Semantic Layers: The Missing Foundation for Agentic AI in Enterprise
Senior Cloud Data Architect
Yazaki
Over the last two decades, I've been on a mission to prove that data changes everything — from factory floors to patient care. As a Senior Cloud System Data Architect and Microsoft Certified Azure Solutions Architect, I bring real-world stories from industries like automotive, healthcare, and finance to the stage. But what drives me most is advocacy. I'm passionate about creating space for women in data and technology, and I use every speaking opportunity to share not just technical know-how, but the belief that this industry gets better when more diverse voices are part of the conversation.
Your company is deploying agentic AI. Exciting. Then your AI agents make technically correct but business-wrong decisions. Why? Because raw data isn't enough. AI agents need semantic understanding—structured knowledge about entities, relationships, and business rules.
A semantic layer is the bridge. It transforms your data platform from a static repository into an AI-ready knowledge base where agents can reason, understand context, and explain decisions. In this case study, we'll share real implementation patterns using knowledge graphs, ontologies, and metadata standards to enable agentic AI across your organization. You'll learn architecture patterns, governance practices that scale without blocking teams, and ROI metrics that justify the investment.
Leave with a diagnostic framework to assess your organization's AI readiness, a 3-month roadmap to build semantic architecture, and confidence that your AI agents will make smart decisions grounded in business context.