Moving SAP workloads to the cloud has long been one of enterprise IT's most demanding undertakings - affecting finance, supply chain, manufacturing, and customer operations simultaneously, often requiring years of planning and execution. AWS has now announced a set of integrations with SAP designed to compress that timeline and extend the reach of AI tools into SAP data and systems, covering everything from code modernisation to live data access for analytics applications.
Migration Complexity Has Always Been the Bottleneck
SAP environments are among the most deeply embedded systems in large organisations. Many run on ABAP, a proprietary programming language developed by SAP in the 1980s, and accumulated codebases can represent decades of business logic that must be assessed, modified, or replaced before migration. AWS is addressing this directly by adding support for its AI coding assistants - including the recently introduced Kiro - with SAP's ABAP Model Context Protocol. The intent is to give developers an automated aid for reviewing and modernising that code, which typically constitutes a significant share of the work in any migration project.
On the migration orchestration side, AWS and SAP are extending the RISE with SAP System Transition Workbench with automated orchestration capabilities. AWS describes this as among the first instances of an SAP migration tool directly coordinating a cloud provider's native services for data transfer in file-based migration scenarios - a technical distinction that matters because it reduces the manual handoffs and configuration steps that traditionally slow projects down. Consulting group Accenture has built an agent-driven delivery platform on Amazon Bedrock around this approach, automating issue triage, integration mapping, and data migration validation.
Network setup has also been a persistent source of delay. SAP Seamless Private Connectivity using AWS Resource Gateway is intended to reduce the time required to link existing enterprise infrastructure to SAP environments from weeks to days, by removing the need for network redesigns and temporary workarounds that characterise conventional connectivity configurations.
Opening SAP Data to AI Without Moving It
Once SAP systems are running in the cloud, the next challenge is making their data usable for analytics and AI applications without creating fragmented copies or triggering data governance complications. The SAP Business Data Cloud Connect for Amazon Athena product addresses this by providing bi-directional, zero-copy access to live SAP data. Companies can query and analyse operational and transactional information directly, without extracting or duplicating datasets - a significant practical advantage for organisations subject to data residency rules or with strict controls over data movement.
Two additional integrations extend AI capabilities into customer operations. Amazon Connect Customer will be integrated with SAP Service Cloud and SAP Enterprise Service Management, connecting AI-driven customer interaction tools to SAP's service systems. Separately, Model Context Protocol support on Amazon Bedrock AgentCore will allow AI agents to access SAP ERP data through SAP Integration Suite, giving those agents the ability to act on live business information rather than operating in isolation from core enterprise systems.
The Competitive Stakes Behind the Announcements
AWS reports that thousands of organisations run SAP on its infrastructure, and that more than half have deployed SAP Cloud ERP. It has held a leadership position in SAP HANA Infrastructure Services according to ISG for five consecutive years and now offers a 99.95% service level agreement for RISE with SAP workloads. These figures matter commercially because SAP's installed base is large, concentrated among enterprises in regulated and capital-intensive sectors, and represents a substantial pool of workloads that major cloud providers are competing to host and, increasingly, to build upon.
The competition is no longer purely about infrastructure costs or reliability. It has shifted toward who can offer the most capable migration path and the richest post-migration environment for AI development. AWS's AI and Data Co-Innovation Program, involving partners including Accenture, Capgemini, Cognizant, and Deloitte, is structured around precisely that: identifying where AI can add value in SAP-heavy environments and accelerating the path from concept to production deployment.
Early customer examples point toward where that value may concentrate first. Hyundai is using Amazon QuickSight with SAP-related operations data; Mercedes-Benz is applying AI-based analysis drawn from SAP data in manufacturing and customer experience work. Both represent the broader pattern - enterprises using the cloud transition as an opportunity to unlock analytical and automation capabilities that were impractical when systems sat on-premises, behind proprietary infrastructure and without the adjacent data services that public cloud platforms provide.
Expanding Regional Availability for Regulated Industries
AWS also confirmed plans to extend availability of SAP Business Data Cloud, GROW with SAP, and SAP Business Technology Platform services to additional AWS regions. For organisations in financial services, healthcare, or public sector - where data sovereignty and regulatory compliance govern where data can be processed and stored - regional expansion is not an incidental detail. It determines whether certain workloads can move to the cloud at all.
Reducing latency for geographically distributed operations is the other driver. Manufacturing companies running production systems across multiple countries, for example, require infrastructure close enough to local facilities to support real-time data flows. Broader regional coverage makes the cloud migration argument more straightforward to make internally, particularly in businesses where operational continuity is non-negotiable and any architectural risk demands a conservative approach to planning.
