AI's potential in African agriculture, energy and climate action – GSMA

A new GSMA report puts forward the huge potential for artificial intelligence (AI) to support Africa's socio-economic growth particularly for sectors like agriculture, energy and climate action.

The report titled: AI for Africa: Use cases delivering impact identified over 90 AI use case applications in Kenya, Nigeria, and South Africa with 49% related to agriculture, 26% to climate action and 24% to the energy sector.

"While many AI use cases are relatively nascent, with some being deployed as part of projects or pilot schemes, a number of commercially viable solutions have also emerged," the report said. "Often, AI is being incorporated into existing digital products and services, acting as an enabler to make digital solutions more relevant and efficient, amplify their impact, and facilitate scaling," it added.

The GSMA report also used existing research and interviews with leaders across civil society, non-governmental organizations (NGOs), academia and the private sector.

Percentage of AI use cases across the relevant sectors. (Source: GSMA's 'AI for Africa: Use cases delivering impact' report from July 2024)

Africa represents just 2.5% of the global AI market, but emerging applications could boost the continent's economic growth by US$2.9 trillion by 2030 according to data from AI4D Africa.

"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. Better training programmes are essential, particularly in the face of a global brain drain on AI talent," Max Cuvellier Giacomelli, head of Mobile for Development at the GSMA, said in a statement about the report.

The research explained that increasing availability of data generated by remote sensing technologies, such as on-the-ground sensors, drones with high-resolution cameras, and satellites, has led to the development of many AI-driven use cases across sectors.

Analysis of geospatial and remote sensing data, powered by machine learning (ML), can support a wide range of use cases and activities such as monitoring soil conditions for effective crop management, mapping energy access in off-grid areas to inform energy planning, and monitoring climate change impacts on ecosystems.

Agricultural AI use cases dominate

The GSMA observed that the agritech sector is seeing most of the AI innovation, especially in Kenya and Nigeria where agriculture continues to play a significant role in the economy.

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.

AI is already being used for agricultural advisory – with companies like TomorrowNow and ThriveAgric providing farm-level insights to farmers – and for financial services with companies like Apollo Agriculture developing alternative credit assessment methods.

The GSMA found that most AI use cases in agriculture involve ML-enabled digital advisory services, which equip farmers with data-driven advice to adopt climate-smart farming practices and optimize productivity.

These solutions typically reach farmers via mobile devices, highlighting the importance of device ownership, digital skills and literacy, and user-friendliness.

AI's impact on energy services

Africa faces significant challenges to energy access and reliability, with half of its population living without access to electricity.

AI is being deployed in the energy sector, especially in Nigeria, where emerging technologies like the Internet of Things (IoT) act as an entry point for advanced data analytics in smart energy management.

"Use cases such as energy access monitoring and productive use asset financing, developed by companies like Nithio, remain at a developing or nascent stage but present significant potential to reduce energy poverty," the report's authors said.

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.

The GSMA highlighted 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.

AI supporting climate action

With nearly one-fifth of the world's population, Africa accounts for just 4% of the world's energy-related carbon dioxide (CO2) emissions to date and has the lowest emissions per capita of any region, according to the GSMA research.

Yet Africans are already disproportionately experiencing the negative effects of climate change, including water stress, reduced food production, increased frequency of extreme weather events and lower economic growth – all of which are fuelling regional instability.

Forecasts suggest that climate change could reduce African GDP by 8% by 2050, with losses of around 15% in regions like East Africa.

"AI is supporting climate use cases especially for biodiversity monitoring and wildlife protection in Kenya and South Africa, driven by large tech companies like Microsoft's AI for Good Lab and nonprofit organizations such as Rainforest Connection," the report said.

"The overwhelming majority (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 said.

The data deficit

One of the key challenges outlined in the report was the lack of locally relevant data for Africa which "poses a major obstacle to developing and deploying tailored solutions" that address challenges that are unique to the continent, according to the GSMA.

In addition to barriers in accessing government and domain-specific data, one of the most significant gaps is in language data. The scarcity of local language data limits the relevance of AI-enabled services and poses a significant barrier to the development of generative AI solutions that rely on language models.

"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," the group said.

This map shows how often 1,933 datasets were used (43,140 times) for performance benchmarking across 26,535 different research papers from 2015 to 2020. Countries are distorted by frequency of usage. (Source: Mozilla Foundation).

The GSMA believes that unlocking the potential of AI will require overcoming critical barriers including the limited number of African data centers and expensive technology investments.

It suggested that countries can leverage mobile-first markets to develop distributed or hyperlocal edge computing – where tasks occur on devices including phones and laptops – reducing reliance on high-powered data centers.

"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," the GSMA explained.

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"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. Policies must also evolve to address inequality, ethics, and human rights concerns in AI deployment," Cuvellier Giacomelli added.

"As African countries shape their own unique AI strategies, 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," he concluded.

*Top image source: Image by freepik- AI-generated.

— Paula Gilbert, Editor, Connecting Africa