A GSMA report: ‘AI for Africa: Use cases delivering impact’, developed from existing research and interviews with leaders across civil society, NGOs, academia and the private sector, identifies over 90 AI use case applications in frontrunning technology markets (Kenya, Nigeria and South Africa) that can drive socio-economic and climate impact.
“To harness the transformative potential of AI across Africa, there needs to be a strong focus on increasing skills for both AI builders and users, especially among underserved populations,” says Max Cuvellier Giacomelli, head of mobile for development at the GSMA.
“Better training programmes are essential, particularly in the face of a global brain drain on AI talent. To ensure Africa doesn’t get left behind, strong partnerships are required across a broad ecosystem of partners including ‘big tech’, NGOs, governments, and mobile operators.”
Giacomelli adds that policies must evolve to address inequality, ethics, and human rights concerns in AI deployment.
As African countries shape their own unique AI strategies, he believes that active engagement in global forums will be pivotal in defining regulatory frameworks that promote ethical AI development and safeguard societal interests, moving toward sustainable solutions that benefit all African communities.
However, there are hurdles to move past to unlock the potential of AI in Africa including the limited availability of data centres and expensive technology investments.
By addressing digital skills gaps and increasing the availability of smartphones, the report states that mobile-based AI solutions may offer a practical way to circumvent current limitations and tap into AI’s full potential across the continent.
Use cases
Today the majority of African AI use cases are related to agriculture (49%), climate action (26%) and energy (24%).
Agriculture employs 52% of the African working population and contributes 17% on average to GDP. In Sub-Saharan Africa, up to 80% of food is produced by smallholder farmers who often use traditional techniques and lack access to information that would help improve yields.
The GSMA finds that the majority of AI use cases in agriculture involve machine learning (ML) enabled digital advisory services, which equip farmers with data-driven advice to adopt climate-smart farming practices and optimise productivity.
These solutions typically reach farmers via mobile devices, highlighting the importance of device ownership, digital skills literacy and user-friendliness.
Affordable and reliable energy services
The region faces challenges to energy access and reliability, with half of its population living without access to electricity.
Today, 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 believes that improving energy access and efficiency within the region is vital as 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.
Climate action
Despite contributing less than 3% of global energy-related CO2 emissions, Africans disproportionately suffer from climate change; without intervention, climate-related emergencies could reduce African GDP by 8% by 2050.
The GSMA finds that the increasing availability of remote sensing technologies and satellite imagery has supported the development of use cases for Natural Resources Management, where AI is 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 already being improved by ML models, significantly improving forecasting in data-scarce regions.
Almost all (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, it says, will be key to driving long-term socio-economic benefits.
Unlocking African AI
To train AI models effectively, extensive, diverse and representative data is essential. The GSMA says datasets must 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 on the continent.
AI development also requires robust infrastructure and computing power. As AI applications expand, the energy demands of data centres and the cost of hardware and software will rise.
Africa already faces a shortage of data centres and, in countries such as South Africa and Kenya, the cost of a Graphics Processing Unit (GPU) is high, representing 22% and 75% of GDP per capita, respectively – making it significantly more expensive than in high-income countries.
As local compute ecosystems grow, countries can leverage mobile-first markets to develop distributed or hyperlocal edge computing, where tasks occur on devices including phones and laptops, thereby reducing reliance on high-powered data centres.
After foundational models are trained on large datasets, AI models can be transferred to smartphones for fine-tuning. With smartphone penetration at 51% and expected to reach 88% by 2030, mobile-based edge computing will be central to expanding the proliferation and capabilities of AI in Africa.
