Seagate Technology’s 2026 Data Infrastructure Readiness Report, released September 14, found that AI is increasing storage demands faster than many organizations can prepare for them. The findings show that organizations need to plan for the growing amount of data that AI systems create, process and retain.

The research surveyed 2,712 enterprise technology decision-makers across the United States, China, India, the United Kingdom, Germany, France and Japan. Organizations are responding by investing in data-center infrastructure, data strategy, governance and storage. 

Among the challenges listed in the survey, data quality and readiness ranked first, cited by 53% of respondents, followed by storage infrastructure at 43%.

AI Is Making Storage a Strategic Concern

According to the Seagate survey, 99% of respondents expect AI to increase storage requirements over the next three years, but only 38% consider their organizations fully prepared.

Seagate also found that 98% of organizations agree that AI is turning storage into strategic business infrastructure. More than three-quarters, or 76%, rank data center investment among their top three infrastructure priorities. For 20%, it is their highest infrastructure investment priority.

Data Readiness Is Part of the Problem

Storage capacity is only one part of the challenge. Seagate found that data quality and readiness ranked as the biggest obstacle to AI deployment, cited by 53% of respondents. Storage infrastructure followed at 43%.

AI strategy maturity, budget and resources, and data management and governance were also cited as barriers to greater preparedness.

This puts greater focus on how organizations manage data before it reaches AI systems. 

Efficiency Is Shaping Storage Plans

The survey also found that organizations are looking at the efficiency and lifespan of their infrastructure as storage needs grow. Ninety-seven percent said extending infrastructure lifecycles can improve sustainability, while 94% expect their storage operations to become more sustainable over the next five years.

At the same time, 77% said they have delayed or changed AI infrastructure expansion because of sustainability or energy concerns. AI-driven energy consumption was the most frequently cited environmental concern, at 52%, followed by carbon emissions from energy consumption at 51%.

For chief data officers (CDOs), the findings highlight a practical issue in AI planning. Growing AI use can increase the amount of data an organization needs to store and manage. That makes data quality, storage capacity, AI governance and lifecycle planning part of the same conversation. 

The report also found that 86% of organizations report moderate or significant returns from their AI investments. Therefore, it is also important for CDOs to assess whether AI deployments are delivering the business outcomes they were expected to produce, particularly as infrastructure and storage requirements grow.