Every vendor pitch makes data center automation vs orchestration sound simple.

Buy this platform, implement these workflows, watch efficiency soar.

But there's a critical conversation getting lost in all those product demos – the practical distinction between automation and orchestration, and more importantly, finding the people who can make either one work.

The reality is that 65% of data center operators prioritize automation, yet the talent to actually implement it remains increasingly scarce.

With the data center automation market projected to grow from $10.16 billion in 2024 to $36.99 billion by 2034, organizations are racing to modernize their operations.

But tools don't configure themselves. This is a practical breakdown of what these technologies actually mean for your infrastructure, your budget, and your hiring strategy.

High Level Takeaways

• Automation handles single tasks (backups, provisioning, patch management); orchestration coordinates complex workflows across multiple systems and tools.
• Both require skilled people: The industry needs 325,000 new full-time positions globally in 2025, with 58% of operators citing multi-skilled data center operators as their top hiring priority.
• Market growth is explosive: The automation market is growing at 13.79% CAGR, but talent shortages are constraining how quickly organizations can actually adopt these technologies.
• Implementation success depends on two things: Understanding which problems need automation vs orchestration, and having the right talent to execute.

What Data Center Automation Actually Does

Data center automation tools handle individual tasks without human intervention. Think of it as teaching your infrastructure to do one thing really well, over and over again.

The most common automated tasks include:

• Server provisioning spins up new virtual machines or containers based on predefined templates. 
• Patch management deploys security updates across hundreds or thousands of systems on schedule. 
• Backup scheduling runs automated backup jobs at specified intervals without manual triggers. 
• Configuration management automation ensures systems maintain their intended state and flags drift.

The business case is straightforward. Automated processes reduce human error, free up staff for higher-value work, and deliver consistent results regardless of who's on shift. When your backup runs at 2 AM, you don't want it dependent on someone remembering to click a button.

Where data center automation tools fall short: they're excellent at executing individual tasks, but they don't coordinate those tasks toward a larger business outcome. That's where orchestration enters the picture.

What Orchestration Brings To The Table

If automation is a skilled worker who does one job perfectly, infrastructure orchestration is the project manager who coordinates an entire team toward a deadline.

Workload orchestration manages multiple automated tasks, sequences them correctly, handles conditional logic, and responds when things go wrong. 

It's the difference between having automated backups and having a complete disaster recovery sequence that automatically fails over to a secondary site, spins up replacement infrastructure, validates data integrity, and notifies stakeholders without human intervention.

Modern orchestration platforms like Kubernetes, Terraform, and Apache Airflow provide this coordination layer. They enable:

• Automated provisioning across hybrid environments with a single workflow
• Workload balancing automation that responds to real-time demand
• Automated failover systems that execute multi-step recovery procedures
• Automated compliance monitoring that triggers remediation workflows when violations occur
• Data center workflow automation that sequences dependent tasks across teams and systems

Here’s a practical example: deploying a new application environment. With automation alone, you might have separate scripts for provisioning VMs, configuring networking, deploying containers, and updating load balancers. Each runs independently. 

With orchestration platforms, these become a single coordinated workflow. If the networking configuration fails, the orchestrator rolls back the VM provisioning rather than leaving partial infrastructure sitting idle. That's the difference between tools that execute tasks and systems that manage outcomes.

And here's the catch: these tools don't configure themselves. The more sophisticated your orchestration, the more specialized talent you need to implement and maintain it.

The Skills Gap No One Talks About When Discussing Automation

There's a counterintuitive reality facing every organization investing in data center operations automation: the more you automate, the more you need skilled humans.

The Uptime Institute projected the industry would need 2.3 million full-time staff by 2025, up from around 2 million in 2019. According to AFCOM's State of the Data Center Report 2025, 58% of data center managers identified multi-skilled data center operators as their top area of growth, while 50% signaled increasing demand for data center engineers.

What's driving this demand? The skills required for modern data center management have fundamentally shifted.

Analysis of in-demand roles reveals that nearly 60% of required skills are actually non-technical. Problem-solving, critical thinking, adaptability, and communication top the list. Why? Because automation handles the routine work. What remains is troubleshooting when things break, designing new solutions, and coordinating across increasingly complex systems.

For technology leaders focused on data center efficiency automation, this creates a hiring paradox: you need people who understand both the technical foundations and can think strategically about how automated systems interact.

Why The Confusion Exists Between Automation And Orchestration

Ask three vendors to define data center automation vs orchestration and you'll get four different answers. The confusion isn't accidental.

Vendor marketing blurs the lines. Most data center management platforms now include both capabilities, so sales teams use the terms interchangeably. A "complete automation solution" often includes orchestration features, while an "orchestration platform" usually handles basic automation tasks.

The tools have converged. Infrastructure as code tools like Terraform handle both automated resource allocation and multi-step orchestration. Configuration management automation platforms like Ansible can execute single tasks or coordinate complex workflows. The technical distinction matters less at the feature level than it once did.

Historical context adds complexity. Automation came first, scripting individual tasks to reduce manual work. Orchestration evolved later as infrastructure grew more complex and organizations needed to coordinate across hybrid environments. Legacy terminology doesn't always map cleanly to modern capabilities.

When does the distinction actually matter? Two scenarios:

1. Planning implementation: Understanding whether you need task-level automation or workflow coordination helps you scope projects accurately and avoid over-engineering solutions for simple problems.
2. Hiring technical talent: An automation specialist writes scripts and maintains individual tools. An orchestration architect designs end-to-end workflows and manages dependencies across systems. Different skills, different roles, different compensation bands.

When You Need Automation, Orchestration, Or Both

The orchestration vs automation benefits debate misses the point. Most organizations need both but the question is which problems require which approach.

The reality for most enterprises: 90% of organizations operate hybrid or multi-cloud environments, driving orchestration demand. But those same organizations still need task-level automation for the foundational work that orchestration coordinates.

The decision framework comes down to infrastructure complexity, business process maturity, and existing capabilities. 

Organizations with mature DevOps practices and complex multi-cloud environments will lean heavily on data center orchestration. Those pursuing hybrid cloud automation or earlier in their automation journey may get more value from focused automation investments first.

The Talent Challenge Behind The Technology

Let's address the elephant in the room: over half of data center operators struggle to attract and retain qualified staff.

If you're a technology director watching your automation initiative stall because you can't find the right people, you're not alone. Projects fall behind schedule. Your existing team picks up the slack and burns out. The tools you purchased sit underutilized while everyone scrambles to keep basic operations running.

The data center skills gap isn't just about finding people who know Terraform or Kubernetes. It's a multi-layered challenge:

1. The aging workforce problem. In North America and Western Europe, much of the existing workforce is approaching retirement age. The "silver tsunami" threatens institutional knowledge that can't be easily documented or transferred.
2. Over-ambitious job requirements. Uptime Institute research consistently shows that employers set minimum requirements higher than necessary. Many positions don't actually require university degrees as equivalent experience can substitute for formal education. But job postings don't reflect this, shrinking the candidate pool unnecessarily.
3. Training infrastructure gaps. Technical colleges and vocational programs are only beginning to develop data center-specific curricula. Microsoft's Datacenter Academy and AWS Workforce Accelerator represent industry responses, but these programs are still scaling.

For organizations trying to implement infrastructure automation strategies, this creates real constraints. You can purchase the best cloud orchestration tools on the market, but without people who can configure, maintain, and troubleshoot them, the investment stalls.

Building Your Automation And Orchestration Capabilities

So, where does this leave technology directors trying to modernize data center operations?

Start By Assessing Your Current Infrastructure Honestly

What's actually causing pain today? If your team spends hours on manual provisioning, task-level automation delivers immediate value. If you're struggling to coordinate deployments across multiple environments, orchestration is the priority. Map your existing workflows before buying any tools.

Match Your Approach To Your Talent

Data center DevOps automation requires different skills than basic scripting. If your team has strong development backgrounds, orchestration platforms may be more accessible. If your strength is operations, start with automation tools that have gentler learning curves.

Consider Phased Adoption

Rather than attempting a complete transformation, identify high-impact workflows and automate or orchestrate them first. Success builds organizational momentum and develops internal expertise that can tackle more complex initiatives. 

Many organizations find value in starting with infrastructure as code for configuration management automation, then layering orchestration capabilities on top once that foundation is solid and the team has built confidence.

Don't Overlook IT Infrastructure Automation Fundamentals

Before investing in sophisticated orchestration, ensure your basic automated data center management is reliable and well-documented. Orchestration that coordinates unreliable automated tasks just creates more complex failures that are harder to diagnose and resolve.

Plan For The Hiring Reality

Recruiting data center technicians with automation and orchestration experience takes longer than traditional IT hiring. Organizations that build relationships with technical colleges, apprenticeship programs, and specialized data engineering recruiters have an advantage. 

The data center skills gap means competition for qualified candidates is intense, so starting your talent search before you finalize your technology decisions is often the smarter play.

Building The Team That Makes It Work

Technology alone doesn't deliver results. Many organizations facing the data center skills gap turn to managed service providers to bridge capability gaps while building internal teams, and that hybrid approach often delivers faster results than trying to do everything in-house.

MSH's data center staffing expertise sits at the intersection of these challenges. We pre-vet candidates so you're not wasting time interviewing people who can't actually do the job. 

Whether you're building automation capabilities or need orchestration architects for a hybrid cloud transformation, we typically deliver qualified candidates within 48-72 hours for urgent roles.

Connect with MSH to discuss your specific needs.