How AI-ready Data Centers Help GCCs Scale Enterprise AI

For years, Global Capability Centers (GCCs) could build a new application or platform, or drive a tech innovation without worrying about the infrastructure underneath it. The one-size-fits-all strategy worked just fine, and capability was never the roadblock. Those were the simpler times, but with the rise of AI, the rules are changing fast. A workload that once needed a handful of CPU servers can now require dense GPU infrastructure, high-speed interconnects, massive amounts of power, and a very different approach to cooling.

Unlike other tech innovations that had longer adoption cycles, AI has entered the mainstream almost overnight. Due to its massive potential, enterprises are aggressively betting on the technology to drive their operational capability. As a result, some enterprises have started to realize that the infrastructure supporting their AI ambitions was designed for a very different kind of workload.

When it comes to GCCs, the implications go well beyond adding more compute. AI is increasingly finding its way into product engineering, software development, customer platforms, analytics, and business operations. These workloads have different demands depending on respective business requirements and larger goals. Training requires sustained compute and predictable power. Inference needs low latency and the ability to handle sudden changes in demand. Enterprise applications also bring data security, regulatory, and connectivity requirements into the equation.

That puts the data center in a different position. It is no longer simply the place where a GCC keeps its technology running. The underlying combination of power, cooling, compute, connectivity, security, and data residency can determine how quickly an AI workload moves from development to production, and how well it performs once it gets there.

AI Changes the Infrastructure Equation

The biggest change is density. A conventional enterprise rack typically draws around 8–12 kW. AI infrastructure can push that figure well beyond 100 kW per rack as GPU clusters are packed into the same physical footprint. That changes more than the amount of power a facility needs. It affects how that power is delivered, how quickly heat can be removed, and how the entire facility is designed around the workload.

Cooling is one of the clearest examples. At higher rack densities, conventional air cooling becomes increasingly difficult to operate efficiently. Liquid and direct-to-chip cooling can move heat away from GPUs far more effectively, allowing high-density systems to run without throttling.

The network becomes equally important. AI workloads distribute processing across large numbers of GPUs, which means those systems need to exchange data constantly. Slow or congested connections can leave expensive compute capacity waiting for data. High-speed interconnects and low-latency network architecture therefore become part of AI performance, rather than something that sits around the compute layer.

Additionally, AI workloads do not all behave the same way. Model training can run for days and depends on stable power and sustained compute. Inference can create sharp changes in demand as applications move into production and user volumes fluctuate. An infrastructure environment that performs well for one workload may not be the right environment for another.

For GCCs, this changes the question they need to ask of their data center. Capacity alone tells you how much infrastructure you have. AI readiness tells you what that infrastructure can actually do.

How Sify builds AI-ready infrastructure for GCCs

Sify has built its data center infrastructure around the demands of high-density AI workloads, with support for more than 130 kW per rack and advanced liquid cooling across its nationwide AI-ready facilities. Its data centers are connected through a 120+ multi-terabit national long-distance (NLD) network fabric with four cloud onramps, giving GCCs the compute and connectivity needed to run AI workloads across environments.

Coming to regulated enterprises, Sify’s sovereign infrastructure also provides a foundation for keeping sensitive workloads and data within the country. Combined with its experience managing critical enterprise infrastructure, this gives GCCs a way to scale AI without having to solve power, cooling, connectivity, and data residency as separate infrastructure problems.

The real question is readiness

As innovation hubs, GCCs today are driving critical operations for global enterprises. As these responsibilities expand, they need AI-ready infrastructure that can keep pace with evolving workloads without letting constraints around power, cooling, connectivity, compute, or data residency become a barrier to growth.

For GCC leaders, the data center is becoming part of the AI strategy itself. The right foundation gives teams the capability and flexibility to move AI workloads into production, scale them across functions, and support them reliably as demand grows.