The "Usability Gap"

Even when data is perfectly integrated and semantically clear (as discussed in my previous post), there remains a "Usability Gap". This is the distance between a non-technical expert’s question and the technical steps required to extract that answer from a database.

Traditionally, bridging this gap required a human intermediary — a data analyst. My thesis evaluated how Copilot-enabled agentic interfaces can replace this friction with natural language interaction.

Why "Agentic" is Different

We aren't just talking about a chatbot that summarizes text. An Agentic AI system is designed to act. In an industrial context, this means:

Reasoning: Understanding the intent behind a question like "Which batches are trending toward failure?".

Tool Use: Knowing how to query the Gold-layer semantic model in Microsoft Fabric to find the answer.

Validation: Cross-referencing results to ensure they align with the business context provided by our semantic layer.

The Power of Semantic Grounding

One of the most significant findings of my research was the impact of Semantic Grounding. When a Generative AI model (like a Large Language Model) is "loose" on raw data, it often "hallucinates" or misinterprets technical columns.

However, when that same AI is grounded in a robust semantic data model, the results are transformative. My evaluation showed that semantically grounded AI agents could achieve the accuracy of professional SQL engineers while maintaining the usability of a natural language conversation.

Scaling Autonomous Analytics

The combination of a Lakehouse architecture and Agentic AI represents a viable, scalable pathway toward what we call Autonomous Manufacturing Analytics.

In this future, the data platform doesn't just store information; it actively participates in the business. It monitors the battery lifecycle, identifies anomalies via its semantic understanding, and provides proactive insights to human operators through an agentic interface.

Conclusion: The Future of Industrial Intelligence

The era of manual data plumbing and static reporting is ending. By focusing on Semantic Integration to define what our data means, and Agentic AI to make that data accessible, we are finally enabling "Operational Intelligence" that is both powerful and truly self-service.