Sponsor: DataHub

Companies are investing billions into AI, yet more than 80% of AI projects fail to make it past the pilot phase. The problem isn't the technology, it's the data foundation.

AI systems require context (metadata, lineage, relationships) to make sense of the data they are fed. Without it, AI becomes expensive guesswork and a liability.

This practical guide from DataHub addresses the AI Gap No One Talks About by showing you how to build an intelligent metadata foundation that powers successful AI.

Inside this guide, you will explore the three critical layers of context needed for AI success:

• Technical Context: Understanding data provenance, schema, and version control.
• Operational Context: Tracking data behavior, reliability, and service-level agreements (SLAs).
• Business & Social Context: Providing the human layer, ownership, and compliance policies that ground AI in business intent.

DataHub Cloud provides the intelligent, event-driven context your data and AI need to perform, scale, and deliver results. Learn how to get your AI models into production faster and ensure compliance at scale.

Traditional data catalogs were simple inventories for basic SQL needs, but they can't handle the volume, variety, and velocity of today's decentralized data ecosystems. As your organization embraces digital transformation and AI, the limitations of older catalogs create fragmentation, governance chaos, and critical blind spots.

Modern metadata platforms are the solution. They function as dynamic, operational assets that integrate deeply with your production pipelines, enabling systems and AI models to orchestrate and manage data in real time.

This guide from DataHub explores seven compelling reasons why your organization needs to move beyond its traditional catalog and adopt a future-proof metadata platform.

Inside, you will learn the critical differences in:

• Scalability: Handling billions of data objects, not just basic inventories.
• Unification: Breaking down silos by combining discovery, observability, and governance in one platform.
• AI-Readiness: Natively cataloging ML models, feature stores, and training data while using AI to enhance metadata management.
• Future-Proofing: Designing a platform that evolves continuously to meet new standards and regulatory requirements.

The transition is not just a technology upgrade, it's a fundamental shift required to maximize value from your data assets and enable your AI strategy.