AI infrastructure enters a new era as enterprises rethink data strategy

As artificial intelligence continues its rapid evolution, a fundamental shift is underway in how enterprises approach infrastructure. The rise of AI-native systems is forcing companies to move beyond legacy architectures, embracing platforms designed from the ground up to handle the scale, complexity, and performance demands of modern AI workloads.

For years, enterprise infrastructure was built around virtualisation and containerised applications, optimized for CPU-based compute and structured data. But today’s AI landscape is different. It’s GPU-driven, data-intensive, and increasingly reliant on unstructured and distributed datasets. This transformation is pushing organisations to reconsider their foundational technology choices.

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“AI-native infrastructure means building systems that are purpose-built for AI workloads — not retrofitting old ones,” said John Mao, VP of Alliances at VAST Data, in a conversation with Khaleej Times. “You need real-time performance, scalability, and manageability that legacy systems simply weren’t designed to deliver.”

The shift is not just technical — it’s strategic. Enterprises are moving from proof-of-concept experiments to production-scale deployments. This evolution brings new priorities: security, governance, cost-efficiency, and data management. And it’s prompting a reimagining of the enterprise data stack.

Traditionally siloed systems like data warehouses and lakehouses are being augmented with semantic layers that enable richer, more intelligent interactions. Technologies such as vector search, graph analytics, and metadata enrichment are becoming essential, especially for agentic AI systems and enterprise copilots that rely on contextual, real-time data.

“Latency kills in decision-making loops,” Mao emphasized. “If your agent takes 20 seconds to respond, it’s no longer useful. Sub-second performance is critical — and that demands a new kind of infrastructure.”

VAST Data, which has positioned itself at the forefront of this transformation, is helping enterprises build AI-native platforms that support high-throughput, low-latency operations across diverse workloads. The company’s approach reflects broader industry trends: treating data as code, implementing CI/CD pipelines for datasets, and prioritizing data diversity over sheer volume.

“Successful AI teams version their data, test it, and track its lineage,” Mao noted. “Because in AI, the data _is_ the application. The model is just the interpreter.”

Looking ahead, the market is expected to see increased investment in infrastructure that supports real-time, scalable AI operations. Enterprises will likely continue layering new capabilities on top of existing systems rather than replacing them outright — a strategy that balances innovation with operational continuity.

For business leaders navigating this fast-moving landscape, Mao’s advice is clear: “Start with the outcome. Focus on the value you want to deliver, then build backward. And treat infrastructure as a first-class citizen — because you’re not building a science project. You’re building a product.”

Somshankar Bandyopadhyay

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somshankar@khaleejtimes.com

Somshankar Bandyopadhyay is a News Editor with close to three decades of experience. Currently, he manages the business section, ensuring that the top economic and business news of the day reaches its readers.