TL;DR
- AI data centers are creating infrastructure challenges far beyond those of traditional enterprise facilities.
- Speed to market is becoming a major competitive advantage as demand for data center capacity continues to grow.
- Equipment shortages and long lead times require more flexible project execution models.
- Integrated automation platforms help organizations accommodate change while maintaining project schedules.
- Unified automation architectures support the uptime, reliability, and operational visibility AI data centers require.
Data centers are an unquestionably hot topic right now. Around the globe, the rise of AI is driving a need for infrastructure that dwarfs what we have traditionally understood to be a data center. Today’s facilities are massive, campus-spanning operations with complex power requirements, dynamic workloads, and aggressive construction timelines. As organizations race to meet demand, they need automation architectures capable of supporting both rapid deployment and long-term reliability.
In a recent interview with Datacentre Solutions, Emerson’s data center expert Sean Saul explored many of the challenges organizations face as they work to build and operate the next generation of AI infrastructure. He explained that while power and reliability often dominate headlines, speed is becoming an equally important consideration.
Speed Is Critical
As Saul explains:
“First and foremost, what we see is the need for speed. Availability of some of the large capital equipment but also the automation equipment—really all the guts, even the IT infrastructure associated with these data centers—is really important.”
The competition to build AI infrastructure has created intense demand for critical equipment, resulting in supply constraints and extended lead times across the industry.
Moreover, some key equipment, including gas turbines and related power infrastructure, can involve lead times that stretch for years. As a result, organizations often cannot rely on traditional project models where every decision is finalized in advance. Instead, data center projects must remain flexible enough to accommodate changing requirements, evolving equipment availability, and phased construction approaches.
Flexibility might mean selecting equipment based on availability rather than preference, or making significant changes later in the project lifecycle. For that reason, automation platforms must be capable of evolving alongside the project.
Solutions such as the DeltaV™ and Ovation™ distributed control systems are designed to provide that flexibility, helping organizations maintain a cohesive automation architecture even when project plans change. Built on decades of industrial automation experience, these platforms help teams preserve schedule certainty without sacrificing operational consistency.
“The construction that we see is really at a modular pace. You may start with data haul one, and go to data haul twelve across these different facilities. Having a consistent way of how you implement that automation strategy is really a core part of what enables that speed and ability to accommodate change.”
Standardized automation architectures help project teams scale rapidly while maintaining consistency across expanding facilities.
Uptime Is Everything
Once construction is complete, another challenge quickly takes center stage: reliability.
For AI data center operators, uptime is the foundational performance metric. Maintaining 99.99% availability requires more than simply assembling best-of-breed components. It requires an integrated architecture that reduces complexity while improving visibility and control.
As Saul notes:
“The other element is being able to support less complex automation architecture to help achieve the reliability outcomes that the data centers are seeking. With a DCS which is very well known in other mission-critical industries like life sciences and pharmaceutical manufacturing…that redundancy all the way from those intelligent field devices where you get the inputs from the operating equipment all the way up through the software and operations layer where you’re making the decisions on what to change and what you’re monitoring to maintain that uptime is really critical.”
Reducing architectural complexity while maintaining end-to-end redundancy helps operators achieve the reliability levels modern AI facilities demand.
A key part of that strategy is integrating systems through a unified data fabric. By connecting operational data across the facility, organizations can improve visibility, preserve context, and create the foundation needed for advanced control strategies and AI-supported operations. Rather than managing disconnected systems, teams gain a consistent view of infrastructure performance across the enterprise.
AI and Automation Are Converging
The next evolution of the AI data center extends beyond power management and infrastructure orchestration. Organizations are increasingly looking to industrial AI technologies to help operators make better decisions, navigate workforce shortages, and manage increasingly complex environments.
As Saul highlights in the interview, Emerson is incorporating industrial AI capabilities directly into operator interfaces to support personnel and improve decision-making. When combined with a unified automation architecture and contextualized operational data, these tools can help teams identify issues faster and operate facilities more effectively.
Building the Foundation for AI Infrastructure at Scale
The rapid expansion of AI is changing how data centers are designed, constructed, and operated. Organizations must move quickly while managing supply-chain uncertainty, evolving project requirements, and demanding reliability expectations. By implementing integrated automation architectures built on flexible control systems and unified data environments, operators can accelerate deployment, simplify operations, and create the foundation needed to support AI infrastructure at scale.
