Page 14 - AI for Good - Impact Report

AI for Good

A US-based start-up and winner of the AI for Good Innovation Factory competition at the 2024 AI
for Good Global Summit in Geneva, uses an AI-powered generative metaverse gaming platform
to transform education and continuous learning through immersive experiences. It aims to
bridge gaps in the education system by using AI and machine learning to personalize learning
experiences based on individual interests, delivering customized career-relevant educational
pathways that inspire curiosity and drive skill development.

The energy sector is another area where AI is used to improve existing structures. While there
is no doubt that AI consumes a lot of energy , it is also true that AI has the potential to manage
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and optimize the overall power consumption of whole regions.
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AI can improve supply and demand forecasts, and understanding of when renewable electricity
is available and needed is crucial for the next generation of electricity systems. By analyzing
historical data, weather patterns, and market trends, AI can forecast energy consumption with
high precision. This capability is crucial in balancing energy supply and demand, reducing
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waste, and optimizing energy procurement strategies. These smart grids have high potential in
regions like Latin America where improvements of technical and commercial energy loss levels
and enhancements of reliability and quality of service are needed. 26

AI is instrumental in enhancing the efficiency of renewable energy sources like solar, wind, and
hydroelectric power. AI-powered forecasting models can predict energy generation patterns,
allowing for optimal energy storage and dispatch strategies. This is particularly beneficial for
grid-scale energy storage systems, such as batteries and pumped-storage hydroelectricity,
which can use AI-based optimization algorithms to maximize energy capture and storage. AI
also is used to find new materials for photovoltaic systems that are considered more efficient
than traditional ones. 27

In agriculture, AI is used to address food security challenges exacerbated by climate change
by making real-time crop-placement decisions, monitoring crop health and enhancing supply
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chain processes.
AI enhances the data systems necessary for improving agricultural sustainability. By using
advanced analytical tools - such as remote sensing, satellite imaging, and earth observation
systems - AI significantly boosts computing power, accuracy, cost-efficiency, and accessibility.
These tools enable real-time analysis of critical factors like soil health, water availability, weather
trends, and pest control, helping farmers make informed decisions. This capability is crucial in
adapting to climate change disruptions that threaten agricultural productivity and, by extension,
global food security. In India, AI-enabled water management leverages a network of experts and
farmers to build scalable applications that improve agricultural decision-making. By analyzing
large datasets, AI can provide personalized insights to farmers on optimal pesticide use, crop
selection, and irrigation schedules. The collaboration between different organizations highlights
the potential for AI to enhance water management practices, by fostering smarter, collective
actions and more efficient resource use. 29

Furthermore, AI improves agrifood systems by optimizing land use, energy consumption, and
supply chain efficiency. AI-driven insights allow for precise land use decisions by analyzing
climate modelling, ecosystem data, and disaster risk maps to determine the most suitable
crops and farming practices. AI also enhances supply chain processes by predicting market
demand and preparing distribution networks for climate-related disruptions. This ensures that

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