Data Centers Rush: Can Operations Keep Up With the Growth?

The data center industry is growing at a speed few operating models were designed to handle.  

Demand is accelerating, mostly driven by AI and digital transformation, but the most pressing challenges are no longer physical. They are operational, software-driven, and organizational. 

So, as capacity scales, questions emerge quickly: 

• Will the data center industry keep scaling at this pace without changing how it operates?

• Will more capacity, more workloads, and more AI simply be absorbed by existing IT teams and tools?

• Are we approaching a point where traditional operating models can no longer keep up?

Today, we want to explore the incredible growth behind the data center gold rush, and the less visible IT and operational pressure it’s creating. 

Why Is Demand for Data Centers Growing So Fast?

One of the first points that is often highlighted when explaining the growing demand for data centers is the increasing incorporation of GenAI into our daily lives.  

However, unlike previous growth cycles (often driven by virtualization or early cloud adoption), this expansion is structural, sustained, and software-intensive. And this is mostly due to two factors: 

1. AI is stacking on top of our existing workloads, adding continuous compute demand rather than shifting capacity.

2. Hybrid and multi-cloud architectures have become the norm, expanding operational surfaces instead of consolidating them.

This way, scale is now governed by orchestration, automation, observability, and reliability practices, not by physical expansion alone. 

But let’s check the main drivers of the increasing data center demand. 

AI and GenAI Have Changed the Computing Curve

AI (and GenAI in particular) is maybe the most powerful growth driver behind today’s surge in data center demand.  

According to BCG, AI workloads are expected to account for approximately 60% of net new data center demand between 2023 and 2028. Training workloads are growing at roughly 30% CAGR, while inference workloads (used to serve models in production) are growing at an even more dramatic pace, exceeding 100% CAGR. 

This matters because AI workloads are continuous, not episodic.  

This means that, once deployed, models require constant inference, monitoring, retraining, and optimization. And this translates into a persistent demand for compute orchestration, observability, automation, and reliability engineering, which is far beyond what traditional enterprise workloads required. 

Cloud Expansion Has Increased Operational Load

Cloud adoption was once expected to simplify infrastructure operations. In practice, it has redistributed them. Enterprises today operate across private data centers, multiple public clouds, and increasingly, edge environments.  

So, rather than consolidating compute, cloud has multiplied operational surfaces. 

In fact, that same BCG report signals that public cloud services exceeded $330 billion in revenue in 2024, growing at over 20% year over year, but most large organizations now run workloads across multiple environments simultaneously. Each environment introduces its own tooling, operational patterns, and reliability challenges. 

As a result, data centers are now integral nodes in a broader, hybrid computing fabric. And managing that fabric requires more software coordination, more automation, and more specialized roles than ever before. 

Digital Transformation Raise the Baseline

Even without AI, enterprise digital transformation remains a steady driver of computing demand. Core business systems, analytics platforms, customer-facing services, and internal automation continue to expand in scope and scale. 

BCG estimates that by 2028, roughly 55% of data center demand will still come from traditional enterprise workloads. And that portion is still growing in absolute terms. In parallel, more than 75% of enterprises already use AI or advanced analytics in at least one business function, increasing data processing requirements. 

This combination creates a rising baseline: more applications, more data pipelines, more integrations, and tighter availability expectations. 

What IT challenges are data centers facing as they scale?

Now, the expansion of the data center industry has not only increased physical capacity requirements.  

Beyond that, it has also exposed structural weaknesses in IT operations and how they are staffed, organized, and scaled. Because what was once manageable through stable teams, predictable workloads, and incremental change is now strained by hybrid architectures and AI-driven variability that demand constant adaptation.  

And, as demand accelerates, many operators are discovering that their existing operating models were designed for a very different era.

1. Operational Complexity Is Outpacing Human Scale

As we saw in the previous section, modern data center environments are no longer single, contained systems. They have spanned private infrastructure, multiple public clouds, edge locations, and a growing mix of AI and non-AI workloads.  

But, while each of these layers adds dependencies, failure modes, and operational overhead, the issue is not a lack of technology. On the contrary, on a per-site basis, the frequency of impactful outages is decreasing. 

The real challenge lies in the growing gap between system complexity and the ability of teams to manually operate it at scale. In fact, according to the Uptime Institute, human error remains a contributing factor in over 50% of data center outages, remaining stubbornly consistent even as tooling has improved.  

And hiring alone cannot close that gap. Because specialized roles in SRE, cloud operations, platform engineering, and automation are in short supply globally, while operational demands continue to grow year over year. 

2. Tool Proliferation Without Unified Intelligence

In response to complexity, organizations have accumulated tools. In fact, large enterprises routinely manage +20 operational tools, each producing alerts, dashboards, and data streams:  

• Monitoring platforms

• AIOps solutions

• Automation frameworks

• Ticketing systems

• Security controls

• Data observability layers.

Yet these tools often operate in silos.  

And the result is not clarity, but cognitive overload. Mean Time to Resolution (MTTR) often increases in hybrid environments, even when advanced monitoring is in place, because correlating signals across systems still requires human interpretation. 

This fragmentation highlights a key pain point: technology exists, but integration, orchestration, and operationalization still lag behind. 

3. Reliability Expectations Are Rising Faster Than SRE Maturity

As data centers become the backbone for AI, digital services, and real-time business operations, tolerance for downtime continues to shrink.  

In short terms, availability is now more of a business expectation than a technical metric. Yet many operators have not fully adopted modern reliability practices. Defined SLOs, error budgets, automated runbooks, and continuous reliability engineering remain unevenly implemented outside hyperscale environments.  

Industry benchmarks consistently show that organizations with mature Site Reliability Engineering (SRE) practices experience 30–50% fewer critical incidents, but adoption across the broader data center ecosystem remains partial. 

This creates a reliability gap: expectations rise, but operational discipline struggles to keep pace. 

4. Automation Exists…but Often as Technical Debt

On paper, most data center operators already rely heavily on automation. Scripts handle provisioning, alerts trigger workflows, and configuration tools enforce baseline policies.  

So, the problem is not the absence of automation. It’s how that automation was built, evolved, and maintained. 

Much of today’s automation emerged reactively, created to solve immediate operational pain rather than as part of a coherent system design. Over time, this has led to a landscape of brittle scripts, undocumented workflows, and tool-specific logic that only a few individuals fully understand.  

And, as environments scale and change faster, these automations become harder to adapt, test, and trust. So, instead of reducing operational risk, poorly governed automation can amplify it. 

This way, small changes ripple unpredictably across systems, recovery procedures depend on tribal knowledge, and teams hesitate to automate further for fear of breaking what already works.  

In this context, automation shifts from being a force multiplier to a form of accumulated technical debt that slows transformation just as operational pressure is accelerating. 

Why are traditional operating models under pressure?

The way data center teams collaborate and operate is also changing faster than many organizational models can absorb.  

As environments grow in scale and complexity, work is no longer confined to stable handoffs, predictable runbooks, or clearly separated responsibilities. Modern data center operations increasingly depend on continuous coordination across engineering, operations, security, and application teams.  

So, tasks like incident response, capacity planning, and change management now require shared context, real-time communication, and rapid decision-making. Besides, all these often occur across time zones and organizational boundaries. 

However, many operating models were designed for linear workflows. 

Issues are detected, passed along, escalated, and resolved by different groups in sequence. And, in fast-moving, software-defined environments, this approach introduces friction: 

• Context is lost between teams

• Resolution cycles lengthen

• Accountability becomes diffuse

So, as reliability expectations rise and systems become more interdependent, data center organizations are being forced to rethink how teams collaborate, how knowledge is shared, and how operational responsibility is distributed.  

But are there any solutions being applied? 

Where are solutions emerging for data centers?

So, the growth of the data center industry is no longer testing physical limits alone. It is basically testing how operations scale, adapt, and stay reliable under constant change.  

However, the main problem today is not a lack of solutions, but a clear shift in where operators are already investing their attention. 

Across the industry, data center teams are moving away from reactive, tool-heavy operations and toward more integrated, software-driven ways of managing complexity. And these changes are already underway, shaping how modern data centers are operated day to day: 

Now, looking toward 2026, the organizations that will stand out will not be those “building data centers,” but those operating critical computing environments as strategic assets. 

Whether they are hyperscalers running global platforms or enterprises in industries like manufacturing, energy, or finance running data centers to support core business operations, the differentiator will be the same: the ability to absorb growth without linear increases in people, incidents, or operational complexity.  

That operational leverage (more than raw capacity) will define competitive advantage in the years ahead. 

That’s why software, automation, and operational intelligence are becoming the real scaling engines. And the core of these innovations must be the same: turning data centers from static environments into continuously evolving systems designed to operate under permanent demand. 

And, at Inclusion Cloud, we help organizations across the data center ecosystem build remote teams around critical data center operations.  

Based in Dallas (one of the fastest growing data center and AI hubs in the U.S.) we focus on staffing cloud, platform, networking, automation, and reliability roles aligned with U.S. time zones. If you’re looking for hard-to-find talent at the intersection of data centers, cloud, and AI, we’d be glad to start a conversation. 

Book a discovery call with our team and let’s take the first step together.