ASEAN shows structural readiness for greater AI adoption, driven by growing investments in AI infrastructure backbone. The region’s digital economy is valued at around $300 billion and is projected to approach $1 trillion by 2030. Data centre capacity is expanding rapidly, and major power markets including Indonesia, Viet Nam, Thailand, Malaysia and the Philippines perform above the global average in AI readiness indicators. Utilities across these systems have initiated AI-enabled projects in forecasting, predictive maintenance and operational optimisation.
Yet adoption remains uneven and fragmented. AI is often applied to individual assets rather than embedded into system-wide planning, dispatch frameworks or regional coordination mechanisms.
The report also warns that AI deployment in power systems introduces technical and governance risks that must be carefully managed. Power grids are engineered as deterministic and highly reliable systems, while AI models are inherently probabilistic. This raises challenges around validation, explainability and liability, particularly when AI tools support safety-critical power system operations. In many ASEAN markets, power system data remain fragmented or non-standardised, increasing the risk of biased outputs or unpredictable behaviour, which diverges from the goals of system operators or policy makers.
Cybersecurity and institutional readiness pose additional constraints. As grids become more digitalised and interconnected, especially with growing distributed energy resources and cross-border electricity trade, the attack surface expands. AI systems themselves may be vulnerable to data manipulation or model exploitation if safeguards are insufficient.
At the same time, unclear regulatory and liability frameworks, combined with institutional caution in safety-critical infrastructure, could slow large-scale deployment. The report underlines that these risks are manageable, but require clear governance standards, robust cybersecurity protections and phased implementation anchored in human oversight.
“The deployment of AI in energy systems must be guided by ethical principles and trustworthy AI frameworks. As machine learning models often operate as black boxes, transparency and explainability become critical to ensure accountability and regulatory compliance.” Said Dr. Pol Torres, Head of Energy & Agrifood AI solutions at EURECAT.
AI represents a commercially available set of tools capable of improving operational efficiency and enabling higher renewable penetration in ASEAN’s power systems. The scale of economic and emissions gains will ultimately depend on the pace of renewable expansion and whether AI moves beyond isolated pilots toward coordinated, system-wide implementation.
