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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.

Organizations invest in IT infrastructure with one expectation: results, delivered fast. But when deployments drag on, the fallout goes far beyond slipping timelines. Slow rollouts can erode ROI, heighten operational risk, and strain already‑stretched IT teams. They also keep organizations from innovating at the speed today’s market demands—and the true cost of those delays often isn’t obvious until performance starts to suffer.

Artificial intelligence workloads are reshaping how memory is produced, priced, and prioritized. Not because the supply chain has fundamentally broken, but because manufacturers are making deliberate decisions about where to place capacity and capital. Wafer lines are being steered toward high-margin, long-term AI demand, not toward broad, undifferentiated expansion. HBM, advanced DRAM, and other AI-optimized memory now command the majority of investment and forward planning.

In every industry, leaders are facing the same reality: mission critical applications and databases can’t run on yesterday’s infrastructure. When core systems slow down, the entire business feels the drag from customer experiences to employee productivity to innovation velocity, VSP One is the chosen solution for many wanting to turn data strategy into business momentum. Here’s how two organizations turned their mission critical applications and databases into engines of innovation.

We’ve entered a new era where AI is accelerating every part of business—innovation, decision‑making, and unfortunately, cyberthreats. That means right now is the most critical moment for IT and business leaders to strengthen resilience. The window for “getting ahead of risk” is no longer measured in months or years; it’s measured in minutes.

When you look at the pace of change in industrial AI, one truth stands out: no single company can do it alone. The complexity of power grids, transportation networks, and manufacturing systems demands collaboration — and that’s exactly what analysts are watching closely.

NVIDIA just announced NVIDIA Nemotron 3, a new family of open models, datasets, and libraries designed to support long-context reasoning and multi-step AI workflows. With the ability to work across enterprise ecosystems, this family of models empowers enterprises to build and deploy reliable multi-agent systems at scale, offering an important set of technologies at a pivotal moment in AI evolution.