AI storage demands outpace enterprise infrastructure readiness, Seagate survey finds

Nearly all IT leaders expect AI to increase storage needs, but only 38% say their organizations are fully prepared for the added demand.

Nearly all enterprise technology leaders expect artificial intelligence to increase their organizations' storage requirements over the next three years, but fewer than four in ten say they are fully prepared for those demands, according to new research published by Seagate Technology.

Seagate's 2026 Data Infrastructure Readiness Report found that 99% of respondents expect AI to increase storage requirements. Only 38% said their organizations are fully prepared to support AI's long-term data needs. However, 83% said they were either fully or mostly prepared.

The research was conducted by Recon Analytics among 2,712 enterprise technology decision-makers across seven global markets. It covered AI infrastructure readiness, investment priorities, storage planning, and sustainability.

Storage readiness trails expected AI demand

Nearly one-third of respondents, or 32%, expect their storage requirements to increase by more than 50% over the next three years. The findings also showed that 98% of respondents agreed that AI is changing the role of storage within their organizations.

Storage infrastructure ranked among the main challenges organizations identified when deploying AI. Data quality and readiness was cited by 53% of respondents, followed by storage infrastructure at 43%.

Storage infrastructure was cited more frequently as a deployment challenge than compute availability, at 27%, or energy constraints, at 24%.

The survey also found that data center investment is a priority for many of the organizations surveyed. Seventy-six percent ranked it among their three main infrastructure investment priorities, while 20% identified it as their highest priority.

Security and compliance infrastructure ranked as the most commonly cited investment priority at 44%, followed by data management and governance at 43%. AI and GPU infrastructure and storage hardware refresh were each cited by 39% of respondents.

Organizations that said they were not fully prepared for future AI data requirements identified several barriers. AI strategy maturity was cited by 16% of respondents, while budget and resources and data management and governance were each cited by 14%.

The survey also examined returns from existing AI investments. Eighty-six percent of respondents reported moderate or significant returns from AI projects, including 33% who reported significant measurable ROI.

Separately, 70% of respondents expect storage requirements to rise by at least 26% over the next three years.

Melyssa Banda, senior vice president of Edge Storage Business at Seagate Technology, said the growing volume of data is changing how organizations plan their infrastructure.

"AI is reshaping the way organizations plan, build and operate infrastructure," Banda said. "As data volumes grow, so does the value organizations can derive from the data."

Energy and lifecycle factors shape infrastructure planning

Ninety-seven percent of respondents agreed that extending infrastructure lifecycles improves sustainability, while 94% expect their storage operations to become more sustainable over the next five years.

Energy and sustainability concerns have already affected some AI infrastructure plans. Seventy-seven percent of organizations said they had delayed or restructured AI infrastructure expansion because of these concerns, including 36% that said they had substantially restructured their plans.

AI-related energy consumption was the most commonly cited environmental concern, identified by 52% of respondents. Carbon emissions associated with energy consumption followed at 51%.

Power consumption was the most commonly considered factor when respondents assessed the environmental footprint of storage hardware, cited by 62%. Device lifespan and durability followed at 55%.

Seagate refers to its approach to balancing capacity growth with infrastructure efficiency and lifecycle planning as "Sustainable Scaling."