Machine Learning Adoption Among Technopreneurs in Gweru, Zimbabwe

Abstract

This paper explored the present condition, operating challenges and facilitators of Artificial Intelligence (AI) and Machine Learning (ML) among Technopreneurs at Gweru, a secondary city in Zimbabwe. The research incorporated the use of qualitative exploratory design and Technology-Organization-Environment (TOE) framework in which semi-structured interviews were held with 23, purposely sampled, Technopreneurs in different fields. The results showed that there is a young-adoption environment, with merely 26 percent of Technopreneurs already taking action with AI/ML, and 35 percent are still in the investigative stages. The sectoral analysis showed that there was more implementation in fintech (37.5) and strong exploration in agritech (30%). The greatest impediments identified were infrastructural constraints which were common and presented as an untrustworthy power, an expensive and slow internet, and expensive cloud service. The other impediments were critical knowledge gaps that require informal learning and lack of familiarity with AI/ML ventures by investors. On the other hand, collaborative peer learning networks, resource sharing and incremental adoption models became some of the important enablers. Specifically, the paper has emphasized the exponential nature of an "AI divide" necessitated by computational needs and suggested locally based suggestions, such as the creation of AI hubs, the development of local cloud infrastructure, formalizing training and mentorship, and training investors on how to faster accelerate AI-based Technopreneurship. This study bridged a significant gap in the knowledge on AI/ML adoption outside of main sources of innovation in emerging economies.