Systems | Development | Analytics | API | Testing

Sustainability often gets treated like a policy or a pledge. But real progress comes from concrete operational habits that IT teams can adopt directly and measure over time. The urgency is growing. The International Energy Agency projects that global data center electricity consumption will more than double to around 945 TWh by 2030, with AI the most important driver of that growth. That makes efficiency not only a sustainability goal, but an operating requirement.

As enterprises scale AI initiatives from the core data center to the edge, many organizations are thwarted by weak data practices and fragmented systems. The attempt to use a one-size-fits-all architectural approach for workloads that have fundamentally different needs is no longer a solution. If your AI ambitions are outgrowing your storage: the solution isn’t just adding more capacity, but matching the right architecture to the right stage of the AI lifecycle.

The next real advantage in artificial intelligence will not come from the next AI tool or application. It will come from a stronger data foundation beneath it. At Hitachi Vantara we work every day with customers on the data supporting their systems. That vantage point has led me to a simple conclusion: the leaders who pull ahead will not be the ones with the most advanced AI. The leaders will be those whose data foundations are strong enough so that AI can be trusted to act.

Ask any storage team what has changed over the last five years, and you'll hear a version of the same answer: everything grew and became more complex all at once. More applications, more data, more platforms, more places for a problem to hide. Complexity outpaced the teams meant to manage it. The staffing math makes it worse. Two-thirds of data center operators now struggle to hire or retain qualified staff.

SAP environments are built to support the business as it grows. Hitachi Vantara’s Virtual Storage Platform One (VSP One) Block High End is certified by SAP to support up to 1,008 SAP HANA nodes. That certification gives organizations a validated benchmark for the scale the platform can support, providing greater confidence as they consolidate, modernize and grow large SAP environments. But scale is only part of what mission-critical SAP systems require.

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.