Vertiv and Deloitte Release AI Infrastructure Guide
The new framework helps organizations align physical facility capabilities with emerging enterprise AI workload requirements.
Updated on Oct. 28, 2026 in Artificial Intelligence
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Vertiv and Deloitte have released a new framework for enterprise AI infrastructure planning. The guide, developed with STL Partners, provides a structured methodology to help organizations transition AI projects from pilot phases to full production.
Why it matters
As enterprises move AI beyond experimental stages, leaders need standardized ways to determine if existing facilities can support high-compute demands. This guide offers a framework to map specific workload requirements to physical infrastructure designs, addressing a common bottleneck in production scaling.
The framework classifies AI infrastructure into four distinct models: Edge AI Cluster, Small AI Cluster, Large AI Cluster, and High-Density AI Cluster. These are mapped against six enterprise workload archetypes based on scale, performance, and operational requirements.
The players
Vertiv
A global provider of critical digital infrastructure, including power and cooling systems for data centers.
Deloitte
A multinational professional services network that offers business strategy, technology consulting, and research services.
STL Partners
A research and consulting firm specializing in telecommunications, edge computing, and digital transformation.
The details
The planning methodology operates through a four-step process: defining business outcomes, evaluating workload requirements, determining optimal deployment locations, and aligning these needs with physical site readiness. This approach forces technical teams to categorize workloads by their specific power, cooling, and latency demands before selecting from the four defined infrastructure clusters. The goal is to ensure facility architecture matches the thermal and power density constraints inherent in modern AI hardware.
Timeline
October 28, 2026: Vertiv and Deloitte released the enterprise AI infrastructure guide.
The Tech Race
As companies rush to scale AI, the focus has shifted from algorithm development to the physical realities of data center capacity and power density. This collaborative framework establishes a standardized language for enterprise infrastructure planning that competes with custom, site-specific design approaches currently dominating the market.
IT leaders and infrastructure architects can use this framework immediately to assess facility readiness for AI deployment. The guide assists teams in evaluating whether current data center sites require modifications to handle high-density AI clusters.
The takeaway
This guide provides a standardized diagnostic for infrastructure gaps that often emerge when scaling AI systems. Organizations should use the four-step planning process to audit current site capabilities against the specific requirements of their AI workload archetypes.
Further reading
For broader trends in scaling AI, see the latest analysis in Artificial Intelligence.
Source note: This article includes information reported by DataCenterNews US.
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Do you plan to increase investment in physical infrastructure to support AI projects at your organization?
