DataHub commissioned independent research firm TrendCandy to survey 250 IT and data leaders about the state of context management in 2026. The findings reveal a market at an inflection point: high confidence, real infrastructure gaps, and a correction already underway.

What you'll learn:

• Why organizations that self-assess at the highest stage of context management maturity still struggle with the fundamentals
• How “good enough” context solutions that work in pilots consistently break down at production scale
• Why 83% of IT and data leaders now believe agentic AI cannot reach production value without a dedicated context platform
• What the surge in context management infrastructure investment reveals about where the market is heading
• The three imperatives for data and IT leaders in 2026

Context engineering was supposed to solve AI’s scalability challenges. Teams are building RAG pipelines, crafting prompt templates, and implementing memory systems—each application starting from scratch. As AI scales organization-wide, these tactical approaches hit fundamental limits: fragmented systems, inconsistent outputs, and no organizational context intelligence.

This keynote explores the essential building blocks of an enterprise context platform and introduces context management as the emerging discipline that changes how organizations approach this challenge.

Key Takeaways:

• Understand the fundamental differences between metadata for humans and context for AI agents
• Learn why current context engineering approaches create compounding technical debt that kills enterprise AI scaling
• Discover the emerging architectural pattern that transforms context from bottleneck to competitive advantage
• Explore the transformative possibilities that context management will unlock for enterprise AI

DataHub commissioned independent research firm TrendCandy to survey 250 IT and data leaders about the state of context management in 2026. The findings reveal a market at an inflection point: high confidence, real infrastructure gaps, and a correction already underway.

What you'll learn:

• Why organizations that self-assess at the highest stage of context management maturity still struggle with the fundamentals
• How “good enough” context solutions that work in pilots consistently break down at production scale
• Why 83% of IT and data leaders now believe agentic AI cannot reach production value without a dedicated context platform
• What the surge in context management infrastructure investment reveals about where the market is heading
• The three imperatives for data and IT leaders in 2026

Context engineering was supposed to solve AI’s scalability challenges. Teams are building RAG pipelines, crafting prompt templates, and implementing memory systems—each application starting from scratch. As AI scales organization-wide, these tactical approaches hit fundamental limits: fragmented systems, inconsistent outputs, and no organizational context intelligence.

This keynote explores the essential building blocks of an enterprise context platform and introduces context management as the emerging discipline that changes how organizations approach this challenge.

Key Takeaways:

• Understand the fundamental differences between metadata for humans and context for AI agents
• Learn why current context engineering approaches create compounding technical debt that kills enterprise AI scaling
• Discover the emerging architectural pattern that transforms context from bottleneck to competitive advantage
• Explore the transformative possibilities that context management will unlock for enterprise AI