Seagate Technology has recently published its Data Infrastructure Readiness Report 2026, highlighting the growing importance of data infrastructure as enterprises move from AI experimentation to large-scale deployment. On the sidelines of this report, Rajeev Ranjan, Editor, Digital Terminal, speaks with Sameer Bhatia, Senior Regional Director for India, Middle East, Turkiye and Africa at Seagate Technology, about the report’s key findings, India’s AI readiness, rising storage demands and the infrastructure priorities shaping enterprise AI adoption.
Rajeev: What is the biggest shift in enterprise infrastructure thinking revealed by Seagate’s Data Infrastructure Readiness Report 2026 as organizations move beyond AI experimentation?
Sameer: The biggest shift is that organizations are beginning to look beyond compute and consider the broader data infrastructure required to support AI at scale. As AI moves from experimentation to wider deployment, organizations are creating and retaining more data, while the potential value of that data is also increasing.
Research for our Data Infrastructure Readiness Report 2026, conducted among 2,712 enterprise technology decision-makers across seven markets, found that 99% of organizations globally expect AI to increase their storage requirements over the next three years, with 100% of respondents in India expecting the same. The findings also show that storage is increasingly seen as strategic business infrastructure. This means organizations need to plan not only for growing data volumes, but also for the long-term value of that data. AI is not only creating more data; it is making more data worth keeping.
Rajeev: Organizations in appear confident about their AI readiness and expected returns. What do the findings reveal about their actual preparedness to scale AI infrastructure?
Sameer: Indian organizations are already seeing value from AI. Our research found that 91% of respondents in India report moderate or significant returns from their AI investments. However, only 38% globally believe they are fully prepared to meet the storage demand created by AI.
This shows that there is still work to do as AI moves into broader deployment. Organizations will need to strengthen data readiness, governance, infrastructure planning and efficiency alongside their AI ambitions. Making these decisions today will help them scale AI effectively and continue to create value from their investments over time.
Rajeev: As AI deployments generate and consume massive volumes of data, how are organizations priorities changing around data storage, management, accessibility, and long-term infrastructure planning?
Sameer: Organizations increasingly need to think about data as a long-term business asset. As AI becomes embedded across more applications and workflows, data retained today may support future learning, context, insights and innovation. This is putting greater emphasis on how organizations manage data throughout its lifecycle and ensure it remains useful over time.
The research reflects this shift. More than three-quarters of global organizations globally rank data center investment among their top three infrastructure priorities. This shows that organizations increasingly recognize the importance of the infrastructure behind AI. But investment alone is not enough. They also need to focus on the quality and readiness of their data and build infrastructure that can scale with their AI ambitions.
Rajeev: Why should organizations give equal strategic importance to data infrastructure alongside compute and GPUs when building AI capabilities at scale?
Sameer: Compute is critical to AI but compute alone cannot create lasting AI value. AI depends on large volumes of data that can be retained, accessed and reused over time. If the underlying data environment cannot support those requirements effectively, adding more compute does not solve the broader challenge.
Our research reinforces this point. Globally, data quality and readiness (53%) and storage infrastructure (43%) rank as greater challenges to AI deployment than compute availability (27%). As AI scales, organizations need to plan compute, data and storage infrastructure as interconnected parts of the same strategy.
Rajeev: How can organizations balance the growing performance and capacity demands of AI with infrastructure efficiency and long-term sustainability goals?
Sameer: Organizations need to look beyond capacity alone and consider how efficiently their infrastructure is being used. Different AI workloads have different requirements for performance, accessibility, retention and cost. Matching each workload to the infrastructure best suited to its needs can help organizations balance performance and capacity with cost and efficiency.
This is what we mean by Sustainable Scaling: increasing AI capacity and business value while continuously improving the efficiency of the infrastructure supporting it. Our research found that globally, nearly all organizations agree that extending infrastructure lifecycles significantly improves sustainability. This reinforces the importance of considering efficiency and lifecycle planning alongside capacity as organizations scale AI infrastructure.
Rajeev: Based on the report's findings, what should CIOs in India, and technology leaders prioritize today to build infrastructure capable of supporting AI at scale over the coming years?
Sameer: CIOs and technology leaders should start by treating data infrastructure as a core part of their AI strategy, not as a downstream storage decision. That means assessing how data volumes will grow, which data needs to be retained, how it will remain accessible and governed, and where infrastructure efficiency can be improved as AI adoption expands.
They should also take a workload-based approach to infrastructure planning rather than relying on a one-size-fits-all model. Our research found that globally, 43% of organizations cite storage infrastructure as a key operational constraint to AI deployment, underscoring the importance of getting the underlying data foundation right. Making these decisions today will help organizations preserve valuable data, scale AI efficiently and create long-term value from their AI investments.
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