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AI ready data is the missing link keeping enterprise AI stuck in pilot mode

Enterprises spent billions on AI infrastructure. The real bottleneck? They don't have AI-ready data.

Reporting by SiliconAngle 1 min brief
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The KeyNews take

Why it matters

Enterprise AI is stalled not by compute constraints but by data quality and preparation gaps. This represents a critical market inflection where data infrastructure becomes the competitive moat for AI deployment at scale.

The key facts

8 to know

  1. Most enterprise AI deployments remain in pilot/experimentation phase despite massive infrastructure investment

  2. Data readiness identified as primary blocker — not GPU capacity or cloud infrastructure

  3. Gap between 'owning data' and 'AI-ready data' is the defining obstacle for enterprise value generation

  4. Billions invested in AI infrastructure (GPUs, cloud, tooling) but ROI stalled in non-production deployments

  5. Billions invested in AI infrastructure (GPUs, cloud, tooling) by enterprises

  6. Most deployments remain in pilot/experimentation phase

  7. Data preparation, not compute, identified as primary bottleneck

  8. Gap between data ownership and AI-ready data is the defining obstacle

Go to the source

SiliconAnglesiliconangle.com

Publisher excerpt: Enterprises have poured billions into artificial intelligence infrastructure — GPUs, cloud capacity, model tooling — yet most deployments remain mired in experimentation rather than generating measurable business value. The bottleneck is not compute. It is AI ready data. The gap between owning data…
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