What does the Designing Infrastructure for Enterprise AI guide actually help companies decide?

It helps business, IT and facilities leaders determine where AI workloads should run and whether the available physical infrastructure can support them, using a workload-based framework and reference designs.

What are the four physical infrastructure models in the guide?

They are Edge AI Cluster, Small AI Cluster, Large AI Cluster and High-Density AI Cluster, each a starting point for decisions on power, cooling, controls, connectivity, resilience and future growth.

Where can I find the full guide?

It is available at https://www.vertiv.com/en-us/insights/industry/designing-infrastructure-for-enterprise-ai/.

Top Companies' AI MovesAI Business & IndustryTop Companies AI1d agoOct 15, 2026, 06:30 JST

Vertiv, Deloitte Center for Integrated Research launch AI infrastructure guide

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3 Key Points

What happened
Vertiv and the Deloitte Center for Integrated Research introduced a collaboration and released the Designing Infrastructure for Enterprise AI guide, developed with STL Partners, built on six workload archetypes and four physical infrastructure models.
Why it matters
The guide gives business, IT and facilities leaders a workload-based framework to evaluate deployment location, power, cooling, connectivity and facility readiness before committing to infrastructure, so constraints surface earlier.

WHO IT HITSEnterprise IT and facilities teams planning AI deployments, along with data center operators and colocation providers, gain a structured way to test whether existing sites can host production AI workloads.

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Context & Analysis

The guide arrives as enterprises shift AI workloads from one-off experiments into sustained production, a move the underlying research says hinges on infrastructure readiness. Available power, cooling, connectivity and operational capacity can decide whether a workload fits an existing facility or needs a different deployment model.

Rather than advocating for or against the cloud, the guide treats enterprise AI as a portfolio of workloads. Exploratory workloads may stay in the cloud, sustained or business-critical ones may favor hybrid or enterprise-owned capacity, and workloads needing faster response times, greater control or data proximity may run at the edge.

The research pairs six workload archetypes with four physical infrastructure models, then a four-step process: define the business outcome and workload, evaluate scale and operating requirements, choose a deployment location, and match it to an infrastructure model and reference design. An early facility-readiness assessment, the research suggests, can surface constraints before they block deployment.

FAQ

What does the Designing Infrastructure for Enterprise AI guide actually help companies decide?: It helps business, IT and facilities leaders determine where AI workloads should run and whether the available physical infrastructure can support them, using a workload-based framework and reference designs.
What are the four physical infrastructure models in the guide?: They are Edge AI Cluster, Small AI Cluster, Large AI Cluster and High-Density AI Cluster, each a starting point for decisions on power, cooling, controls, connectivity, resilience and future growth.
Where can I find the full guide?: It is available at https://www.vertiv.com/en-us/insights/industry/designing-infrastructure-for-enterprise-ai/.

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