• Seagate study finds 99% of firms expect AI to increase their storage requirements within three years
• Only 38% of organizations consider themselves fully prepared for future demands
• Storage infrastructure ranks among the biggest obstacles facing AI deployment

New data from Seagate has claimed AI is increasing the business importance of stored information while exposing weaknesses in infrastructure readiness.

The company’s research found 99% of respondents expect AI workloads to increase their storage requirements in the next three years.

Yet only 38% describe their organizations as completely prepared, while 43% identify storage infrastructure among the leading obstacles to wider AI deployment.

AI is raising the stakes for storage planning

The survey gathered responses from more than 2,700 technology decision-makers and found that 86% reported moderate or significant returns from AI investments.

Among those respondents, 33% said their organizations were already seeing substantial measurable financial or operational benefits from AI initiatives.

Those returns increase the importance of retaining information that can support future models, applications and decisions across changing business requirements.

Nearly 7 in 10 respondents expect storage requirements to increase by at least 26%, including 32% anticipating growth exceeding 50%.

Furthermore, 98% of respondents regard storage as strategic infrastructure rather than simply an administrative technology function.

The survey nevertheless shows that preparedness remains incomplete, with data quality and readiness identified by 53% as another major deployment difficulty.

Storage therefore ranks ahead of compute availability, cited by 27%, and energy constraints, which were identified by 24% of respondents.

The figures indicate that adding computing capacity alone does not resolve the infrastructure limitations organizations encounter when deploying increasingly data-intensive AI systems.

Sustainability is becoming part of the storage equation

Infrastructure expansion is also encountering sustainability concerns, with 77% of respondents reporting delays or changes to planned growth.

Within that figure, 36% said investments had undergone major changes, while another 41% experienced smaller postponements linked to sustainability considerations.

Power consumption was the most frequently considered environmental factor for storage hardware, cited by 62% of respondents.

Equipment lifespan followed at 55%, while respondents broadly connected longer hardware service periods with improved sustainability across data center operations.

Current confidence remains below future expectations, with 39% describing their storage operations as highly sustainable under present conditions, according to survey responses.

That proportion rises to 61% when respondents assess where their organizations expect storage sustainability to stand five years from now.

Funding levels also influence those expectations, with 70% of organizations holding sustainability budgets above $100 million expecting highly sustainable operations within five years.

By comparison, 48% of organizations with annual sustainability budgets below $1 million anticipate reaching that same level during the period.

Training material, active datasets, model checkpoints and retained outputs can impose substantially different infrastructure requirements throughout an AI system’s lifecycle.

For organizations expanding AI operations, those differences make storage architecture, equipment lifespan, energy consumption and capacity planning increasingly interconnected decisions.

The survey does not establish that every organization will face identical costs, although its findings indicate that storage growth is widely anticipated.