Vertiv Helps Enterprises Move from AI Pilots to Production
Vertiv Helps Enterprises Move from AI Pilots to Production |
| [14-October-2026] |
New deployment guide, informed by Vertiv and STL Partners research, helps organizations determine their AI archetypes and where the supporting infrastructure should be located Workload-based approach connects business requirements to right-sized infrastructure models and reference designs to reduce deployment risks COLUMBUS, Ohio, Oct. 14, 2026 /PRNewswire/ -- Vertiv Holdings Co. (NYSE: VRT), a global leader in critical digital infrastructure, and Deloitte Center for Integrated Research today introduced a collaboration to help enterprises address a critical challenge in moving artificial intelligence from experimentation to production: determining where AI workloads should run and whether the available physical infrastructure can support them.
The new Vertiv Designing Infrastructure for Enterprise AI Guide, developed by Vertiv and STL Partners, a digital infrastructure research firm, provides business, IT and facilities leaders with a workload-based framework for making those decisions before committing to an infrastructure plan. Starting with the intended business outcome and AI workload, organizations can evaluate deployment location, infrastructure requirements and facility readiness, then align each workload with an appropriate physical infrastructure model and reference design for further planning and technical validation. The underlying research highlights a critical consideration as enterprises move AI workloads into sustained production: infrastructure readiness can determine where workloads can be deployed and how quickly they can move into production. Available power, cooling, connectivity and operational capacity can determine whether a workload can be supported in an existing facility or requires a different deployment model. The guide translates these considerations into a four-step process: (1) define the intended business outcome and AI workload, (2) evaluate the workload's scale, performance and operating requirements, (3) determine the most appropriate deployment location and (4) match the workload and location to a physical infrastructure model and reference design. "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. "By starting with the workload, enterprises can make infrastructure decisions earlier, identify constraints sooner and build a more credible path from AI ambition to production." "The economics and operating requirements of AI change as workloads move into production," said Nicholas Merizzi, principal, Silicon2Service U.S. Lead, Deloitte Consulting LLP. "Organizations need to consider business value, governance, operating ownership and infrastructure together. Bringing those questions into the planning process earlier gives leaders a stronger basis for deciding where AI workloads should run and how they should be supported over time." The process recognizes that enterprise AI is a portfolio of workloads rather than a single infrastructure decision. Exploratory workloads may remain in the cloud, while sustained or business-critical workloads may favor hybrid infrastructure or capacity managed by the enterprise. Workloads requiring faster response times, greater control, data proximity or specific model performance may need to run at the edge or in another controlled environment. The workload-specific approach allows public cloud, colocation, enterprise-owned capacity, hybrid environments and edge infrastructure to each play a role. Rather than advocating broadly for or against the cloud, the guide provides a consistent method for determining the appropriate operating environment for each AI workload. The research categorizes enterprise AI workloads into six archetypes covering model development, retraining and four categories of production use. These archetypes help organizations identify workloads with similar performance, operating and infrastructure requirements, creating a more consistent basis for planning. Each workload can then be aligned with one of four physical infrastructure models: Edge AI Cluster, Small AI Cluster, Large AI Cluster and High-Density AI Cluster. The four models provide a starting point for decisions involving power, cooling, controls, connectivity, resilience, operational support and future growth. Corresponding reference designs can then be adapted based on site assessment, workload requirements and detailed engineering. Facility readiness is critical across all four models. The guide helps enterprises evaluate whether a proposed location has sufficient power, cooling, connectivity, operational capacity and room for growth. Conducting this assessment early can help identify infrastructure constraints before they become barriers to deployment. The full guide, Designing Infrastructure for Enterprise AI, details the six workload archetypes, four physical infrastructure models and a four-phase roadmap for planning, deploying and evolving enterprise AI infrastructure. It is available at https://www.vertiv.com/en-us/insights/industry/designing-infrastructure-for-enterprise-ai/. About Vertiv Holdings Co. Forward-looking statements For media inquiries, please contact:
SOURCE Vertiv Holdings Co | ||
Company Codes: NYSE:VRT |
View original content to download multimedia:
