Vertiv Holdings Co., a global leader in critical digital infrastructure, has teamed up with Deloitte Center for Integrated Research to address a significant challenge faced by enterprises: transitioning artificial intelligence (AI) from experimental pilots to full-scale production. The collaboration, announced on October 14, 2026, introduces the Vertiv Designing Infrastructure for Enterprise AI Guide, developed in partnership with STL Partners, a digital infrastructure research firm. This guide is designed to help organizations navigate the complexities of AI deployment by providing a structured framework for decision-making.
The guide begins by emphasizing the importance of aligning AI workloads with the intended business outcomes. It outlines a four-step process: defining the business goal and AI workload, evaluating the workload’s scale, performance, and operational requirements, determining the most suitable deployment location, and matching the workload and location to a physical infrastructure model and reference design. This approach ensures that enterprises can assess their infrastructure readiness and identify potential constraints early in the planning phase.
The underlying research underscores a critical consideration for enterprises: the readiness of their physical infrastructure can significantly impact where AI workloads can be deployed and how quickly they can transition into production. Factors such as available power, cooling, connectivity, and operational capacity play a pivotal role in determining whether an existing facility can support a workload or if a different deployment model is required.
Giordano Albertazzi, CEO of Vertiv, highlighted the importance of this new phase in enterprise AI, stating, “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.” By starting with the workload, enterprises can make more informed infrastructure decisions, identify constraints sooner, and build a more credible path from AI ambition to production.
The Vertiv Designing Infrastructure for Enterprise AI Guide serves as a comprehensive tool for business, IT, and facilities leaders, offering a workload-based framework that connects business requirements to right-sized infrastructure models. This guide aims to reduce deployment risks and help organizations successfully transition their AI initiatives from pilot projects to sustained production.
The transition from AI pilots to production isn’t just about scaling up models; it’s about aligning the technical infrastructure with the workload’s demands. Let’s break down the structural and technical challenges enterprises face when moving AI into production, using Vertiv’s framework as a lens.
First, every AI workload has unique requirements. Take, for example, a real-time fraud detection system. It needs low latency, high throughput, and consistent performance. If the infrastructure can’t handle the scale—say, processing thousands of transactions per second—it’ll bottleneck, leading to delays or failures. Vertiv’s guide starts by defining these requirements, ensuring that the infrastructure planning isn’t just a guess but a calculated effort.
Next, the physical infrastructure’s readiness is a critical factor. Power availability is often the first hurdle. Running AI models in data centers can be power-intensive, especially with GPUs and TPUs. If the facility doesn’t have enough capacity or the right power distribution, it could limit the workload’s performance or require costly upgrades. Cooling is another technical challenge. High-density computing generates a lot of heat, and inadequate cooling can lead to hardware failures or reduced efficiency. Vertiv’s approach evaluates these factors early, helping organizations avoid costly retrofitting.
Connectivity is another layer. AI workloads, especially those requiring real-time processing, need reliable, high-speed networks. Latency spikes or bandwidth shortages can degrade performance. For instance, an AI system analyzing video streams for quality control needs seamless data flow to avoid processing delays. The guide helps determine if the current network infrastructure can support these needs or if a different deployment model, like edge computing, is more suitable.
Operational capacity is often overlooked but equally important. Even if the infrastructure is technically capable, the operational team’s expertise and resources can determine how smoothly the workload transitions to production. For example, managing a large-scale AI deployment requires skilled personnel to monitor performance, handle updates, and troubleshoot issues. Without this capacity, even the best infrastructure can underperform.
Vertiv’s four-step process addresses these technical and structural challenges systematically. By starting with the workload’s requirements, it ensures that the infrastructure planning isn’t just a one-size-fits-all solution but tailored to the specific needs of the AI application. This approach not only reduces deployment risks but also accelerates the transition from pilot to production by identifying constraints early and providing actionable solutions.
In essence, the technical challenge lies in matching the workload’s demands with the infrastructure’s capabilities across power, cooling, connectivity, and operational readiness. Vertiv’s guide provides a structured, data-driven method to tackle these challenges, making the transition to AI production more feasible and efficient.
The challenges outlined by Vertiv’s framework are not unique to a single industry or organization. In fact, many enterprises across various sectors are grappling with similar issues as they seek to scale their AI initiatives. For instance, financial institutions are increasingly adopting AI for fraud detection and risk assessment, while healthcare providers are leveraging AI for diagnostics and patient care optimization. Each of these applications demands specific infrastructure capabilities, making the transition from pilot to production a universal challenge.
A notable precedent is the experience of the automotive industry, particularly in the realm of autonomous driving. Companies like Tesla and Waymo have faced significant infrastructure challenges in scaling their AI-driven systems. For example, training autonomous driving models requires massive computational resources, often exceeding the capacity of existing data centers. This has led to investments in specialized facilities with enhanced power and cooling capabilities, mirroring the strategies recommended by Vertiv’s guide.
In the retail sector, AI is being used for inventory management and customer behavior analysis. However, scaling these applications often reveals gaps in connectivity and operational readiness. For example, a retail chain using AI to optimize stock levels across multiple locations may find that their network infrastructure cannot handle the high volume of data required for real-time analysis. This has led to a growing trend of companies partnering with infrastructure providers to ensure their systems can support AI workloads effectively.
The response to these challenges has been multifaceted. Many enterprises are adopting a hybrid approach, combining cloud-based solutions with edge computing to balance performance and infrastructure demands. For instance, deploying AI models at the edge can reduce latency and alleviate the strain on central data centers, as seen in applications like smart city surveillance or industrial IoT systems. Additionally, there has been a surge in investments in green infrastructure, with companies prioritizing energy efficiency to mitigate the environmental impact of AI deployment.
Vertiv’s collaboration with Deloitte and STL Partners represents a significant step forward in addressing these challenges. By providing a structured framework, the guide enables organizations to proactively assess and mitigate infrastructure gaps, ensuring a smoother transition to AI production. This approach is particularly relevant as enterprises continue to recognize the importance of aligning their technical capabilities with the demands of their AI applications, whether in finance, healthcare, automotive, or retail.
The lessons from Vertiv’s framework extend far beyond the specific case of AI deployment, offering a broader strategy for enterprises navigating the complexities of scaling advanced technologies. At its core, the framework emphasizes the importance of aligning technical infrastructure with the specific demands of the workload, a principle that applies to any technology transition, from IoT to edge computing.
One of the key takeaways is the necessity of starting with the workload requirements. Too often, enterprises attempt to retrofit infrastructure to fit emerging technologies, leading to inefficiencies and high costs. By defining the technical needs upfront, organizations can design solutions that are both scalable and efficient, reducing the risk of bottlenecks and ensuring smoother transitions.
The focus on infrastructure readiness—evaluating power, cooling, connectivity, and operational capacity—provides a template for assessing any large-scale technology deployment. For instance, as more industries adopt edge computing, the principles outlined by Vertiv can help determine whether existing facilities can support distributed workloads or if new infrastructure investments are necessary.
Moreover, the structured, four-step approach underscores the value of a data-driven, systematic methodology. This approach is not limited to AI but can be applied to various technological initiatives, ensuring that organizations avoid common pitfalls such as over-reliance on generic solutions or underestimating operational challenges.
In essence, Vertiv’s framework teaches enterprises to approach technology transitions with a clear-eyed, requirements-based strategy, ensuring that infrastructure decisions are aligned with business goals and technical demands. This lesson is universally applicable, offering a roadmap for any organization looking to scale advanced technologies effectively.
Tech-Bee is an independent technology publication. This piece is drawn from reporting and sourcing noted by the publisher.