The race to scale artificial intelligence is not being won in the data center. It is being won in the supply chain that builds, powers, and coordinates it. The real risk is not just complexity, it is committing billions of infrastructure before knowing whether the ecosystem can actually deliver.
Over the next five years, North America alone is expected to require roughly 92 gigawatts of additional power to support AI-driven data centers. Meeting that demand could require building thousands of new facilities. At the same time, data centers are projected to invest around $650 billion in 2026. These are not incremental shifts. They represent a massive expansion of digital infrastructure, where decisions are made earlier, faster, and with far greater financial consequence.
How AI data center growth is reshaping supply chain complexity
This is not just a cloud provider story. The buildout of AI infrastructure depends on a broad ecosystem that includes semiconductor manufacturers, networking providers, component suppliers, energy utilities, and construction firms.
Each of these players is responding to the same surge in demand, but with different constraints, timelines, and dependencies. Every decision now has knock-on effects across the system. A delay in one area can cascade across multiple layers, undermining plans that have already been committed.
At the same time, resource competition is intensifying. Data centers are projected to consume up to 70 percent of global memory chip production, driving cost increases across industries. What appears to be a localized infrastructure decision increasingly creates ripple effects across the global supply chain.
Why traditional supply chain planning breaks in AI infrastructure environments
Many organizations are struggling not because they lack data, but because their operating models were not designed for this level of interdependence and speed. Supply chains were traditionally built to manage variability within known bounds. Today, companies are being forced to lock in commitments earlier than ever, often before they have full visibility into whether supply, capacity, and dependencies will align.
Demand from AI workloads is volatile and difficult to predict. Supply constraints across chips, energy, and critical components continue to tighten. At the same time, different teams operate from different assumptions, creating multiple versions of the truth. Instead of enabling confident decisions, planning becomes an exercise in reconciliation.
ERP and MRP systems remain essential for execution, but they cannot model uncertainty, evaluate trade-offs dynamically, or ensure that decisions made upstream will hold when real conditions unfold.
The hidden cost of poor coordination in data center supply chains
The consequences of this gap become clear at the moment of execution. Infrastructure must be deployed in sequence, with power and cooling established before networking equipment is installed and servers are activated. When one component is delayed, the entire system stalls, and commitments that were made earlier begin to break down.
As servers sit idle, capacity cannot be monetized. In some cases, each unutilized rack can represent hundreds of thousands of dollars in lost annual revenue. At scale, delays compound quickly, turning planning assumptions into missed revenue, delayed launches, and reactive firefighting.
These outcomes are not caused by a lack of effort. They are the result of committing to plans without a synchronized, system-wide understanding of what can actually be delivered.
The real challenge: ensuring decisions hold under pressure
In this environment, the challenge is not simply deciding what to build or where to invest. It is ensuring that those decisions hold when demand materializes and constraints emerge.
Organizations must commit to capacity, timelines, and customer expectations before they have full certainty. Every commitment introduces risk, because it depends on a network of interdependent factors that are constantly shifting.
The challenge is not a lack of data. It is the ability to see trade-offs clearly, align teams around a single version of reality, and make decisions that remain valid as conditions change. Without that, plans drift, execution breaks down, and confidence erodes.
From supply chain planning to orchestration in AI-driven operations
Addressing this level of risk requires more than better planning. It requires a different approach to how decisions are made and managed. Organizations need to connect demand, supply, inventory, and capacity decisions in a single environment, where trade-offs can be evaluated continuously and decisions can be adjusted before they break. This is where orchestration becomes essential.
Kinaxis enables this by bringing these decisions into a concurrent environment, where changes in one part of the network are immediately reflected across the rest. Teams can test scenarios, understand the downstream impact of commitments, and adjust before execution is at risk.
This allows organizations to move beyond static plans and toward decisions that hold under real-world conditions.
Why confidence at the moment of commit is now the competitive advantage
The scale and pace of AI infrastructure growth are forcing organizations to commit earlier and at greater risk. The companies that succeed will not be those that plan more. They will be those that commit with confidence.
Confidence in this context means knowing that a decision can be executed as intended, even as conditions change. It requires visibility across the network, alignment across teams, and the ability to continuously evaluate and adjust trade-offs.
Kinaxis supports this by enabling organizations to make connected decisions that remain valid as reality unfolds. In environments defined by volatility and interdependence, this is what separates execution that holds from execution that breaks.
AI may be driving demand, but the ability to commit with confidence and deliver on those commitments will determine who captures that value.
For more on the supply chain of AI, check out our feature story.