The effects of machine learning on organizations in Zambia.

This thematic literature review explores the multi-dimensional impact of Machine Learning (ML) on organizations in Zambia, situating the national experience within broader global and regional contexts. As organizations globally transition towards Industry 4.0, Machine Learning has emerged as a critical catalyst for business model innovation, operational efficiency, and predictive decision-making. Through a systematic analysis of 62 empirical studies and over 2,700 regional documents published between 2005 and 2025, this review identifies several core themes: the theoretical drivers of technology adoption, the specific sectoral transformations in Zambian mining, banking, agriculture, and education, and the systemic barriers, including the digital divide and ethical governance gaps. Key findings reveal a significant performance disparity between private and public enterprises, with private firms achieving a 65% adoption rate compared to 30% in the public sector. While awareness of generative AI tools in higher education is as high as 88%, actual organizational readiness is constrained by infrastructure deficits and a lack of localized data. The review highlights the "paradox of potential," where the ambition for technological leapfrogging is tempered by structural dependencies on Western-trained models. This study contributes to the literature by proposing a context-sensitive conceptual framework for AI governance and identifying critical directions for future research into indigenous language processing and decolonial AI strategies.