Seagate Study: 99% of IT Leaders See AI Driving Storage Growth, Only 38% Say They're Ready

Enterprise IT leaders are finally seeing returns on their AI investments, but the infrastructure underneath those projects is straining. A study commissioned by Seagate found that 99% of IT decision-makers expect AI to increase their organizations' data storage requirements, while just 38% believe they are prepared to meet that demand. The gap between expectation and readiness is the central finding of the report, which surveyed IT leaders at companies running AI workloads.

The numbers point to a problem that has been building quietly as generative AI moves from pilot programs into production. Training and running models generates far more data than traditional analytics, and much of it has to be retained for compliance, reproducibility, or future fine-tuning. Seagate's research suggests most storage teams have not scaled capacity, performance, or data management policies at the same pace as the AI projects they support.

That mismatch matters because storage is rarely the first thing companies budget for when they stand up an AI initiative. Compute gets the attention, and storage tends to be treated as a commodity that can be expanded later. The survey results suggest later is arriving faster than expected, and organizations that underestimated growth are now facing procurement cycles, cost overruns, and architectural rethinks.

Seagate has an obvious commercial interest in the findings, since higher storage demand translates into sales of drives and related infrastructure. Still, the readiness figure lines up with broader industry signals: cloud providers have reported rising storage consumption tied to AI, and hardware vendors have spent the past year pitching denser drives and tiered architectures aimed at exactly this problem. The survey adds a concrete number to what has largely been anecdotal evidence.

What the study does not resolve is whether the 38% figure reflects a genuine technical shortfall or a budgeting and planning failure. Those are different problems with different fixes, and the answer likely varies by company size and sector. The next useful data point will be whether readiness improves over the coming year as AI budgets mature, or whether the gap widens further as model sizes and data retention requirements keep climbing.

For IT leaders, the practical takeaway is that storage planning now belongs in the earliest stages of an AI roadmap rather than the procurement checklist at the end. The organizations that close the gap first will be the ones able to scale their AI work without hitting a wall. The ones that don't may find their ambitious pilots capped by something as unglamorous as disk capacity.