By: Narinder Kapoor, Senior Vice President and Managing Director, APAC, HPE

As the world increasingly relies on artificial intelligence (AI), Asia Pacific finds itself at a crossroad. Will AI be the key to a sustainable future, or could it exacerbate environmental challenges that are already too pressing to ignore?

With the region set to maintain a 50% share of global primary energy demand and 60% share of global carbon emissions by 2050, managing AI’s environmental impact has never been more urgent. Training a single large AI model has been shown to generate over 626,000 pounds of CO2 equivalent – nearly five times the lifetime emissions of an average American car. Hence, to scale AI effectively, organisations must determine whether AI is the most resource efficient tool for the task, while also ensuring it is ethical and privacy-enabled.

Every stage of the AI lifecycle – from data selection to training and deployment – presents an opportunity to minimise environmental impact. Governments, industries, and innovators must partner to promote sustainable energy solutions, equitable access to low-carbon energy, and harmonised regulations that prioritise innovation and the environment. Here are four priorities we must embrace to scale AI responsibly in Asia Pacific.

1. Ensure AI is the right tool for the job

AI will revolutionise industries and scientific discoveries globally, but organisations and technology leaders need to use AI intentionally, given what it takes to run it. For instance, a single generative AI (GenAI) query consumes about 15 times more energy than one search engine query, yet the allure of cutting-edge AI may encourage overuse. AI should be reserved for tasks where it adds significant value, not as a one-size-fits-all solution.

Organisations should develop clear AI business plans that include expected business outcomes, data needs, AI model requirements, and energy and cooling demands.

Can your use case effectively and efficiently account for these risks and mitigate them to promote trust? Diligently working through such considerations can help identify where alternatives to AI, or what type of AI tools, might be more appropriate.

2. Optimise across the entire AI lifecycle

Scaling AI sustainably requires optimisation across its lifecycle – from data selection to model design to training and deployment. Although much attention has been given to advancements in AI infrastructure and equipment efficiency – such as advanced chips or a 100% direct liquid cooling system architecture that can reduce power consumption by 90% – we must think beyond the equipment.

The AI lifecycle must be studied to improve software, data, energy, and resource efficiency. For instance, from data efficiency standpoint, selecting relevant datasets and leveraging pre-trained models can significantly reduce energy consumption by eliminating the need for redundant model training. Avoiding unnecessary data processing and fine-tuning only, when necessary, can also cut energy waste.

Monitoring tools are equally necessary. Despite the clear need for efficiency,  only 44% of enterprises actually monitor AI-related energy use. Detailed performance metrics can help researchers identify opportunities to reduce emissions by up to 80% simply by adjusting the time that models are trained to align with windows where renewable energy is more plentiful.

3. Push toward a cleaner energy future, everywhere

AI’s environmental impact is directly linked to the energy used to power it. To scale AI sustainably, Asia Pacific must accelerate the shift to lower-carbon energy sources. Technology innovators and policymakers must form strategic partnerships to spark innovation and develop a more sustainable energy roadmap that includes reliable, low-emission energy sources.

Moreover, developed nations have a responsibility to help developing economies decarbonise their energy grids. Many countries in Asia Pacific lack the infrastructure for low-carbon energy sources due to financial or political constraints. It is essential that the region collectively invests in renewable energy sources to ensure all countries can sustainably access the benefits of AI.

4. Implement smart regulatory policies for responsible AI

Policymakers and the private sector must collaborate to develop smart and practical regulatory policies that incentivise sustainable IT innovation, monitor AI-related energy consumption, and harmonise standards across borders. International frameworks, such as those from the Institute of Electrical and Electronics Engineers (IEEE), can guide the development of sustainable AI systems.

As Asia Pacific intensifies efforts to strengthen data governance, privacy, and security, a more comprehensive regulatory approach is needed. Although temporary measures, such as bans on new data centre builds, bought us time, impactful, long-term solutions with a regional view must prioritise the integration of sustainability with broader ethical considerations

Public-private partnerships are crucial for driving radical innovation while safeguarding natural resources. Governments, industry leaders, and civil society must collaborate to develop and implement responsible and sustainable AI practices, ensuring AI contributes to the long-term prosperity and well-being of the global community.

A defining moment for AI in Asia Pacific

The trajectory of AI in Asia Pacific will shape the region’s economic and environmental future. AI can be a catalyst for sustainable development, but only if its deployment is intentional, measured, and guided by ethical and sustainability considerations.

By prioritising responsible AI use, optimising energy consumption, transitioning to clean energy, and enacting robust regulatory policies, Asia Pacific has the opportunity to lead the world in sustainable AI adoption. Governments, businesses, and civil society must align efforts now to ensure AI drives prosperity without compromising the planet. The choices made today will determine whether AI becomes a force for good or an unchecked driver of environmental degradation.