AI Is Creating a Storage Boom: Generative AI is producing and consuming enormous amounts of data. As businesses deploy AI at scale, storage infrastructure is becoming a bigger part of technology planning, from training datasets to model outputs and archived information.
Data Volumes Are Rising: A Western Digital and IDC study found that 94.7% of organisations reported increased data volumes because of AI adoption. More than 61% saw data growth of at least 25% over the past year.
Training Needs Massive Datasets: Businesses must store text, images, video, code and other information used to develop increasingly capable models, creating sustained demand for high-capacity storage systems.
Training Needs Massive Datasets: Businesses must store text, images, video, code and other information used to develop increasingly capable models, creating sustained demand for high-capacity storage systems.
AI Inference Creates More Data: Every AI interaction can generate prompts, outputs, logs, embeddings and operational information, creating another continuous stream of data that organisations may need to store, analyse and secure.
Old Data Is Becoming Useful: The Western Digital study found 75.9% of respondents were reactivating cold-tier data for AI workloads, turning previously dormant information into valuable training and analysis material.
Faster Storage Matters: AI workloads need more than capacity. They also require fast access to data, high throughput and low latency, particularly when GPUs and other accelerators are processing enormous datasets.
5 billion in 2026, with demand for GPUs, networking, scalable storage and continuous AI inference driving infrastructure growth.
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