Artificial Intelligence (AI) is at the forefront of the Fourth Industrial Revolution. AI has the promise of transforming various sectors, based on its wide application, and driving an inclusive growth. AI is projected to contribute around $15.7 trillion to global GDP, with $6.6 trillion coming from increased productivity and $9.1 trillion from consumption effects by 2030.

Across the globe, artificial intelligence (AI) is steering revolutionary progress, yet Africa has yet to fully harness its potential. The continent’s readiness for the Fourth Industrial Revolution (4IR) lags behind more developed counterparts, particularly in crucial aspects such as infrastructure, technology access, education, skills, and data availability. Notably, most of Africa’s languages lack the digital corpus (low-resource languages) needed to develop robust AI tools, in contrast to resource-rich languages in continents like Europe. Bridging these gaps is imperative for Africa to actively participate and benefit from the ongoing AI advancements.

Not just artificial intelligence (AI), it is evident that the majority of African firms reports moderate to very low levels of business preparedness for hot technologies like IOT, big data, 3D technologies, blockchain technologies among others. Africa has the potential to leapfrog into this revolution and accelerate development of high-end artificial intelligence tools in various sectors.

Investigating the AI landscape in Africa, this article delves into the pertinent challenges hindering the development and utilization of effective AI models and tools on the continent. By shedding light on these issues, this article emphasizes the critical need to address them in order to unlock the full potential of AI in the African continent. As Africa seeks to integrate AI into various sectors, from healthcare to finance, addressing these obstacles becomes paramount for ensuring the successful development and deployment of impactful AI solutions. Understanding and tackling these challenges is essential for fostering a conducive environment for AI innovation and application.

• Data availability challenge – The success of any machine learning model lies in the high-quality and the diversity of the training data. In Africa, there is a challenge in ensuring that AI systems are trained on data that accurately reflects the local population and addresses the unique challenges faced by the continent. Most of Africa’s languages lack an available digital corpus critical for developing AI tools and applications. There is no readily available labelled data for modeling in Africa as most of the languages are low-resourced.

On the African continent, we are not collecting as much data as we are supposed to collect, and much of the data we collect in Africa often tends to be incomplete. In addition, much of the data collected is gathered in the global North, so machines have limited understanding of the African context (Marwala, 2017). Professor Marwala noted that “the algorithms made in the North assume the data is complete. How do we design AI that is able to work even in the presence of missing information?”

The absence of structured data greatly affects model training, which results in poor performing and inaccurate models. Therefore, a collaborative approach is needed to collect this data, enhance labelling and structuring, and make it publicly available across different domains.

1. Inadequate basic and digital infrastructure – Artificial Intelligence development relies on good infrastructure and seamless connectivity, which is still a challenge in Africa. Most parts of the African continent are yet to get an internet connection which impedes data collection, model training, and deployment. Alternative approaches like edge computing, and infrastructure localization can spearhead AI development in Africa as we look forward to improving the necessary infrastructure for AI development.
2. Skills gap – lack of relevant skills, especially among the young population, greatly impedes the adoption of artificial intelligence in Africa. The digital gap between Africa and the rest of the world keeps widening in the digital revolution. It is crucial for Africa to move with speed to upskill their young people in order to fully harness the power of the transformative technologies and industries and to avoid being left behind in the digital revolution.
3. Diver demographics and languages – Africa boosts a rich demography and the number of spoken languages. However, this poses a challenge for the development of advanced technologies like artificial intelligence. It is intricate to train artificial intelligence models to understand and interact with various languages and cultural nuances. Since most of the languages in Africa are low-resourced, they lack of adequate representation, which can introduce biases in the trained models. Also, understanding the diverse demographics in the African continent is a challenge to the adoption and the training of effective AI models in Africa. Africa, should therefore address these issues by vibrant community engagement, use of experts like linguists, data augmentation, and having an inclusive model development initiative.

Artificial Intelligence has the potential to transform the productivity and GPD potential of the African continent. Therefore, Africa should put concerted effort to address these challenges, and strategically invest in different types of artificial intelligence to live this reality.