements to their user interface and integration capabilities. These factors will heavily influence how smoothly AI features can be adopted in your environment.

4. Seek Expertise: AI projects can be complex, requiring knowledge of data science, infrastructure, and software integration. Don’t hesitate to reach out for specialized consulting to pinpoint viable use cases. As SAP refines its offerings and Deepseek’s model evolves, tapping into expert advice can help you seize opportunities without getting lost in the hype.

5. Closing Thoughts: Embrace the Future, but Do So Strategically

The emergence of Deepseek’s AI model represents a tangible shift in how enterprises consider deploying and managing AI workloads. Meanwhile, SAP continues to evolve its “Business AI” strategy, aiming to bring advanced intelligence into the everyday workflows of its massive global user base. For business leaders, these innovations underscore the need for a pragmatic, use-case-driven approach to AI adoption.

If you have questions or need help discovering AI use cases that can genuinely move the needle for your business, don’t hesitate to reach out. The journey to harness AI effectively—especially within SAP’s ecosystem—may be complex, but the potential benefits are hard to ignore. With the right strategy, Deepseek-inspired innovations and SAP’s robust ERP platform could open doors to unprecedented operational efficiency, customer engagement, and competitive advantage.

Need help pinpointing AI use cases in your SAP environment?

Contact us to explore how these emerging technologies can be tailored to your specific business challenges. Let’s turn AI-driven possibilities into real-world outcomes.

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