As organizations invest in AI, modernization, and hybrid cloud, they are facing a new challenge: Rising infrastructure costs and increasing complexity across compute, storage, networking, security, and data protection. Preparing for AI is no longer about refreshing a single technology. It requires a coordinated strategy that aligns infrastructure investments with future workloads and business priorities.
In a recent interview with Mike Vizard of TechStrong TV, Irwin Teodoro, senior vice president of Advisory and Transformation Services at GDT, discussed why traditional infrastructure refresh cycles are no longer enough. As AI workloads evolve, organizations must take a holistic approach to planning, balancing performance, cost, security, and scalability across the entire technology stack.
“You can’t just buy one piece of technology and solve your overall strategy. What you’re having to do is look across the stack,” said Teodoro.
Teodoro explained that modern infrastructure decisions are deeply interconnected. A network modernization initiative, for example, often requires organizations to evaluate compute, storage, cybersecurity, data protection, and cloud strategy together to ensure the environment is ready to support AI workloads.
The conversation also explored how IT leaders can control rising infrastructure costs through workload-centric planning, right-sizing resources, and using FinOps practices to better understand and optimize technology investments. Rather than planning around individual hardware refreshes, organizations should develop flexible technology roadmaps and reference architectures that can adapt as AI use cases continue to evolve.
As AI adoption accelerates, CIOs and CTOs have an opportunity to rethink how they design and manage enterprise infrastructure. Organizations that align infrastructure strategy with business goals, rather than isolated technology upgrades, will be better positioned to support innovation while managing cost and complexity.
