OpenText and Google target the data layer gap holding back enterprise agentic AI
Enterprise agentic AI is stalling. The bottleneck isn't model performance—it's data.
Why it matters
As enterprises rush to deploy AI agents, a critical infrastructure gap is emerging: most organizations lack the data governance and context engineering layer needed to feed agents with clean, actionable information from legacy systems. This represents a strategic inflection point for enterprise AI ROI.
The key facts
8 to knowContext engineering identified as key blocker for enterprise agentic AI deployment
Legacy systems contain decades of unstructured, ungoverned data
Google Cloud Next 2026 signals pivot toward agentic enterprise
Data layer governance gap is limiting model performance impact in production
Context engineering identified as key differentiator for agent performance
Decades of unstructured, ungoverned data in legacy systems blocking deployment
Data layer infrastructure gap highlighted as constraint on enterprise AI adoption
OpenText and Google partnership targeting data governance for agentic workflows
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Publisher excerpt: Organizations racing to deploy agentic AI are discovering that raw model performance is only part of the equation — context engineering is the key to managing decades of unstructured, ungoverned data trapped inside legacy information management systems. As Google Cloud Next 2026 signals a complete…