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CIOs are being asked to accelerate AI adoption, manage cyber risk, and ensure their businesses remain resilient when facing disruption — a set of demands that leaves a whopping 91% of CIOs with moderate or high stress, according to a survey by cybersecurity platform SAFE. To maintain these responsibilities sustainably, modern enterprises must increasingly be built on a foundation of data that drives AI-powered business processes. For CIOs, that means new risks must be faced.

Most mainframe storage refreshes begin with a notice. A support statement gets revised, a lifecycle date is published, a lease comes due, or a capacity report shows the estate crossing a threshold two years earlier than anyone planned for. A purchase that had been sitting comfortably in a future budget cycle now has to close in this one. Under deadline pressure, the instinct is to buy the current generation of the platform you already run.

During the past year, AI has transformed the infrastructure market. Organizations are rapidly expanding data center and cloud capacity to support new AI workloads, while facing growing pressure to manage cyber risk, control costs, and demonstrate measurable business value. As infrastructure investments increase, technology leaders must balance innovation with operational efficiency and financial accountability.

Corporate sustainability has no shortage of ambition. Over the past several years, companies have set increasingly ambitious climate targets, expanded disclosures, and invested significantly in understanding and measuring their environmental impact. But working in the technology infrastructure industry has made one thing clear to me: Targets don't reduce emissions. Decisions do.

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.