ZDNET’s key takeaways
- A new study finds 62% of organizations are ill-prepared to tackle surging storage needs.
- Organizations must lean in to AI’s growing data demands and infrastructure readiness.
- “Sustainable scaling” may be the key to optimizing AI growth, according to Seagate’s study.
Artificial intelligence is forcing organizations to evaluate increases in data storage like never before, but less than 40% of businesses believe their infrastructure is equipped to handle the expansion, according to new research from Seagate Technology.
Seagate Technology’s 2026 Data Infrastructure Readiness Report found that 99% of IT leaders expect AI to increase their organization’s storage needs over the next three years. In fact, 32% anticipate their storage demand will grow by more than 50% as a result of AI.
Also: AI failed to properly patch software flaws 74% of the time, 1Password’s study warns
Despite this, only 38% of organizations say they are prepared to meet AI’s growing data demands. That’s less than four out of ten.
Seagate’s findings, published on September 14, are based on a survey of 2,712 enterprise technology decision-makers across the US, China, India, the UK, Germany, France, and Japan. Recon Analytics conducted the research on behalf of Seagate during May and June 2026. The survey aimed to examine respondents’ perspectives on their organizations’ AI readiness, infrastructure investment, storage architecture, infrastructure efficiency, sustainability, and long-term infrastructure planning.
The report’s data suggests a widening gap between the pace of enterprise AI adoption and the underlying data infrastructure (i.e., storage) needed to support it. In recent years, the AI conversation has stubbornly centered on computing power. Now, businesses and organizations across the globe are increasingly encountering challenges in how data
Also: 91% of professionals say their firm still falls short on AI – how to fix that
The most commonly reported challenge to deploying AI among respondents is data quality and readiness, cited by 53% of respondents. The runner-up is storage infrastructure, cited by 43% of respondents. These two roadblocks are reported at significantly higher rates than others, such as compute availability (27%) and energy constraints (24%).
AI increases the value of data, but it needs somewhere to go
The increased push for more robust infrastructure arrives as some businesses obtain measurable returns from AI. Seagate’s report details that 86% of organizations are seeing “moderate or significant” returns on their AI investments, with one-third reporting “significant measurable” returns.
As AI creeps into more business operations and setups, the data supporting those systems is becoming a longer-term business asset, and the industry knows it. Nearly every respondent of Seagate’s survey (98%) agreed that AI is transforming the seemingly basic component of storage into a strategic element of business infrastructure.
And with more storage comes the need for more places to house it. According to Seagate’s findings, investments in data centers are moving higher on organization priority lists. Just over three out of four organizations (76%) ranked data centers among their top three infrastructure investment priorities, with one out of five even identifying it as their single highest priority.
Also: The cloud vs. clouded leopard: America’s data center backlash on display at Nashville Zoo
The data center debate is getting a lot of attention, with fights against data centers popping up around the country. Regardless, the industry’s push for more investment into storage will likely continue, though it’s not the only factor preventing businesses from feeling fully prepared.
Immature AI strategies, limited budgets and redentified by survey respondents as significant barriers to their organization’s preparedness
‘Sustainable scaling’
While data centers and other forms of AI infrastructure have ruffled the feathers of many towns, cities, states, and individuals, Seagate’s report indicates that sustainability and energy are influencing how organizations shape, plan, and expand their AI infrastructure.
Of organizations surveyed, 77% said they had delayed or restructured AI infrastructure expansion due to sustainability or energy concerns, with 36% admitting they had significantly revised expansion plans as a result.
AI-associated energy consumption was the top environmental concern, with 52% of respondents indicating so, followed by carbon emissions and energy use at 51%.
Also: This CIO doesn’t ‘hire engineers to write code’: 3 AI fundamentals he prioritizes instead
Ninety-seven percent of respondents also agreed that extending the usable lifecycle of infrastructure can improve sustainability, and 94% anticipated their storage operations becoming more sustainable within the next five years.
Seagate defines this critical need for continued investment in data strategy, government, and AI infrastructure as a new imperative, which thre company calls sustainable scaling.
“Sustainable scaling is the ability to increase AI capacity and business value while continuously improving the efficiencies of the infrastructure that supports it,” according to the report. “As policymakers, regulators and the public place more scrutiny on the growth of AI infrastructure, sustainable scaling will play a critical role in the long-term
The bottom line
Seagate anticipates that the next phase of AI will create more data, but capacity alone will not determine which organizations succeed. The report concludes that the defining factor will be each organization’s “ability to keep data available and ready for use while efficiently managing the infrastructure demands that come with growth.”
And while this work may already be underway for 38% of organizations, for most, the gap between being mostly prepared and fully prepared remains wide.
Also: Why your RAM options cost 4X more now than last year – even legacy tech prices aren’t immune
Seagate’s report indicates that closing this gap requires an infrastructure strategy built around the “full data lifecycle.”
“Organizations need to understand what data they will create, how quickly different workloads need to access it, how long it may retain value and which operational measures will guide growth. Those decisions provide the foundation for sustainable scaling and for lasting value from AI.”
