The data storage industry is constantly pushing boundaries. We demand speed, efficiency, and reliability. But how do we truly measure the distance between “good enough” and “mission-critical”? In our world, that distance is measured in 9s. And the cost is certainty. You've likely heard your cloud providers talk about the industry standard for availability. For many, this has become a synonym for “five 9s” (99.999% uptime). On paper, that sounds impressive, right?

As enterprises navigate rising virtualization costs and increasing infrastructure complexity, many are rethinking their approach to modernization. One organization leading this transformation is Alior Bank, a forward-looking financial institution that successfully modernized its IT environment to improve agility, resilience, and cost efficiency.

For the modern IT leader, managing a hybrid cloud often feels like navigating a series of operational constraints rather than executing a strategy. You’re caught between the board’s demand for immediate AI results with disparate data silos, rising egress costs, inflexible consumption models, overworked employees, and the looming impact of hardware refresh cycles. There’s a constant friction between the agility of the cloud and the resilience of your on-premises core.

Enterprises are facing one of the most significant infrastructure pivots in a decade. Between rising AI adoption, escalating data‑sovereignty requirements, and the industry‑wide shift away from legacy virtualization stacks, organizations are under pressure to move faster—without compromising resilience, control, or budget. Recent industry data underscores this urgency.

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.