Research Shows AI Is Pushing Organizations to Optimize Infrastructure to Unlock Long-Term Data Value
SINGAPORE — September 14, 2026 — Seagate Technology (NASDAQ: STX) today introduced its inaugural 2026 Data Infrastructure Readiness Report, new global research examining how businesses are preparing data infrastructure to support the next phase of AI adoption.
The report points to a shift in enterprise AI planning: as AI moves from early use cases into broader deployment, data infrastructure is becoming more central to how organizations store, access, manage and use data at scale. Nearly all (99%) organizational IT leaders expect AI to increase storage requirements, but only 38% believe they are fully prepared to meet that demand.
Drawing on independent research conducted by Recon Analytics among 2,712 enterprise technology decision-makers across seven global markets, the report finds:
- Nearly nine in ten organizations (86%) report moderate or significant ROI from AI investments, including one-third (33%) reporting significant measurable ROI.
- Nearly every organization (99%) expects AI to increase storage requirements over the next three years, with nearly one-third (32%) expecting storage needs to increase by more than half.
- Data quality and readiness (53%) and storage infrastructure (43%) rank as the leading challenges to deploying AI, with storage infrastructure ranking ahead of compute availability (27%) and energy constraints (24%).
- Nearly all organizations (98%) agree that AI is transforming storage into strategic business infrastructure.
Together, the findings reveal that organizations are broadening their AI infrastructure focus beyond compute to include the data foundations needed to support rising storage requirements, improve accessibility and governance, and scale more efficiently over time. Seagate defines this approach as Sustainable Scaling: increasing AI capacity and business value while continuously improving the efficiency of the infrastructure that supports it. In practice, that means making infrastructure decisions to scale with rising AI data requirements while improving efficiency, sustainability and long-term data value.
“AI is reshaping the way organizations plan, build and operate infrastructure," said Melyssa Banda, senior vice president of Edge Storage Business at Seagate Technology. "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."
- AI Returns Are Raising the Business Value of Data
AI is already delivering measurable business value for many organizations. These returns are putting greater focus on data infrastructure as the foundation of AI.
- Nearly all organizations (98%) agree that AI is transforming storage into strategic business infrastructure.
- Nearly all organizations (99%) expect AI to increase storage requirements over the next three years, yet only 38% of organizations say they are fully prepared for AI’s long-term data demands.
As AI becomes more embedded in business operations, organizations are facing a dual shift: the volume of data they need to store and manage is rising, while the potential value of that data is also increasing. The findings suggest data is becoming more than an input; it is a long-term asset that organizations need to preserve, manage and use effectively to create business value over time.
- Data Infrastructure Is Becoming Central to AI Strategy
As AI increases storage requirements, preparing data infrastructure to support AI at scale requires more than adding capacity, yet only 38% of organizations say they are fully prepared for AI’s long-term data demands. The research shows organizations are assessing data readiness, storage infrastructure, governance, budget and AI strategy maturity as they move toward broader AI deployment.
- Only 38% of organizations say they are fully prepared for AI’s long-term data demands.
- More than three-quarters of organizations (76%) rank data center investment among their organization's top three infrastructure priorities, while one in five (20%) now consider it their single highest infrastructure investment priority.
- AI strategy maturity (16%), budget and resources (14%), and data management and governance (14%) are the leading barriers to greater preparedness.
- Data quality and readiness (53%) and storage infrastructure (43%) rank as the leading challenges to deploying AI, with storage infrastructure ranking ahead of both compute availability (27%) and energy constraints (24%).
These findings point to a broader shift in enterprise AI planning. Organizations are not only asking how to deploy AI, but how to build the data foundation needed to make AI useful, scalable and valuable over time.
- Efficiency and Lifecycle Planning Are Shaping How AI Infrastructure Scales
The survey also shows that efficiency, sustainability and lifecycle considerations are becoming a significant part of how organizations plan for AI-driven data growth. As storage requirements rise, organizations are evaluating how infrastructure can scale capacity more efficiently, extend usable lifecycle and support long term data value.
- Nearly all organizations (97%) agree extending infrastructure lifecycles significantly improves sustainability, while 94% expect their storage operations to become more sustainable over the next five years.
- Nearly eight in ten organizations (77%) have delayed or restructured AI infrastructure expansion because of sustainability or energy concerns, including 36% that have significantly restructured expansion plans.
- AI-driven energy consumption (52%) and carbon emissions from energy consumption (51%) rank as organizations' leading environmental concerns.
These findings suggest sustainability is becoming part of infrastructure strategy: not a standalone objective, but a factor shaping how organizations scale capacity, improve utilization and support growing AI data demands over time.
"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.”
For more information and to read the full research report, visit the Data Infrastructure Readiness Report 2026 homepage.
Methodology
The research was conducted by Recon Analytics on behalf of Seagate. Between May and June 2026, the study surveyed 2,712 enterprise technology decision-makers across the United States, China, India, the United Kingdom, Germany, France and Japan. The survey examined respondents’ perspectives on their organizations’ AI readiness, infrastructure investment, storage architecture, infrastructure efficiency, sustainability and long-term infrastructure planning to better understand the decisions shaping the AI infrastructure at scale. The results represent the reported views, expectations and practices of the decision-makers surveyed.
About Seagate Technology
Seagate (NASDAQ: STX) is a pioneer in mass-capacity data storage, accelerating ability to harness the full value of data. Our portfolio of advanced storage solutions helps hyperscale cloud providers, enterprises, and consumers protect, create and manage the data that powers their transformation and growth. For more than 45 years, Seagate has driven breakthrough innovations that bring sustainable, high-performance storage to the world at-scale. Learn more at www.seagate.com, and follow us on LinkedIn, YouTube, X and Facebook.
