SAP and Microsoft’s expanded partnership to accelerate the transition to S/4HANA
A major shift is happening in enterprise tech. SAP is ending mainstream support for its legacy on-premises ERP Central Component platform in 2027. That’s not far off. And it’s no longer optional for large enterprises to wait and see. They’ve got to move, fast. To make that transition smoother, SAP is teaming up with Microsoft to expand their cloud ERP alliance around S/4HANA. It’s not just a vendor relationship. This partnership is designed to take real-world complexity, finance, supply chain, operations, and break it down into manageable pieces for faster cloud ERP adoption.
At the heart of this push is the Business Suite Accelerator program, launched with Microsoft as the lead hyperscaler. It’s engineered to help companies update core processes, replace old infrastructure, and add advanced capabilities around data, analytics, and AI, all in one go. These aren’t vague benefits. They’re practical tools, delivered through scalable frameworks that let partners and clients deploy faster while avoiding surprises.
SAP isn’t simply offering a new licensing model or re-platforming advice. It’s rethinking how its clients move to the cloud. By designing shared playbooks and tooling together with Microsoft, SAP is letting companies pick the pieces they need, optimize them, and gradually evolve into cloud-native, AI-enabled systems, on their own terms. This is how the world’s largest tech-driven enterprises will stay adaptive and competitive in the next decade.
Karl Fahrbach, SAP’s Chief Partner Officer, sums up the value well: partners now get “proven frameworks, shared resources, and scalable tools” to tackle these migrations. That’s what moves the needle for global companies trying to turn legacy systems into innovation engines.
Strengthening multi-cloud ERP integration with AWS and Google Cloud
In enterprise IT, flexibility means survival. SAP is taking a realistic, high-impact approach by strengthening its ties with the other hyperscalers, AWS and Google Cloud. That means customers running SAP systems aren’t forced into a single cloud stack. Instead, they now have multi-cloud interoperability that works across Azure, AWS, and Google Cloud. The result is more freedom, plus faster innovation.
This is more than just storage or computing choice. SAP’s Business Data Cloud is now integrated with Databricks on Azure and Google Cloud’s BigQuery. It connects data from SAP systems with third-party sources, so decision-makers can analyze everything in one place. Businesses get a real data layer, not hundreds of disconnected systems. Leaders can act with confidence, seeing across functions in real time.
SAP is also rolling this out in stages. According to its announcement on May 20, the Business Data Cloud will go live across three regional Google Cloud data centers later this year. That kind of phased deployment gives enterprises stability while scaling. Teams can test, validate, and optimize in smaller areas before going wide.
From the top down, SAP is backing the strategy with solid leadership. Philipp Herzig, SAP’s CTO and Chief AI Officer, is one of the key drivers. His team is building tech that delivers AI in specific use cases. It’s this kind of executive-led innovation that serious companies are looking for when modernizing core operations.
Integrating across hyperscalers is a necessity. And SAP is turning it into something usable, secure, and ready for execution at scale.
Collaborative generative AI initiatives with AWS to enhance ERP capabilities
SAP isn’t just modernizing ERP, it’s injecting it with intelligence. Through a tight collaboration with AWS, SAP has kicked off a generative AI development initiative designed to bring practical automation to the enterprise core. This isn’t a theoretical roadmap. It’s being built now, with real use cases. The target? Complex business challenges that demand fast, accurate decisions, like spotting financial inconsistencies the moment they happen, or keeping supply chains running despite uncertainty.
The engine behind this is AWS’s Bedrock platform, which supports the development of “agentic” tools. These tools aren’t standalone AI models. They’re decision-support layers embedded in business processes. Firms like Accenture and Deloitte are already developing on this infrastructure, building AI-enabled solutions powered by SAP’s deep ERP data. What matters here is not AI for its own sake, but AI designed and deployed with clear operational value.
According to Philipp Herzig, SAP’s CTO and Chief AI Officer, the initial focus is on high-impact scenarios: financial anomaly detection in real time and supply chain disruption mitigation. These are two areas where milliseconds matter. When executed right, AI can cut risks, prevent instability, and simplify executive decisions before problems gain size.
This SAP-AWS initiative is an ecosystem play. It allows partners to tap into SAP’s transactional data, already standardized and structured, and combine it with Bedrock’s generative capabilities and model diversity. There’s a real advantage here for companies in regulated industries or complex operations. You aren’t just using AI, you’re applying it directly where ERP lives, with full context and scale. For C-suite leadership, this means measurable outcomes from AI initiatives instead of proof-of-concepts stuck in silos.
Advanced AI orchestration through integration with Google Cloud
SAP’s integration with Google Cloud is pushing enterprise AI into a new phase, full orchestration. It’s not about isolated tools or scattered APIs. SAP has signed on to Google Cloud’s Agent2Agent open protocol, which allows AI systems to execute in concert. These connected systems can make decisions, execute actions, and loop insights back, all through structured coordination.
With this, SAP customers gain access to Google’s Gemini large language models (LLMs), integrated through the SAP Business Technology Platform’s generative AI hub. These models don’t act alone, they connect to SAP’s Agentspace tool-building platform, enabling more advanced, customized automation. It’s an architecture that prioritizes interactivity between systems.
From a business perspective, this means more than just faster workflows. It creates AI agents that understand enterprise processes at scale, finance, HR, supply chain, and work in sequence to improve performance. This orchestration removes the need to jump between tools or rewire integrations every time a new model becomes available.
SAP announced on May 20 that it will deploy its Business Data Cloud in three Google Cloud regions later this year. That matters from both compliance and performance perspectives. Enterprises operating under regional data laws need cloud partnerships that deliver flexibility without sacrificing control.
By giving SAP systems seamless access to Google Cloud’s AI ecosystem, executive teams gain powerful tools that are both experimental and immediately applicable. It simplifies how companies pilot and scale AI-driven efficiencies across departments, with engineered alignment from core data to decision output.
Driving the transformation to an AI-driven enterprise
SAP’s long-term strategy is direct, enable enterprises to become AI-native, not just AI-compatible. The recent platform integrations, partner programs, and multi-cloud deployments all point toward a single outcome: giving businesses the architecture and tools to automate intelligently, adapt faster, and scale with intent. For executive leadership, the focus is now shifting from experimentation to execution. AI is moving from the edge to the core.
SAP is leveraging hyperscaler relationships with Microsoft, AWS, and Google Cloud to unify data pipelines, simplify infrastructure decisions, and put AI capabilities directly into operational centers. This is structured to eliminate the fragmentation executives typically face when scaling AI, from tool selection to governance. With SAP Business Data Cloud integration and Agent2Agent orchestration, companies are no longer tied to isolated innovation. They can apply AI across finance, procurement, logistics, and workforce management, all within a governed enterprise framework.
Joint statements from SAP and Google Cloud reflect this direction: “Our objective is to enable all enterprises to streamline data integration and data science, enhance their analytical workflows, and accelerate their transformation into an AI-driven enterprise.” That’s the shift. Standard ERP workflows are being reengineered to support AI-driven decisions without adaptation lags or fragmented data.
For C-suite teams, the payoff is efficiency at scale, a clearer view of operations, reduced overhead in decision cycles, and the ability to deploy predictive and generative AI solutions across the value chain. It also streamlines regulatory alignment and security, since these platforms are already designed for enterprise-grade compliance and availability.
This is trajectory-level change in how modern enterprises operate, grow, and compete. SAP is not treating AI as an add-on, it is positioning AI as a core operating layer. And that’s the most scalable approach leaders can invest in today.
Main highlights
- SAP and Microsoft are accelerating enterprise cloud migration: Leaders should evaluate the Business Suite Accelerator to streamline their shift to S/4HANA, minimizing migration risk while gaining access to AI and analytics capabilities that future-proof operations.
- Multi-cloud ERP is now table stakes: Decision-makers must architect their ERP systems for interoperability across Azure, AWS, and Google Cloud to increase agility, reduce vendor lock-in, and unify real-time data access across platforms.
- AI-powered ERP tools are moving from concept to deployment: Leaders should partner with ecosystem integrators like Accenture and Deloitte to explore actionable use cases in finance and supply chain powered by generative AI and AWS Bedrock.
- AI orchestration is becoming a competitive advantage: Executives can improve enterprise responsiveness and automation accuracy by leveraging SAP’s integration with Google Cloud’s Agent2Agent AI protocol and Gemini large language models.
- Enterprise AI transformation starts with unified data: Leaders should prioritize investments in platforms that centralize data integration and automate workflows, positioning the organization to scale AI adoption across core functions without added complexity.
