Seagate Technology has released its inaugural 2026 Data Infrastructure Readiness Report, highlighting a widening gap between expected AI-driven storage demand and enterprise preparedness.

Based on research among 2,712 enterprise technology decision-makers across seven global markets, the report found that 99% of organisations expect AI to increase storage requirements over the next three years. However, only 38% said they are fully prepared to meet long-term AI data demands.

The study also found that 86% of organisations are seeing moderate or significant returns from AI investments, including 33% reporting significant measurable returns.

Data quality and readiness emerged as the leading challenge to AI deployment, cited by 53% of respondents, followed by storage infrastructure at 43%. Storage ranked ahead of compute availability at 27% and energy constraints at 24%.

Nearly all respondents, at 98%, said AI is transforming storage into strategic business infrastructure.

According to Seagate, the findings indicate that enterprises are broadening their AI infrastructure strategies beyond computing capacity to include storage, data accessibility, governance and long-term infrastructure planning.

More than three-quarters of organisations, or 76%, ranked data centre investment among their top three infrastructure priorities, while 20% identified it as their highest priority.

AI strategy maturity, budget and resources, and data management and governance were also identified as key barriers to stronger preparedness.

“AI is reshaping the way organisations plan, build and operate infrastructure,” said Melyssa Banda, Senior Vice President of Edge Storage Business at Seagate Technology.

She added that organisations need data infrastructure capable of preserving, accessing and using increasing volumes of data over longer periods.

Sustainability is also influencing AI infrastructure planning. The report found that 97% of organisations believe extending infrastructure lifecycles improves sustainability, while 77% have delayed or restructured AI infrastructure expansion because of energy or sustainability concerns.

AI-driven energy consumption was cited by 52% of respondents as a leading environmental concern, followed by carbon emissions from energy use at 51%.

Seagate describes its approach as “Sustainable Scaling”, which focuses on expanding AI capacity while improving infrastructure efficiency, lifecycle performance and long-term data value.

The research was conducted by Recon Analytics between May and June 2026 across the US, China, India, the UK, Germany, France and Japan.