Is SAP’s Business Data Cloud the Future of AI?

A few weeks ago, we saw what’s being called a “landmark partnership” between SAP and Databricks. But I think this is just the beginning of something much bigger—one that could reshape the entire industry. After all, when the world’s leading business software provider teams up with the most widely used data intelligence platform, it’s bound to make waves. But is there more to it than meets the eye??

Another game-changing technology is already redefining the way we do business: AI agents. And according to SAP’s CEO, Christian Klein , the connection between SAP’s Joule AI agents and the new Business Data Cloud platform runs deep. But what does this mean for the future of business software??

How SAP Business Data Cloud Supercharges Joule AI Agents?

SAP’s Business Data Cloud (BDC) is built to power Joule AI agents by providing a structured, trusted data foundation through a knowledge graph. Essentially, it organizes and connects all of a company’s knowledge—best practices, policies, documents, and more—so employees can instantly access the right information when they need it.?

Instead of digging through outdated manuals or scattered files, employees can simply ask a question and receive an accurate, up-to-date answer in seconds. This is critical for AI systems, ensuring responses are reliable and data-driven rather than just educated guesses.?

In Joule’s case, it leverages BDC to provide trustworthy AI-driven insights while also automating complex workflows, improving decision-making, and enabling data-driven functions (e.g. risk assessment). Besides, BDC’s partner insight apps further expand AI-driven business intelligence.??

By contextualizing enterprise data, BDC transforms Joule from a regular chatbot into an autonomous AI agent that optimizes operations and enhances productivity.?

What Are “Knowledge Graphs”??

To understand how AI agents work, it's essential to grasp the concept of knowledge graphs. These are structured representations of information that connect different entities—such as people, products, or locations—through their relationships.?

For example, in a retail business, a knowledge graph could link a customer to their previous purchases, preferred payment methods, and customer service interactions. This structure helps AI agents understand how pieces of data are connected, enabling them to provide personalized recommendations, automate workflows, and make smarter decisions.?

Why Are Knowledge Graphs Important for AI Agents??

1. Understands Context Better?

Instead of just reading text, AI connects the dots—like recognizing that “Tesla” could refer to a company, a person, or a car model—leading to smarter answers.?

2. Thinks and Learns Like a Human?

AI can infer new knowledge by analyzing relationships within the graph, allowing for smarter, more human-like interactions. For example, if it sees that “high employee turnover” is linked to “low job satisfaction” in company reports, it can suggest solutions rather than just listing statistics.?

3. Improve Search and Recommendations?

Knowledge graphs help search engines understand queries better and provide richer, more relevant results. Like Google’s search engine, they provide AI with more relevant answers—so when you ask, “What’s our PTO policy?” you get the latest company-approved response instantly.?

4. Prevents Misinformation?

Instead of making up answers, AI pulls from structured company data, ensuring responses are accurate and up to date.?

5. Facilitate Integration Across Systems?

Knowledge graphs unify data from multiple sources, allowing AI agents to operate seamlessly across different platforms and domains. For example, they help AI agents to pull customer insights from Salesforce and policy details from SharePoint?

Getting ready for the upcoming agentic wars?

As the AI hype settles, we’re starting to see a clearer picture of what enterprise AI will truly look like—and the key technologies needed to make it happen. SAP’s move to position Business Data Cloud (BDC) as the foundational data layer is a strategic step toward the next major phase of business AI: the rise of AI agents.?

The reality is, enterprises are already juggling multiple AI-powered products across their operations. The real question now isn’t if they’ll adopt AI agents, but which platform they’ll choose. That’s why tech vendors are in a race to refine their AI agents and build the most powerful, adaptable platforms possible. The competition isn’t just about AI—it’s about who will own the future of enterprise intelligence.?

While SAP has taken a major step in this race, creating agents that connect a whole enterprise won’t be easy. Cleaning, harmonizing, and contextualizing data is still a hard process. So, building the foundations of these systems will be an onerous multi-year job for most organizations.?

I think that we have the challenge to think differently about ROI in this new vision of agentic AI. We must take care of immediate profits but also consider the huge potential value that we could create across a business if we take the job to really unlock our data and provide advanced decision support.??

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