How Will AI and ML Surge Reshape Data Center Infrastructures by 2028?

The digital revolution, spearheaded by the burgeoning fields of artificial intelligence (AI) and machine learning (ML), is catalyzing transformative change in how data centers are built and operated. As these technologies hunger for more processing power, data center physical infrastructures (DCPI) must evolve to meet the challenge. By 2028, the value of the DCPI market is expected to surpass $46 billion, propelled by an impressive compound annual growth rate of 11%. This surge underscores the industry’s effort to amplify data centers’ capabilities to handle the intensive computational demands of AI and ML. Such a growth trajectory signals not only a technological but also an infrastructural evolution in the realm of data storage and computation, ensuring the next generation of data centers is equipped to support the advanced requirements of these cutting-edge technologies.

Power and Cooling Innovations

To keep pace with the intense requirements of AI-driven applications, data center infrastructures are undergoing significant enhancements, particularly in power and cooling systems. Rack power densities, once content with 15 kW/rack, are on the brink of a dramatic escalation, estimated to rocket to 60–120 kW/rack. This upsurge is set to transform the prevalent air-cooled heat management traditions, ushering in the era of liquid cooling solutions. These more efficient and effective cooling strategies, indispensable for managing the heat produced by high-density server racks, are expected to flourish, with revenue predictions soaring past $3 billion by 2028.

Market Dynamics and Regional Growth

The majority of growth in the Data Center Physical Infrastructure (DCPI) market is expected to be driven primarily by Cloud and Colocation services, leveraging their vast scales and operational efficiencies for expansion. While the enterprise segment may see slower growth, substantial progress is predicted within regions like Asia Pacific (sans China), North America, and EMEA. In contrast, China and Latin America are on track for more modest advancements. Dell’Oro’s Lucas Beran refers to the current phase as a “calm before the storm,” indicating a time of strategic preparations across the industry. As the sector gears up for the incoming wave of Artificial Intelligence (AI), companies are bracing for the extensive infrastructure investments required to support the swift development anticipated in the AI space. This forward-looking stance embodies the industry’s readiness to tackle the imminent challenges and seize opportunities presented by the next technological frontier.

Explore more

AI and Generative AI Transform Global Corporate Banking

The high-stakes world of global corporate finance has finally severed its ties to the sluggish, paper-heavy traditions of the past, replacing the clatter of manual data entry with the silent, lightning-fast processing of neural networks. While the industry once viewed artificial intelligence as a speculative luxury confined to the periphery of experimental “innovation labs,” it has now matured into the

Is Auditability the New Standard for Agentic AI in Finance?

The days when a financial analyst could be mesmerized by a chatbot simply generating a coherent market summary have vanished, replaced by a rigorous demand for structural transparency. As financial institutions pivot from experimental generative models to autonomous agents capable of managing liquidity and executing trades, the “wow factor” has been eclipsed by the cold reality of production-grade requirements. In

How to Bridge the Execution Gap in Customer Experience

The modern enterprise often functions like a sophisticated supercomputer that possesses every piece of relevant information about a customer yet remains fundamentally incapable of addressing a simple inquiry without requiring the individual to repeat their identity multiple times across different departments. This jarring reality highlights a systemic failure known as the execution gap—a void where multi-million dollar investments in marketing

Trend Analysis: AI Driven DevSecOps Orchestration

The velocity of software production has reached a point where human intervention is no longer the primary driver of development, but rather the most significant bottleneck in the security lifecycle. As generative tools produce massive volumes of functional code in seconds, the traditional manual review process has effectively crumbled under the weight of machine-generated output. This shift has created a

Navigating Kubernetes Complexity With FinOps and DevOps Culture

The rapid transition from static virtual machine environments to the fluid, containerized architecture of Kubernetes has effectively rewritten the rules of modern infrastructure management. While this shift has empowered engineering teams to deploy at an unprecedented velocity, it has simultaneously introduced a layer of financial complexity that traditional billing models are ill-equipped to handle. As organizations navigate the current landscape,