AI ready data is the missing link keeping enterprise AI stuck in pilot mode
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 and having AI ready data is proving to be the defining obstacle of this infrastructure cycle. A commissioned IDC Global AI Readiness Survey found that 94% of information technology leaders identify data quality as the primary factor in AI success — yet most enterprise data remains unclassified, ungoverned and unfit for production AI workloads. Everpure Inc. and Nvidia Corp. are jointly targeting this problem through co-engineering that spans data intelligence, vectorization, and GPU-accelerated inference pipelines, according to Jason Hardy (pictured, right), vice president of storage technology at Nvidia Corp.
“There is this nervousness to fully commit — there is a cost attached to it, but it’s also where to start,” Hardy said. “They get overwhelmed, and then they kind of freeze out. So where we like to see how we can streamline through that is, hey, let’s shrink this down into a very focused path and then help walk through what does that mean from the infrastructure, but also the data side of it.”
