This is an extract from a recent report “AI to unlock the next wave of renewable integration in ASEAN” published by EMBER.
AI integration is strengthening, but uneven adoption threatens impact
ASEAN’s expanding digital economy and growing data centre capacity provide a structural foundation for AI adoption. The region’s digital economy is now at around $300 billion and it is projected to reach $1 trillion by 2030. Data centres are crucial for efficient digitalisation in the power sector, enabling big data analytics, AI, and smart grid operation. The region is actively prepared to adopt AI with large investment in data centre infrastructure, and ASEAN’s data centre market is projected to grow from $14 billion in 2024 to $30 billion by 2030. Major investments from global cloud service providers are also flowing into the region, such as the $1 billion in investments by Google into Thailand’s expanding cloud infrastructure. Substantial development of digitalisation and data centres position ASEAN countries high in assessments of readiness to adopt AI in the power sector. In fact, several major power systems, including Indonesia, Viet Nam, Thailand, Malaysia and the Philippines, score above the global average on AI readiness indicators, suggesting institutional capacity to deploy AI in public services, including the power sector. Utilities across these power systems have initiated pilot applications in forecasting, predictive maintenance and optimisation, and successfully gained positive results from AI adoption.
However, infrastructure quality remains uneven across the region, with significant gaps in connectivity, data governance, cybersecurity and interoperability standards. AI applications are concentrated in leading markets, while smaller or lower-income systems risk falling behind. This uneven deployment creates the risk of a regional “digital divide,” in which countries with stronger digital infrastructure attract greater renewable investment and system efficiency gains, while others capture fewer benefits. Without deliberate regional coordination, ASEAN could develop pockets of digital sophistication rather than an integrated, intelligent power system.
The scale of untapped system-wide benefits
Global evidence demonstrates that AI in power systems can generate substantial efficiency gains. Deloitte estimates that by 2030, AI-enabled energy efficiency improvements could deliver more than 3,700 TWh of energy savings worldwide far exceeding the projected energy consumption of data centres while generating approximately $200 billion in cost savings and reducing 660 million tonnes of CO2 equivalent emissions.
ASEAN cost savings and emission reduction potential
The potential cost savings and emissions reductions are quantified under two AI adoption scenarios of Deloitte: Baseline and Widespread, which reflects levels of AI preparedness, access and exposure; and two policy pathways: Low-VRE and High-VRE scenarios from IEA’s projections on ASEAN’s power outlook. On an annual basis, as AI adoption rates increase from 2026 to 2035, from 17.5% to 55% under Baseline adoption scenario and 20% to 64% in Widespread adoption scenario, AI could enable ASEAN to realise increasing savings on power sector costs. Annual savings could rise from about $2.5–3.5 billion in 2026 to around $7–10.5 billion in 2035. These estimates correspond to the Baseline-Low VRE and Widespread-High VRE scenarios, respectively.
Cumulatively, this translates into $45–67 billion in cost savings over the period 2026–2035. In terms of emissions, the largest absolute reductions are observed under the Widespread AI adoption–Low VRE scenario. This outcome reflects the fact that the Low VRE scenario assumes lower penetration of VRE and consequently higher baseline emissions, which are approximately 25% higher than under High-VRE scenario. As a result, AI driven efficiency gains yield greater absolute emissions reductions in this scenario, while scenarios with higher VRE shares exhibit lower marginal emissions benefits from AI. Overall, AI adoption could reduce ASEAN power-sector emissions by approximately 290 to nearly 400 million tonnes of CO2 by 2035, under the Baseline–High VRE and Widespread–Low VRE scenarios, respectively.
The cost to deploy AI might be minimal compared to the benefits
Beyond the scale of cost savings and emissions reductions, a key question for policymakers and utilities is whether AI deployment costs are justified by the returns. Although this question is case-specific, initial piloting projects show that deployment costs are modest relative to potential savings when applied strategically at scale. The cost of deploying AI solutions varies by application and level of model sophistication. For example, deploying a large language model (LLM) typically requires relatively low upfront cost with a capital expenditure (CAPEX) of $50200 thousand and operating expenditure (OPEX) $0.0048-0.015 per inference but more advanced analytical or system optimisation models may entail higher costs. For generators, a standard AI asset management solution subscription including predictive maintenance might cost around $100 thousand but could yield $900 thousand in savings. Training compute costs can be $50-100 million for frontier models not including R&D and data acquisition costs. However, even these higher costs are small relative to the cumulative cost savings and emissions benefits identified in the preceding analysis, especially when amortised over the lifetime and scale of utility operations.
Risks of AI integration and policy recommendations
AI challenges and risks
Although AI could play a big role in the energy transition, it is a probabilistic approach and should not be viewed as the silver bullet. Many existing systems in the region were not designed for AI integration, meaning deployment may require significant upgrades or redesign. It is important to note that AI facilitates the resolution of grid challenges associated with the energy transition but cannot, on its own, resolve them all completely. At the same time, AI is driving rapid growth of data centres, further complicating the landscape as it stresses existing power grids. By 2030, data centres could account for 2–30% of national electricity demand across ASEAN (excluding Viet Nam). In addition, these facilities require stable and continuous loads, a demand profile renewables can only partially meet today. As a result, many rely on natural gas now, substantially increasing emissions. Beyond these systemic pressures, AI deployment in power systems could introduce additional technical, operational, and governance risks.
Data limitation can lead to unintentional AI failures
The effectiveness of AI in the power sector is fundamentally constrained by data availability, accessibility, and quality. Levels of digitalisation vary widely across regions and segments of the power system, placing less-digitalised utilities and operators at a disadvantage in capturing AI’s potential benefits. Even where data exist, access remains a major barrier. Energy systems are highly fragmented, often built and enhanced over many decades with data non-standardised and distributed across multiple companies and organisations that may be unwilling or unable to share information due to confidentiality, regulatory, or competitive concerns. Data quality further compounds these challenges. High quality data characterised by completeness, accuracy, coverage, and timeliness are essential for reliable AI performance, yet they are costly and resource-intensive to produce.
The lack of quality data results in models trained and validated on synthetic or simulated data, which may not capture the full spectrum of variability and noise present in live grid conditions. These data limitations directly increase the risk of unintentional AI failure modes. Incomplete or unrepresentative datasets can lead to bias, where AI decisions systematically deviate from intended objectives. Limited or narrow training data raise the risk of extrapolation errors, causing models to behave unpredictably when exposed to conditions outside their training experience, an especially critical concern in safety critical power system operations. Finally, ambiguous objectives or misaligned training signals can result in model misalignment, where AI actions diverge from the goals of system operators or policymakers.
Regulatory uncertainty and liability risks
Power systems are traditionally engineered as deterministic and highly reliable infrastructures, while AI models are inherently probabilistic. Integrating AI into real time and critical operations can therefore introduce uncertainty, complicate validation and verification, and challenge existing reliability standards. Utilities therefore rely on extensive offline testing, digital twins and virtual commissioning to validate AI behaviour before deployment. AI adoption also raises complex liability and accountability questions. When AI tools are used for forecasting, dispatch, protection, or autonomous control, responsibility for system failures may be unclear. Accountability can span utilities, system operators, AI developers, software vendors, and data providers.
The unclear nature of many AI models further complicates accountability attribution, as it can be difficult to trace how a specific decision was made. In addition, existing power system regulations and grid codes were not designed to address AI decision-making, creating legal uncertainty and discouraging large scale adoption. Without clearly defined liability frameworks and market standards, utilities may face increased legal risk, while regulators struggle to enforce compliance and ensure system reliability. One of the solutions is to implement human-in-the-loop systems where AI can suggest actions but operators approve final decisions. Also, explainable AI models are more interpretable and will be easier to audit, justify and eventually improve trust in AI. The implementation of AI solutions will face regulatory hurdles. The desire to preserve some element of human control is likely to persist.
Rising cybersecurity vulnerabilities in a digitalised grid
ASEAN’s transition toward a more decentralised energy system is expected to drive rapid growth in DERs alongside cross-border electricity trade through the ASEAN Power Grid (APG). These developments will require secure and trusted data sharing including sensitive operational information, which underscores the critical importance of robust cybersecurity. Cybersecurity readiness remains uneven across the region. While Singapore and Malaysia rank high in cybersecurity capacity, and the Philippines’ government issued the 2023-2028 National Cybersecurity Plan in 2024, other countries, including Cambodia, Myanmar, and Lao PDR are below world average and thus require additional support to strengthen cybersecurity. Poor measures in several member states can pose risks to regional data security and could hinder cross-border energy collaboration. Cyber threats to the energy sector have intensified significantly in recent years. Analysis shows that a typical gas and electricity utility faced over 1500 attacks per week in 2024, triple the number four years earlier. In 2023, Southeast Asian businesses reportedly experienced more than 36,000 online attacks on a daily basis. AI systems also face additional risks from adversarial attacks, including data poisoning, evasion, and model extraction, which exploit vulnerabilities unique to machine-learning systems.
Recommendations
Align regulation with accurate VRE forecasting
An effective regulatory framework should incentivise accurate VRE generation forecasting. Grid codes need to define data standards and transparency requirements, including regulations that mandate the deployment of weather sensing and monitoring infrastructure to ensure the availability of high quality, real time meteorological data. Such data are essential for training and operating AI forecasting models. Where electricity markets exist, mechanisms that reward forecasting accuracy and penalise significant deviations between scheduled and actual generation can incentivise AI adoption, driving both operational efficiency and market performance.
Strengthen data foundations and model governance
AI can significantly improve weather and VRE generation forecasting, but this technique requires extensive, diverse, and reliable datasets. Establishing decentralised, secure and collaborative data ecosystems such as data spaces will help improve data interoperability, trust, value and governance. ASEAN governments should therefore build and improve these data frameworks for power system operations. Secure data sharing platforms between system operators, utilities, and regulators are essential to unlock AI applications in forecasting, dispatch, and flexibility management. Public investment and concessional financing should support digital grid infrastructure, including advanced metering, sensors, and communication systems.
Embed data protection and cyber security by design
AI deployment in power systems must safeguard sensitive utility data and comply with established cybersecurity standards. A secure data foundation is essential, incorporating robust access controls for data integrations. Advanced technologies, for instance post-quantum cryptography, can defend against potential cyberattacks on AI models. Additionally, decentralised machine learning techniques such as federated learning can enhance user privacy and reduce data transmission costs. Besides that, since AI models are sensitive to input data, adversarial data manipulation or poisoning can deliberately disrupt model performance and lead to unsafe or misleading outcomes.
Build AI-ready institutions and workforces
Successful AI adoption in utilities requires leadership commitment and a workforce equipped with the necessary digital and analytical skills. Utilities should systematically map AI and digital competencies across operational domains to identify and address skill gaps in high value use cases such as predictive grid maintenance, load forecasting, and DERs optimisation. Targeted training and upskilling programmes are essential to reduce organisational resistance and position AI as an augmentation of human expertise rather than a replacement. Also, partnerships between governments, private sector organisations, and educational institutions can play a critical role in designing and delivering relevant AI skill-development initiatives, ensuring alignment with industry needs and labour market demands, particularly in ASEAN countries.
Phased roll-out, close monitoring and plan additional budget
AI deployment should be prioritised in power system operations and planning where near-term impact is greatest. AI sandboxes can further manage risks by enabling utilities and generators to experiment and pilot applications in controlled environments with defined risk parameters while accelerating learning, innovation, and scaling. Utility adoption should follow a phased, impact-focused rollout designed for long-term resilience. Budgets and timelines should realistically account for integration challenges, sensor upgrades, and the complexity of connecting AI systems to legacy infrastructure.
Access the report here