A new Seagate survey of 2,712 enterprise technology leaders finds that 99 percent expect AI to raise their storage requirements over the next three years, yet only 38 percent feel fully prepared for it. The report argues the next phase of AI will be decided as much by how organizations store and manage their data as by how much computing power they can buy.

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Most of the money and attention in the AI build-out has gone to processors, the chips that train and run the models. A new report from Seagate Technology argues that a harder constraint is forming in the systems that hold the data those models depend on.

Released on September 14, Seagate's inaugural Data Infrastructure Readiness Report draws on independent research by Recon Analytics among 2,712 enterprise technology decision-makers across seven markets. Its central finding is a mismatch. Nearly every organization, 99 percent, expects AI to increase its storage requirements over the next three years, and almost a third, 32 percent, expects those needs to grow by more than half. Only 38 percent say they are fully prepared to meet that demand.

The Readiness Gap Behind the AI Boom

Nearly nine in ten organizations, 86 percent, report moderate or significant return on their AI investments, and a third, 33 percent, report significant measurable returns. As those returns climb, the data feeding the models becomes more valuable, which is why 98 percent of respondents agree that AI is transforming storage into strategic business infrastructure rather than a passive cost.

As AI settles deeper into daily operations, organizations face a twin shift, with the volume of data they need to keep rising at the same time as its potential worth. Data, in this case, is starting to look less like a disposable input and more like a long-term asset that has to be preserved, managed, and put to use to keep generating value.

Storage Moves Ahead of Compute as a Constraint

The most striking part of the survey is that storage sits among the obstacles of deploying AI. Data quality and readiness, at 53 percent, and storage infrastructure, at 43 percent, rank as the two leading challenges, both ahead of compute availability at 27 percent and energy constraints at 24 percent. In a market that has spent its attention on graphics chips and power supply, the data foundation has become the tighter bottleneck. More than three-quarters of organizations, 76 percent, place data center investment among their top three infrastructure priorities, and one in five, 20 percent, now call it their single highest priority. Respondents point to AI strategy maturity at 16 percent, budget and resources at 14 percent, and data management and governance at 14 percent.

“AI is reshaping the way organizations plan, build and operate infrastructure,” said Melyssa Banda, Senior Vice President of Edge Storage Business at Seagate. “As data volumes grow, so does the value organizations can derive from the data. They need data infrastructure that helps them preserve, access and use more of that data over time. Sustainable Scaling is about making those infrastructure decisions more efficient, more durable and more connected to long-term data value.”

Efficiency and Sustainability Enter the Plan

The report also finds that efficiency and lifecycle planning are becoming part of how companies scale. Nearly all organizations, 97 percent, agree that extending the working life of infrastructure meaningfully improves sustainability, and 94 percent expect their storage operations to grow more sustainable over the next five years. Those concerns are already reshaping decisions. Nearly eight in ten, 77 percent, have delayed or restructured AI infrastructure expansion over sustainability or energy worries, including 36 percent that have significantly restructured their plans. AI-driven energy consumption, at 52 percent, and the carbon emissions that come with it, at 51 percent, rank as the leading environmental concerns. Seagate frames its own answer as Sustainable Scaling, growing AI capacity and business value while continuously improving the efficiency of the infrastructure underneath it.

“The next phase of AI will require capacity growth, but capacity alone will not be enough,” Banda said. “It will be defined by smarter infrastructure decisions on how effectively organizations scale, organize, retain and use the data that AI depends on. The companies that create lasting value from AI will be the ones that treat data infrastructure as a business strategy.”

The survey, conducted between May and June 2026, spanned the United States, China, India, the United Kingdom, Germany, France, and Japan.

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