Data Center

How AI Is Changing Businesses' Infrastructure Strategies

The economics and operational realities of artificial intelligence are pulling compute back on-premises.

Brett Schmidt is senior vice president of commercial territory at CDW

For years, the dominant narrative in enterprise IT was clear: Move as much as possible to the cloud and shrink the data center footprint over time. That strategy made sense when workloads were predictable, elastic and relatively lightweight. Artificial intelligence has changed the equation.

AI workloads are fundamentally different from the applications that drove early cloud adoption. They are compute-hungry, data-intensive and aren’t usually susceptible to lots of ups and downs. As organizations move from experimentation to production AI, many are discovering that running these workloads exclusively in the cloud can be financially and operationally unsustainable. The result is not a retreat from the cloud but a reset of hybrid strategy.

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Why AI Is Pulling Compute Back On-Premises

There are four primary drivers of this reset. The first is cost. Training and running large models can consume massive amounts of GPU resources over extended periods. In the cloud, those costs accumulate quickly and can be difficult to predict. For steady-state AI workloads, owning infrastructure can provide more stable and often more favorable long-term economics.

The second factor is data gravity. AI systems are only as good as the data they ingest, and many enterprises have large, sensitive data sets that already reside on-premises. Moving that data back and forth to the cloud introduces latency, costs and risk. Keeping compute closer to the data improves performance and simplifies architecture.

Compliance and security considerations also loom large. Data residency, access controls and auditability are all prerequisites for cyber-resilient organizations, and highly regulated industries face strict requirements around them. Running AI workloads on-premises can make it easier to meet these obligations, particularly when dealing with proprietary or sensitive information.

The final factor is performance. For inference workloads that support real-time decision-making, latency matters. On-premises or edge deployments often deliver more consistent performance than cloud-based alternatives, especially when network conditions are unpredictable.

The Infrastructure Ripple Effects of AI

Bringing AI back into the data center is not as simple as repurposing existing infrastructure. AI places new demands on nearly every layer of the stack.

Power and cooling are immediate constraints. High-density GPU servers draw significantly more power and generate more heat than traditional systems. Many facilities were never designed for these loads, forcing organizations to rethink capacity planning and, in some cases, facility upgrades.

Networking also becomes critical. AI workloads depend on fast, low-latency interconnects to move data efficiently between compute, storage and accelerators. Storage systems must scale not just in capacity but in throughput to keep models fed with data.

At the same time, hybrid architectures are becoming more sophisticated. Organizations are designing environments that support burst capacity in the cloud for training spikes, manage model lifecycles across locations, and enable distributed inference closer to users or devices. Hybrid is no longer about static workload placement; it is about dynamic orchestration.

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What Hybrid Infrastructure Looks Like in the AI Era

The hardest part of this transition is not necessarily choosing where workloads run but managing them consistently across environments. Security policies must span on-premises and cloud resources. Observability tools need to provide end-to-end visibility into performance and cost. Finance teams want clear cost attribution for AI initiatives that may span multiple platforms.

These challenges expose a hard truth: Hybrid success in the AI era depends less on any single platform and more on orchestration, automation and operational maturity. Without strong governance and tooling, hybrid environments quickly become fragmented and inefficient.

This is where experience matters. Designing, building and operating AI-ready hybrid infrastructure requires expertise across compute, networking, facilities and cloud services.

AI is not killing the cloud, and it is not resurrecting the data center of the past. It is forcing a more nuanced, pragmatic approach to hybrid IT — one grounded in workload realities rather than ideology. Organizations that recognize this shift and invest accordingly will be better positioned to turn AI from an experiment into a durable competitive advantage.

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