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Explore how to overcome the fundamental challenge of "data readiness" in AI systems through an innovative agentic semantic layer approach in this 17-minute conference talk. Learn why rapid advances in LLM capabilities haven't translated to increased reliability for business-critical AI applications, with the root cause being unprepared data infrastructure. Discover the limitations of current conversational analytics systems that struggle with query verification, RAG-based systems that require extensive knowledge graph preparation, and agentic AI workflows that resort to hard-coded processes rather than delivering true autonomous functionality. Understand how the lack of a well-prepared semantic data layer causes LLM planning and reasoning to fail in capturing user intent and domain context. Examine the challenges of enterprise data silos, varying quality levels, and the constantly evolving nature of business definitions and metadata requirements. See live demonstrations of building and maintaining an AI-powered semantic data layer that automatically adapts to changing data landscapes and business needs. Learn how this approach significantly enhances existing RAG, text-to-SQL, and tool calling techniques while opening pathways to reliable AI deployments in production environments.
