Mumbai, October 19, 2026: Vertiv Holdings Co. (NYSE: VRT) and the Deloitte Center for Integrated Research have announced a collaboration to help enterprises address the challenge of moving artificial intelligence (AI) workloads from pilot phases to production, focusing on determining optimal workload locations and assessing infrastructure readiness.
The initiative includes the release of the Vertiv Designing Infrastructure for Enterprise AI Guide, developed with digital infrastructure research firm STL Partners. The guide offers a workload-based framework for business, IT, and facilities leaders to evaluate deployment locations, infrastructure requirements, and facility readiness before committing to an AI infrastructure plan.
Framework for AI workload deployment
The guide outlines a four-step process: defining business outcomes and AI workloads, evaluating scale and operating requirements, determining deployment locations, and matching workloads to physical infrastructure models and reference designs. The research emphasizes that available power, cooling, connectivity, and operational capacity are critical factors in deciding whether existing facilities can support AI workloads or if alternative deployment models are needed.
Six AI workload archetypes identified
Enterprise AI workloads are categorized into six archetypes, covering model development, retraining, and four types of production use. These archetypes help organizations group workloads with similar performance and infrastructure needs, providing a consistent basis for planning. Each workload can be aligned with one of four physical infrastructure models: Edge AI Cluster, Small AI Cluster, Large AI Cluster, and High-Density AI Cluster.
The guide also details how public cloud, colocation, enterprise-owned capacity, hybrid environments, and edge infrastructure can each play a role in supporting different AI workloads, depending on requirements such as response time, control, data proximity, and model performance.
Facility readiness and reference designs
Facility readiness is highlighted as a critical factor across all four infrastructure models. The guide assists enterprises in assessing whether proposed locations have adequate power, cooling, connectivity, operational capacity, and room for growth, helping to identify constraints early in the planning process.
Corresponding reference designs for each infrastructure model can be adapted based on site assessments, workload requirements, and detailed engineering, providing a roadmap for planning, deploying, and evolving enterprise AI infrastructure.
Executive perspectives on AI infrastructure
“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 Vertiv.
Nicholas Merizzi, principal and Silicon2Service U.S. Lead at Deloitte Consulting LLP, added that considering business value, governance, operating ownership, and infrastructure together during the planning process gives leaders a stronger basis for deciding where AI workloads should run and how they should be supported over time.
Vertiv is headquartered in Westerville, Ohio, USA, and operates in more than 130 countries, offering power, cooling, and IT infrastructure solutions and services.
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