Sameer Bhatia, Senior Regional Director for India, Middle East, Türkiye and Africa at Seagate Technology, examines the growing demands of AI-driven data, the importance of storage infrastructure and the need for sustainable scaling
Artificial intelligence is rapidly reshaping enterprise infrastructure priorities, placing unprecedented demands on data storage, accessibility and long-term management. While organisations are beginning to realise measurable returns from AI investments, their infrastructure readiness has yet to keep pace with growing ambitions.
Seagate Technology’s Data Infrastructure Readiness Report reveals that 86% of organisations are experiencing moderate or significant returns from AI investments, while 99% anticipate increased storage requirements over the next three years. However, only 38% consider themselves fully prepared to manage AI’s long-term data demands, highlighting a significant gap between adoption and infrastructure preparedness.
With data quality, storage capacity and governance emerging as critical challenges, enterprises are increasingly recognising the importance of looking beyond computing power to build scalable, efficient and resilient data foundations. The report also highlights a growing emphasis on sustainable infrastructure strategies that balance performance, cost, capacity and environmental responsibility.
Sameer Bhatia, Senior Regional Director for India, Middle East, Türkiye and Africa at Seagate Technology, discusses the evolving role of storage in enterprise AI strategies, the need for workload-aligned architectures and how Seagate’s Sustainable Scaling approach can help organisations address expanding data requirements while supporting long-term business value and sustainability objectives.
Interview Excerpts
What does Seagate’s Data Infrastructure Readiness Report reveal about the gap between enterprise AI ambitions and infrastructure preparedness?
Organisations are already seeing value from AI. Eighty-six percent report moderate or significant returns from their AI investments, while 99% expect AI to increase their storage requirements over the next three years. However, only 38% describe themselves as fully prepared for AI’s long-term data demands. As AI moves from early use cases into broader deployment, data infrastructure is becoming more central to how organisations store, access, manage and use data at scale. AI is not only creating more data, it is also increasing the potential value of that data. This makes the ability to retain, access, govern and reuse data an increasingly important business capability. That shift is already reflected in how organisations view storage, with 98% agreeing that AI is transforming it into strategic business infrastructure.
Why must organisations look beyond compute when developing infrastructure strategies for AI at scale?
Compute is an important part of AI infrastructure, but our research shows that the challenges organisations face as they scale AI extend well beyond compute. Data quality and readiness is a leading challenge to AI deployment (53%), followed by storage infrastructure (43%). Both rank ahead of compute availability (27%). These findings show that organisations need to broaden 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.
How should cloud and data-center strategies evolve to manage the growing volume, quality and retention demands of AI data? Cloud and data center strategies need to account for the full lifecycle of AI data. Nearly every organisation surveyed expects AI to increase storage requirements over the next three years, with 32% expecting those requirements to grow by more than 50%. AI is not only creating more data; it is making more data worth keeping. Data retained today can support future learning, context, insights and innovation. This makes the ability to retain, access, govern and reuse data increasingly important as AI scales. Organisations therefore need to understand what data they will create, how quickly it needs to be accessed, how long it may retain value and how infrastructure can support that growth efficiently.
The focus should be on building a data foundation that can evolve with AI while keeping valuable data available and ready for use over time.
Why do different AI workloads require distinct approaches to storage performance, accessibility and cost?
Different data and workloads have different requirements for performance, accessibility, retention, capacity and cost. Training data, active datasets, inference context, model checkpoints, source content and retained outputs do not all place the same demands on infrastructure. This is why organisations need a workload-aligned approach to storage architecture. Rather than applying a single architecture across the entire data lifecycle, they can align storage technologies to the requirements of different workloads, balancing performance, capacity, efficiency and long-term value. As AI scales, this approach can help organisations improve efficiency, lower operational costs and build a more resilient foundation for long-term growth.
How can Seagate’s Sustainable Scaling model help organisations expand AI infrastructure efficiently while supporting long-term business and sustainability goals?
Seagate defines Sustainable Scaling as increasing AI capacity and business value while continuously improving the efficiency of the infrastructure that supports it. In practice, this means making infrastructure decisions that can support rising AI data requirements while improving efficiency, sustainability, and long-term data value. Lifecycle planning is an important part of this approach. Nearly all organisations (97%) agree that extending infrastructure lifecycles significantly improves sustainability, while 94% expect their storage operations to become more sustainable over the next five years. Sustainable Scaling is therefore not about treating sustainability as a standalone objective. It is about scaling capacity while improving how efficiently infrastructure uses power, space and other resources, and extending infrastructure lifecycles where possible. The aim is to support growing AI data requirements while creating lasting value from the infrastructure organisations invest in.
Source: Tahawul Tech