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Recent headlines are impossible to ignore. AI adoption is driving an enormous surge in demand for energy to power data centers and the storage systems operating within them. Energy constraints have become a primary bottleneck for data center development, with long grid connection queues and capacity backlogs.

Living in South Florida, I've spent a lot of my career talking to customers about disaster recovery through the lens of hurricanes. Those conversations are easy because everyone understands the threat. We can watch a storm develop for days. Weather stations track every shift in direction. Data centers activate contingency plans. Business continuity teams prepare for impact.

As enterprises race to adopt AI, weak data foundations are preventing more than half (58%) of organizations in the United States and Canada from realizing value and contributing to an estimated $108 billion in wasted global AI investment each year, according to a report from Hitachi Vantara. The reason is rarely bad models or lack of ambition.

Most enterprises are spending on AI faster than they’re becoming ready to run it. Over the next two years, IT leaders plan to grow their AI investment by 70%, according to our 2025 State of Data Infrastructure Global Report. The capability keeps arriving. The readiness to operate it does not keep pace.

Data center teams are skilled at solving familiar problems such as storage outages, missed forecasts, and late refresh cycles. These are known quantities. Teams have playbooks for them. But 2026 has brought a different kind of pressure. After years of enterprise AI investment concentrated almost entirely on model training, the industry has crossed a threshold: the workload that now defines AI infrastructure isn’t building models. It’s running them. Continuously. At scale. Every day.

AI runs on data, and global enterprises are awash with petabytes of data. That might suggest that it’s easy for companies to advance their businesses through the power of AI. Yet enterprise data is often fragmented across departmental and technological silos, and that data is often inconsistent, ungoverned and disconnected from mission-critical systems. As a result, many AI initiatives stall before they can deliver operational value, and the root cause is rarely the model.

Hybrid cloud was meant to simplify IT — but for many organizations, it has done the opposite. As data spreads across on-premises systems, multiple clouds and edge environments, complexity (not flexibility) has become the defining challenge. With AI initiatives now dependent on distributed, high-quality data, this complexity directly impacts performance, governance, and cost. The lack of a unified view and thereby management of data is the biggest issue spurred by complexity.

For years, enterprise infrastructure treated security, storage, networking, and compute as mostly separate operational layers. Disruptive technologies tend to blur those lines. We saw this with backup and recovery, virtualization, and now with AI.

Modern infrastructure transformation should accelerate innovation — not add complexity. As organizations modernize to support mission-critical workloads, hybrid architectures, AI data activation, and third-party environments, they need flexibility, visibility, and trust. That’s why Hitachi Vantara is simplifying infrastructure acquisition and management by delivering an outcome-driven experience for the data center.

There is no shortage of ambition when it comes to AI. It shows up in every boardroom conversation, every strategy document, every budget cycle where AI is no longer a novelty project but a line item with real expectations attached to it. Yet, very few organizations actually execute AI in a consistent, repeatable way that’s tied to reliable business outcomes. The problem with readiness is that we tend to treat it like a milestone: something you reach and then move on from.