Supporting AI’s towering performance capabilities requires a formidable foundation. Without AI-ready infrastructure, many initiatives are likely to collapse. In a quick-hitting Episode 3 of Winners Circle, NexusTek’s expert-led webinar series, Peter Newton, NexusTek SVP of Cloud Services, explains how hybrid cloud infrastructure is essential to forward-looking AI strategies.
Just as a skyscraper depends on its foundation, AI capabilities require infrastructure that can support high-powered workloads. Modern AI workloads demand immense computational resources—thousands of GPUs, petabytes of data, and split-second response times—that traditional single-cloud and on-premises setups simply can’t deliver.
These technical requirements present a core challenge: how to balance the hyperscale compute power AI needs with the data control and security businesses require.
Strategic AI workload placement—what Gartner calls “cloud-adjacent AI”—addresses three key constraints to success: computational elasticity to manage workload spikes, data gravity solutions for rapid access to massive datasets, and a way to resolve the sovereignty paradox by leveraging cloud capabilities while maintaining data privacy and compliance.
Orchestration supports these goals by placing workloads where they perform best across hybrid environments. Aligning IT strategy with business objectives in this way can turn infrastructure into a true competitive advantage, Newton says. He cites a McKinsey study showing that organizations aligning their AI and infrastructure strategies are 2.5 times more likely to see measurable business impact.1
NexusTek recently saw this with a financial services client that used AI to achieve 40 percent faster fraud detection and 60 percent fewer false positives.
The key to enhancing agility without adding complexity or friction lies in architectural discipline, Newton adds—creating an intelligent fabric rather than bolting cloud and on-premises systems together. NexusTek calls this approach “workload-aware-cloud.” As agentic AI raises performance expectations, this kind of infrastructure will be increasingly vital—capable of thinking as fast as the agents it powers.
The biggest mistake Newton sees? Treating AI infrastructure as a technology project rather than a strategic investment. “If you don’t invest in AI infrastructure, you’re not saving money—you’re simply giving away market share,” he says. By investing in AI-ready infrastructure, organizations gain competitive advantages including faster time-to-market, the ability to build AI-native products, greater efficiency and automation, and platform effects that accelerate future AI rollouts.
Embracing AI infrastructure as a value-generating imperative rather than a cost center also helps organizations avoid other common pitfalls, such as underestimating the scale of change required.
Future-proofing AI infrastructure means adopting modular, interoperable systems, with standardized APIs and advanced capabilities for observability and intelligent workload placement. Yet more than half of infrastructure teams aren’t ready to support AI workloads, according to Gartner,2 highlighting the urgent need for AI fluency across other IT functions such as security.
What’s most exciting, Newton says, is how accessible AI infrastructure is becoming. Just a few years ago, running an LLM “required a Ph.D. and at least a million-dollar budget.” Today, even midsize companies, with the right hybrid cloud strategy, can build AI that rivals the world’s biggest tech players.
And the disruption is only the beginning. The window is short for executives to build smart infrastructure strategies that can keep pace with a rapidly evolving AI landscape.
Ready to transform your infrastructure into the high-performance pillar of your AI strategy—and a competitive edge? Contact our team
Sources
1. McKinsey & Co., “How High-Performing Companies Develop and Scale AI.” March 2020.
2. Gartner, “Lack of AI-Ready Data Puts AI Projects at Risk.” February 2025.