Most enterprise AI initiatives don't fail because of the AI. They fail because of the data.
Companies have invested heavily in machine learning, business intelligence, and generative AI - but the operational data that would actually make those investments useful is still trapped inside complex ERP systems, fragmented pipelines, and legacy architectures that were never designed with agents in mind.
That's the problem Incorta and Google Cloud solve together. And as a Google Cloud partner, Incorta is a foundational part of how enterprises get from raw operational data to production-ready AI agents - fast.
The data delivery problem no one talks about
Every analytics initiative starts with the same assumption: the data will be there when you need it. In practice, it rarely is.
Traditional ETL processes strip away the transaction-level detail that AI and analytics actually need. ERP databases like SAP and Oracle can contain 10,000+ tables. Reverse-engineering them into usable pipelines takes months—sometimes years. And once you've built those pipelines, they're fragile. A vendor patch or schema change can break the whole thing, and the institutional knowledge required to fix it often lives with just one or two engineers.
The result: data latency measured in hours or days, partial data sets, and AI models that can't act on your most valuable operational information.
Incorta solves this with Direct Data Mapping™ (DDM)—patented technology that connects directly to source systems like Oracle Fusion, SAP, Workday, and Salesforce, ingests data in its native form (3NF), and delivers 100% of it to Google BigQuery without traditional ETL. No deconstructing. No manual transformations. No data loss.
With over 240 pre-built connectors and automated schema detection, Incorta eliminates the engineering bottleneck entirely. What traditionally takes 18–24 months can happen in weeks. Hormel Foods, for example, replaced an entire Oracle data warehouse with an Incorta-to-BigQuery pipeline in just 10 weeks.
A data foundation built for AI Agents
Delivering data faster is only part of the story. The bigger shift happening right now is agentic AI - and it requires something fundamentally different from what most data architectures were built to provide.
A dashboard needs aggregated, high-level trends. An AI agent needs something else entirely:
Granular data. Agents need transaction-level detail to analyze exceptions, detect anomalies, and take precise action. Pre-aggregated data makes their reasoning shallow and incomplete.
Real-time access. Whether an agent is reconciling financial entries, processing invoices, or optimizing supply chain flows, it needs to act on current data—not a batch from last night.
Business context. Raw numbers aren't enough. Agents need to understand why an invoice belongs to a vendor, how an expense rolls up into a cost center, how a shipment ties to an order. Traditional architectures flatten or abstract this context away, leaving agents blind to business logic.
Incorta addresses all three with a semantic layer built directly into its platform. When data is ingested via DDM, Incorta automatically captures source relationships and keeps them intact - translating complex ERP data models into logical, business-centric datasets. Business schemas organize these relationships, standardize calculations, and enforce governance, so every downstream consumer—whether a dashboard or an AI agent—is working from the same trusted definitions.
Incorta calls this enterprise truth: the verified, context-rich operational knowledge that accurately represents how your business actually works. It includes live data from ERP, CRM, and HR systems; historical records; business logic; and trusted third-party information: delivered to BigQuery as a continuously updated foundation for intelligent automation.
Powering AI Agents in Google Gemini Enterprise
Once enterprise truth is in BigQuery, Google Gemini Enterprise can do something genuinely powerful with it.
Gemini Enterprise is Google's secure, enterprise-grade environment for building, deploying, and managing customizable AI agents at scale. Using the Agent Development Kit (ADK), developers can define agents that connect to BigQuery, reason over live enterprise data, and take autonomous action.
Here's what that looks like in practice.
A financial analyst at a large enterprise might spend hours manually reviewing invoices for discrepancies against purchase orders and contracts. With an Incorta-powered Gemini agent, the workflow changes entirely:
- The analyst asks the agent to find invoices for a specific amount.
- The agent queries BigQuery - where Incorta has already delivered clean, contextualized data from Oracle Fusion—and surfaces the matching invoice.
- The analyst spots an overcharge and asks the agent to place a hold.
- The agent updates the holds table in Oracle Fusion with the correct hold ID, type, and reason automatically.
- Incorta recognizes the change and refreshes all related tables in BigQuery so the next interaction has current data.
The same agent can generate reports, create visualizations, and answer business questions: all from a single interface, grounded in real operational data.
This is what separates production-grade agentic AI from a compelling demo. The intelligence is in the model. The reliability is in the data foundation.
The Incorta + Google Cloud partnership
The joint Incorta and Google Cloud solution brings together:
- Incorta's Direct Data Mapping™ for no-ETL data delivery from complex ERP and business systems directly to BigQuery
- Data Apps: Incorta's prebuilt solution accelerators for specific business domains like finance and supply chain—for rapid deployment without extensive modeling work
- Google BigQuery as the scalable, trusted enterprise truth layer that agents query and act on
- Google Gemini Enterprise for building, deploying, and managing intelligent agents that reason and take action on live business data
The result is a data pipeline that goes from months to weeks, AI agents grounded in operational reality rather than generic model training, and an analytics architecture that scales across departments without duplicating effort or fragmenting governance.
Ready to build?
If your AI initiatives are stalling at the pilot stage, the bottleneck is almost certainly the data - not the model. Incorta and Google Cloud give you a clear, faster path to fixing that.
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