Today, 95% of enterprise AI projects fail to move beyond the Proof of Concept (POC) stage. The root cause is rarely the AI model itself. It is the data.
When Artificial Intelligence is fed with scattered data, uncertain quality, and a lack of business context, the result is inevitable: AI hallucinations and untrustworthy outputs. To make “Enterprise AI” finally work, organizations must bridge the semantic gap between raw systems and AI agents.

A bidirectional synergy

In this exclusive technical webinar, data expert Marc de Burlet explores why Data Vault 2.0 (DV2) is emerging as the most robust foundation for AI. But the relationship goes both ways: while AI needs Data Vault for context, Data Vault engineering can be massively accelerated by AI.
We will break down this bidirectional synergy into three actionable pillars

1. Building AI-Ready foundations
   AI agents cannot reason without context and history. We will demonstrate how the Data Vault 2.0 methodology (Hubs, Links, and Satellites) naturally provides a unified, quality-checked semantic layer. Learn how to feed your LLMs with auditable, point-in-time data to definitively eliminate hallucinations.
2. AI as an omplementation accelerator
   Designing and building a Data Vault is traditionally resource-intensive. Discover how modern agentic workflows and the Model Context Protocol (MCP) are changing the game. We will discuss how AI agents can assist data engineers to model, map, and deploy Data Vault pipelines significantly faster.
3. AI outputs as a data source
   AI isn’t just a consumer of data; it’s a powerful generator. Learn the architectural best practices for taking unstructured AI outputs—such as sentiment analysis, entity extraction, or document summaries—and securely ingesting them back into your Data Vault as structured, auditable satellite data.

• Speaker: Marc de Burlet, Data & Architecture Expert
• Format: 30-minute technical presentation followed by a 15-minute live Q&A.

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Register here to our webinar.