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For years, data teams focused on making enterprise data more accessible. Data replication solved an important problem: access.
Once data reached modern cloud platforms, teams could analyze it, report on it, and build applications around it. For many analytics initiatives, that was enough.
AI introduces a different set of requirements.
Analytics and AI consume data differently. Analysts can interpret missing context, reconcile inconsistencies, and apply business knowledge on their own. AI systems rely much more heavily on the structure embedded in the data itself.
A replicated purchase order may contain all of the underlying data, but not the relationships that connect it to suppliers, inventory, approvals, and downstream business processes. When those relationships are lost, AI can still retrieve information correctly, but struggle to reason across systems, workflows, and decisions.
Building an AI-ready data foundation
Incorta helps organizations bring complex ERP and operational data into BigQuery without stripping away the relationships, hierarchies, and business rules embedded within it. Using Direct Data Mapping™ and its semantic layer, Incorta preserves the business structure already embedded in source systems, reducing the need to reconstruct that logic after data has been moved.
On Google Cloud, BigQuery provides a scalable environment where operational data, analytics, and AI can work from the same foundation. Google’s Gemini models and other AI-driven workloads can build on the structure already preserved within the data instead of requiring teams to recreate it.
AI-ready data becomes more valuable as organizations extend the same foundation across analytics, AI, and future workloads. By preserving business structure at the data layer, Incorta collaborates with Google Cloud to make it possible to support AI assistants, forecasting, analytics, and future agentic workflows from the same trusted foundation.
From data movement to AI value
When the relationships and business rules behind enterprise data remain intact, the same foundation can support analytics, AI, and future use cases without creating new pipelines, semantic definitions, or data models each time requirements change. That means teams can spend less time reconstructing logic and more time extending a shared data foundation into new analytics and AI workflows.
For data and AI leaders, that trusted foundation creates a more scalable path to operationalizing AI. New analytics, AI, and agentic use cases can build on the same trusted business structure, reducing the effort required to bring AI into additional workflows, teams, and processes.
The value of AI-ready data extends beyond analytics. It creates the foundation for systems that can increasingly support decisions and actions across the business.
Learn more: Download Onramp to the Integrated Data Superhighway for AI Agents for a deeper look at how Incorta collaborates with Google Cloud to bridge data movement and AI readiness. Or explore Incorta on Google Cloud Marketplace to get started.
