Introduction: A Critical Crossroad for the Global South

As the world accelerates towards an AI-driven future, food security and energy security remain two of the most pressing issues in the Global South—regions spanning Asia, Africa, and Latin America. These challenges are exacerbated by climate change, which disrupts traditional agricultural cycles and increases energy demands. Meanwhile, economic growth in these regions demands a steady supply of sustainable and affordable energy, particularly as industries digitalize and automation scales up. Addressing these interconnected challenges requires innovative AI-driven solutions that can enhance resilience, optimize resources, and create sustainable pathways for the future.

Transitioning to Food Security: AI’s Role in Sustainable Agriculture

To address the growing challenges of food security in the Global South, it is essential to explore innovative solutions that leverage AI. As climate change disrupts traditional agricultural cycles, AI-driven technologies can help smallholder farmers and policymakers mitigate risks while improving productivity. The following sections explore how AI is shaping the future of sustainable agriculture.

1. Climate-Smart Precision Agriculture

AI has revolutionized precision farming, allowing smallholder farmers to maximize yield while minimizing resource wastage. AI-driven analytics process real-time data on soil health,weather patterns, and crop conditions, providing predictive insights for better decision-making.

• In India, Microsoft’s AI-driven sowing application helped farmers optimize planting times using 30 years of climate data (World Economic Forum, 2023). This resulted in a 30% increase in productivity per hectare, all through a simple SMS-based advisory system—proving that AI does not have to be complex to be impactful.
• In Uganda, AI-powered smartphone apps diagnose plant diseases based on computer vision analysis of leaf images (International Institute of Tropical Agriculture, 2023). This technology has significantly reduced yield losses by enabling farmers to take early corrective measures before disease outbreaks spread, improving crop resilience by 25%.

2. AI in Food Distribution: Reducing Post-Harvest Losses

AI is not only transforming food production but also optimizing supply chains to reduce losses and ensure food reaches consumers efficiently.

• Twiga Foods in Kenya leverages AI-driven logistics to connect small farmers directly with urban retailers, cutting post-harvest losses from 30% to just 4% (World Bank, 2022) by optimizing supply routes and market demand predictions.
• Governments and humanitarian organizations are using machine learning models to forecast food shortages (FAO, 2023) up to 30 days in advance, allowing proactive intervention in food-insecure regions.

By integrating AI into both farming and food distribution, significant advancements have been made in optimizing logistics, reducing losses, and improving food accessibility. For example, in Kenya, Twiga Foods has successfully implemented AI-powered logistics to streamline supply chains, enabling small farmers to directly connect with retailers (World Bank, 2022). Similarly, in India, AI-driven predictive models help manage food inventories, ensuring efficient distribution and reducing waste (FAO, 2023). These case studies highlight how AI integration is not just a technological innovation but a practical solution to pressing food security challenges in the Global South. With these advancements, we can enhance agricultural resilience against climate change while ensuring food reaches those who need it most.

Energy Security: AI’s Role in Sustainable Power Solutions

1. AI for Renewable Energy Optimization

With economic growth in the Global South comes a surging demand for energy. However, the reliance on fossil fuels is unsustainable. AI is enabling the transition to renewable energy by predicting and optimizing energy generation from solar and wind power.

• Atlas AI and ENGIE Energy Access use machine learning models to identify off-grid communities where renewable energy mini-grids can be most effectively deployed (World Resources Institute, 2024).
• In India, AI-driven predictive maintenance for solar farms has reduced downtime and boosted energy efficiency by 15% (IRENA, 2023), ensuring a stable energy supply.

2. AI in Smart Grids: Reducing Energy Waste

Energy loss due to inefficiencies and theft is a major problem in developing economies. AI-powered smart grids and predictive analytics are now helping utilities reduce these losses.

• In Bihar, India, an AI-powered fraud detection system identified over 136 cases of electricity theft within a month (International Energy Agency, 2023), reducing non-technical losses that had previously drained resources from the energy sector.
• AI-powered microgrids in rural Africa use real-time demand forecasting to balance energy distribution, ensuring that stored solar energy is used efficiently without unnecessary wastage (International Renewable Energy Agency, 2024).

With AI, we are witnessing the rise of self-regulating energy systems that enhance both access and affordability of clean energy, paving the way for sustainable industrialization.

The Need for AI Innovations Tailored to the Global South

One of the biggest challenges in AI development is that most innovations are designed for developed nations, where infrastructure and resources are vastly different. The Global South has unique needs that require AI solutions specifically tailored to their realities.

In developed nations, AI-driven agricultural solutions often focus on high-tech automation, robotics, and advanced IoT systems that rely on strong internet connectivity and significant capital investment. In contrast, developing nations require AI solutions that are affordable, decentralized, and adaptable to low-resource environments. Similarly, in the energy sector, while developed economies prioritize AI for grid automation and smart city initiatives, the Global South benefits more from AI solutions that enable decentralized renewable microgrids and predictive maintenance for off-grid solutions.

These solutions include:

• Offline AI Applications: Many rural areas have limited or no internet access. AI solutions must be designed to function offline, using mobile-based or SMS-enabled advisory systems.
• Low-Cost AI Hardware: Traditional AI models rely on expensive computing power. More research should be invested in affordable AI hardware, such as lightweight AI sensors for smallholder farmers or low-power microgrids for energy distribution.
• Localized AI Training Data: Many AI models are trained on datasets from the Global North, which do not reflect agricultural patterns, energy usage, or climate variables in developing nations. Investing in local datasets will improve AI accuracy and adoption.
• AI for Decentralized Energy Networks: Instead of large power grids, developing nations can benefit from AI-driven decentralized microgrids that optimize renewable energy sources and prevent blackouts.

By focusing on these context-specific innovations, AI can truly address the unique challenges of food and energy security in the Global South while reinforcing the necessity of AI advancements that differ from those in developed nations.

The Intersection of AI, Climate Change, and Economic Growth

The climate crisis is no longer a distant threat—it is already affecting food supply chains, water resources, and energy infrastructure in the Global South. Meanwhile, economic growth in these regions relies heavily on energy availability, especially as businesses digitalize, factories automate, and digital services expand. The AI revolution presents a paradox: while AI itself demands massive energy consumption, it also offers the most effective tools to mitigate climate-related food and energy challenges.

Policy and Implementation Considerations

For AI to become a scalable and inclusive solution, governments, businesses, and academia must collaborate to:

1. Invest in AI literacy: Farmers, energy providers, and policymakers must be trained in AI adoption to ensure that solutions are utilized effectively.
2. Leverage mobile-first AI solutions: Given that mobile penetration is higher than internet access in many parts of the Global South, AI solutions must be optimized for SMS, feature phones, and offline functionalities.
3. Build data-sharing ecosystems: AI models are only as good as the data they learn from. Governments and private sectors must create open-access agricultural and energy datasets to improve AI efficiency.
4. Ensure AI ethics and inclusivity: AI-driven energy pricing, predictive farming models, and automated supply chains must be designed to benefit local communities rather than exacerbate inequalities.

Indonesia as a Pioneer in Global AI Governance

Indonesia has the potential to take the lead in advocating for AI governance that represents the interests of the Global South. Recognizing the unique challenges faced by developing nations, Indonesia’s Minister of Digital Communications (Komdigi) Meutya Hafid raised these issues at the Artificial Intelligence Action Summit (AIAS) in Paris, France, on February 10-11, 2025. These two issues could become key discussion points at the AI Global South Forum next year. Organized by UNESCO, this forum serves as a platform for developing and applying Artificial Intelligence (AI) technologies for developing countries in Asia, Africa, and the South Pacific. Indonesia has also put forward a bid to host the event, positioning itself as a leader in AI governance for the Global South.

By positioning itself at the forefront of AI governance discussions, Indonesia can influence global policies on ethical AI, equitable AI access, and technology transfer. The country can advocate for frameworks that ensure AI solutions address the unique socioeconomic and environmental conditions of developing nations. Additionally, Indonesia can foster regional cooperation among Global South countries, encouraging AI research partnerships and capacity-building initiatives to ensure AI benefits are distributed equitably.

Conclusion: AI as the Great Equalizer

The Global South faces the dual challenge of climate vulnerability and economic transformation. AI presents an unprecedented opportunity to leapfrog traditional development hurdles and build climate-resilient food and energy systems. By investing in smart, data-driven solutions, we can future-proof these regions against climate risks while ensuring inclusive economic progress.

However, AI alone is not a silver bullet—it must be combined with the right policies, infrastructure investments, and grassroots adoption. If harnessed effectively, AI has the power to redefine food security, energy access, and sustainable growth in the Global South, ensuring a more resilient and prosperous future for millions.