The compute and network resources for a major sporting goods chain struggle during a holiday sale as its aging infrastructure can’t handle the surge in real-time data from thousands of simultaneous transactions and AI-driven inventory updates. Slow point-of-sale processing, delayed stock replenishment alerts and fragmented visibility across stores result in frustrated customers, lost sales, brand damage and overwhelmed IT teams.

In an industry where customers can switch brands in a click, retailers are under pressure to deliver key features that enable great experiences, such as instant inventory updates, frictionless checkout and personalized experiences at every touchpoint. Providing these services requires a modern retail edge compute and network infrastructure that can support massive AI-driven workloads and keep stores, warehouses and distribution centers running at speed.

For retailers, the question is no longer whether to modernize. In a sector defined by thin margins and intense competition, the companies that move first gain an advantage. Those that wait risk losing market share—or worse.

When legacy infrastructure becomes a liability

AI readiness is still not widespread. In a recent study, only 6% of retail companies said their IT environment can support mature AI capabilities. Most retailers rely on fragmented, aging systems that were never designed for today’s AI technologies, data volumes and pace of commerce. Store networks, fulfillment operations and back-end systems often operate in silos, limiting visibility and slowing decision-making.

These legacy systems leave retailers vulnerable. A network bottleneck can lead to frustrated customers and delayed transactions at the point of sale. Weak visibility across distributed locations makes it harder to respond to inventory problems or security threats. Too many tools and too many vendors leave IT teams with more complexity than control.

In retail, infrastructure problems don’t stay inside the server room. They show up on the sales floor, in the warehouse and ultimately, in customer experiences. That’s what makes the retail edge such a high-stakes part of the business.

The cost of waiting

The biggest mistake retailers can make is treating modernization as something they can defer until budgets loosen or legacy systems fail outright. Waiting is expensive in ways that don’t always appear on a balance sheet.

There are direct costs, like keeping inefficient systems running, paying to support end-of-life equipment and absorbing the expense of patching security gaps. But there are also hidden costs. Every delay slows the ability to use AI for demand forecasting, personalization, operations and loss prevention. Every delay gives competitors more time to get faster, smarter and more responsive.

AI is raising the stakes

The urgency is being amplified by the rise of AI and the massive workloads that come with it. With 83% of enterprises planning to deploy AI agents, workloads are expected to surge in the next two years. Retailers must run more data-intensive applications across the edge, from real-time analytics to automation and AI inferencing.

This is where the competitive gap widens. Early adopters are building the infrastructure they need for AI-driven operations now. In one study, 44% of retailers with AI-ready environments reported faster service and reduced wait times and 38% reported more accurate inventory availability. These companies are deploying personalization at scale, responding to consumer sentiment in real time and operating leaner, faster and more securely than their competitors.

Start smart, scale fast

Modernization is not a rip-and-replace exercise. Adopting a flexible, full-stack architecture that can address today’s application but also adapt and support AI use cases for years to come is critical. Retail enterprises can begin by addressing immediate pain points:

  • Consolidate vendor sprawl with fully integrated platforms
  • Extend robust security to every edge node without adding complexity
  • Build a high-performance, low-latency foundation capable of supporting AI workloads

An ecosystem of partners—including Intel, NVIDIA, Red Hat and Nutanix — validate this full-stack approach, ensuring retailers can modernize incrementally while preparing for an AI-infused future. For example, Cisco is helping Nutanix and its customers create a modernized edge architecture through a strategic partnership that delivers a unified, flexible and AI-ready infrastructure platform from the data center to the edge.

A modernized retail edge defined

Imagine a retail infrastructure where you can observe and control what’s happening across your network, from data centers to every store or office location. This infrastructure enables real-time AI inferencing for inventory management, consumer sentiment analysis and loss prevention. Security is embedded across distributed sites, not bolted on. A single management platform replaces the chaos of multiple tools, simplifying operations and enhancing agility.

Be on the right side of the competitive divide

The retail industry has always rewarded speed, adaptability and execution. That’s especially true now, as AI accelerates the pace of change and customers expect more from every interaction. For retailers, modernization isn’t just about infrastructure readiness. It’s about business readiness. 

Retail modernization isn’t a typical IT project. It’s a revenue, customer experience and network resilience strategy—all using the same infrastructure. Retailers who understand that distinction are already building the edge environments that will define the next decade of commerce. The question is no longer whether your infrastructure can handle today. It’s whether it can handle what comes next.

Find out more about data center modernization.