Adoption of Artificial Intelligence in the Zimbabwean Manufacturing Sector: A Critical Review and Research Agenda

Abstract

Artificial intelligence (AI) is reshaping manufacturing through quality control, maintenance, and supply chain planning, yet adoption in Sub Saharan Africa is uneven. The researchers synthesize evidence on AI adoption with a focus on Zimbabwe, guided by Technology Organization Environment (TOE), the Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology (TAM/UTAUT), the Resource Based View (RBV), and Dynamic Capabilities. The researcher applies a transparent selection process and includes n = 26 studies: Zimbabwe (n = 2), South Africa (n = 2), Africa/regional (n = 0), and global/other (n = 22). Zimbabwe specific evidence is thin and concentrated in SMEs and a food manufacturing case. The review provides a consolidated synthesis of drivers and barriers, clarifies the methodology, and presents a practical research agenda tailored to Zimbabwe. The paper offers a compact framework linking organizational and ecosystem conditions to use cases and outcomes, and a roadmap for firm actions, policy levers, and measurement priorities to scale AI adoption.