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.
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 knowMost enterprise AI deployments remain in pilot/experimentation phase despite massive infrastructure investment
Data readiness identified as primary blocker — not GPU capacity or cloud infrastructure
Gap between 'owning data' and 'AI-ready data' is the defining obstacle for enterprise value generation
Billions invested in AI infrastructure (GPUs, cloud, tooling) but ROI stalled in non-production deployments
Billions invested in AI infrastructure (GPUs, cloud, tooling) by enterprises
Most deployments remain in pilot/experimentation phase
Data preparation, not compute, identified as primary bottleneck
Gap between data ownership and AI-ready data is the defining obstacle
Go to the source
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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…