Enterprise AI is hitting a hidden bottleneck: data storage infrastructure. While 99% of organizations expect artificial intelligence to increase their storage requirements over the next three years, only 38% say they are fully prepared to meet that demand, according to new research from Seagate. This gap between expectation and readiness reveals a critical blind spot in how companies are planning their AI deployments.

The finding comes from Seagate's inaugural 2026 Data Infrastructure Readiness Report, which surveyed 2,712 enterprise technology decision-makers across seven global markets, including the United States, China, India, the United Kingdom, Germany, France, and Japan. The research examined how businesses are preparing their data infrastructure to support the next phase of AI adoption as the technology moves from early pilots into broader, company-wide deployment.

Why Is Storage Becoming the Bottleneck for AI?

As AI becomes embedded in business operations, organizations are facing a dual challenge: the volume of data they need to store and manage is rising, while the potential value of that data is also increasing. Nearly all organizations (98%) now agree that AI is transforming storage into strategic business infrastructure, yet many are unprepared for the scale of the shift.

The research reveals that data quality and readiness (53%) and storage infrastructure (43%) rank as the leading challenges to deploying AI at scale. Notably, storage infrastructure ranks ahead of both compute availability (27%) and energy constraints (24%), suggesting that companies have been focused on the wrong infrastructure priorities. This misalignment reflects a broader shift in enterprise AI planning: organizations are not only asking how to deploy AI, but how to build the data foundation needed to make AI useful, scalable, and valuable over time.

One-third of organizations (32%) expect their storage needs to increase by more than half over the next three years, a dramatic expansion that requires strategic planning well in advance.

What Are the Key Barriers to Infrastructure Readiness?

Beyond storage capacity itself, several factors are preventing organizations from preparing adequately for AI-driven data growth. The research identified the leading barriers to greater preparedness:

• AI Strategy Maturity: Only 16% of organizations cite this as a barrier, but it remains a foundational challenge for those lacking a clear roadmap for AI deployment.
• Budget and Resources: 14% of organizations struggle with insufficient funding and staffing to support infrastructure expansion, limiting their ability to invest in new storage systems.
• Data Management and Governance: Another 14% face challenges in organizing, securing, and managing data effectively, which is essential before scaling storage infrastructure.

More than three-quarters of organizations (76%) rank data center investment among their top three infrastructure priorities, while one in five (20%) now consider it their single highest infrastructure investment priority. This shift underscores how central storage has become to enterprise AI strategy.

How Are Companies Balancing Growth With Sustainability?

Interestingly, sustainability and efficiency are shaping how organizations plan for AI-driven data growth. Nearly all organizations (97%) agree that extending infrastructure lifecycles significantly improves sustainability, and 94% expect their storage operations to become more sustainable over the next five years.

However, the pressure to scale quickly is creating tension. Nearly eight in ten organizations (77%) have delayed or restructured AI infrastructure expansion because of sustainability or energy concerns, with 36% significantly restructuring their expansion plans. AI-driven energy consumption (52%) and carbon emissions from energy consumption (51%) rank as organizations' leading environmental concerns.

"The next phase of AI will require capacity growth, but capacity alone will not be enough. It will be defined by smarter infrastructure decisions on how effectively organizations scale, organize, retain and use the data that AI depends on. The companies that create lasting value from AI will be the ones that treat data infrastructure as a business strategy," said Melyssa Banda, Senior Vice President of Edge Storage Business at Seagate Technology.

Melyssa Banda, Senior Vice President of Edge Storage Business, Seagate Technology

What Does AI ROI Look Like Today?

Despite infrastructure challenges, AI is already delivering measurable business value. Nearly nine in ten organizations (86%) report moderate or significant return on investment (ROI) from their AI investments, with one-third (33%) reporting significant measurable ROI. This success is driving the urgency to scale, but it also highlights the risk: companies seeing strong returns are likely to expand their AI deployments without ensuring their storage infrastructure can support the growth.

The challenge becomes even more acute when considering agentic AI, a more advanced form of AI that makes independent decisions and takes actions. Agentic AI workloads can increase inference transactions (the number of times the AI processes data) by 50 to 100 times compared to traditional generative AI experiences. This exponential increase in data processing demands puts enormous pressure on storage and infrastructure budgets.

Steps to Prepare Your Infrastructure for AI-Driven Data Growth

• Assess Current Data Quality and Readiness: Before expanding storage, evaluate the quality and organization of your existing data. Poor data quality undermines AI effectiveness, so cleaning and structuring data should precede infrastructure investment.
• Develop a Clear AI Strategy and Governance Framework: Define which AI use cases matter most to your business, establish data governance policies, and create accountability for AI spending and outcomes.
• Plan for Sustainable Scaling: Rather than simply adding capacity, design infrastructure that improves efficiency, extends equipment lifecycles, and reduces energy consumption while supporting growing AI data demands.
• Monitor and Measure ROI Continuously: Establish metrics for each AI initiative to track return on investment, not just component costs. This helps identify which projects deliver value and which may need adjustment.

For organizations deploying agentic AI, cost control becomes even more critical. Research shows that 61% of organizations agree that AI agent costs are a major barrier to adoption, and 42% expect to allocate $1 million or more into AI agent initiatives over the following 12 months. ServiceNow's AI Control Tower, a governance platform designed to help enterprises manage and monitor AI costs, reflects this emerging need for better visibility and control over AI spending.

The data is clear: companies that treat data infrastructure as a strategic business priority, not an afterthought, will be better positioned to scale AI effectively and capture its full value. The window to prepare is narrowing, and the cost of falling behind is rising.