AI in ASEAN power systems: momentum is building, but system-wide gains remain untapped

This report finds that widespread AI adoption in ASEAN power systems could deliver up to $67 billion in cumulative cost savings and reduce nearly 400 million tonnes of CO2 by 2035.

• Widespread adoption of AI in ASEAN power systems could result in cumulative cost savings of $45–67 billion and a reduction of approximately 290 to nearly 400 million tonnes of CO2 by 2035.
• AI cost benefits are highest in scenarios with high variable renewable energy (VRE) penetration, specifically where VRE reaches a 42% share of generation by 2035, because renewable systems have higher upfront CAPEX and O&M costs.
• Despite high AI readiness scores in countries like Indonesia, Viet Nam, Thailand, Malaysia, and the Philippines, adoption is currently fragmented and pilot-based, risking a regional "digital divide" due to uneven infrastructure quality in connectivity, data governance, and cybersecurity.
• The cost of deploying AI is generally modest compared to the potential returns; for example, an AI asset management solution for generators costing $100 thousand could yield $900 thousand in savings, and a $2 million machine learning system for transformers saved $8 million annually.

APA

Ember (2026). AI in ASEAN power systems: momentum is building, but system-wide gains remain untapped. https://ember-energy.org/chapter/ai-in-asean-power-systems-momentum-is-building-but-system-wide-gains-remain-untapped/

Chicago

Ember. AI in ASEAN power systems: momentum is building, but system-wide gains remain untapped. 2026. https://ember-energy.org/chapter/ai-in-asean-power-systems-momentum-is-building-but-system-wide-gains-remain-untapped/.

Wikipedia

{{cite report |author=Ember |title=AI in ASEAN power systems: momentum is building, but system-wide gains remain untapped |date=3 March 2026 |url=https://ember-energy.org/chapter/ai-in-asean-power-systems-momentum-is-building-but-system-wide-gains-remain-untapped/ |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{ember2026asean, author = {{Ember}}, title = {{AI in ASEAN power systems: momentum is building, but system-wide gains remain untapped}}, institution = {Ember}, year = {2026}, month = mar, url = {https://ember-energy.org/chapter/ai-in-asean-power-systems-momentum-is-building-but-system-wide-gains-remain-untapped/}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }