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Vertex AI Meets SAP: Google Cloud Unveils New Tools for Intelligent ERP

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Key Takeaways

⇨ Google Cloud is enhancing ERP systems through deep integration of its Vertex AI with SAP solutions, enabling smarter, faster business processes and reducing manual efforts.

⇨ The Cortex framework allows for seamless integration of SAP and non-SAP data into BigQuery, facilitating real-time analytics and performance insights without complicated data handling.

⇨ IT leadership will play a crucial role in guiding AI integration within organizations, focusing on overcoming data fragmentation and skills gaps through dedicated AI Centers of Excellence and structured change management.

At SAP Sapphire 2025, Google Cloud announced a series of innovations designed to make enterprise resource planning (ERP) systems smarter, faster, and more responsive by tightly integrating its Vertex AI platform with SAP’s application and data ecosystems. The goal: deliver intelligent ERP experiences that reduce manual effort, enable predictive insights, and accelerate business outcomes.

Google Cloud Advances AI and Data for SAP

Google Cloud is extending its generative AI capabilities through deep integrations with SAP S/4HANA Cloud and the SAP Business Technology Platform (BTP). By embedding Vertex AI into SAP’s generative AI hub, customers can build domain-specific AI assistants tailored to finance, procurement, and supply chain scenarios. One featured demo showed how a supply chain copilot can analyze live operational data to suggest shortage resolutions, improving planning speed and supplier coordination.

To break down siloed data environments, Google Cloud promoted its Cortex framework—a suite of prebuilt data models and pipelines that bring SAP data into BigQuery. This enables advanced analytics, generative AI, and real-time dashboards without requiring complex ETL or data duplication. Thus, companies can now unify SAP and non-SAP data to power decision-making across the enterprise.

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Google Cloud also showcased its maturing SAP-certified infrastructure, which supports high-availability deployments of SAP S/4HANA and SAP ECC. Enhanced tooling—including Cloud Application Manager and Migrate for Compute Engine—simplifies cloud migration and lifecycle management. Customers benefit from Google Cloud’s sustainability credentials, with all infrastructure powered by renewable energy.

To reduce complexity and fragmentation, Google Cloud highlighted its Cortex data foundation, which provides prebuilt models and pipelines to move SAP data into BigQuery. This enables real-time analytics and federated AI workloads across SAP and non-SAP sources—without replication overhead.

What This Means for SAPinsiders

AI strategy and execution will become a core responsibility. The convergence of Vertex AI and SAP platforms means CIOs and IT leaders can now deliver AI-infused experiences directly within ERP workflows. This shifts the focus from maintaining systems to optimizing them—with AI assistants reducing manual tasks and surfacing insights automatically. For SAPinsiders, the role evolves toward AI governance, cross-system integration, and business enablement.

Technology evaluation will prioritize AI readiness and integration speed. SAPinsiders should prioritize solutions that integrate seamlessly with SAP BTP and SAP S/4HANA. Look for platforms offering prebuilt connectors and models—like Google Cloud’s Cortex—to accelerate deployment. Scalable, secure deployment of generative AI copilots is essential, along with support for cross-system data access without heavy ETL. Finally, consider vendor maturity and industry-proven use cases to ensure reliability and fast time-to-value.

IT leadership must guide cross-functional AI adoption. Widely acknowledged roadblocks to AI integration include fragmented data and skills gaps. Enterprises—particularly in the energy sector—have addressed common roadblocks like these by establishing AI Centers of Excellence and retraining staff on Google Cloud and SAP BTP. Successful change management often hinges on early stakeholder engagement and a phased implementation model.

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