Seagate report: 99% expect AI storage surge, only 38% prepared
- Seagate's 2026 report finds 99% of IT leaders expect AI to increase storage needs over three years
- Only 38% of organizations say they are fully prepared for AI's long-term data demands
- Data quality (53%) and storage infrastructure (43%) are the top challenges to AI deployment
- 77% of firms have delayed or restructured AI infrastructure plans due to sustainability concerns

*this image is generated using AI for illustrative purposes only.
Seagate Technology’s inaugural 2026 Data Infrastructure Readiness Report reveals a significant preparedness gap in enterprise AI planning. While 99% of organizational IT leaders expect artificial intelligence to increase storage requirements over the next three years, only 38% believe their organizations are fully prepared to meet that demand.
The research, conducted by Recon Analytics among 2,712 enterprise technology decision-makers across seven global markets including India, the US, and China, highlights a shift in focus from compute power to data foundations. Organizations are increasingly viewing storage not just as capacity, but as strategic business infrastructure essential for long-term data value.
AI Returns and Storage Challenges
The report indicates that AI is already delivering measurable business value, with 86% of organizations reporting moderate or significant ROI from AI investments. One-third (33%) reported significant measurable ROI. Despite these returns, infrastructure bottlenecks persist.
Data quality and readiness emerged as the leading challenge to deploying AI, cited by 53% of respondents. Storage infrastructure followed closely at 43%, ranking higher than compute availability (27%) and energy constraints (24%). Nearly all organizations (98%) agree that AI is transforming storage into strategic business infrastructure.
Sustainability and Efficiency Drivers
Sustainability concerns are actively shaping infrastructure expansion plans. 77% of organizations have delayed or restructured AI infrastructure expansion due to sustainability or energy concerns, with 36% significantly restructuring their plans.
Environmental priorities are clear, with AI-driven energy consumption (52%) and carbon emissions from energy consumption (51%) ranking as the leading environmental concerns. Consequently, 97% agree that extending infrastructure lifecycles improves sustainability, and 94% expect their storage operations to become more sustainable over the next five years.
What the Numbers Show
The data reveals a divergence between perceived strategic importance and operational readiness. While 98% of organizations view storage as strategic infrastructure, only 38% claim full preparedness for long-term data demands. This suggests that while the business case for data infrastructure is widely accepted, the execution capabilities—particularly regarding data quality (53%) and storage architecture (43%)—lag behind strategic intent. The high rate of restructuring due to energy concerns (77%) further indicates that efficiency is becoming a constraint on scale, rather than just an optimization goal.
How will the 62% gap between AI storage expectations and organizational preparedness impact enterprise IT budget allocations for 2026?
What specific architectural innovations or hybrid-cloud strategies are emerging to resolve the data quality and storage infrastructure bottlenecks cited by over 50% of leaders?
To what extent will sustainability-driven restructuring of AI infrastructure delay the deployment timelines for generative AI models in energy-constrained regions?

































