Concentric Staff
AI has collapsed the boundaries between telecom, cable, and data center infrastructure, pulling them into one accelerated buildout unlike anything the industry has seen since the early convergence of the 80s and 90s. The shift today is even more dramatic: every major operator is moving to fiber while racing to support AI workloads that consume far more power and cooling than traditional applications.
With billions being invested each year, speed and consistency have become the real competitive differentiators. And yet, a familiar set of obstacles continues to prevent companies from deploying at the pace AI now requires.
Mike Lee, General Manager of Concentric’s Critical Power Division, spoke with Data Center Dynamics about the operational blind spots and deployment mistakes slowing AI infrastructure projects across the country, and what it will take to fix them.
We’re sharing a few of the themes he explored, and you can read his full perspective in Data Center Dynamics.
What Three Roadblocks Are Slowing AI Infrastructure Deployment?
Across national deployments, the same challenges surface again and again. These issues directly impact how quickly and consistently organizations can scale.
1. The Expertise Gap
Many companies no longer have the internal technical depth required for large, complex power and cooling projects such as data centers. Years of workforce reductions and retirements have left small teams responsible for massive deployments while still managing day-to-day operations. The result is slower decision-making, higher risk, and reduced capacity to support AI growth.
2. Regional Inconsistency Across Projects
Even strong internal teams struggle to maintain uniformity when deploying across multiple regions. Local labor rules, contractor differences, permitting processes, construction practices, and material availability all vary by market. These inconsistencies lead to unpredictable timelines, uneven quality, and long-term service challenges. A national partner also provides a single point of accountability for standards, performance, and timelines across every site.
3. Procurement Sequences That Create Delays
Traditional procurement often starts by choosing equipment and then finding someone to install it. In practice, this adds unnecessary delays and limits flexibility. The fastest-moving organizations begin by selecting an experienced implementation partner who can help guide equipment choices based on availability, performance needs, and service coverage. This approach shortens timelines and improves consistency. This matters even more for power infrastructure, where capacity and cooling decisions influence every other system in the deployment.
The Path Forward
Succeeding in the AI infrastructure race requires more than budget. It requires a deployment strategy built for speed, national alignment, and operational discipline. Companies that involve partners early, standardize work across regions, and optimize procurement around deployment goals will be best positioned to keep pace with rising AI demand.