Infrastructure impact on SAP cloud services

Many SAP customers have initiated or are in the process of transitioning their SAP ERP to SAP S/4HANA on Rise with SAP. However, there is often missing information regarding the technical aspects of Rise with SAP's offerings, migration requirements, and its benefits. This information gap sometimes leads to misinformation in Requests for Proposal (RFPs), resulting in numerous irrelevant or out-of-scope tasks and deliverables being included in the scope of an SAP partner, necessitating subsequent amendments. In my experience, some of the common technical areas where gaps are evident and questions arise during client discussions include: 👉🏼 How will users connect to SAP in the cloud? Via the internet or an internal network? 👉🏼 What does the network architecture entail, including VPNs, WAN, or MPLS connectivity? 👉🏼 Which SAP instances (Sandbox, Dev, QA, Prod) can be migrated to Rise with SAP? 👉🏼 What is FUE, and why is there a need for a minimum of 135 FUE when actual requirements may be lower? 👉🏼 Why are SAP partner Basis consultants unable to perform traditional tasks such as backup/restore, system copy, and kernel update? 👉🏼 Who will configure HA/DR for SAP S/4HANA? 👉🏼 Is Rise with SAP a Software as a Service (SaaS) product? 👉🏼 Who is responsible for covering the cloud subscription expenses? 👉🏼 Does Rise with SAP provide unlimited data egress/ingress? 👉🏼 Can older versions of SAP S/4HANA be migrated and run on Rise with SAP? 👉🏼 How is their SAP infrastructure secured from external threats in Rise with SAP? 👉🏼 Is using SAP BTP services alongside SAP S/4HANA on Rise with SAP mandatory? To ensure that these queries are comprehensively addressed and to facilitate informed decisions with the right mindset during the transition of SAP ERP to Rise with SAP, I strongly believe customers must seek proper guidance, advisory, and consulting from SAP or their trusted SAP partners at the very beginning of the planning phase of the transformation journey. Feel free to share your thoughts and experiences too!! #risewithsap #saponcloud #s4hana

AI isn’t magic - it’s math + data + infrastructure. And if your SAP system is still on-prem, you’re cutting off its fuel source. I’ve spent over two decades in SAP environments. Today, I’m watching a new wave of demand hit CIOs hard: “Can we plug AI into our SAP data?” Not if your system is stuck in an old stack. At Sapphire last week, SAP pushed Joule - their AI assistant - front and center. But what they also made clear is this: to even access that capability, you need to be in the cloud. Modernization used to mean agility, cost savings, and faster provisioning. Now it means AI readiness. And most teams haven’t connected the dots yet. Here’s what I’ve learned after 25 years of SAP migrations: 1. AI requires speed and scale Legacy systems, even those virtualized, hit performance ceilings. Cloud-native SAP environments are the only way to handle the speed AI needs. 2. Your current stack might block SAP AI features If you’re still running on older infrastructure, you won’t be able to use services like Joule. SAP made that clear: cloud is the new baseline. 3. Migration without transformation is wasted effort Moving to the cloud isn’t enough. If you don’t modernize your operational processes - monitoring, integration, automation - the costs won’t go down, and the performance won’t go up. 4. AI needs context, and that starts with orchestration Without streamlined workflows and clean data pipelines, AI doesn’t just underperform - it confuses and misfires. 5. You don’t need to move everything at once, but you do need a blueprint I’ve helped organizations migrate in phases, with minimal disruption. But every successful one started with a clear assessment and roadmap. Cloud migration is no longer a tech decision. It’s a business enablement move - one that sets the stage for AI, analytics, and real-time decisions. If you want your SAP system to think like the future, you need to get it into the infrastructure of the present.

Your SAP AI is only as good as your Data infrastructure. No clean data → No business impact. SAP is making headlines with AI innovations like Joule, its generative AI assistant. Yet, beneath the surface, a critical issue persists: Data Infrastructure. The Real Challenge: Data Silos and Quality Many enterprises rely on SAP systems - S/4HANA, SuccessFactors, Ariba, and more. However, these systems often operate in silos, leading to: Inconsistent Data: Disparate systems result in fragmented data. Poor Data Quality: Inaccurate or incomplete data hampers AI effectiveness. Integration Issues: Difficulty in unifying data across platforms. These challenges contribute to the failure of AI initiatives, with studies indicating that up to 85% of AI projects falter due to data-related issues. Historical Parallel: The Importance of Infrastructure Just as railroads were essential for the Industrial Revolution, robust data pipelines are crucial for the AI era. Without solid infrastructure, even the most advanced AI tools can't deliver value. Two Approaches to SAP Data Strategy 1. Integrated Stack Approach:   * Utilizing SAP's Business Technology Platform (BTP) for seamless integration.   * Leveraging native tools like SAP Data Intelligence for data management. 2. Open Ecosystem Approach:   * Incorporating third-party solutions like Snowflake or Databricks.   * Ensuring interoperability between SAP and other platforms. Recommendations for Enterprises * Audit Data Systems: Identify and map all data sources within the organization. * Enhance Data Quality: Implement data cleansing and validation processes. * Invest in Integration: Adopt tools that facilitate seamless data flow across systems. * Train Teams: Ensure staff are equipped to manage and utilize integrated data effectively. While SAP's AI capabilities are impressive, their success hinges on the underlying data infrastructure. Prioritizing data integration and quality is not just a technical necessity → It's a strategic imperative.