GSMA says AI is the answer to socio-economic growth in Africa

The report, called AI for Africa: Use cases delivering impact, was put together from existing research and from interviews with ‘leaders across civil society’, NGOs, academia and the private sector. 

The vast majority of the use cases it has identified are related to agriculture (49%), climate action (26%) and energy (24%).

The GSMA finds that the majority of those AI use cases in agriculture involve machine learning enabled digital advisory services, which can give farmers data-driven advice to adopt ‘climate-smart’ farming practices and optimise productivity. These solutions are typically deployed via mobile devices, which the report says highlights ‘the importance of device ownership, digital skills and literacy and user-friendliness.’  

In energy, the report says AI is improving both on-grid infrastructure and off-grid systems with things such as predictive maintenance, smart energy management, energy access assessment and productive use financing to monitor and extend services in energy-scarce areas.

In terms of climate change, 98% of AI use cases in Africa are predictive applications, due to the availability of historical datasets, ease of application and lower computation requirements compared with generative AI models, we’re told.

However the report says AI development also requires ‘robust infrastructure and computing power’, and that in order to facilitate it the energy demands of data centres and the cost of hardware and software will rise.

Africa already faces a shortage of data centres, we’re told, but developing distributed or ‘hyperlocal edge computing’ can reduce reliance on high-powered data centres.

“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,” said 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.

“Policies must also evolve to address inequality, ethics, and human rights concerns in AI deployment. 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.” 

The report also cites AI4D Africa which claims emerging AI applications could boost Africa’s economic growth by $2.9 trillion by 2030.

It’s hard to see how more technology and more infrastructure would not lead to growth in any given area, however it does get a bit tricky when it comes to specifically pointing to AI and totting up the amount of cash it will generate. It’s all still quite new, but to be fair what mostly seems to be emphasised here is less flashy but perhaps more practical things like predictive maintenance and energy management, as opposed generative AI which is usually centre of the conversation.  

While jaws dropped when Chat GPT began showing off what the latest LLMs could do a couple of years ago, it feels early to be able to pin down the precise value the tools will provide a country, sector, region or even a business, and how. While the hype train is still roaring on, a recent report claimed almost all c-level execs are feeling pressured into deploying generative AI, but many raised concerns that the enthusiasm is diverting resources that could be better deployed elsewhere.