As organizations race to harness the power of artificial intelligence, a critical question emerges: Does governing AI simply fall under the umbrella of data governance, or does it require an entirely new framework? This keynote will explore the intersection—and divergence—between traditional data governance and the emerging discipline of AI governance. We’ll examine why governing AI models, algorithms, and ethical considerations introduces complexities beyond data quality and stewardship. Attendees will gain clarity on overlapping principles, key differences, and practical strategies for integrating both disciplines into a cohesive governance approach. Join us to uncover how these governance paradigms can coexist and evolve to ensure trust, compliance, and innovation in the age of AI.

To-date, data management initiatives were designed to support reporting and analytics.  With the rise of generative AI , data strategies must now enable reasoning, decisioning, and autonomous systems at a higher scale and magnitude than ever before. Enterprises have largely focused on cloud, data‑lake modernization and data catalog initiatives, but these requirements were not designed for AI. The lack of "AI-ready data" is proving costly, yielding inaccurate results, or failing in production outright. Enterprises are realizing that without "context" AI cannot meet expectations.AI systems need context to reason over enterprise data. Business definitions, logic, and concepts that often lie in tribal knowledge or unstructured forms are not presenting in AI workflows. Ontologies and "context layers" are rapidly emerging as critical building blocks to successful AI outcomes. This webinar demystifies these semantic concepts and outlines how they transform fragmented data assets into an AI-ready foundation.

AI and data innovation are advancing faster than governance can adapt, creating a growing gap between risk, compliance, and business strategy. As new AI regulations emerge globally and privacy frameworks evolve to meet them, organizations are realizing that compliance can’t live in silos. Without a unified governance framework, even well-intentioned data programs become fragmented, reactive, and vulnerable. And the pressure is higher for organizations in complex regulatory environments such as Financial Services, Insurance, Life Sciences, and Government who will need a centralized approach to AI Governance.Join James Howard, former Chief Privacy Officer at Wells Fargo and Nimit Mehta, CEO of TopQuadrant, for a candid fireside chat on how governance can evolve to meet this moment. They’ll explore how data and AI governance offers a framework for AI oversight, why intentional “governance by design” must become standard practice, and how organizations can prepare for rapidly changing regulatory expectations.

Discover how to build a scalable semantic architecture to enable AI-ready data in this webinar featuring Ritu Arora of Bristol Myers Squibb and Laurie Nelsen of TopQuadrant, formerly Mayo Clinic. Learn practical strategies for metadata management, semantic enrichment, and cross-domain data discovery. Get actionable steps to prepare your teams and data for AI initiatives, no matter your current maturity. Leave with a clear roadmap to stress less and scale more in your AI journey.

Join Jessica Talisman, Holger Knublauch, Boris Pelakh, Pete Rivett, and Mike Pool for a panel discussion on the evolving roles of OWL and SHACL in semantic modeling. Explore how these two approaches—once seen as competing—can complement each other in building knowledge-based systems and ontologies. Hear insights from leading experts, including the co-creator of the SHACL W3C standard, and get guidance on choosing and combining these standards for your projects. Gain practical clarity to navigate the future of semantic technology.

Your content—including videos, images, documents, and spreadsheets—holds the answers that your teams and AI need in order to learn and drive better customer experiences. However, extracting this knowledge and finding the right content, especially across systems like SharePoint, AEM, Collibra, or Databricks, can be nearly impossible. Content is tagged manually and inconsistently from system to system, leading to wasted time, poor discoverability, and ultimately bad customer experiences.Discover how automated tagging, controlled vocabularies, and knowledge graphs can help you turn your content into a strategic enterprise-wide initiative, ensuring visibility, consistency, and governance across your entire content ecosystem.

This session explores Graph Retrieval-Augmented Generation (Graph RAG), combining knowledge graphs with LLMs for improved accuracy, governance, and scalability. Using medical journal data, we demonstrate how semantic search, controlled vocabularies, and structured metadata refine retrieval and enhance AI-driven insights.See a step-by-step build of a Graph RAG application with Databricks, Weaviate, and TopBraid EDG, plus real-world applications in drug discovery, biomedical research, and compliance. Learn how knowledge graphs bring transparency, explainability, and domain expertise to AI.

Managing policies can be complex, but it doesn’t have to be. Discover how your organization can simplify and accelerate your approach to policy and compliance enforcement, making it easier to align data with business terms, regulatory requirements, and financial reporting standards. In this webinar, Norm Friend, Senior Semantic Solutions Engineer at TopQuadrant, will explain how a more agile and efficient approach to compliance allows for quick and flexible policy management across your organization.