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WorkThe story, in brief

Reimagining tech infrastructure for (and with) agentic AI

Nobody is talking about this: agentic AI's real bottleneck isn't the model—it's your data infrastructure.

Reporting by McKinsey Insights 1 min brief
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The KeyNews take

Why it matters

As agentic AI deployment accelerates, infrastructure and data governance emerge as critical success factors. Leaders need to shift focus from model capability to building scalable, trustworthy data foundations that agents can reliably interpret and act on.

The key facts

9 to know

  1. McKinsey framework positions data governance as prerequisite for agentic AI scaling

  2. Focus on converting unstructured data into governed, reusable assets

  3. Emphasis on shared technical foundations and standards enforcement

  4. Strategic insight relevant to enterprise AI infrastructure planning

  5. Focus on data governance as prerequisite for agentic AI deployment

  6. Emphasis on converting unstructured data into governed, reusable assets

  7. Shared foundations and standards enforcement identified as key enablers

  8. Targets data leaders and infrastructure decision-makers

  9. Published Apr 2026 — timing aligns with agentic AI acceleration narrative

Go to the source

McKinsey Insightsmckinsey.com

Publisher excerpt: Scaling agentic AI requires turning unstructured data into governed, reusable assets that systems can interpret and trust. Data leaders can start by building shared foundations and enforcing standards.
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