This study aims to explore the determinants of AI adoption and its impact on HRM effectiveness in Tanzanian medium enterprises (MEs). With a focus on providing insights for HR professionals and decision makers, data from 185 respondents comprising HR professionals, IT professionals, and CEOs who have adopted AI were analyzed using PLS-SEM, which includes factors of Relative Advantage, Complexity, Compatibility, Security/ Privacy, Top management, Organizational readiness, Competitive pressure, External support, and Government support for AI adoption. The results highlight relative advantage, compatibility, and competitive pressure as the key drivers of AI adoption in the Tanzanian context, which in turn enhances the effectiveness of HR systems. The study bridges the existing gap and offers recommendations for the integration of AI into HRM practices. Implications for managers and solution providers are discussed to facilitate a better understanding of the determinants influencing the adoption process in SEs in Tanzania. The study builds on theoretical knowledge of AI adoption by utilizing the TOE model, which incorporates technological, organizational, and environmental factors. The study recommends future exploration of additional factors and inclusion of a larger sample to enhance the universality of the results.

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This study intends to explore the determinants of AI adoption and its impact on HRM effectiveness in Tanzanian medium enterprises (MEs). With a focus on providing insights for HR professionals and decision-makers, data from 185 respondents comprising HR professionals, IT professionals, and CEOs who have already adopted AI was analyzed using PLS-SEM, in which factors of Relative advantage, Complexity, Compatibility, Security/Privacy, Top management, Organization readiness, Competitive pressure, External support and Government support were tested for the adoption of AI. Results highlight relative advantage, compatibility, and competitive pressure as key drivers of AI adoption in Tanzania's context, subsequently enhancing HR systems' effectiveness. The study bridges the existing gaps and offers recommendations for AI integration into HRM practices. Implications for managers and solution providers were discussed to facilitate a better understanding of the determinants influencing the adoption process within Tanzanian MEs. The study underlies the theoretical knowledge of AI adoption by utilizing the TOE model, incorporating technological, organizational, and environmental factors. This study recommends future exploration of additional factors and including a larger sample to enhance the universality of results.