AI: Catalyst or Hindrance for Sustainability in Asia-Pacific

Artificial Intelligence (AI) consumes significant amounts of energy, relying on power-intensive data centres and computing infrastructure. In much of the Asia-Pacific region, this energy still comes from fossil fuels, raising concerns about AI’s growing carbon footprint. Training a single large model can generate over 626,000 pounds of carbon dioxide equivalent, underscoring the environmental cost of rapid innovation.

Despite these concerns, AI can play a constructive role in advancing sustainability. When applied responsibly, it helps optimise energy use, reduce waste, and improve efficiency across sectors. From managing smart grids to streamlining logistics, AI offers tools that support more sustainable operations and decision-making.

The challenge now is to ensure AI development aligns with climate goals. Companies must embed sustainability into their AI strategies, invest in cleaner technologies, and establish strong governance frameworks.

Industry leaders and experts share their insights on whether AI is hindering or enabling sustainability, and the steps organisations must take to lead responsibly.

Tee Jyh Chong, Senior Vice President, Asia Pacific, Alcatel-Lucent Enterprise, highlights that AI has transitioned from a research-driven field into a commercially viable, industry-defining force. NCS Australia has developed a cloud-based digital twin model providing real-time monitoring and analysis of water management parameters in Melbourne. By combining historical data and integrating data from multiple platforms, they can forecast the quality of recycled water using machine learning algorithms. In Singapore, the NCS AI Centre of Excellence team has leveraged AI and Machine Learning, IoT to reduce the carbon footprint of their data centre by improving its cooling performance and energy efficiency.

Serene Nah, Managing Director and Head of Asia Pacific, Digital Realty, notes that AI has redefined requirements, demanding that infrastructure can handle increasingly dense, high-performance workloads without compromising sustainability. Digital Realty prioritises energy efficiency through direct liquid cooling and their proprietary AI platform, Apollo, which uses operational data to optimise energy usage across their global portfolio.

Vincent Caldeira, Chief Technology Officer, APAC at Red Hat, explains that AI impacts enterprises, with many struggling to scale AI workloads and transition generative AI from pilot to production. Red Hat aims to democratise AI through open-source contributions, enabling efficient deployment across hybrid cloud environments. Enterprises are leveraging AI for hyper-automation to streamline operations and optimise resource management, addressing challenges in sectors such as finance, material science, and healthcare.

Susanna Hasenoehrl, Head of Sustainability Solutions, SAP Asia, believes the full promise of AI can increase enterprise productivity by up to 30 per cent. SAP’s data centres run on 100 percent green electricity as part of their 2030 Net Zero commitment. They are also helping customers find the most efficient LLM for their needs, which often means the one that uses the least amount of energy.

Suvig Sharma, Regional Head, Asia, Confluent, highlights that Confluent integrates real-time data streaming with generative AI, LLMs, and advanced analytics, supporting a wide range of enterprise AI use cases. The foundation of data streaming drives Confluent’s ability to curate and stream relevant, high-quality data to AI models, reducing the volume of data processed and stored, and cutting down computational power for AI training and inference.

Daniel Pointon, Group Chief Technology Officer, ST Telemedia Global Data Centres, states that AI is reshaping the data centre landscape and is a core pillar of the company’s growth strategy. STT GDC is proactively designing and delivering purpose-built, AI-ready data centres and adapting existing facilities across key markets. They use AI-enhanced cooling control systems, which utilise reinforcement learning to reduce energy consumption in their data centres, with initial results indicating energy savings of 10 percent, with the potential to reach up to 30 percent savings in their cooling energy consumption.

Tee Jyh Chong, Senior Vice President, Asia Pacific, Alcatel-Lucent Enterprise, shares that they use AI not only to optimise internal operations but also to offer advanced functionalities in service of their customers. They favour traditional machine learning models over large language models if they can deliver the same results efficiently and are investing in optimised fine-tuning methods to minimise memory usage and computational requirements. Their AI-powered smart building solutions provide the digital foundation needed for sustainable operations.

Danny Elmarji, Vice President, Presales, APJC, Dell Technologies, emphasises that the road to efficient AI implementation starts with a focus on efficient infrastructure. They invest in hardware designed for optimal performance per watt and use advanced cooling technologies like direct-to-chip or rack-level liquid cooling. Dell uses AI to automate the collection and analysis of usage data and data centre operations, making sustainability reporting more accurate, efficient, and auditable.

As AI continues to evolve, the key to balancing innovation and environmental responsibility lies in responsible development, transparent governance, and strategic investment in sustainable technologies.