A Comparative Analysis of AI HRM Governance Approaches Across African Countries: Continental Patterns and Divergences.

Background: The governance of artificial intelligence within human resource management across the 54 sovereign states of Africa presents distinctive challenges that differ markedly from those observed in Euro-American contexts. African organizations have adopted AI-driven workforce tools at annual growth rates exceeding 25% since 2018; however, governance frameworks remain in their nascent stages and are characterized by fragmentation. Current comparative scholarship has largely overlooked innovative governance developments within the African continent, often positioning Africa as a recipient rather than an active innovator in AI regulation. Methods: This study employed a qualitative comparative analysis encompassing eleven focal countries, representing five regional clusters. Data sources included legislative documents, professional standards, civil society instruments, and expert practitioner insights. The analysis focused on five key governance dimensions: legislative architecture, professional standards, digital infrastructure, philosophical traditions, and mechanisms established by civil society. Results: Three overarching governance patterns emerged across the continent: (1) the functioning of data protection laws as de facto governance frameworks for AI in human resource management; (2) the significant influence exerted by international development finance institutions on national AI policy formulation; and (3) ongoing tensions between digital transformation aspirations and institutional capacity limitations. Variations across countries were structured along three axes: the specificity of regulations pertaining to AI-driven employment decisions; the integration of African philosophical traditions such as Ubuntu, Harambee, Ma'at, and Teranga; and the degree of regional economic community harmonisation initiatives. Based on these insights, the African AI HRM Governance Typology classified countries into four ideal types. Conclusions: Achieving effective AI HRM governance across Africa necessitates tailored harmonisation strategies that respect the varying levels of governance maturity. Critical actions include the development of an African Union Model Law on AI in Employment, the establishment of minimum standards within the framework of the African Continental Free Trade Area, and the promotion of regional economic community governance coalitions. Future research should focus on empirically assessing governance effectiveness, exploring employer practices across the continent, and analysing the impact of trade liberalisation on regulatory convergence.