Solution Lab: Building the Enterprise Context Layer: Making Data Meaningful for Generative and Agentic AI
Webinar Speaker: Fern Halper, TDWI VP Research, Senior Research Director for Advanced Analytics
Date: Wednesday, December 2, 2026
Time: 9:00 a.m. PT / 12:00 p.m. ET
As organizations move from traditional AI to generative and agentic AI, providing the right business context is becoming increasingly important. AI systems need more than access to enterprise data. They need to understand what data means, which customers, products, and other entities it refers to, how information is connected, and what information is relevant to a particular task.
Yet enterprise information is often fragmented across systems, and the context AI needs is not always readily available. A generative BI assistant may calculate revenue differently from a finance dashboard. A customer service agent may fail to recognize the same customer across multiple systems. A procurement agent may overlook important supplier relationships, while an AI assistant may retrieve an outdated policy. These challenges highlight the need for capabilities that help organizations establish, connect, govern, and deliver trusted business context to AI applications.
TDWI research underscores the need for these capabilities. In a 2026 TDWI Blueprint survey, about 40% of respondents reported having a central semantic layer, a third reported using a taxonomy or business glossary, and slightly more than a quarter reported using knowledge graphs. Meanwhile, vendors are introducing new technologies and approaches for providing context to AI, which can make it difficult for organizations to determine which capabilities they need and how they fit together.
In this TDWI Solution Lab, TDWI VP of research Fern Halper, Ph.D., will provide an independent perspective on the emerging enterprise context layer, drawing on TDWI research and the new TDWI Solution Navigator. Industry solution specialists will demonstrate approaches to providing enterprise context for AI and discuss how organizations are addressing different business requirements. They will join Fern to explore how organizations can evaluate available solutions, integrate them with existing data architectures, and determine which capabilities best support their AI initiatives.
Data, analytics, and AI leaders will learn:
- Why generative and agentic AI require business context beyond access to enterprise data
- What capabilities are needed to establish, connect, govern, and deliver trusted context to AI applications
- How different approaches to providing context address different business requirements and AI use cases
- What to consider when evaluating solutions and integrating context capabilities into existing enterprise architectures