AI has rewritten the enterprise data playbook. Workflows no longer just create temporary operational data, but vast amounts of high-value assets, from LLM training datasets and model outputs to logs, metadata and archived knowledge that may need to be preserved for years.
The AI infrastructure discussion is typically framed around the cost of data centers, the power requirements, and the compute needed to train and run models, including GPUs and high-performance storage. That’s hardly surprising given the eye-watering investment numbers occupying the headlines.
When that dreaded “Storage Almost Full” warning pops up, most of us panic-delete apps or scramble to erase our favorite memories. We assume our devices are bloated simply because we take too many photos or record too many long videos.
