What are the biggest challenges in deploying AI infrastructure at scale?

Organizations moving from pilot AI projects to global deployments face challenges around standardization, supply chain logistics, and data center readiness. Ensuring that infrastructure can be replicated consistently across multiple sites requires careful planning and integration expertise. UNICOM Engineering's Jeff Hudgins highlights that the shift from custom builds to repeatable models is a key inflection point.

How does UNICOM Engineering support AI infrastructure deployment?

UNICOM Engineering provides solution-ready platforms and integration services designed to help OEMs, ISVs, and enterprises accelerate hardware deployment. Their global services team, led by Hudgins, focuses on reducing time-to-deployment and operational complexity for AI workloads. They act as an intermediary between hardware manufacturing and end-site installation.

Why is standardization important for AI infrastructure rollouts?

Standardization allows organizations to move beyond expensive, time-consuming custom deployments and instead leverage repeatable processes that scale efficiently. Without standardized configurations, each deployment site introduces new variables that increase cost and risk. A repeatable model also simplifies maintenance, upgrades, and global supply chain management.

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Simplifying AI Infrastructure: From Data Center to Deployment (Part 1)

Jeff Hudgins from UNICOM Engineering discusses the real-world challenges of deploying AI infrastructure at scale, including design, integration, cooling, logistics, and installation. Organizations benefit from partnering with a single accountable provider and leveraging strong ecosystem relationships to simplify global AI infrastructure delivery.

This story was produced through MarketScale. See how Engineering & Construction teams put it to work with Partner & Channel Enablement.

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By MarketScale · May 19, 2026 · Updated July 1, 2026Ai InfrastructureIsvsJeff HudginsOems

Key takeaways

In this episode of the Flawless Execution podcast, Jeff Hudgins, VP of Global Services at UNICOM Engineering, breaks down the real-world challenges of deploying AI infrastructure at scale.

As AI moves from one-off builds to repeatable global deployments, OEMs, ISVs, and enterprises face increasing complexity across design, integration, cooling, logistics, and installation.

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In this episode of the Flawless Execution podcast, Jeff Hudgins, VP of Global Services at UNICOM Engineering, breaks down the real-world challenges of deploying AI infrastructure at scale. As AI moves from one-off builds to repeatable global deployments, OEMs, ISVs, and enterprises face increasing complexity across design, integration, cooling, logistics, and installation. Jeff discusses how a single accountable partner and strong ecosystem relationships help organizations simplify AI infrastructure delivery—from the data center to the partners responsible for bringing it online.

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About the Experts

MarketScale

Host, Flawless Execution Podcast / Content Team, MarketScale

MarketScale is a B2B media and content platform that produces industry-focused podcasts, video series, and digital content. The Flawless Execution podcast covers operational and infrastructure topics relevant to technology and engineering sectors. MarketScale works with subject matter experts across industries to surface insights for professional audiences.

Jeff Hudgins

VP of Global Services

UNICOM Engineering

Jeff Hudgins serves as VP of Global Services at UNICOM Engineering, a provider of solution-ready platforms and integration services for OEMs, ISVs, and enterprises. He focuses on the operational and logistical challenges of deploying AI infrastructure at scale across global markets. His work spans data center readiness, supply chain, and repeatable deployment models for AI workloads.

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