SAP and Google Cloud are working together to deploy a new agentic commerce framework designed to automate complex marketing and retail processes across large-scale enterprises.
According to SAP research, 78% of businesses view AI as critical for customer retention in 2026. Yet the same findings show that less than 40% of companies share customer data effectively—only 37% integrate it across customer experience platforms, and just 39% do so within CRM systems.
To fix this underlying data fragmentation, infrastructure-level changes are necessary. SAP and Google Cloud have enhanced their collaboration to create an agentic customer experience platform that integrates data, AI, customer engagement, and commerce workflows into a unified system.
This setup fundamentally changes how AI connects with backend retail systems. Traditional digital commerce relies on siloed APIs. SAP Commerce Cloud now uses the Universal Commerce Protocol to create standardized interactions between retailers, payment processors, and autonomous software agents. With this protocol, AI can independently manage the entire shopping journey—from search and purchase to post-sale support.
Deploying the Universal Commerce Protocol
By adopting the Universal Commerce Protocol, engineering teams enable seamless communication between intelligent agents and commerce systems. This standardization reduces integration overhead and speeds up adoption in AI-powered sales channels.
SAP and Google plan to collaborate so that merchant products appear naturally within the Gemini app and Google Search, including in AI Mode. Shoppers interact with these interfaces while the backend handles inventory checks, cart updates, and payments—all without retailers needing to overhaul their current tech stacks.
SAP Commerce Cloud integrates Google Gemini’s AI to power its dedicated Shopping Assistant. Brands can deploy this assistant for their customers to support interactions via chat, voice, and text. The system maintains context throughout the entire purchase process. It pulls in real-time behavioral signals, current stock levels, and live marketing data to create personalized product bundles and tailored shopping experiences. Continuous refinement ensures recommendations are both highly relevant and logistically feasible.
A common failure in enterprise systems occurs when marketing campaigns drive demand that exceeds available inventory. When front-end storefronts aren’t synced with warehouse systems, customers often encounter “out of stock” errors during checkout—especially after clicking promotional links or opening emails. Delayed fulfilment tracking leaves support teams without visibility into real-time order status. The joint SAP–Google Cloud solution was built specifically to eliminate these breakdowns.
Rather than managing disconnected touchpoints, the new architecture links every step of the customer journey. In legacy systems, shoppers must repeatedly provide the same information, and customer service agents often lack a complete view of interactions. This integration ensures the system instantly recognizes the user and their context across all digital channels.
Bidirectional data flows
Effective marketing depends on precise, real-time data pipelines. SAP Engagement Cloud and Google Cloud have developed a multi-agent autonomous framework powered by SAP Business Data Cloud Connect for Google BigQuery. This setup uses bidirectional, zero-copy data links governed by strict security policies—eliminating data duplication while reducing storage costs and network delays.
Google BigQuery processes external variables such as weather, location, and ad engagement metrics. SAP Customer Experience solutions contribute internal data including customer profiles, purchase history, service interactions, and consent records. SAP Engagement Cloud uses this combined intelligence to deploy autonomous agents that personalize customer interactions at every stage.
Because data flows through Business Data Cloud while BigQuery handles real-time logic, inventory stays synchronized instantly. The Shopping Assistant checks live stock levels before suggesting any product. The system verifies physical availability against customer demand prior to making a recommendation.
Generative execution in production environments
Advanced generative AI drives the localized output of marketing campaigns. Google Gemini models—including the Nano Banana 2 version—offer specialized agentic capabilities. These models automatically produce localized copy, tailored visuals, and campaign variations based on inputs from the bidirectional data stream.
The platform upgrades standard messages into rich, interactive experiences using Google Rich Communication Services (RCS). Ad creative evolves in real time based on engagement feedback. The system analyzes how users respond, updates their profile, and directs Nano Banana 2 to adjust the next message accordingly.
Marketing teams gain significant efficiency by moving away from manual campaign setup. Instead of defining rigid parameters, they set business objectives and grant SAP Engagement Cloud access to enterprise data. Autonomous agents then handle the rest: segmenting audiences using BigQuery analytics and generating targeted content via Gemini models.
Evaluating the infrastructure impact
This architecture transforms how commerce operates. Consumers express intent through search engines or conversational AI; embedded agents interpret that intent, connect via the Universal Commerce Protocol, and complete purchases directly within enterprise systems.
Even when transactions happen on third-party platforms like Google Search or Gemini, retailers retain ownership of the customer relationship. Consent-compliant engagement data is captured and fed back into SAP Customer Experience solutions. The updated profile gives Gemini models fresh context before the next interaction.
Campaign performance improves autonomously over time. The multi-agent system evaluates the effectiveness of a sent RCS message and adjusts variables automatically before the next outbound communication.
See also: Computer vision deployments drive retail productivity gains
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