Less than four out of ten organizations say they're equipped to handle the storage demands that artificial intelligence will create, even as virtually all IT leaders expect AI to drive significant expansion in data needs over the next three years. That's the central finding of Seagate Technology's 2026 Data Infrastructure Readiness Report, released September 14 and based on a survey of 2,712 enterprise technology decision-makers across seven countries. The research, conducted by Recon Analytics in May and June 2026, reveals a widening divide between how quickly businesses are adopting AI and whether their underlying infrastructure can keep pace.

The numbers paint a stark picture: 99% of IT leaders anticipate AI will boost their organization's storage requirements within the next three years, with nearly one-third projecting growth exceeding 50%. Yet only 38% describe their organizations as ready to meet those swelling data demands. When asked to identify the biggest obstacles to deploying AI, 53% of respondents pointed to data quality and readiness, while 43% cited storage infrastructure—both far ahead of compute availability at 27% and energy constraints at 24%. Meanwhile, 86% of organizations report seeing moderate or significant returns on their AI investments, with a third describing those returns as significant and measurable. Nearly all respondents—98%—agreed that AI is elevating storage from a mundane component into a strategic pillar of business infrastructure.

The report found that 76% of organizations now rank data centers among their top three infrastructure investment priorities, with one in five naming it their single highest priority. But sustainability and energy concerns are reshaping how companies plan that expansion: 77% of surveyed organizations said they had delayed or restructured AI infrastructure buildouts due to environmental or energy considerations, and 36% acknowledged making significant revisions to their plans. AI-related energy consumption topped the list of environmental worries at 52%, closely followed by carbon emissions and energy use at 51%. Nearly all respondents—97%—agreed that extending infrastructure lifespan can improve sustainability, and 94% expect their storage operations to become more sustainable within five years.

The report attributes the preparedness gap to a mix of immature AI strategies, tight budgets, and challenges around data management and governance. Seagate argues that the defining factor for success in the next phase of AI won't be capacity alone, but rather each organization's ability to keep data accessible and ready while efficiently managing the infrastructure demands that accompany growth. To address this, the company introduces the concept of "sustainable scaling"—increasing AI capacity and business value while continuously improving the efficiency of supporting infrastructure. The report concludes that closing the readiness gap requires an infrastructure strategy built around the full data lifecycle, including understanding what data will be created, how quickly different workloads need to access it, how long it retains value, and which operational measures will guide growth. Those decisions, according to Seagate, provide the foundation for sustainable scaling and lasting value from AI. The tension between rapid AI adoption and infrastructure constraints may force enterprises to choose between speed and sustainability, a trade-off that could reshape competitive dynamics as data becomes a longer-term strategic asset rather than a fleeting operational input.