As artificial intelligence moves into mainstream enterprise operations, storage is no longer a passive back-end function. It is rapidly evolving into a high-performance foundation that supports mission-critical operations, accelerates decision-making, and enables the next phase of digital transformation. In an exclusive conversation with Digital Terminal, M G Rejith, Head of HIT & DWP Service Line, Global Delivery Unit at Fujitsu, shares how enterprise storage strategies are being reshaped in 2026 to meet the demands of AI-driven organizations.
From Data Lakes to Active AI Lakehouses
Highlighting a major trend in enterprise storage, Rejith said, “One of the key trends is the transition from traditional data lakes to active AI lakehouses, combined with data mesh principles. Earlier data lakes often turned into data swamps, limiting usability. In 2026, storage is increasingly data-product centric, with business domains owning their storage stacks and operating against clearly defined SLAs.”
“Performance is another major driver. Technologies such as NVMe over Fabrics and GPU-Direct Storage are being adopted to remove bottlenecks in training and real-time inference. By allowing GPUs to access data directly from storage and bypass the CPU, enterprises can achieve the sub-millisecond access needed for multi-step AI workflows,” Rejith explained.
“Storage management itself has become automated and predictive. AI-driven tiering now moves data dynamically between hot and cold tiers based on upcoming training needs. As a result, CIOs are tracking “cost per token” as a core efficiency metric, reinforcing the importance of optimized and automated data placement,” he added.
Security and resilience are also central to modern storage strategies. In 2026, immutable storage has become mandatory to protect against autonomous ransomware, while quantum-safe encryption is moving into planning as enterprises prepare for the next phase of data security,” Rejith noted.
He summarized the impact of these trends by saying, “Together, these trends are reshaping storage from a capacity-led function into a strategic capability aligned with performance, efficiency, and trust.”
Architecting Storage for the Edge and AI Workloads
Rejith emphasized that CIOs must rethink storage architecture to fully enable digital transformation. “To power the next phase of digital transformation, CIOs need to fundamentally reimagine enterprise storage across three critical pillars,” he said.
The first pillar focuses on inference at the edge. “First, CIOs must architect for inference at the edge. By 2026, nearly 75% of enterprise data will be processed at the edge. Instead of pulling all data into a central cloud, organizations should deploy micro-storage at the edge to support local AI agents, while synchronising only refined insights to the core,” Rejith explained.
The second pillar centers on Zero-Trust data governance. “Second, storage must become an active enabler of Zero-Trust data governance. As AI agents gain greater autonomy, the storage layer becomes the final line of defence. Implementing object lock and cryptographically verifiable retention ensures that every access request is validated, positioning storage as a core component of the Zero-Trust architecture,” he said.
“Finally, CIOs need to eliminate storage silos through universal APIs. AI depends on diverse data, and silos remain a leading cause of AI project failure. Moving toward vendor-neutral, S3-compatible storage across hybrid environments allows AI models to seamlessly access data across on-premises, public cloud, and sovereign cloud environments—without costly migrations or egress challenges,” Rejith noted.
Building the Foundation for AI-First Enterprises
Summing up, Rejith emphasized the strategic impact of these changes. “Together, these shifts enable storage to move from a passive backend function to a strategic foundation for digital transformation,” he concluded.
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