Challenges to the implementation of artificial intelligence and machine learning in emerging economies: A systematic review of the case of zimbabwe

Aim: This paper sought to synthesize literature onbarriers to effective artificial intelligence (AI) and machine learning (ML) implementation in Zimbabwe. Methodology: A systematic review methodology was adopted. It followed a multi-stage review structure. The search technique comprised of keyword Boolean syntax queries on scholarly databases. The PIO framework was applied to screen items for relevance. After filtering, a 28-article evidence base was subjected to thematic assessment and synthesis. Results: The systematic analysis of 28 articles revealed significant obstacles across cross-cutting dimensions vis-a-vis rapid AI advancement. Findings from the review caution against technologically determinist perspectives which are detached from policy choices, instead underscoring Zimbabwe’s strong potential to equitably integrate AI innovations through tackling adoption barriers by means of coordinated strategies optimizing accessible tools. Conclusion: While this review highlights daunting challenges, mapping these impediments illuminates pathways for maximizing responsible adoption suited for contextual realities. The paper proposes an Emerging economy AI/ML Adoption Framework developed along four key interconnected components which include contextual factors, core adoption barriers, strategic enablers and adoption outcomes.