Beyond Data Silos: Powering the Autonomous Enterprise with SAP’s Next-Generation Data Fabric and AI

The next era of enterprise transformation is not just about making data available. It is about making data intelligent, contextual, trusted, and actionable.

Artificial Intelligence is rapidly transforming how businesses operate. From demand forecasting and supply chain optimization to financial planning and customer engagement, AI agents are moving beyond generating insights to actively supporting business decisions and executing complex workflows.

But there is one fundamental challenge: AI is only as intelligent as the business context it understands.

Enterprise data is often scattered across applications, databases, data warehouses, and cloud platforms. Even when organizations have access to vast amounts of information, disconnected systems, inconsistent master data, and fragmented business definitions make it difficult for AI to understand how the business actually operates.

At SAP TechEd Berlin 2026, SAP introduced a new wave of innovations in SAP Business Data Cloud (SAP BDC) to address this challenge and accelerate the journey toward the autonomous enterprise.

With advancements in open lakehouse architecture, zero-copy data sharing, enterprise master data management, and business knowledge modeling, SAP is taking an important step toward creating an intelligent data foundation where AI agents can understand business operations, make informed decisions, and act with greater autonomy and confidence.

Let’s explore the innovations shaping this transformation.

1. Breaking Data Silos: An Open Lakehouse Built for the AI Era

Enterprise data should not be restricted by platforms, storage locations, or technology boundaries. However, traditional data architectures often require organizations to replicate information across multiple systems, creating additional costs, operational complexity, and inconsistent data.

SAP is addressing this challenge by strengthening its open lakehouse foundation through the completed acquisition of Dremio.

The vision is simple: make enterprise data accessible wherever it resides, without constantly moving or duplicating it.

Key innovations include:

• Open lakehouse architecture: Native Apache Iceberg support and a universal catalog powered by Apache Polaris provide an open foundation for discovering, governing, and sharing enterprise data.

• Zero-copy data sharing: SAP BDC Connect enables interoperability with platforms such as Databricks, Snowflake, Google Cloud, AWS, and Microsoft, minimizing unnecessary data duplication.

• Federated data access: Powered by Dremio’s high-performance SQL query engine, organizations can query distributed data across SQL Server, MongoDB, Oracle, and PostgreSQL without physically moving it.

• Autonomous query optimization: Dremio Reflections intelligently optimize query performance by learning workload patterns and automatically precomputing frequently accessed results.

An especially exciting development is the integration with Joule Work, allowing business users to ask complex questions in natural language and receive answers grounded in trusted enterprise data.

The business impact: Less time managing data pipelines, fewer data silos, faster analytics, and a scalable data foundation for AI agents operating across enterprise environments.

2. SAP and Databricks: Accelerating the Next Generation of Data and AI Innovation

The future of enterprise AI requires collaboration between business applications, data engineering platforms, and advanced AI technologies.

SAP’s expanded partnership with Databricks brings additional industry-leading capabilities into SAP Databricks within SAP Business Data Cloud.

The expanded capabilities include:

• Lakeflow

• Agent Bricks

• Unity Gateway

• Lakebase

Through SAP Application Data Foundation, organizations can access SAP application data directly within their preferred SAP BDC engine, including SAP Databricks.

This creates an opportunity to combine the rich business semantics of SAP data with the advanced analytics and AI capabilities of Databricks.

Consider a real-world manufacturing scenario.

A manufacturer wants to predict future product demand, optimize inventory, and improve supply chain efficiency.

Traditionally, this could involve extracting historical sales data, integrating inventory information, combining external market intelligence, and building complex data pipelines before developing forecasting models.

With SAP Business Data Cloud and SAP Databricks, data professionals can access SAP data, enrich it with third-party information, and create custom data products on a semantically rich foundation.

AI models can then generate forecasts that are aligned with the business logic and relationships already embedded in SAP systems.

The business impact: Accelerated AI development, reduced integration complexity, lower total cost of ownership, and faster realization of business value from cloud transformation.

3. SAP Reltio: Turning Fragmented Master Data into a Trusted 360-Degree Business View

One of the biggest barriers to enterprise AI adoption is inconsistent master data.

Imagine an organization where the same customer exists in multiple systems with different customer IDs, addresses, names, and transaction histories.

A human employee might recognize that these records represent the same customer. But an AI agent needs reliable entity resolution and connected business relationships to reach the same conclusion.

This is where SAP Reltio becomes important.

Following the completion of its acquisition, SAP Reltio is now generally available within SAP Business Data Cloud, bringing multi-domain Master Data Management (MDM) capabilities natively into the business data fabric.

Its AI-based entity resolution capabilities help organizations:

• Identify and reconcile duplicate records across SAP and non-SAP systems.

• Harmonize inconsistent customer, supplier, and product information.

• Build unified 360-degree views of critical business entities.

• Connect business attributes, relationships, and interactions.

• Publish trusted entity profiles as governed data products.

The significance goes beyond traditional master data management.

When Joule and Joule Agents access unified business entity profiles, they gain a more complete understanding of customers, suppliers, products, and their relationships across the enterprise.

For example, a customer service agent can potentially understand a customer’s purchase history, outstanding invoices, service interactions, and business relationships through connected and trusted information.

The business impact: Better data quality, improved decision-making, stronger customer intelligence, and a reliable foundation for context-aware enterprise AI.

4. SAP Knowledge Graph and Ontology Manager: Giving AI Agents the Context to Think and Act

This is arguably one of the most important developments in SAP’s journey toward the autonomous enterprise.

AI agents do not simply need access to information. They need to understand what that information represents, how different business entities are connected, and which business rules govern their decisions.

For example, knowing that an invoice is overdue is useful. But understanding the customer associated with that invoice, the agreed payment terms, the credit exposure, and the organization’s collections policies is what enables meaningful business action.

SAP Knowledge Graph provides this connected business understanding by linking enterprise entities, relationships, and business rules.

At SAP TechEd Berlin 2026, SAP introduced SAP Business Data Cloud Ontology Manager, an AI-assisted experience that enables organizations to create domain-specific views of SAP Knowledge Graph.

Rather than exposing an entire enterprise knowledge model to every AI agent, organizations can create curated views containing only the information relevant to a particular business scenario.

Consider a collections AI agent.

To support collections operations effectively, the agent needs to understand:

• Customer relationships.

• Outstanding invoices.

• Payment terms.

• Credit risk.

• Financial transactions.

• Relevant business rules and policies.

Using Ontology Manager, organizations can create a dedicated collections domain view that connects these entities and relationships.

Joule Agents can then use this context to generate more relevant answers, recommendations, and business actions.

Even more importantly, these domain-specific views inherit business definitions and governance from SAP Knowledge Graph.

This means organizations can reuse the same business semantics across collections, cash flow management, order management, and other business scenarios without repeatedly redefining the underlying concepts.

The business impact: More intelligent AI agents, consistent business semantics, reusable enterprise knowledge, and decisions grounded in how the organization actually operates.

5. The Bigger Picture: SAP Is Moving Beyond Data Management Toward Business Intelligence for Autonomous Operations

The real significance of these announcements is not any individual technology or product integration. It is how these capabilities come together to create a unified foundation for enterprise intelligence.

Consider how the different innovations complement each other:

Together, these capabilities establish a powerful foundation for the next generation of enterprise AI.

The transformation can be understood through four interconnected layers:

This approach shifts the enterprise data strategy from simply collecting and processing information to creating a connected business intelligence ecosystem.

6. What Enterprises Should Take Away from These Announcements

For business leaders, enterprise architects, data engineers, and AI transformation teams, these innovations highlight several important priorities:

1. Think beyond traditional data integration. Build open architectures that allow data to remain accessible across platforms without unnecessary duplication.

2. Treat business context as a strategic asset. High-quality data alone is not enough. Business definitions, relationships, policies, and semantics must also be available to AI.

3. Invest in trusted master data. AI-driven decisions require consistent customer, supplier, product, and financial information across systems.

4. Make enterprise knowledge reusable. Domain-specific ontologies can help organizations develop AI agents that understand individual business processes without rebuilding business semantics for every use case.

5. Move from AI experimentation to business execution. The ultimate objective is to enable AI agents to support real business workflows with the appropriate governance, context, and controls.

Conclusion: The Future Belongs to Enterprises That Connect Data, Context, and Intelligence

SAP TechEd Berlin 2026 marks an important milestone in SAP’s vision for the autonomous enterprise.

With the evolution of SAP Business Data Cloud, SAP is bringing together open lakehouse architecture, cross-platform data interoperability, advanced analytics, unified master data, enterprise knowledge graphs, and AI-powered business agents.

The bigger opportunity is not simply to make enterprise data available to AI. It is to make enterprise data understandable and actionable.

Imagine an enterprise where AI agents can understand customer relationships, recognize supply chain dependencies, interpret financial policies, evaluate business risks, and recommend or execute actions based on trusted organizational knowledge.

That is the direction SAP Business Data Cloud is helping organizations move toward.

The next competitive advantage will not come from having more data or deploying more AI models. It will come from connecting data with business meaning and enabling intelligent systems to turn that understanding into action.

The autonomous enterprise is no longer just a vision of the future. With trusted data, connected business context, and intelligent AI agents, organizations are beginning to build its foundation today.

Reference

Original SAP Announcement: Advancing SAP Lakehouse and New Data Fabric Innovations in SAP Business Data Cloud for the Autonomous Enterprise 

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