Africa’s path towards sustainable economic prosperity is full of challenges and opportunities. As the continent seeks to harness its vast potential, Artificial Intelligence (AI) emerges as a promising tool to accelerate growth, drive innovation, and address socio-economic disparities.

Despite the unequal landscape in many African and other low- and middle-income countries (LMICs), AI holds promise as a catalyst for economic growth across critical sectors such as health, power, agriculture, and education. This piece explores AI’s transformative use cases within these sectors, highlighting its potential and associated challenges, including ethical considerations and governance issues. Correctly navigating these complexities is crucial in leveraging AI to foster progress across the continent.

One important domain of AI application in Africa is agriculture, the continent’s economic backbone. Through precision farming, AI is being used to analyse soil health, crop needs, and weather data to maximise yields and reduce waste. This has the potential to optimise resource use, boost food security, and increase farmers’ income. 

As an example, a Tanzanian tech start up, Agripoa’s mobile app uses image classification to help farmers detect and treat poultry diseases early. By uploading images of their birds’ diseases on Agripoa’s mobile app, farmers are able to quickly identify the specific type of bird disease and to receive information and guidance on medical treatment options. The platform also integrates an interactive platform that enables users to receive information and guidance from other farmers. 

The major challenge with implementing AI-powered farming systems on a large scale in Africa is engendering participation and ownership. Many African farmers operate on a subsistent scale and lack the required digital skills to effectively take advantage of this. Easily, decisions about how their data is used is left to the developers, and alternative models of imagining the technology are lost on the farmers.

AI also has the potential to transform African states’ healthcare delivery systems by making services more accessible and efficient. One commonly cited example is predictive analytics and personalised medicine, i.e. using large health data sets to identify patterns and make more accurate predictions about diseases and treatment. Generative adversarial networks (GANs), a generative AI framework, are also being used to produce synthetic data and clinical scenarios, which healthcare practitioners leverage to understand disease patterns better and to simulate treatment and diagnosis. Disease diagnosis through medical image analysis or genomic sequencing is another important area of AI application. Ubenwa, a Canada-based start-up founded by a Nigerian computer scientist, uses AI to analyse babies’ cries and detect illnesses from the data. AI-driven platforms like HelpMum are also being used to promote immunisation and vaccination uptake among pregnant women in underserved areas of Nigeria. 

Additionally, innovative solutions like Kolat Care AI, currently in its early development, aim to tackle maternal health crises, such as eclampsia among women in Africa. These initiatives all show the potential of AI to address critical health challenges on the continent. 

However, one key concern about health AI is privacy, wherein extremely sensitive patients’ information is at risk of misuse without proper regulation. There is also the risk of inaccuracy; misdiagnosis as a result of inaccurate information obtained from AI — especially generative AI platforms— could be potentially more damaging in the healthcare sector. Other concerns include poor digital literacy levels on the continent and the amount of resources required to upskill frontline workers and re-design health centres’ organisational environment for effective human-technology interaction. 

In education, AI-driven platforms can personalise learning, making resources more accessible and effective. Examples of these include M-Shule, an AI-powered mobile learning platform based in Kenya, which provides personalised educational content and assessments for primary school students. The app uses AI algorithms to adapt to each student’s learning needs and provides feedback to both students and teachers. Additionally, Kukua, another Kenyan start-up, employs adaptive AI technology to tailor learning experiences for kids using gamified and animated visuals. By aligning AI education programs with market needs, Africa can cultivate a skilled workforce ready to participate in a global economy that is increasingly reliant on the digital.

AI can also aid in sustainable urban planning and the development of smart cities. Especially useful in African urban settings is AI-powered traffic management; using real-time GPS data to manage traffic flows and optimise public services (e.g. predictive scheduling can prevent bus-bunching and improve service efficiency). In Zambia, a new public-private partnership is piloting the creation of an automated digital base map of the capital city of Lusaka, which will aid planning and administration across a range of social infrastructure and services. Though yet to incorporate an automation system, the city of Cape Town in South Africa has also recently launched SoundThinking, a system that uses smart sensors and gunfire sounds to detect gunshot incidents, providing crucial geolocation data that can enable more rapid response by security enforcement agencies. These initiatives have the potential to reduce economic losses from service inefficiencies and position African cities as hubs of innovation and investment, but much responsibility lies with the regulatory environment. 

Policy’s role both in driving AI adoption and regulating development cannot be overstated. For African countries, the major task is the political will to incentivise innovation through substantial investment commitment and innovation-enabling policies. 

One key challenge with AI progress in Africa is the cost of compute (the hardware inputs required to train AI models). While new technology investments in AI can spill into other industries and enhance job creation, strategic planning and policy support are required. With other critical areas demanding the government’s investment attention, there is a tendency to not prioritise investments in new technologies. Therefore, both domestic and international public-private partnerships will become crucial in mobilising resources for AI development in Africa. Government efforts must, however, go beyond investment to promoting continuous learning and adaptation by supporting responsible AI experimentation  – for example, through public service adoption of the technology or participating in red teaming sessions that consequently improve the government’s capacity to anticipate potential risks – and upskilling civil servants’ capacity both to use and regulate AI.

The other challenge is navigating the global economic ramifications of AI. With the global economy becoming increasingly knowledge-intensive, research already shows that businesses that use ‘digital’ and data — whether by embedding these in existing products/services’ value chains or by leveraging them to create new ones — are more productive than their non-digital-using peers. It is clear that the economic gains from AI are likely to benefit only a select few, not only because digital innovations take time to diffuse to the rest of society but also because of AI’s uneven supply landscape. 

GOLOKA™ offers a multilayered solution that combines AI and human agency to help businesses and government organizations collect diverse data and dispersed sources. 

For example, inputs like human capital (data scientists, ML engineers), computational resources, tacit knowledge and capital are much more lacking in many African and other low- and-middle-income countries. AI-leading countries like the USA and China are also engaging in a cut-throat competition to develop the most-novel AI models. This could potentially crowd out the currently growing global focus on AI safety and ethics, and fragment international coordination, with negative consequences for both the AI innovation value chain and its regulation. For Africa, its key unique advantages in areas like massive talent pool and a diverse demographic (a rich data source) must be its major point of negotiation in the global AI value chain.

Data usage will also bring to the fore the crucial topic of Africa’s digital and data sovereignty. African states’ must ensure their own active participation in global AI governance mechanisms; and in such contexts, the demand for global cooperation in AI safety, standards, and the broader value chain must be balanced with digital self-sufficiency. The perennial difficulty of taxing Big Techs in Africa and the increasing ‘Americanisation’ of operation standards and legal structures of US tech firms operating in Africa are some of the key issues that are important to address in this context.

Domestically, closing the digital divide is an urgent task. But equally pressing is implementing ethical and governance guardrails around privacy, bias, misinformation, and market dominance. Public awareness of the risks and benefits of AI is essential, especially given the open access nature of many Gen AI platforms (arguably the most used type of AI today). Such awareness initiatives should focus not only on encouraging the public to use AI responsibly but also on engaging them in discussions about AI’s role in Africa’s future and seeking public perception of government policies and initiatives.

Latest AI models like GPTs, which are largely domain-agnostic, would require broader organisational innovation to tailor them for specific public service usage. If anything, this shows that realising AI’s potential requires a harmonised approach that includes strategic investments, organisational reforms, and innovative governance approaches. Africa’s AI journey is not just about technology but about shaping a prosperous, equitable, and sustainable future for all its citizens — a goal that requires public sector innovation beyond the development of AI itself.

{The article is originally published on Dataphyte}

About authors: Ayantola Alayande is a researcher in digitalisation, innovation and the digital economy. He is currently Civic Innovation & Knowledge Consultant at Dataphyte. He can be reached on ayantola@dataphyte.com

Olukunle Owolabi, PhD is an AI Software Engineer and founder of Apex Bridge AI. He previously worked at Meta as a Research Scientist. He can be reached on olu.owolabi@apexbridge.ai