Welcome to AI Unraveled, the briefing for enterprise leaders building production AI.

Today, we're dissecting the industrial challenge: How do you feed a massive LLM like Gemini or Copilot data from a decades-old oil pipeline and a brand-new inspection drone, all at once? The answer is a new, three-stage architecture of data fusion. We’ll show you how Energy and Construction are moving from data silos to unified, prescriptive intelligence.

Stick with us. This is Industrial AI Unlocked.

But first, a crucial message for our corporate partners:

🚀STOP MARKETING TO THE MASSES. START BRIEFING THE C-SUITE.

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SOURCE:

The source material available at our linkedin page here explains how the Energy and Construction sectors are adopting Industrial AI to overcome historical data silo problems. This transformation moves from descriptive to prescriptive AI by fusing legacy operational technology (OT) data with new, unstructured sensor data from devices like drones. A new three-part Data Pipeline Architecture is required, consisting of Edge Pre-processing to filter raw sensor data, a Contextual Data Fabric utilising a Vector Database to unify diverse data types, and a Large Language Model (LLM) like Gemini or Copilot, which acts as the Reasoning Engine using Retrieval Augmented Generation (RAG) to provide actionable, prescriptive insights to field operators. The transcript highlights case studies, such as Skanska and Imperial Oil, demonstrating how this architecture provides auditable intelligence and predictive maintenance capabilities by creating a shared data ontology.

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