AI readiness does not start with the model—it starts with the data architecture beneath it. As more organizations deploy AI agents, copilots, and automated decision engines, data teams face a set of questions far harder than simply which model to use: can our data layers be trusted enough to ground AI safely, accurately, and at scale?
In this recorded panel discussion, four industry practitioners dive deep into how structuring data with Medallion Architecture helps teams transition from raw data ingestion to trusted, governed, and AI-ready data products. The speakers explore how Bronze, Silver, and Gold layers should be designed in real-world environments where data quality, lineage, semantic consistency, and governance all matter. They also debate a critical emerging question for AI readiness: should AI agents be trained or grounded only on fully curated Gold layer data, or can Silver layer and semantic layer patterns also play an important role?
Watch the recording to discover how Medallion Architecture supports AI-ready foundations, what belongs in each layer, and why boundaries matter. The panel breaks down the case for and against grounding AI exclusively on the Gold layer, where Silver and semantic layers fit into modern architectures, and how to design for lineage, validation, governance, and explainability from the start. You will also learn about the common mistakes that make Medallion Architectures harder to scale or trust, leaving you with clear, practical steps your team can take now to build confidence in the data foundations behind enterprise AI, analytics, and future automation.
This session is moderated by Alexander Perry, Solutions Architect at WhereScape, and features expert panelists Mike Magalsky (CEO & Founder at infoVia), Paul Watson-Gover (Senior Solutions Architect at WhereScape), Simon Meacher (MD & Founder at Engaging Data), and Trung Ta (Senior Consultant at Scalefree). Fill out the form to get instant access to the full recording.
