What Separates AI-Ready Enterprises From Everyone Else?

Worldwide spending on AI is forecast to total $2.52 trillion in 2026, a 44% increase year-over-year, according to Gartner.

As spending increases, the pressure for results is mounting. Mentions of AI disruption on corporate management calls nearly doubled in Q4 2025 compared to the previous quarter, according to a Bloomberg analysis of earnings call transcripts. Amid this surge, investors are now responding, attempting to sort the winners from the losers.

Enterprises are all-in on the AI future, but most are running on infrastructure built for the past. The era of AI pilots and proof of concept is coming to an end, replaced by deployment at scale. However, this implementation phase frequently exposes cracks in the foundations. 

“Experimentation was maybe proving that things could work,” says Kumaran Siva, Corporate Vice President, Enterprise AI at AMD. “Now, it’s where rubber hits the road.”

How fragile foundations stall AI scaling efforts 

AI initiatives often sink between ambition and results because the existing physical infrastructure to execute the strategy was built for a different era entirely.

In 2023, before the generative AI boom began, vacancy rates in North American data center markets had already fallen below 3%, according to research firm Data Center Hawk. Considering how little capacity was left, one could argue that AI didn’t create the infrastructure crisis so much as expose it. 

Energy budgets are stretched. Compute resources are nearing capacity. Bandwidth is at its limits. The IEA reported that data center power demand grew 17% in 2025, and according to BloombergNEF, electricity demand from AI training and services is set to quadruple within a decade. Inside enterprises, the picture is just as stark, as two-thirds of surveyed organizations believe their IT environments require upgrades to meet future demand, according to S&P Global Market Intelligence. 

The foundational difference between companies that have refreshed with cutting-edge hardware and those running older systems is already producing winners and losers. 

“Some companies are actually pretty far ahead,” says Siva. “They’ve done the hard work and they know what they’re doing.” 

The data tells the story: The gap between AI-first and AI-unready organizations is widening.

When organizations decided it was time for an overhaul

The AI foundation question applies across industries. One example is telecom, where operators are facing a cascade of challenges, including the rapid expansion of 5G, the convergence of cellular and broadband networks and the need for real-time AI applications. Meanwhile, telecom IT leaders must manage surging data traffic at ultra-low latency and support a wide range of workloads, all while addressing sustainability goals. The old infrastructure was built for a slower era. 

Take Swisscom, Switzerland’s leading telecom company, which ran into cloud infrastructure problems. The company had relied on aging servers for years, and the hardware was showing its limits. Performance bottlenecks, rising energy costs and architecture that made workload deployments difficult had complicated Swisscom’s efforts to modernize its on-premise hardware. 

Swisscom rebuilt its foundation, upgrading to AMD EPYC™ server CPUs, which nearly doubled virtual CPU capacity per server, while cutting power consumption by 24% and reducing vCPU power use by more than half. The new infrastructure runs today’s telecom workloads, and is built for the expanding requirements of AI training and inference in the coming years.

The foundation Swisscom built for its own workloads turned out to be capable of supporting third-party customers running cloud-native applications, a line of business it hadn't originally planned to offer.

Swisscom isn't alone. Chunghwa Telecom Information Technology Group (CHT ITG), the IT services arm of Taiwan’s largest telecom company, deployed AMD EPYC™ server CPUs across its cloud services business, cutting rack space needs by 1.6x and clearing the runway for the coming wave of AI training and inference.

The infrastructure decisions Swisscom and CHT ITG made have unlocked additional capacity to run today’s workloads and handle tomorrow’s AI demands.

Purpose is at the center of successful implementations

As AI workloads grow more complex, agentic and energy-hungry, demands on the underlying infrastructure increase. 

Meeting these demands requires computing that places demanding workloads front and center, rather than relying on a one-size-fits-all approach. AMD describes its approach as “compute with purpose,” and delivers processors that excel in many contexts and with specialized workloads. As the energy cost of AI grows, the efficiency of the underlying compute structure becomes as strategically important as the AI model running on top of it.

Foundation decisions carry risk: Enterprises that build too rigidly can find that when the technology shifts, changing course can become prohibitively costly.

“Lock-in is real,” says Siva. “Companies invest in a particular technology and have a very hard time moving off of it.”

The enterprises positioned for the AI era build compute structures that give them the flexibility and the freedom to adapt as their needs evolve. AMD has built its enterprise AI platform around an open-source software stack, a hardware portfolio spanning CPUs, GPUs, FPGAs and networking, with a roadmap designed to keep pace with the demands of enterprise AI.

“Enterprise customers are at the beginning stages of making decisions that are going to impact them years from now,” not unlike the infrastructure decisions that define a generation of enterprise computing, says Siva. “Those decisions last for years.”

Explore AI-Ready enterprise solutions from AMD.