Artificial intelligence is no longer just a software story.

Behind every AI model, generative AI application, recommendation engine, computer vision system, and intelligent automation platform is a growing demand for compute, storage, networking, power, and cooling infrastructure.

As AI adoption accelerates, the role of the data center is changing significantly. Traditional infrastructure designed primarily for conventional enterprise workloads must increasingly accommodate higher compute density, larger datasets, faster networking, and more demanding thermal requirements.

In Indonesia, this transformation is already taking shape. Industry analysis highlights a shift from capacity-led data centers toward intelligent, AI-ready infrastructure capable of supporting AI training and inference, high-density computing, automation, and sustainable operations.

So, what does the rise of AI mean for businesses and their data center strategy?

1. AI is Increasing the Demand for Compute Infrastructure

Traditional enterprise workloads often run across relatively moderate compute environments. AI workloads are different.

Training and running AI models can require large numbers of GPUs or other accelerators operating simultaneously. As businesses move from experimenting with AI to deploying AI applications at scale, infrastructure requirements increase accordingly.

This means businesses need data centers capable of supporting:

  • High-performance computing
  • GPU-based workloads
  • High-density servers
  • Large-scale data processing
  • High-speed storage
  • Low-latency networking
  • Scalable infrastructure

Indonesia’s AI infrastructure landscape is expanding rapidly, with major investments and projects being developed to support increasingly demanding AI workloads. In August 2026, the Indonesian government highlighted the development of an AI Factory projected to reach up to 1 GW of capacity within three years, illustrating the scale of infrastructure required for the next phase of AI adoption.

2. Higher Compute Density Means Higher Power Requirements

One of the biggest changes brought by AI is density.

AI servers equipped with multiple GPUs can consume significantly more power than conventional enterprise servers. As more compute is concentrated into a smaller physical footprint, power availability becomes a critical consideration when selecting data center infrastructure.

Power is therefore no longer simply an operational requirement. It can become a strategic factor in determining whether an organization can scale its AI environment.

Globally, the rapid expansion of AI data centers is already creating significant pressure around electricity availability and grid connections. Recent industry developments show that power infrastructure is increasingly becoming one of the biggest constraints on data center expansion.

For businesses, this means evaluating:

  • Available power capacity
  • Power redundancy
  • Rack-level power density
  • Backup power systems
  • Scalability for future workloads
  • Power efficiency

An AI-ready data center needs to be designed not only for today’s workloads but also for tomorrow’s increasing compute requirements.

3. Cooling is Becoming a Critical Data Center Challenge

More compute means more heat.

For decades, air cooling has been the standard approach for most data center environments. However, increasing server and GPU densities are pushing traditional cooling technologies to their limits.

This is accelerating interest in liquid cooling and other advanced thermal management technologies.

In Indonesia, industry discussions in 2026 have increasingly focused on liquid cooling as AI-driven computing density rises.

The objective is not simply to keep equipment cool. Effective cooling contributes directly to:

  • Hardware reliability
  • Performance stability
  • Energy efficiency
  • Equipment lifespan
  • Operational resilience

For businesses deploying AI infrastructure, cooling capability should therefore be part of the data center evaluation process—not an afterthought.

4. Connectivity Matters as Much as Compute

AI infrastructure is not just about GPUs.

AI workloads continuously move large volumes of data between compute, storage, users, cloud platforms, and other infrastructure environments.

This makes network performance increasingly important.

High-speed and low-latency connectivity can help businesses improve data movement between workloads while reducing potential bottlenecks.

A modern data center therefore needs to provide an ecosystem where compute infrastructure can efficiently connect to:

  • Cloud platforms
  • Internet networks
  • Enterprise networks
  • Other data centers
  • Internet Exchanges
  • Content and application providers

This is particularly important for organizations implementing hybrid cloud, multi-cloud, AI, and distributed workloads.

The industry’s focus is increasingly moving beyond compute alone toward networking and connectivity as another potential AI infrastructure bottleneck.

5. Resilience Becomes Even More Important

The more businesses depend on AI and digital applications, the more costly infrastructure downtime can become.

An interruption affecting an AI workload may impact more than a single application. It can potentially disrupt customer-facing services, data processing, automation, analytics, and other business-critical operations.

This makes data center resilience essential.

Businesses should consider infrastructure capabilities such as:

  • Redundant power systems
  • Backup generators
  • Multiple power sources
  • Network redundancy
  • Environmental monitoring
  • Physical security
  • Disaster recovery capabilities
  • Business continuity planning

The objective is simple:

AI infrastructure must be built for continuous operation, not just high performance.

6. AI is Changing How Businesses Evaluate Data Centers

Historically, businesses may have evaluated data centers based on factors such as:

Location → Rack space → Power → Connectivity → Price

The AI era requires a broader perspective.

Organizations should increasingly evaluate:

Compute density → Power availability → Cooling capability → Network connectivity → Resilience → Scalability

This is because infrastructure decisions made today can determine how easily an organization can scale its AI and digital workloads in the future.

A facility that works well for conventional workloads may not necessarily be optimized for increasingly dense AI environments.

7. What Should Businesses Look for in an AI-Ready Data Center?

Before selecting a data center provider, businesses should consider several key factors.

High-density infrastructure

Can the facility support higher-density servers and compute environments?

Reliable power

Does the facility provide redundant power infrastructure and sufficient capacity for future expansion?

Effective cooling

Can the cooling architecture accommodate increasing thermal loads?

Strong connectivity

Can workloads connect efficiently to carriers, cloud providers, IXs, and other networks?

Resilience

Does the facility have appropriate redundancy and disaster recovery capabilities?

Scalability

Can the infrastructure grow as compute and storage requirements increase?

Operational expertise

Does the data center operator have the technical capability to support increasingly complex infrastructure environments?

These considerations are particularly important as businesses transition from AI experimentation to production-scale AI deployment.

8. Colocation Can Help Businesses Build AI Infrastructure More Efficiently

Building an enterprise-grade data center from the ground up requires significant capital, technical expertise, operational resources, and long-term planning.

For many organizations, data center colocation can provide a more practical alternative.

With colocation, businesses can deploy their own IT infrastructure within a professionally managed data center environment while leveraging existing facilities, power systems, cooling, physical security, and connectivity ecosystems.

This can allow organizations to focus their resources on their applications and business objectives while relying on specialized infrastructure providers for the underlying facility.

For AI workloads, the ability to access reliable power, appropriate rack capacity, connectivity, and resilient infrastructure can become particularly valuable.

9. Indonesia’s Data Center Market Is Entering a New Phase

Indonesia’s digital economy continues to create strong demand for cloud, AI, data center, and cybersecurity infrastructure.

The Indonesian government has identified data centers, AI, and digital infrastructure as important components of the country’s digital economic development.

At the same time, the industry is moving beyond simply adding more data center capacity.

The next phase is about building infrastructure that is:

AI-ready.
Connected.
Resilient.
Scalable.
Efficient.

This creates an opportunity for businesses to rethink their infrastructure strategy before AI workloads become significantly more demanding.

Preparing Your Infrastructure for the AI Era

AI is changing more than the way businesses process data. It is changing the infrastructure required to support digital business.

As AI workloads become more compute-intensive, businesses will need to pay greater attention to power, cooling, connectivity, resilience, and scalability when choosing their data center environment.

The data center of the future will not simply provide space for servers.

It will become a critical foundation for AI, cloud, digital services, and business innovation.

For businesses preparing to scale their digital infrastructure, choosing the right data center partner today can help create a stronger foundation for tomorrow.

Ready to build a more resilient and future-ready infrastructure?

Discover how OMNI Data Center can support your business with reliable data center infrastructure, colocation, connectivity, and disaster recovery solutions.

Contact OMNI Data Center today to discuss your infrastructure requirements.