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The definition of sustainability is being re-written in the age of AI. Yes, the current discourse that focuses on green IT considerations, including resource efficiency, carbon accounting and water use, is necessary. But it is incomplete. Sustainability in the age of AI implies sustaining the long-term flourishing of people, businesses, societies, and planetary systems together, not just minimizing energy use or carbon.

With the rapid expansion of AI adoption, data center construction is accelerating around the world. Behind this boom, however, lies a growing concern: a serious shortage of electric power, as supply struggles to keep pace with soaring demand. Nowhere is this issue more visible than in the United States.

Across all industries in the market, enterprises face rising pressure to modernize quickly, reduce risk, and enable data‑driven innovation — all while optimizing cost and simplifying increasingly complex hybrid environments. Hybrid cloud has become the operating model for this shift, yet organizations still struggle with fragmented tools and unpredictable expenses.

If you’re responsible for keeping storage reliable, secure, and cost-efficient, 2026 planning is shaping up to be uniquely challenging. A perfect storm of pressures like ongoing semiconductor constraints, concentrated manufacturing, and unprecedented AI-driven demand are reshaping day-to-day infrastructure operations. The challenges introduced by the global supply chain crunch, however, are especially risky.

For years, SAP leaders have been told a familiar story: Scale carefully. Don’t outgrow your infrastructure. Hope your next acquisition fits inside your existing SAP footprint. Behind the scenes, many SAP teams have been managing risk not by innovating, but by working around the limits of their storage platforms. CIOs, for example, are increasingly prioritizing platform consolidation, with 75% of organizations pursuing vendor consolidation as fragmented, aging architectures become harder to manage.

In an always-on industrial economy, fragmented data is a liability. Your analytics reports may look flawless, but if they’re built on data silos scattered across edge, core, and cloud, they’re built on a fault line. Data silos drive-up costs, distort the critical decisions meant to drive competition, and prevent organizations from reaching a state of data singularity — where data becomes unified, portable, and continuously usable for AI.

Most enterprises have made the commitment to AI. They’ve approved the budgets, stood up the pilots, and named it a strategic priority. So why are 95% of them getting zero return on $30–40 billion in GenAI investment? According to MIT research cited in Hitachi Vantara’s 2025 State of Data Infrastructure Global Report — which surveyed more than 1,200 IT leaders across 15 markets — the failure isn’t the model. It’s the infrastructure underneath it.

As organizations scale their AI initiatives from experimentation into production, CTOs face a pivotal architectural challenge as storage emerges as one of the most common—and most expensive—constraints. While organizations continue to invest aggressively in GPU compute, studies consistently show that infrastructure inefficiencies outside the GPU account for the majority of wasted AI spend.

Enterprises in 2026 are under increasing pressure to modernize applications, adopt hybrid cloud architectures, and streamline operations—but their expanding and aging VMware footprints have become a major obstacle. As VMware licensing models evolve and operational costs climb, reducing or restructuring this footprint has become just as critical as adopting new platforms.

Imagine being forced to buy twice the storage you'll ever use, or watch your AI workloads grind to a halt when petabyte-scale data growth from training models exhausts capacity mid-project? Many teams remember when a few well-tuned arrays and RAID groups felt like more than enough, long before AI pipelines and container sprawl started eating capacity for breakfast. And then there’s reliability.