NTT DATA, a global leader in AI, digital business and technology services, released Can Data Centers Keep Pace with AI? A Global Data Center Outlook, a report examining whether the infrastructure required to support the next wave of artificial intelligence can scale fast enough to meet demand. Developed in partnership with NTT Global Data Centers and economic consultancy ThoughtLab, the report models three scenarios for global data center expansion through 2030. It finds that while demand is expected to grow between 23% and 30% annually in the most likely scenarios, capacity constraints across power, equipment supply chains, land availability and labor could create a capacity crunch if not addressed through coordinated action.

"AI demand is accelerating faster than many parts of the underlying infrastructure system can respond," said Doug Adams, CEO and President, NTT Global Data Centers. "The challenge now is not simply scaling capacity, but removing the operational and supply-side constraints that delay deployment and erode the economics of AI investment. This report is intended to help the market move from recognizing the challenges to acting on practical solutions."

In its stress test of likely growth paths, NTT Global Data Centers found that:

• Power availability and grid connections are becoming decisive constraints in major markets, particularly in the United States and Europe.
• Processors, transformers, switchgear and backup generators are emerging as significant choke points, with long lead times and limited manufacturing capacity affecting how quickly new projects can be fitted out and energized.
• Rising community opposition and land constraints are delaying approvals in prime markets, while shortages in specialized construction labor are increasing execution risk and extending delivery timelines.

The report argues that these constraints are addressable and provides a roadmap for enterprises, operators, investors and policymakers to unlock capacity and improve the performance economics of AI infrastructure. Among its recommendations:

• Co-plan power and grid infrastructure with utilities early to align new projects with generation, transmission, storage and interconnection realities before bottlenecks delay deployment.
• Strengthen supply-chain resilience by diversifying suppliers, securing longer-term procurement agreements, standardizing equipment specifications and treating long-lead components as strategic priorities rather than late-stage purchasing decisions.
• Drive efficiency-focused innovation through advanced cooling, workload optimization, liquid and direct-to-chip cooling and AI-enabled operations that reduce pressure on energy and water resources.
• Set stronger and more transparent performance benchmarks, including more consistent use of power usage effectiveness (PUE) and water usage effectiveness (WUE), to improve planning and investor confidence around efficient capacity expansion.
• Improve community engagement and siting strategy so projects can move faster with clearer public understanding of economic benefits, infrastructure impacts and mitigation measures.

"AI infrastructure demand is no longer a future scenario. It is here now," Adams added. "The organizations that move fastest over the next several years will be those that understand where the real constraints are, act early to mitigate them, and build with efficiency, resilience and long-term value creation in mind."