TL;DR
- AI training facilities operate very differently from traditional enterprise data centers.
- High-density AI workloads create dynamic power and cooling requirements that traditional infrastructure was not designed to handle.
- Many AI data centers rely on behind-the-meter power generation, effectively managing both a data center and a power plant.
- Flexible automation platforms help project teams adapt as rapidly evolving AI infrastructure projects change over time.
- Reliability and cybersecurity must be integrated from the beginning to deliver the availability AI operations require.
The rapid growth of AI is creating unprecedented demand for data center capacity. However, many people still view these facilities through the lens of traditional enterprise data centers. In reality, the infrastructure supporting AI training environments looks far more like an industrial operation than a commercial building. As developers race to build new capacity, the organizations that recognize these differences early will be better positioned to execute projects successfully and maintain long-term operational performance.
It feels as though we hear the words “data center” all the time these days. However, for people not directly involved in the AI industry, the term is often misunderstood. We’ve had data centers a very long time, but the data centers driving the training of AI and making the headlines are a very different breed of facility than the traditional enterprise data centers of the last couple decades. Aaron Crews, Emerson’s AI infrastructure expert, explores this distinction in a recent article in Automation World.
“Traditional enterprise data centers were largely static facilities. Their energy loads were relatively predictable, and their cooling requirements were much more stable. As a result, these facilities were often constructed and managed similarly to large commercial buildings.”
Traditional enterprise data centers were designed around predictable operating profiles, making them fundamentally different from today’s AI-driven facilities.
In contrast, the hyperscale data centers being used for AI training have extremely high rack density and much more dynamic workloads, which comes with an associated spike in power consumption. That power has to come from somewhere, and in many cases long wait lists for connection mean it doesn’t come from the grid. Instead, many are using behind-the-meter generation onsite, which means they’re effectively responsible for a data center and a power plant.
New Infrastructure Demands New Project Models
Because AI training facilities are so much different, they cannot be constructed the same way as their traditional counterparts. The market for building these data centers is incredibly hot, and the associated rush to get them built means that equipment availability and supply chain issues often cause projects to change many times before completion.
“Consider a modern AI factory project. Modular construction, multiple phases of execution as the project expands across a campus, ‘build while you go’ execution models, and even the potential for building onsite energy generation as a core capability are all increasingly common.”
Modern AI infrastructure projects require execution strategies that can accommodate constant change while minimizing delays and rework.
Traditional project execution has never been good at absorbing change, so new needs call for a new model. At the core of that model is an automation system that can accommodate change without added expense from redesign every time the project plan shifts.
Automation Built for Flexibility
Modern automation systems like the DeltaV™ Automation Platform and Ovation™ Automation Platform are built specifically to deliver flexibility throughout a project. Electronic Marshalling capability empowers teams to add I/O anywhere in the facility without having to redesign and rebuild cabinets. In addition, the systems feature seamless integration across the infrastructure, built on a unified data fabric provided by AspenTech Inmation™.
The seamless automation infrastructure supporting the most effective AI data center facilities also includes reliability technologies. Sensing devices like the AMS suite of solutions and critical software platforms like AMS Machine Works, AMS Device Manager, and AMS Optics also seamlessly integrate as part of a unified data fabric. For an industry like AI data centers that thrive on 99.99% availability, carefully managing reliability means delivering the highest possible ROI.
“The seamless automation infrastructure supporting the most effective AI data center facilities also includes reliability technologies.”
Reliable operations depend on visibility into asset health and infrastructure performance across the entire facility lifecycle.
Cybersecurity Cannot Be an Afterthought
AI data centers are increasingly becoming critical infrastructure. As such, they need an effective plan for securing against cybersecurity threats. The most advanced automation systems for data centers are secure by design, built around strong Zero Trust cybersecurity principles.
“The most advanced automation systems for data centers will be secure by design, built around strong Zero Trust cybersecurity principles.”
Embedding cybersecurity into foundational architecture helps organizations reduce risk while supporting long-term operational resilience.
Building the Next Generation of AI Infrastructure
To truly build out a secure, effective, reliable data center, project teams can no longer follow the path of traditional enterprise data centers. Modern hyperscale AI data centers are more sophisticated and more complex, requiring a robust, purpose-built automation architecture. Organizations that adapt early will be better positioned to manage power, reliability, cybersecurity, and project complexity while capturing competitive advantage in an increasingly important market.
