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Enterprise data contains the relationships, workflows, and business logic that make organizations run. A shipment, for example, is more than just a record. It connects suppliers, inventory, forecasts, fulfillment timelines, and customer commitments. An invoice is tied to contracts, approvals, and payment terms.
As organizations deploy AI more broadly, those connections become increasingly important because AI needs to understand how the business actually works, not just what each record says. Yet the relationships that connect data across the business can become harder to preserve as information moves between operational systems, warehouses, and analytics environments. When AI can only see fragments of the underlying business process, recommendations become less reliable and harder to operationalize.
Preserving context from ERP to AI
Incorta and Google Cloud help make enterprise systems usable for AI without requiring organizations to reconstruct the relationships and business logic embedded in their data from scratch.
Incorta helps organizations bring complex ERP and operational data into BigQuery without stripping away the relationships and rules that give it meaning. Using Direct Data Mapping™ and its semantic layer, Incorta preserves the business structure already embedded in source systems, so teams can bring ERP and operational data into BigQuery without losing the relationships, hierarchies, and business rules that make it useful for analytics and AI.
On Google Cloud, that contextualized data becomes available in BigQuery, where it can support analytics, Gemini, and other AI-driven workloads. Instead of starting with raw replicated records, organizations start with data that retains the relationships, hierarchies, and business rules needed to make data truly AI-ready.
Together, Incorta and Google Cloud help establish a shared foundation for analytics and AI, reducing the effort required to reconstruct business logic every time a new use case emerges.
Turning business understanding into AI advantage
When enterprise relationships, business rules, and operational context remain intact, the same foundation can support analytics, AI, and emerging agentic workflows without requiring teams to rebuild business logic for every use case.
A supply chain model can work from the same operational context used by an AI agent. A financial forecast can incorporate the same business relationships already used for reporting and analysis. Rather than creating separate pipelines, semantic definitions, and data models for every new initiative, organizations can extend a trusted business structure across analytics and AI.
For data and AI leaders, that operational foundation creates a more scalable path to operationalizing AI. New analytics, AI, and agentic use cases can build on the same trusted data foundation rather than requiring separate pipelines, semantic definitions, or data models for every initiative.
As enterprises move beyond experimentation, helping AI understand how the business works is becoming just as important as giving it access to the data in the first place. That understanding is what enables organizations to move from reporting on the past to acting in the present.
Learn more: Download Onramp to the Integrated Data Superhighway for AI Agents to explore how Incorta and Google Cloud help create a trusted foundation for enterprise AI. Or find Incorta on the Google Cloud Marketplace to get started.
