A significant disconnect exists between the financial benefits organizations are realizing from artificial intelligence and their technical readiness to support it, according to a new report from Seagate Technology. While 86% of businesses report moderate to significant returns on AI investments, a majority—62%—lack the storage infrastructure necessary to handle the resulting data explosion.
The 2026 Data Infrastructure Readiness Report, conducted by Recon Analytics on behalf of Seagate during May and June, surveyed 2,712 enterprise technology decision-makers across the United States, China, India, the UK, Germany, France, and Japan. Published on September 14, the study highlights a widening gap between the speed of AI adoption and the underlying data systems required to sustain it.
Nearly all respondents (99%) anticipate that AI will increase their storage requirements over the next three years, with 32% expecting growth exceeding 50%. Despite these projections, only 38% of organizations feel prepared to meet these expanding data needs.
The conversation around AI challenges is shifting. For years, the primary focus was on computing power; however, businesses are now encountering substantial obstacles regarding how AI-sourced data is accessed, stored, retained, and managed. Data quality and readiness remains the top deployment barrier, cited by 53% of respondents, followed closely by storage infrastructure at 43%. These figures significantly outweigh concerns about compute availability (27%) and energy constraints (24%).
As AI integrates deeper into business operations, data is increasingly viewed as a long-term strategic asset. A staggering 98% of survey participants agreed that AI is transforming storage from a basic utility into a critical component of business infrastructure. Consequently, investment in data centers has risen in priority: 76% of organizations ranked them among their top three infrastructure investments, and 20% identified them as their single highest priority.
However, expansion faces hurdles beyond mere capacity. Sustainability and energy consumption are heavily influencing infrastructure planning. According to the report, 77% of organizations have delayed or restructured AI infrastructure projects due to sustainability or energy concerns, with 36% making significant revisions to their plans. AI-related energy use was the leading environmental concern for 52% of respondents, while carbon emissions were a concern for 51%.
Despite these challenges, optimism around green infrastructure remains high. Ninety-seven percent of respondents believe extending the lifecycle of infrastructure improves sustainability, and 94% expect their storage operations to become more sustainable within five years.
Seagate characterizes the need to balance infrastructure growth with efficiency as “sustainable scaling.” This concept involves increasing AI capacity and business value while simultaneously improving the efficiency of the supporting infrastructure. The report suggests this approach will be vital for the long-term viability of the AI economy as regulatory and public scrutiny intensifies.
The report concludes that success in the next phase of AI will not be determined by capacity alone, but by an organization’s ability to keep data available and efficiently manage growing infrastructure demands. Closing the preparedness gap requires a strategy focused on the full data lifecycle, including understanding data creation, access speed requirements, retention value, and operational measures to guide growth.
Sustainability concerns delaying projects makes total sense given energy costs. Green infrastructure needs to be part of the plan, not an afterthought.
Are they serious? 86% see returns but can’t store the data. That sounds like a recipe for costly technical debt down the road.
I would have guessed compute was the bigger bottleneck. Storage and data quality seem to be the real unsung heroes here.
It is wild that 62% of companies are flying blind on storage. The ROI is there, but the plumbing is clearly missing.