Not that long ago, discussions about AI were theoretical, experimental and speculative, with enterprises thinking and talking about proofs of concept and future possibilities. Today, however, AI is fast becoming the cornerstone of business transformation worldwide.
The technology is being used to do everything from automate routine tasks to generate new ideas for products and services, and its impact is only expected to accelerate.
According to McKinsey’s State of AI report, 65% of organizations worldwide had already integrated AI into at least one business function as of last year (up from 50% in 2023). Meanwhile, IDC estimates that global data creation will reach 175 ZB this year, driven heavily by AI workloads, machine learning and real-time data processing.
As the data center market explodes, AI will be the key growth driver. It’s reshaping the physical infrastructure that supports digital transformation. As AI workloads make their way to your data center, is your infrastructure ready for them?
AI in the data center: disruption in real time
Modern AI applications are pushing the limits of current data center design. From managing internal workloads that use machine learning algorithms to improving energy efficiency and security through predictive models, AI is driving a new level of operational intelligence.
Powering this transformation are high-density data centers equipped with GPU clusters capable of managing massive parallel workloads for model training and inference.
But this isn’t a one-size-fits-all transition. Each region, organization and facility has a different pace of adoption, which makes it essential to understand how AI data centers are evolving.
AI data center infrastructure: a global perspective
While the data center industry is maturing rapidly, AI adoption varies significantly by region. For example:
- North America owns more than 40% of the global data center market share, with capacity expected to increase by 2.5 times in the next few years.
- Countries like Ireland, Denmark and Germany are emerging as data center hubs, driven by favorable tax policies, robust connectivity and a focus on sustainability.
- The Asia Pacific region is expected to register the highest growth rate (CAGR of 13.3% from 2025 to 2030), led by China, Japan, India and Singapore.
As these ecosystems evolve, so must the underlying infrastructure.
3 phases of deploying AI-driven data centers
Integrating AI into data center operations typically unfolds in three phases:
- Data preparation: During this phase, AI gathers data from various resources like databases, APIs, logs, images, videos, sensors and other sources that may or may not be in real time. These data are then tagged/labeled; errors are removed and converted to a format that AI models understand. This is the base of model accuracy and performance.
- Training: AI systems start teaching the AI model on how a task needs to be completed by using the data preparation phase. The neural network of the AI model learns the data, its composition, its pattern and its relationship. This is also known as the deep learning phase. This phase demands GPU-rich, high-density data center environments to process AI workloads with minimal latency.
- Inference/autonomy: This is where the AI model starts integrating seamlessly with external ecosystems, the outer world with new data. This is where the final decisions and predictions are made. This is also where AI infrastructure will require cabling, real-time data feeds and deeply integrated systems.
Overcoming infrastructure challenges to support AI-driven data centers
To achieve AI autonomy, several foundational challenges must be addressed.
1. Port density and rack space
AI workloads often rely on GPU clusters interconnected via high-speed, low-latency links. This leads to high port density, significantly increasing space and cooling requirements. Traditional rack designs can’t keep up. Without purpose-built infrastructure, the very hardware meant to accelerate AI can become a bottleneck.
2. Cable media selection
Choosing between copper and fiber is no longer a technical debate—it’s a strategic one. AI networks demand high bandwidth and low latency over longer distances. Fiber is generally preferred in high-performance environments, but only when properly planned and installed. Missteps here can lead to signal degradation and performance loss, especially in high-noise, high-interference areas.
3. Convergence of IT and BAS/BMS
AI-enabled data centers require seamless, real-time integration across all building systems, making the convergence of IT with building automation systems (BAS) and building management systems (BMS) essential.
However, this integration is often hindered by legacy infrastructure, disparate control protocols and overlooked gray space areas housing critical support systems like UPS units, chillers, power distribution and HVAC controls.
For AI to optimize energy, cooling and security in real time, these gray space components must be unified and reliably interconnected through well-planned cabling. Otherwise, fragmented oversight and poor connectivity can lead to significant performance and downtime issues.
The case for action: build smart, build now
There is no time for delay. As AI continues to permeate business models, customer expectations and digital workflows, data centers must keep pace. Addressing these challenges is non-negotiable for long-term competitiveness.
The infrastructure decisions you make today will determine whether your data center can scale with the rapid evolution of AI technologies tomorrow.
Modernizing for AI means futureproofing your infrastructure. Our complete connection solutions offer a comprehensive portfolio engineered for the most demanding AI data center environments.
Are you ready to design an AI-ready data center that performs under pressure? We’ll help you build infrastructure that powers the future.
Learn about Belden data centers.
Related links:
- Data center resilience is about evaluating risks and adapting practices
- Introducing ANSI/TIA-942-C: Recent Updates to Data Center Standards
- Data Center Trends Driven by Faster Speeds and More Bandwidth