AI's Hidden Bottleneck: Why Networks Are Stalling Enterprise Momentum

DENVER – October 27, 2026 — For the past year, the artificial intelligence industry has been consumed by a narrative of scarcity: a shortage of advanced GPU chips and a lack of electrical grid capacity to power them. But for enterprise IT leaders tasked with actually deploying AI workloads, the most acute pain point isn't measured in megawatts or silicon. It is measured in milliseconds.

According to the 2026 State of AI Infrastructure Report released today by data center and hybrid IT provider Flexential, an overwhelming 96% of enterprises reported network-related performance issues affecting their AI workloads over the past 12 months. The data signals a critical paradigm shift in how businesses must evaluate their infrastructure. Latency—the time it takes for data to travel across a network—has more than doubled as a primary operational concern since 2024, surging past bandwidth shortages and unreliable connections to become the undisputed top hurdle for AI deployments.

The Great AI Split: Compute vs. Data

To understand why networks are suddenly choking, one must look at how enterprise AI architectures have evolved. The report highlights a growing phenomenon that industry insiders are calling "the great AI split." Over the past year, enterprise deployment of GPUs in the public cloud jumped from 30% to 54%. Conversely, the share of organizations housing their AI data in those same public clouds plummeted from 59% to 26%.

This decoupling is driven by a combination of data gravity, security compliance, and exorbitant cloud egress fees. Companies are eager to rent highly elastic, on-demand compute power from hyperscalers to train their models. However, they are increasingly repatriating their massive, proprietary training datasets to private colocation facilities or on-premises environments to maintain control and cap costs.

The result is a hybrid AI architecture that forces unprecedented volumes of sensitive traffic across wide-area networks. When the brain (the GPU) and the memory (the data) are geographically separated, the nervous system connecting them—the network—must work overtime. Legacy network architectures were simply not designed to handle the sustained, high-bandwidth shuttling of massive datasets required for continuous AI training and real-time inference. Consequently, this constant data transit results in severe network congestion, packet loss, and performance degradation that ultimately stalls AI momentum.

Reconciling the Power Paradox

The prominence of network latency as the chief operational bottleneck stands in stark contrast to the prevailing public discourse, which remains fixated on the data center industry's power crisis. Macro-level reports from commercial real estate and data center analysts consistently cite grid bottlenecks and power density requirements as the primary barriers to AI growth. Yet, the survey of over 350 IT decision-makers found that difficulty scaling data center space and power affected only 25% of respondents. A mere 4% identified power as their sole operational issue.

This apparent paradox is easily reconciled when distinguishing between hyperscale developers and enterprise operators. While securing new, greenfield power for a massive AI research lab is a multi-year grid challenge, enterprise IT teams typically lease space from colocation providers who have already secured the necessary electrical infrastructure. For the enterprise, the power is often already there; the challenge is getting the data to the compute fast enough to utilize that power efficiently.

"Power determines where AI can run, and the network determines whether it performs once it's operational," noted Ryan Mallory, CEO of Flexential, in the report's release. "Organizations are discovering that a site with megawatts available can still fail them if the fiber, the carriers, and the cloud on-ramps aren't in place. Our customers count on us to get that right, and it comes down to planning, due diligence, and execution to build the right ecosystem inside every data center."

The Physical Reality of Fiber Constraints

The network crisis is ultimately a physical one. Despite advancements in software-defined networking, data must still travel over physical glass strands. According to the research, 91% of organizations reported that fiber availability, carrier diversity, and low-latency connectivity have been limiting factors even at sites with favorable power availability. For 35% of these organizations, this limitation happens very often.

The telecommunications industry has been scrambling to keep pace with the demand for 400 Gigabit Ethernet and beyond. However, labor shortages, permitting delays, and the sheer cost of trenching new lines have created localized fiber shortages. These constraints have tangible business impacts. Over the past 12 months, fiber availability increased deployment costs for 54% of respondents, delayed AI rollouts for 54%, and forced half of the surveyed enterprises to change their deployment locations entirely.

Furthermore, 42% of respondents now consider access to high-capacity fiber and multiple carrier options to be just as critical as power and space when selecting locations for AI workloads. Another 50% count it among their major considerations. This shift is forcing colocation giants to pivot their value propositions. Providers are increasingly competing not just on raw square footage or power density, but on their interconnection capabilities—the density of their carrier ecosystems and the presence of direct, low-latency cloud on-ramps. The Denver-based data center operator, for instance, recently upgraded its own private network backbone to 400 Gbps across major U.S. metro markets to facilitate this exact type of heavy AI traffic between disparate compute and data environments.

Navigating the Interconnected Future

As inference models evolve and become more geographically distributed to sit closer to end-users, the reliance on robust, regional network infrastructure will only intensify. The workloads currently in development—ranging from autonomous decision engines to advanced customer-facing assistants—are highly sensitive to latency and will test network limits harder than any historical enterprise application.

In response, enterprise operators are rapidly evolving their management strategies. The report notes that software-defined networking adoption reached 58% this year, a massive 20-percentage-point jump from 2024. This indicates a desperate need for network agility, allowing enterprises to dynamically provision and route high-bandwidth connections across hybrid architectures. Additionally, third-party colocation for edge processing rose to 46%, while traditional on-premises AI hardware deployments fell to 24%.

"Customers used to ask how much space and power we could give them. Now they want to know what their traffic will do once it leaves the building," stated Patrick Doherty, Chief Revenue Officer at Flexential. "They're running GPUs in one environment and keeping data in another, and they need a partner whose network can close that gap. Interconnection has moved to the center of every infrastructure conversation we're having."

The data paints a clear picture of the modern AI landscape: the initial land grab for GPUs and power is transitioning into an operational phase where network transit dictates success. Businesses that fail to prioritize fiber availability, carrier diversity, and low-latency interconnections risk stranding their expensive AI investments on digital islands, unable to process the data they were built to analyze. As the gap between data storage and compute widens, the network will remain the ultimate arbiter of enterprise AI momentum.