From Promise to Plateau: Breaking Through the AI Data Bottleneck—Without Rebuilding Your Stack Recording
Join us as we discuss how to move from pilots to assurance-grade AI using an Enterprise Knowledge Model (EKM) and AI-enabled data agents that automate quality checks, lineage, enrichment, and continuous monitoring across silos, functions, processes, data types, and sources.
Enterprise AI generally doesn’t fail because the models are weak. It fails when data can’t be governed, understood, evidenced, or explained at scale. In this OCEG™ webcast, Infinity Data AI co-founders Lee Dittmar and Iggy Geyer show how to move from pilots to assurance-grade AI using an Enterprise Knowledge Model (EKM) and AI-enabled data agents that automate quality checks, lineage, enrichment, and continuous monitoring across silos, functions, processes, data types, and sources.
You’ll see how this approach embeds governance “in the data,” and aligns with emerging standards (NIST AI RMF, ISO/IEC 42001) and regulations (e.g., EU AI Act) and avoids costly rip-and-replace projects. We’ll ground it in a bank-grade use case to show how better data quality and lineage translate directly into faster onboarding, stronger risk models, and cleaner regulatory reporting.
SPEAKERS:
Lee Dittmar, Co-founder, Infinity Data AI, and OCEG™ Fellow
Iggy Geyer, Co-founder, Infinity Data AI
Featured in: AI / Artificial Intelligence
