AI adoption is accelerating at a pace most governance systems were never designed to handle. Models are trained, data is shared, and insights are generated in near real time—while many organizations still rely on governance approaches built for slower, more static data environments.

This creates a critical question: Is a metadata solution enough, or do you need a full data governance and intelligence platform?

In this session, we’ll challenge common assumptions about metadata-first strategies and explore where metadata delivers value—and where it falls short as AI scales. We’ll examine why metadata alone often stops at visibility, while modern governance platforms extend metadata into automation, enforcement, and continuous control.

You’ll learn how organizations are:

• Preserving and reusing existing metadata investments without disruption
• Deciding when metadata is sufficient—and when it becomes a constraint
• Moving from documentation to execution with data quality scoring, lineage, secure data sharing, and AI-ready governance

Through a live demonstration of watsonx.data intelligence, we’ll show how metadata can remain the foundation—while governance intelligence ensures data is trusted, compliant, and usable at the speed AI demands. This session isn’t about replacing what you have.

It’s about understanding what’s required when AI outpaces governance—and choosing the right approach for your environment.

When you interact with IBM, this serves as your authorization to DATAVERSITY or its vendor to provide your contact information to IBM in order for IBM to follow-up on your interaction. IBM’s use of your contact information is governed by the IBM Privacy Policy

Website: watsonx.data intelligence