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AI Predicted to Drive $2.9 Trillion Growth in Africa by 2030

2024-07-17 11:23:23.918000

Artificial intelligence (AI) has the potential to support Africa's socio-economic growth, according to a report by the GSMA. The report, titled 'AI for Africa: Use cases delivering impact,' identifies over 90 AI use cases in Kenya, Nigeria, and South Africa that can drive socio-economic and climate impact. The main areas where AI can make a difference are agriculture, climate action, and energy. In agriculture, machine learning-enabled digital advisory services can help farmers adopt climate-smart farming practices. In energy, AI-enabled solutions can improve on-grid infrastructure and off-grid systems. AI can also support climate action through remote sensing technologies and satellite imagery for biodiversity monitoring and early warning systems for climate emergencies. The report highlights that Africa represents just 2.5% of the global AI market but emerging applications could boost the continent's economic growth by $2.9 trillion by 2030. However, there are challenges to overcome, including limited availability of data centers, expensive technology investments, and the need for extensive and diverse data. Mobile-based edge computing is seen as a practical way to expand the proliferation and capabilities of AI in Africa, given the high smartphone penetration rate [8413e6f4].

According to a GSMA report, AI applications in Africa could boost economic growth by $2.9 trillion by 2030. The report identifies over 90 AI use case applications in Kenya, Nigeria, and South Africa that can drive socio-economic and climate impact. The majority of AI use cases in Africa are related to agriculture, climate action, and energy. AI is being used in agriculture to provide data-driven advice to farmers, in energy to improve infrastructure and off-grid systems, and in climate change to support biodiversity monitoring and disaster assessment. The report highlights the need for extensive and representative data, robust infrastructure, and increased digital skills to fully harness the potential of AI in Africa [4cb7ab0d].

In addition to the GSMA report, the International Monetary Fund (IMF) released a report on the top 10 African countries most ready for AI adoption in 2024. The report measures AI preparedness across 174 countries based on four dimensions: digital infrastructure, human capital, technological innovation, and legal frameworks. According to the IMF report, the top 10 African countries most ready for AI in 2024 are Seychelles, Cabo Verde, [missing data], [missing data], [missing data]. These countries have made significant progress in developing their digital infrastructure, investing in human capital, fostering technological innovation, and establishing robust legal frameworks to support AI adoption [a05d8cef].

AI adoption in Africa is gaining momentum, with various initiatives and projects being implemented across the continent. In Sub-Saharan Africa, a Ph.D. candidate in the School of Economics at Georgia Institute of Technology is developing a tool that utilizes AI and machine learning to predict food insecurity in the region. The tool aims to assist policymakers and community organizations in implementing timely interventions to alleviate hunger and achieve food security. The primary causes of food shortages in Sub-Saharan Africa are armed conflicts, climate change-induced droughts, and natural disasters. The goal is to provide policymakers, aid organizations, and communities with valuable insights to effectively address the worsening crisis of food insecurity in Africa [cb3ca318].

Crude oil theft in the Niger-Delta region of Nigeria has been a long-standing issue, causing economic losses and environmental damage. Nigeria, being one of the largest oil-producing countries in Africa, can use AI technologies such as monitoring and surveillance with drones and satellite imaging, predictive analytics, enhanced security systems, and collaboration with tech companies to combat oil theft. By investing in AI and improving surveillance and security systems, Nigeria can protect its oil installations, resources, and promote sustainable development in the country [e574c2fa].

The Horn of Africa is also utilizing AI to combat extreme weather events. Scientists at Oxford University are developing an AI system that can predict extreme weather using satellite data and weather forecasts. This system, known as the 'laptop weather forecaster,' provides a 48-hour warning for communities to prepare for floods, potentially saving lives and livelihoods. The success of this pilot program could make AI a powerful tool in mitigating the impact of climate change worldwide [6a20d0bb].

In Nigeria, blockchain technology and AI are being highlighted as tools to address illicit financial flows (IFFs) in Africa. African countries lose $88.6 billion annually due to IFFs. The chairman of Nigeria's Financial and Monetary Crimes Commission emphasizes the need for strong legal frameworks, capacity building, and adopting advanced technologies like data analytics, blockchain, and AI for asset tracking and recovery efforts. Nigeria has recently embraced blockchain technology and AI, with the National Information Technology Development Agency restructuring the National Blockchain Policy Steering Committee and advocating for the incorporation of AI into the country's security framework [6b41d863].

AI is also making significant contributions to agriculture in Africa. AI models can gather data from weather stations, satellites, and historical records to provide accurate weather forecasts for farmers. This helps farmers make informed decisions about planting, irrigation, and harvesting, especially in the face of climate change. AI-powered sensors and drones can collect data on soil conditions, allowing farmers to monitor moisture levels, nutrient content, and pH balance. Machine learning algorithms in AI models can analyze this data to provide real-time insights for farmers to optimize crop management. AI tools can also help identify pests and diseases in crops through image recognition and machine learning, enabling early detection and targeted treatments. The use of AI in agriculture can reduce waste, optimize resource usage, and increase crop yield and quality [98a07327].

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