AI Report on Southern Africa http://misa.org When creating and applying an AI system, it is necessary to integrate expert knowledge. Although IT experts, such as software developers and engineers, are responsible for the design and development of AI applications, they are not the main users of AI. The African market has a significant challenge of a shortage of professionals who are equipped with the necessary skills for AI. AI development incorporates machine learning and Natural Language Processing (NLP) techniques that use complicated algorithms; therefore programming abilities are required. The question to be asked is: How can AI be programmed to perform accurate operations? As a result, ICT proficiency and programming skills are among the necessary skills for the efficient adoption and usage of AI applications (Komarova et al. 2019). The Data and Infrastructure Pillar: A country’s infrastructure and data capacity go a long way in determining its AI readiness. A strong and reliable data infrastructure is crucial for AI development as it provides the necessary foundation for collecting, storing, and analysing large amounts of data. Additionally, a country’s ability to effectively manage and utilise its data resources is essential for maximising the potential of AI technologies. Therefore, investing in robust data infrastructure and building data capacity are key factors in ensuring successful AI adoption and usage. To prevent bias and error, AI tools require an abundance of high-quality data that should represent all citizens in each country (data representativeness). As a result, to harness this data’s potential, the infrastructure required to fuel AI tools and distribute them to the public must be established. There is a substantial requirement for increased high-quality data to advance AI development in Southern Africa. The development of regional data ecosystems is still in its nascent phase. Several Southern African nations require more data collection mechanisms and improved data governance frameworks, leading to enhanced data quality. AI systems are constructed using complex algorithms, and data is utilised to train these algorithms. Africa is now facing a scarcity of data, and most of the available data must honestly represent the continent’s reality. Concerns about data scarcity and the potential that many algorithms are not appropriately adapted to the culture, language, and contexts of local inhabitants were voiced by participants at the Johannesburg meeting. 10