Organizations Struggle with AI Data Storage Demands

Key Insights from Seagate’s Research

A significant 62% of organizations are not prepared for increasing storage demands driven by AI.
Companies must address the growing data requirements associated with AI and ensure their infrastructure is ready.
Seagate’s study suggests that “sustainable scaling” is crucial for optimizing AI growth.

Recent findings from Seagate Technology highlight an urgent need for organizations to reassess their data storage capabilities in the wake of AI advancements. According to the 2026 Data Infrastructure Readiness Report, less than 40% of businesses feel their infrastructure can meet the anticipated surge in storage requirements.

The report reveals that 99% of IT leaders predict that AI will necessitate increased storage over the next three years, with 32% expecting their storage needs to rise by more than 50% due to AI influences.

Despite these projections, only 38% of organizations report readiness to manage the growing demands of AI data. This statistic, derived from a survey involving 2,712 enterprise technology decision-makers across multiple countries including the US, UK, and Germany, indicates a troubling gap in readiness and infrastructure investment.

Seagate’s research, conducted by Recon Analytics in mid-2026, further emphasizes a disconnect between rapid AI adoption and the foundational data infrastructure required to support it. While discussions around AI have largely focused on computing power, organizations are now facing significant challenges regarding data management, accessibility, and retention.

Among the challenges identified by survey respondents, 53% cited data quality and readiness as a major barrier, followed closely by 43% who pointed to inadequate storage infrastructure. Other issues such as compute availability and energy constraints were reported at lower rates.

The Need for Enhanced Data Infrastructure

As organizations increasingly recognize the value of data generated by AI, there is a pressing need for improved storage solutions. The report indicates that 86% of businesses are experiencing either moderate or significant returns on their AI investments, with one-third observing substantial measurable returns. This growing reliance on data positions it as a long-term asset, with 98% of survey participants acknowledging that AI is transforming storage into a strategic business component.

With this shift, the importance of data centers has surged, with 76% of organizations ranking them among their top three infrastructure investment priorities. Approximately 20% identified data centers as their highest priority. However, the conversation around data center expansion is fraught with challenges, including public opposition and resource limitations, which could hinder preparation efforts.

Survey respondents also raised concerns about immature AI strategies, budgetary constraints, and data governance as additional barriers to readiness.

Sustainable Scaling: A New Imperative

The report underscores sustainability as a critical factor shaping AI infrastructure development. A notable 77% of organizations reported delaying or restructuring their AI infrastructure plans due to sustainability and energy concerns, with 36% making significant revisions to their expansion strategies.

Energy consumption linked to AI was cited as the primary environmental concern by 52% of respondents, followed closely by carbon emissions at 51%. Furthermore, 97% agreed that extending the lifecycle of their infrastructure could enhance sustainability, with 94% expecting improvements in their storage operations’ sustainability over the next five years.

Seagate articulates the necessity of sustainable scaling, which involves enhancing AI capacity and business value while improving infrastructure efficiency. The report highlights that as scrutiny increases from regulators and the public regarding AI infrastructure growth, sustainable scaling will be essential for a thriving AI economy.

Conclusion

Looking ahead, Seagate anticipates that the next wave of AI will generate even more data. However, mere capacity will not dictate organizational success; instead, the ability to maintain data accessibility while effectively managing the accompanying infrastructure demands will be paramount. As of now, only 38% of organizations are making strides toward full preparedness, leaving a significant gap for many.

Closing this preparedness gap will require a comprehensive infrastructure strategy that addresses the entire data lifecycle. Organizations must consider the types of data they will produce, the speed of access required for various workloads, and the operational measures that will guide their growth, thereby establishing a foundation for sustainable scaling and lasting AI value.