Context Is the New Foundation for AI-Ready Governance

As organizations accelerate AI and analytics initiatives, one challenge keeps surfacing: enterprise data is only as useful as the context behind it.

Business definitions, ownership, metadata, lineage, and data quality signals all play a critical role in whether teams can trust, govern, and activate their data effectively. But in many organizations, that context is incomplete, outdated, or scattered across systems. As a result, governance programs struggle to keep pace, data catalogs lose relevance, and AI initiatives face greater risk around accuracy, reliability, and accountability.

This panel discussion will explore how data leaders are rethinking governance and stewardship for the AI era. Panelists will discuss why traditional, manual approaches no longer scale, how trusted business context improves analytics and AI outcomes, and where automation can help teams keep governance programs current without removing human oversight.

Join us for a practical conversation on what it takes to build AI-ready data foundations — and how organizations can move from static documentation to living, trusted context that supports better decisions, stronger governance, and more reliable AI.

In this session, we’ll discuss:

• Why business context is becoming essential to AI and analytics success
• How stale metadata and unclear ownership create governance and AI risk
• What scalable data stewardship looks like in modern enterprises
• How AI can support governance workflows without replacing human judgment
• Practical ways to keep data assets trusted, documented, and ready for use

Register here