Data Infrastructure Readiness Report 2026

AI demands are outpacing infrastructure readiness. New global research commissioned by Seagate among more than 2,700 enterprise technology decision-makers found that 99% expect AI to increase storage requirements over the next three years, yet only 38% say they are fully prepared.

This readiness gap is becoming more consequential as AI enters a new phase. The first era of AI was defined by breakthroughs in models and compute. The next era will be shaped by the data infrastructure decisions required to scale AI efficiently, sustainably and in ways that unlock long-term value. Organizations are confronting a new reality: AI success depends on infrastructure innovations, with data now a strategic business asset rather than a cost center.

Against this backdrop, the research explored several dynamics organizations are navigating as they prepare for the future of AI infrastructure:

• AI-driven data growth
• AI infrastructure readiness
• Infrastructure investment priorities 
• Sustainability and operational efficiency
• Storage strategy and long-term scalability

Underscoring a critical need for continued investment in data strategy, governance and the systems required for AI success, these dynamics define a new imperative: sustainable scaling.

Sustainable scaling is the ability to increase AI capacity and business value while continuously improving the efficiencies of the infrastructure that supports it. As policymakers, regulators and the public place more scrutiny on the growth of AI infrastructure, sustainable scaling will play a critical role in the long-term viability of a robust and healthy AI economy.

The five strategic shifts that follow illustrate how organizations are putting sustainable scaling into practice and where the greatest opportunities and challenges lie.

AI is already producing measurable business value. Eighty-six percent of respondents describe their organization’s return on AI investments as moderate or significant, including 33% who report significant measurable returns. Those results raise the stakes for the data that makes AI possible. When models, applications and decisions depend on data, storing it is no longer a background function. It becomes part of the organization’s ability to improve AI outcomes over time. The pattern is broadly shared across markets, with moderate or significant returns reported in the US (87%), China (91%), India (91%), Japan (75%), France (82%), the UK (86%) and Germany (82%).

The expected growth is substantial. Nearly all respondents expect AI to increase storage volume requirements within three years. Seven in 10 anticipate an increase of at least 26%, including 32% who expect requirements to rise by more than half. The expectation is nearly universal across markets, ranging from 98% in Japan to 100% in India, France, the UK and Germany, with the US and China both at 99%.

The implication is clear: organizations must plan for both more data and more value embedded in that data. A short-term approach focused only on immediate capacity can leave information fragmented, inaccessible or expensive to reuse. A longer-ranging view can connect retention, access and governance decisions to the future value the organization expects AI to create.

That shift is already visible in how leaders describe storage. Ninety-eight percent agree that AI is transforming data storage from a back-office function into strategic business infrastructure. The finding signals a change in priority: storage decisions now directly influence how quickly organizations can put data to work and how well they can preserve its value. This view remains consistent across markets, ranging from 96% in Japan and Germany to 100% in India, with the US and the UK at 99%, China at 98% and France at 97%.

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Most organizations express confidence in their direction, yet a closer look reveals more nuanced reality. While 83% say they are fully or mostly prepared for AI’s long-term data demands, only 38% consider themselves fully prepared. Perceived preparedness varies widely by market, from 96% in China to 55% in Japan, 89% in India, 88% in the UK, 86% in both the US and Germany and 74% in France.

The findings suggest that AI readiness depends on more than compute capacity or investment alone. Respondents identify AI strategy maturity, budget and resources, data management and governance as their most common gaps. Deployment challenges tell a similar story. Among key operational constraints, 53% cite data quality and readiness and 43% cite storage infrastructure, compared with 27% for compute availability and 24% for energy constraints.

Respondents cite data quality and readiness (53%) and storage infrastructure (43%) as the leading AI deployment challenges, ahead of compute availability and energy constraints.

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Respondents cite data quality and readiness (53%) and storage infrastructure (43%) as the leading AI deployment challenges, ahead of compute availability and energy constraints.

The readiness gap is ultimately a data foundation challenge. As a result, organizations need a coordinated data foundation that aligns data retention, governance, storage and compute to scale AI effectively.

Organizations increasingly view data center investment as a strategic business priority rather than a necessary operational expense. More than three-quarters rank it among their top three infrastructure priorities and one in five identify it as their single highest priority.

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The spending outlook also shows that AI infrastructure extends beyond GPUs. Security and compliance infrastructure top the list of organizations’ investment priorities (44%), with data management/governance ranking second (43%). AI and GPU infrastructure and storage hardware refresh tie for the third largest investment priority (39%).

The strategic question is therefore not which single layer in the stack receives the largest budget. It is whether the full infrastructure can support the flow of data into AI systems, preserve data that may gain value later and scale without creating new operational constraints. Investment creates more value when organizations evaluate these dependencies together.

Energy and sustainability considerations are already affecting AI infrastructure planning. Seventy-seven percent of respondents say their organization has delayed or restructured an expansion because of these concerns. That includes 36% that significantly restructured planned investments and 41% that experienced minor delays.

This finding turns efficiency from a future ambition into a current planning constraint. Organizations need to add capacity, but they also need to understand the power, space, cooling and equipment lifecycle implications of that growth. The most useful decisions are the ones leaders can connect to measurable operating requirements.

Respondents expect progress. Thirty-nine percent describe their storage operations as ‘very sustainable’ today. That share rises to 61% when they consider where their operations will be in five years. Expectations also vary with available investment: 70% of organizations with annual sustainability budgets of $100 million or more expect their operations to be ‘very sustainable’ within five years, compared with 48% of those with budgets below $1 million.

Organizations increasingly define and measure sustainable infrastructure through operational efficiency rather than environmental reporting alone. Power consumption is the most frequently considered factor when they assess the environmental footprint of storage hardware, cited by 62%. Device lifespan and durability follow at 55%. Nearly all respondents also agree that extending the lifecycle of data storage equipment has a significant effect on the sustainability of data center operations.

These findings support an operational view of sustainable scaling. Rather than relying on broader environmental language, organizations can evaluate specific infrastructure choices through power use, capacity utilization, equipment lifespan and energy cost per terabyte. Those measures make it easier to connect sustainability goals with the day-to-day economics of infrastructure.

As AI increases the quantity, variety and value of data that organizations need to succeed and grow, the business models underpinned by it are increasingly being shaped by the underlying data infrastructure. Equally important as adding capacity, is optimizing the economics and operational efficiency of growth. This means applying efficiency-led principles to infrastructure design that enable organizations to scale capacity while continuously improving resource utilization, profit margins and long-term return on investment.

A shift toward efficiency-led infrastructure is reflected in the operational practices organizations are adopting:

• 52% use AI to determine storage and operational needs (US: 48%, China: 55%, India: 62%, Japan: 54%, France: 47%, UK: 53%, Germany: 42%).
• 46% invest in renewable energy sources (US: 44%, China: 41%, India: 58%, Japan: 37%, France: 45%, UK: 49%, Germany: 47%).
• 40% deploy power based on actual data needs rather than running infrastructure continuously (US: 34%, China: 44%, India: 48%, Japan: 41%, France: 29%, UK: 41%, Germany: 40%).
• 31% reuse heat generated by data infrastructure (US: 26%, China: 37%, India: 32%, Japan: 24%, France: 38%, UK: 31%, Germany: 33%).

The same efficiency-led thinking is also increasingly shaping storage architecture. A workload-aligned approach is one example of this shift. Rather than applying a single architecture across the entire data lifecycle, organizations can design systems that balance performance, capacity, efficiency and long-term value according to workload requirements. This creates a more practical framework for scaling AI while controlling infrastructure costs and resource demands.

AI environments encompass diverse data types with different performance, capacity and access requirements. Training data, active datasets, inference context, model checkpoints, source content and retained outputs do not all place the same demands on infrastructure. As AI scales, efficiency-led design becomes increasingly important. Organizations that align storage technologies to workload requirements within intelligent, multi-tiered architectures can improve economic efficiency, lower operational costs and build a more resilient foundation for long-term growth.

The next phase of AI will create more data, but capacity alone will not determine which organizations capture its value. The differentiator will be the ability to keep data available and ready for use while efficiently managing the infrastructure demands that come with growth.

The survey shows that this work is already underway. Organizations are prioritizing data center investment, reassessing the role of storage and factoring efficiency and lifecycle considerations into expansion plans. Yet the gap between being mostly prepared and fully prepared remains wide.

Closing that gap requires an infrastructure strategy built around the full data lifecycle. Organizations need to understand what data they will create, how quickly different workloads need to access it, how long it may retain value and which operational measures will guide growth. Those decisions provide the foundation for sustainable scaling and for lasting value from AI.