Vertiv Launches Guide to Help Enterprises Scale AI Infrastructure from Pilot to Production

Vertiv and Deloitte Address Critical Enterprise AI Challenge

Vertiv Holdings Co. and Deloitte Center for Integrated Research launched a collaboration targeting a fundamental obstacle enterprises face: transitioning artificial intelligence from pilot projects to production-ready systems1

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. The partnership introduces the Designing Infrastructure for Enterprise AI Guide, developed with STL Partners, a digital infrastructure research firm. This workload-based framework helps business, IT and facilities leaders determine where AI workloads should run and whether available physical infrastructure can support deployment at scale.

Infrastructure Readiness Determines AI Deployment Success

The underlying research reveals that infrastructure readiness directly impacts where AI workloads can be deployed and how quickly they move into production1

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. Available power and cooling, connectivity and operational capacity determine whether existing facilities can support workloads or require alternative deployment models. "The next phase of enterprise AI will be shaped not only by models and computing, but also by whether organizations can provide the power, cooling, connectivity and operating environment each workload requires," said Giordano Albertazzi, CEO of Vertiv2

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. This assessment becomes critical as organizations transition AI from pilot to production, where infrastructure constraints can become deployment barriers.

Four-Step Process for AI Infrastructure Planning

The guide translates infrastructure considerations into a systematic four-step process. Organizations first define the intended business outcome and AI workload, then evaluate the workload's scale, performance and operating requirements1

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. Next, they determine the most appropriate deployment locations before matching the workload and location to physical infrastructure models and reference designs. Nicholas Merizzi, principal at Deloitte Consulting LLP, emphasized that "the economics and operating requirements of AI change as workloads move into production," requiring organizations to consider business value, governance, operating ownership and AI infrastructure together2

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Six AI Workload Archetypes Guide Infrastructure Decisions

The research categorizes enterprise AI into six archetypes covering model development, retraining and four categories of production use1

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. These archetypes help organizations identify workloads with similar performance, operating and infrastructure requirements, creating a consistent planning basis. The approach recognizes that enterprise AI represents a portfolio of workloads rather than a single infrastructure decision. Exploratory workloads may remain in cloud environments, while sustained or business-critical workloads may favor hybrid infrastructure or enterprise-managed capacity. Workloads requiring faster response times, greater control, data proximity or specific model performance may need edge infrastructure or controlled environments2

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Four Physical Infrastructure Models for Different AI Needs

Each workload archetype aligns with one of four physical infrastructure models: Edge AI Cluster, Small AI Cluster, Large AI Cluster and High-Density AI Cluster1

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. These models provide starting points for decisions involving power, cooling, controls, connectivity, resilience, operational support and future growth. The workload-specific approach allows public cloud, colocation, enterprise-owned capacity, hybrid environments and edge infrastructure to each play distinct roles. Rather than advocating for or against specific deployment models, the guide provides a consistent method for determining the appropriate operating environment for each AI workload2

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Early Assessment Prevents Infrastructure Bottlenecks

Facility readiness proves critical across all four infrastructure models. The guide helps enterprises evaluate whether proposed locations have sufficient power, cooling, connectivity, operational capacity and room for growth1

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. Conducting this assessment early helps identify infrastructure constraints before they become barriers to deployment. By starting with the workload, enterprises can make infrastructure decisions earlier, identify constraints sooner and build more credible paths from AI ambition to production. The full guide details the six workload archetypes, four physical infrastructure models and a four-phase roadmap for planning, deploying and evolving enterprise AI infrastructure, available at Vertiv.com1

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