2026 will be the year when AI becomes a discipline, not an experiment
Lawrence Yeo, ASEAN Solutions Director, Hitachi Vantara
The year 2025 was when artificial intelligence became real. Data centers expanded across Southeast Asia, GPU procurement surged, and AI moved from concept to operational deployment.
However, as adoption increased, various limitations also began to surface:
Power availability,
Operational costs,
Data management maturity, and
Regulatory alignment.
These realities are now shaping both corporate investment directions and national digital infrastructure policies.
Over the past two years, many organizations viewed AI primarily as a computing challenge. The assumption was that more accelerators would lead to greater capabilities. But what’s surprising is that the limiting factor is no longer the models or the hardware.
The real bottleneck is the ability to move, manage, and secure data at scale. The main constraints lie in the data pipelines, the storage and network architectures supporting them, and the operational discipline required to run these systems reliably.
The next phase of AI will be agentic—systems capable of making decisions and taking actions in sectors such as financial services, healthcare, logistics, and manufacturing.
For governments and highly regulated industries, this raises new questions about accountability, transparency, and security. Such systems require accurate, well-managed data that is available within the right context, as well as infrastructure designed for high-capacity training, low-latency inference, and sustained resilience.
This is where conflicts begin to emerge: regional demand for AI capabilities continues to grow, but the physical capacity to support it remains limited. Energy efficiency is becoming a strategic concern.
The expansion of data centers in ASEAN—particularly in Singapore, Malaysia, Indonesia, Thailand, and the Philippines—is now constrained by power and land availability. The region cannot continue expanding capacity indefinitely.
Modernizing storage and compute platforms toward denser, more efficient architectures is becoming essential to ensure that AI remains economically and environmentally sustainable.
Data sovereignty is also rising for similar reasons. Countries are strengthening expectations around data location, operational control, and cybersecurity assurances. The future will not be a binary choice between public cloud infrastructure or on-premises systems but a hybrid model that combines local control with secure data mobility.
What happens in Southeast Asia will have global implications, as the region becomes an example of how economies balance AI growth, sustainability, and sovereignty.
Organizations and countries leading in 2026 will treat data as a strategic asset. They will invest in energy-efficient architectures, resilient data platforms, and ongoing talent development.
AI maturity will not be defined by who has the most compute, but by who can operate the most disciplined and efficient data ecosystem.
(RHZ/QOB)