Before enterprises can run with agentic AI, they need to learn to walk with their data
Nobody is talking about this: enterprises are racing to deploy AI agents while their data infrastructure is still broken.
Why it matters
As companies accelerate agentic AI investments, a critical gap is emerging between ambition and execution—legacy data systems are the bottleneck blocking agent deployment, not the models themselves. Leaders need to address foundational data quality before multi-agent orchestration becomes viable.
The key facts
8 to knowData quality identified as primary blocker for enterprise agentic AI deployment
Legacy systems incompatible with agent-ready data architectures
Gap between AI investment acceleration and operational readiness widening
Multi-agent orchestration requires foundational data infrastructure work
Multi-agent orchestration adoption blocked by data quality issues
Legacy systems incompatible with agentic workflows
Persistent gap between AI investment and operational readiness
Data infrastructure modernization identified as critical blocker
Go to the source
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Publisher excerpt: Multi-agent orchestration is the destination, but for most enterprises, the road is blocked long before the first agent gets deployed by the quality of the data feeding those systems. As organizations accelerate investment in agentic workflows, a persistent gap between AI ambition and operational…