Indonesia’s data center industry is undergoing rapid transformation. The widespread adoption of artificial intelligence (AI) — driven by tools such as OpenAI’s ChatGPT — has accelerated digital infrastructure development across the nation. Businesses, governments, and technology providers are now investing heavily in computing power, storage capacity, and cloud systems to support AI-driven innovation.
However, this growth raises a critical question: Is Indonesia’s data center infrastructure ready to meet the demands of AI?
In Episode 3A of the Nusantara Data Center Academy Podcast, Hazlan Hezri (Senior Program Manager at Microsoft) and Bizardy Ilham (Data Center Solutions Manager at Legrand Indonesia), moderated by Ayu Saptarika (Marketing Development at PT. Primacom Interbuana), explored this topic in depth. Their discussion highlighted the technological, operational, and human resource challenges that Indonesia must overcome to build AI-ready data centers.
The Transformation Toward AI-Driven Infrastructure
The rise of AI is reshaping how data centers are designed and operated. Across Southeast Asia, demand for high-performance computing infrastructure has increased by more than 25% annually, fueled by machine learning, analytics, and automation.
Traditional facilities were built primarily for storage and basic processing. In contrast, AI workloads require real-time computation, massive parallel processing, and ultra-low latency. This evolution demands GPU-optimized architectures, advanced cooling systems, and robust connectivity.
While several Indonesian operators have begun this transition, readiness levels vary depending on scale, location, and technology adoption.
Infrastructure: The Real Bottleneck of AI Adoption
According to the experts, Indonesia’s primary challenge is not hardware availability but infrastructure readiness, particularly regarding network speed and reliability.
AI systems require vast amounts of data to be processed and transmitted instantly. Without high-speed, low-latency connectivity, even the most powerful GPUs cannot deliver real-time AI performance. This limitation affects applications across sectors — from autonomous systems to IoT analytics.
Given Indonesia’s archipelagic geography, developing distributed edge networks and a strong national fiber backbone is essential. Government initiatives under Indonesia Digital Vision 2045 are expected to play a key role in bridging the gap between AI capability and infrastructure capacity.
Cooling and Heat Removal: Beyond Traditional Systems
AI workloads consume far more power than conventional IT systems. Power density in AI servers can exceed 40 kW per rack, compared to 3–5 kW in typical enterprise setups. This surge generates significant heat that traditional air-cooling systems can no longer manage efficiently.
Previously, most data centers maintained ambient temperatures between 19–21°C for optimal stability. However, AI-driven operations can push rack temperatures to 50–60°C, forcing operators to adopt more sophisticated thermal management.
The focus has shifted from general room cooling to targeted heat removal, using solutions such as liquid cooling and hot/cold aisle containment. These technologies enhance thermal efficiency, optimize space, and reduce overall energy costs. Retrofitting existing facilities with smart sensors and zonal temperature control has become a cost-effective strategy for operators seeking to transition toward AI-readiness.
Beyond cooling systems, it is also crucial to understand how energy is distributed within a typical data center environment.
GPU-Centric Architectures: The Core of AI Computing
The transition from CPU-based to GPU-driven processing represents one of the most significant architectural shifts in the industry. GPUs are optimized for parallel computation, enabling them to process massive AI and machine learning workloads more efficiently.
However, this shift brings higher energy demands and increased heat output. To adapt, data centers must redesign their layouts, improve rack spacing, and upgrade power distribution systems.
As Bizardy Ilham noted, “One AI rack can replace three traditional CPU racks — not in function, but in power consumption.”
This chart compares the power usage and heat output between traditional CPU racks and GPU racks. As shown, GPU racks can consume 15–30 kW per rack and generate temperatures up to 60°C, compared to CPU racks that use only 3–5 kW and remain around 20°C.
This underscores the importance of intelligent power planning, airflow design, and redundancy systems to sustain GPU-intensive operations efficiently.
Decentralized AI Data Center Strategy
Unlike Singapore’s compact environment, Indonesia’s vast geography presents unique infrastructure challenges. Centralizing AI operations in a single region increases latency and limits accessibility for users across the islands.
Experts recommend a decentralized data center strategy, developing multiple AI-capable facilities across Java, Sumatra, Kalimantan, and Sulawesi. This distributed approach ensures low latency, strengthens data sovereignty, and supports service continuity during regional disruptions.
Such decentralization also aligns with Indonesia’s goal of digital inclusivity, ensuring that AI-driven services are accessible to users nationwide — not only in major metropolitan areas.
The Symbiosis Between Cloud and Physical Data Centers
The expansion of AI has reinforced the interdependence between cloud computing and physical data centers. Cloud providers rely on physical facilities for compute and storage capacity, while enterprises increasingly depend on cloud scalability for AI workloads.
This creates a symbiotic yet competitive dynamic. Hyperscalers such as AWS, Google Cloud, and Microsoft Azure remain key clients of local operators but also compete directly for enterprise projects.
To stay relevant, Indonesian data centers must adopt hybrid models that blend on-premise reliability with cloud flexibility. Offering scalable, on-demand services will be crucial for sustaining business growth in the AI era.
Human Capital: The Foundation of AI Readiness
Technology alone cannot ensure readiness. Operating AI-driven data centers requires skilled professionals capable of managing complex systems, ensuring uptime, and optimizing energy use.
Expertise in data analytics, network automation, and AI infrastructure management is becoming indispensable. Upskilling through structured training and certification programs remains critical.
As Hazlan Hezri emphasized, “The transition to AI infrastructure is as much about people as it is about technology.”
Human capital will determine how effectively Indonesia can operationalize its AI ambitions.
Hybrid Models: A Practical Path Forward
Given the high cost of full-scale AI infrastructure, many Indonesian data centers are adopting a phased hybrid model. Rather than constructing new facilities, operators are developing AI-ready zones within existing data halls.
This approach allows operators to balance traditional enterprise workloads with AI-driven tasks. Over time, as demand grows, these zones can evolve into fully AI-optimized environments featuring intelligent cooling, power automation, and energy-efficient operations.
Conclusion: Indonesia’s Path to AI-Ready Infrastructure
Indonesia stands at a defining moment in its digital transformation journey. The momentum behind AI adoption is clear, and the nation’s data center ecosystem is rising to meet that challenge.
Although infrastructure limitations persist — particularly in connectivity and energy efficiency — ongoing progress in cooling innovation, GPU integration, and workforce development signals a steady move toward AI readiness.
The continued collaboration between government initiatives, private investment, and global technology partners will be key to ensuring Indonesia achieves a resilient, scalable, and sustainable AI-driven data center landscape.
For more details, listen directly to the podcast on YouTube Nusantara Academy and don’t forget to register for training by contacting https://wa.me/6285176950083
