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  • SAP and Google Cloud deploy agentic commerce architecture

    SAP and Google Cloud are deploying agentic commerce architecture to automate multi-agent marketing and retail operations at enterprise scale.

    SAP research indicates 78 percent of businesses consider AI essential for retaining customers in 2026. However, the same data reveals fewer than two in five companies share customer data across customer experience (37%) or CRM (39%) platforms. 

    Addressing this structural data failure requires direct infrastructure intervention. SAP and Google Cloud expanded their partnership to build an agentic customer experience architecture, connecting data, AI, engagement, and commerce operations.

    The deployment relies on restructuring how AI interacts with backend commercial platforms. Most digital commerce infrastructures rely on fragmented APIs. SAP Commerce Cloud adopts the Universal Commerce Protocol to standardise data exchange among retailers, payment gateways, and autonomous agents. This framework allows software to independently execute the full retail sequence, spanning initial search, transaction processing, and post-sale resolution.

    Deploying the Universal Commerce Protocol

    Engineering teams integrating the Universal Commerce Protocol facilitate direct interactions between intelligent agents and commerce platforms. The standardisation lowers integration costs and accelerates onboarding into AI-driven channels.

    SAP plans to collaborate with Google to ensure merchant products surface organically across the Gemini application and Google Search, specifically incorporating AI Mode functionalities. Consumers interact with these interfaces while the backend architecture processes inventory checks, cart management, and payment processing without requiring retailers to rebuild existing infrastructure.

    SAP Commerce Cloud integrates Google Gemini capabilities to power a designated Shopping Assistant. Brands deploy the assistant directly to their consumers to facilitate chat, voice, and text engagements. State retention remains active throughout the complete shopping cycle. The deployment ingests live behavioural inputs, current warehouse capacities, and active marketing data to assemble distinct merchandise pairings, including full event configurations. By continuously refining recommendations, the application ensures high relevance and strict physical fulfilment capability.

    Enterprise systems often fail when promotional campaigns trigger demand that physical inventory cannot satisfy. Frontend interfaces failing to synchronise with backend warehouse systems frequently halt digital purchases. Users regularly click promotional emails, load the associated mobile application, and face sudden out-of-stock notices during checkout. Fulfilment updates experience severe delays, leaving support agents without a complete operational picture. SAP and Google Cloud engineered their joint solution to correct these specific systemic customer experience failures.

    Instead of managing disconnected points of contact, the architecture unifies the entire sequence. Traditional commercial setups require consumers to repeatedly input previously shared information. Support staff frequently lack access to unified records, preventing them from resolving issues efficiently. The integration targets these operational breakdowns, ensuring the system recognises the user and their precise context instantly across all digital properties.

    Bidirectional data flows

    Marketing execution demands highly accurate data pipelines. SAP Engagement Cloud partners with Google Cloud to formulate an autonomous multi-agent framework. The technical foundation relies on SAP Business Data Cloud Connect for Google BigQuery. The deployment relies on bidirectional, zero-copy data linking secured by strict administrative controls. Leaving vast data stores in place rather than duplicating them drops storage expenses and network latency.

    BigQuery ingests live variables like weather conditions, precise locations, and active advertising interaction rates. SAP Customer Experience solutions supply the internal behavioural context, tracking customer profiles, exact transaction histories, specific service interactions, and consented engagement records. SAP Engagement Cloud activates the combined intelligence, deploying autonomous agents to orchestrate personalised interactions throughout the customer lifecycle.

    Routing information through the Business Data Cloud while BigQuery handles the logic forces immediate inventory synchronisation. The Shopping Assistant actively queries live warehouse records before displaying any product. Software checks physical supply against consumer requests, verifying availability prior to making the suggestion.

    Generative execution in production environments

    Advanced generative models dictate the localised output of the marketing campaigns. Google Gemini models, specifically including the Nano Banana 2 iteration, provide specialised agentic skills. The models dynamically generate localised messaging, customised imagery, and campaign variations based on the exact specifications provided by the bidirectional data flow.

    The deployment upgrades standard text messages into immersive and interactive interfaces via Google Rich Communication Services. Advertising creatives evolve continuously based on incoming engagement data. The system processes the interaction, evaluates the response against the user profile, and instructs the Nano Banana 2 model to adjust the subsequent communication.

    Marketing departments achieve high efficiency by abandoning manual execution. Instead of configuring rigid campaign parameters, teams establish business goals and provide enterprise data access to the SAP Engagement Cloud. The autonomous agents coordinate the necessary steps, segmenting audiences based on Google BigQuery analytics and generating specific content variations through Google Gemini models.

    Evaluating the infrastructure impact

    Deploying the architecture restructures standard commerce operations. Consumers dictate their purchasing intent to search engines and conversational interfaces. The embedded AI agents process the intent, navigate the Universal Commerce Protocol connections, and complete the purchase directly against the enterprise backend.

    Retailers retain full ownership of the customer relationship despite the transaction occurring within a third-party environment. The architecture captures the consented engagement data, feeding the transaction history back into the SAP Customer Experience solutions. The system updates the localised customer profile, providing the Google Gemini models with fresh context prior to the next engagement cycle.

    The system continuously improves campaign performance without requiring direct human intervention. The multi-agent framework evaluates the success of a generated Rich Communication Services text message, adjusting the variables prior to the next automated dispatch.

    See also: Computer vision deployments drive retail productivity gains

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  • Aviva deploys AI to stop £230M in sophisticated insurance fraud

    Aviva has uncovered a record £230 million in insurance fraud claims and is using AI tools to counter the growing problem.

    The battleground has changed, and the culprits are also coming armed with a new generation of tools. We’re now in an environment where AI is being used not just to defend against fraud, but to perpetrate it.

    The insurance industry has long dealt with opportunistic dishonesty. A bumped car suddenly needs four new doors, or a minor slip becomes a life-altering injury. However, according to Aviva’s data, the nature of the deception is getting deeper, more sophisticated, and harder for the human eye to catch.

    Aviva is fighting fire with fire, deploying its own AI to uncover these elaborate schemes.

    Countering the AI-powered insurance fraud factories

    Aviva reports that scammers are now using AI to generate convincing fakes of car accident scenes. These aren’t clumsy photoshop jobs; they’re detailed, plausible images that can easily fool a claims handler working through a heavy caseload.

    The same generative AI tools are being used to create fake documents, from invoices for repairs that were never done, to medical reports that have no basis in fact. Fraudsters don’t need access to a network of corrupt garages or medical professionals to back up their story. They just need a subscription to an AI service and a bit of imagination. The AI handles the rest, producing official-looking documents that can pass a cursory inspection.

    An individual or small group can now generate the supporting evidence for dozens of high-value claims without ever leaving their desk. How do you validate reality when reality itself can be so easily and cheaply faked?

    Aviva’s response has been to build an AI-powered defence system that can operate at the same scale and speed as the threat. While the company is understandably tight-lipped about the exact architecture, you can piece together what a system like this needs to do.

    At its core, the AI detective carries out pattern recognition at scale. The AI sifts through millions of data points from current and past claims, learning what a legitimate claim looks like—and, more importantly, what it doesn’t.

    When a new claim comes in, the system is cross-referencing everything. Does the damage in the photo match the physics of the described accident? Do the timestamps on the documents make sense? Has this vehicle registration number appeared in other suspicious claims? Are the repair costs quoted on the invoice out of line with the thousands of other similar repairs in the database? It’s a level of forensic analysis that would be impossible to perform manually on every one of the thousands of claims filed each day.

    From organised crime to exaggerated claims

    It’s important to note that this isn’t all about organised criminal gangs. A portion of that £230 million figure comes from what the industry calls “claims inflation.”

    Claims inflation is the more common fraud where policyholders or service providers pad the bill. For instance, a garage might add unnecessary repairs to a quote, or an individual might exaggerate the value of items stolen in a burglary.

    Here, too, AI is proving to be a heavy-duty tool. By analysing vast datasets of repair costs and market values, the system can instantly flag when a quoted price is an outlier. It can compare the cost of a replacement part from one garage against the average from hundreds of others in the same region for the same make and model.

    The goal of Aviva’s AI isn’t to outright deny claims, it’s an augmentation tool for their human investigators. The AI acts as a filter, sifting through the noise to surface the most likely instances of fraud. This human-in-the-loop approach is essential for ensuring fairness and preventing the system from becoming a black box that makes decisions without oversight.

    What Aviva is doing provides a potential route for any customer-facing enterprise in the age of generative AI. The same technology that creates these threats is also the most effective way to combat them.

    As it becomes easier to fake everything from identities to invoices, the only viable defence is an intelligent system that can learn, adapt, and spot deception at a scale that humans alone can’t match.

    See also: Weis Markets adds Instacart AI-powered shopping carts to stores

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    Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

    AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

    The post Aviva deploys AI to stop £230M in sophisticated insurance fraud appeared first on AI News.