Executive Summary
Artificial intelligence (AI) is a rapidly growing sec- tor within the African innovation ecosystem. In recent years, the continent has seen an increase in the number of startups focusing on AI and re- lated technologies. These startups are leveraging AI to build innovations that solve problems across different sectors such as agriculture, healthcare, and finance, among others. Despite the growth of AI startups in Africa, their potential for the ap- plication of AI technologies to revolutionize busi- nesses, increase efficiency, and solve social and industrial challenges is still very low. Therefore, the main objective of this project is to map and identify the status, challenges, and opportunities of AI startups in African countries which will pro- vide actionable recommendations that promote excellence in the adoption and use of AI leading to the social-economic growth and sustainable development in Africa.
erature and AfriLabs’ enormous datasets of Inno- vation hubs and associated networks of African startups, investors, hubs, corporates, public sec- tors, and humanitarian and development organi- zations. A snowballing approach was utilized to increase the coverage and diversity of the col- lected data and its stakeholders. The following data collection methods were deployed; desk research of stakeholders by country, review of AI stakeholders’ engagement through key informa- tion interviews and focus group discussions, case studies, and online surveys. A webinar session was used to discuss findings and get feedback and contributions from relevant stakeholders and contributors in the ecosystem. A total of 320 publications with over 900 AI stakeholders were identified of which 669 were AI startups and 165 participants participated in the secondary and primary data collection respectively. The collect- ed data was cleaned and annotated through the use of tools such as Python and MS Excel to un- derstand its dimensions and insights, and then,
The study utilized proprietary vocabulary to dis- cover and extract qualified startups from the lit-
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