Among the Africa AI use case applications identified, the vast majority are related to agriculture, climate action and energy, according to the GSMA
New artificial intelligence (AI) applications could boost Africa’s economic growth by $2.9 trillion by 2030, according to a new GSMA report.
The report, dubbed “AI for Africa”, noted that the continent currently represents just 2.5% of the global AI market, but added that nearly 90 AI use cases in markets including Kenya, Nigeria and South Africa can drive socio-economic and climate impact across the continent.
Among the Africa AI use case applications identified, the vast majority are related to agriculture (49%), climate action (26%) and energy (24%), according to the report.
The GSMA report highlighted that agriculture employs 52% of the African working population and contributes 17% on average to the GDP. The GSMA also found that the majority of AI use cases in agriculture involve machine learning-enabled digital advisory services, which equip farmers with data-driven advice to adopt climate-smart farming practices and optimize productivity.
In the energy sector, AI-enabled solutions in Africa are improving both on-grid infrastructure and off-grid systems, with use cases such as predictive maintenance, smart energy management, energy access assessment and productive use financing to monitor and extend services in energy-scarce areas. The GSMA highlights that improving energy access and efficiency within the region is vital because it creates a virtuous cycle by enhancing internet and digital tool usage, cellular networks and broadband as well as the generation, transmission and distribution of data needed for AI capabilities.
The GSMA report also found that the increasing availability of remote sensing technologies and satellite imagery has supported the development of uses cases for natural resources management, where AI is being used for biodiversity monitoring and wildlife protection. Early warning systems that offer predictive analytics and real-time disaster assessment to provide timely alerts for climate emergencies and other natural disasters are also already being improved by ML models, significantly improving forecasting in data-scarce regions, the GSMA said.
The report highlighted that 98% of AI use cases in Africa fall under predictive AI applications which leverage ML approaches, due to the availability of historical datasets, ease of application and lower computation requirements compared with generative AI models. The GSMA identifies several hurdles that must be overcome to reap the full potential of the AI opportunity including more nascent use cases and generative AI, which will be key to driving long-term socio-economic benefits.
“To train AI models effectively, extensive, diverse and representative data is essential. It is crucial for datasets to reflect the complexities and nuances of African markets rather than mimic data from the Global North. For instance, across Africa today, there is a major gap in the availability of local-language data. Despite efforts by governments and the private sector, high-quality, locally relevant data remains very limited or hard to access, hindering AI development and scaling,” the report stated.
“AI development also requires robust infrastructure and computing power. As AI applications expand, the energy demands of data centers and the cost of hardware and software will rise. Africa already faces a shortage of data centers and, in countries such as South Africa and Kenya, the cost of a Graphics Processing Unit (GPU) is prohibitively high, representing 22% and 75% of GDP per capita, respectively,” it added.
