Seagate’s Data Infrastructure Readiness Report 2026 reveals why storage, governance and long-term data strategy are becoming the foundation of AI success for Indian enterprises
As artificial intelligence moves from experimentation to enterprise-wide deployment, organizations are discovering that AI success depends as much on data infrastructure as it does on algorithms. According to Seagate’s Data Infrastructure Readiness Report 2026, Indian enterprises are showing strong confidence in their AI journeys, but the ability to manage, retain, govern and scale data effectively will determine who ultimately leads in the AI era.
In this exclusive interview with Enterprise IT World, Sameer Bhatia, Senior Regional Director for India, Middle East, Turkiye and Africa, Seagate Technology, discusses how AI is reshaping infrastructure priorities, the challenges organizations face as AI scales, and what CIOs must do today to prepare for the next phase of growth.
How is India’s growing AI adoption changing enterprise infrastructure priorities?
India’s AI adoption is moving from experimentation to broader enterprise deployment, and organizations are already beginning to see measurable business value from their investments.
Seagate’s Data Infrastructure Readiness Report 2026, which surveyed 2,712 enterprise technology decision-makers across seven global markets, found that 91% of respondents in India reported moderate or significant returns from their AI investments. This indicates that organizations are moving beyond pilots and proof-of-concepts and are increasingly integrating AI into mainstream business operations.
As AI becomes more deeply embedded across industries, infrastructure priorities are evolving rapidly. One of the clearest trends emerging from the research is the growing importance of storage infrastructure. In India, 100% of respondents expect AI to increase their storage requirements over the next three years, while also agreeing that AI is transforming storage from a traditional IT resource into strategic business infrastructure.
This shift demonstrates that enterprises are no longer viewing storage simply as a cost centre. Instead, they recognize it as a critical enabler of innovation, competitive advantage and long-term AI success.
As Indian organizations move from AI pilots to production, why is data infrastructure becoming central to long-term AI success?
As organizations expand AI from limited use cases into broader production environments, the importance of managing data effectively becomes significantly greater.
AI is not only generating more data but also increasing the value of data that organizations already possess. Businesses now need to retain, access, govern and reuse large volumes of information across multiple applications, departments and workflows.
What makes this particularly important is that data retained today may become a strategic asset tomorrow. Historical information can support future learning models, provide additional context, generate new insights, and enable innovations that may not yet have been envisioned.
“AI readiness is not just about adding more capacity. It is about using infrastructure more intelligently so organizations can scale AI efficiently and build a data foundation that can support their long-term ambitions.”
– Sameer Bhatia, Senior Regional Director for India, Middle East, Turkiye and Africa, Seagate Technology
As a result, data infrastructure becomes the backbone of sustainable AI adoption. Organizations must invest in infrastructure that can efficiently scale while ensuring valuable data remains accessible and reusable over time. The real opportunity lies in creating a foundation that supports current AI requirements while remaining flexible enough to accommodate future needs.
What does Seagate’s Data Infrastructure Readiness Report suggest about the gap between AI ambition and infrastructure preparedness?
One of the key findings from the report is that organizations are highly optimistic about AI’s potential, but infrastructure readiness still has room to evolve.
In India, 89% of respondents stated that their organizations are fully or mostly prepared for AI’s long-term data demands. This reflects a strong level of confidence among enterprise leaders regarding their readiness strategies.
At the same time, the findings suggest that preparedness cannot be viewed as a one-time achievement. As AI adoption accelerates and use cases become more sophisticated, infrastructure requirements will continue to expand.
Organizations and countries will not lead the AI era simply because they adopt AI technologies quickly. Leadership will belong to those that build resilient, scalable and future-ready data and AI infrastructure capable of supporting increasingly complex workloads and growing data ecosystems.
What infrastructure challenges could emerge as Indian organizations scale AI across more functions and workloads?
As AI extends into additional business functions, the challenge is no longer just about coping with larger data volumes.
Organizations must also ensure that data is accessible, properly governed and ready for use across a variety of AI applications. Infrastructure must support these requirements while maintaining operational efficiency and scalability.
A further challenge is the uncertainty surrounding future AI demands. Technology leaders must make infrastructure decisions today that can accommodate tomorrow’s requirements, even though those requirements are still evolving.
The reality is that data which appears insignificant today could become highly valuable for future AI models, analytical insights or entirely new business applications. Therefore, enterprises need infrastructure architectures that are adaptable and capable of evolving alongside changing workloads, expanding datasets and shifting business priorities.
How can Indian organizations balance capacity, cost, efficiency and sustainability as AI increases data growth?
The most effective approach is to design infrastructure around specific workload requirements rather than adopting a one-size-fits-all strategy.
Not every AI application requires the same level of performance, access speed or retention capability. As AI adoption accelerates across India, organizations must identify which datasets require immediate access, which should be retained for longer periods, and how each category can be managed efficiently.
Taking a workload-centric approach enables businesses to expand capacity intelligently without overinvesting in unnecessary resources. It allows organizations to align infrastructure investments with actual operational needs while simultaneously improving cost efficiency and sustainability outcomes.
Ultimately, matching infrastructure capabilities to different data requirements helps enterprises manage exponential data growth while strengthening long-term resilience and operational effectiveness.
What should CIOs and business leaders prioritise today to build data infrastructure that can support the next phase of AI growth?
CIOs and business leaders should begin by recognizing data infrastructure as a strategic component of their AI roadmap rather than a supporting technology layer.
Planning should extend beyond current AI initiatives and consider how data volumes, workloads and infrastructure needs will evolve as AI adoption scales across the organization. This requires bringing together data readiness, governance, infrastructure investments, lifecycle planning and efficiency objectives into a unified strategy.
Too often, these decisions are treated separately. However, successful AI transformation depends on aligning them closely with broader business and AI goals.
AI readiness is not merely a question of adding more storage or increasing capacity. The real focus should be on using infrastructure intelligently, ensuring that organizations can scale AI efficiently while creating a durable data foundation capable of supporting long-term innovation and growth.
Enterprise IT World Takeaway
India’s AI momentum is undeniable, but Seagate’s latest research suggests that future competitiveness will depend less on AI adoption itself and more on how effectively organizations manage the data behind it. The winners of the AI era will not simply be those deploying advanced models, but those building scalable, intelligent and resilient data infrastructures capable of supporting continuous innovation. As enterprises accelerate their AI ambitions, data readiness is fast becoming the defining factor between experimentation and sustained business value.