Artificial Intelligence and Succession Planning in South African Family Businesses: A Systematic Review of Leadership and Sustainability
DOI:
https://doi.org/10.51137/wrp.ijarbm.703Keywords:
Family-Owned Businesses, Succession Planning, Artificial Intelligence, Decision-Support Systems, Governance and Leadership Continuity, Emerging EconomiesAbstract
This study systematically reviews emerging scholarship on Artificial Intelligence (AI)-enabled succession planning in family-owned enterprises, with a particular focus on South Africa and comparable emerging-economy contexts. Guided by PRISMA 2020 procedures, the review synthesises 24 core scholarly studies published between 2020 and May 2026, complemented by an extended analytical corpus to support theoretical integration. The findings indicate that resistance to AI-supported succession planning is driven less by technological constraints than by governance informality, founder-centric authority, and socioemotional considerations embedded in family firms. Succession processes remain strongly influenced by kinship structures, identity preservation, and relational legitimacy, which often conflict with the perceived objectivity of AI-driven decision-support systems. The analysis presents four key themes: organisational readiness and trust in AI, the influence of culture and emotional logic, ethical risks related to bias and exclusion, and the emergence of hybrid governance models. Evidence suggests that AI is most effective when deployed as a complementary tool within governance structures that retain human judgement while enhancing scenario analysis, leadership development, and succession preparedness. The study contributes to theory by integrating socioemotional wealth, resource-based, and technology acceptance perspectives into an exploratory framework that reconceptualises succession planning as a governance-mediated, socio-technical process. Practically, the findings highlight the need for family firms to formalise governance mechanisms and succession criteria prior to AI adoption. The study is limited by the emergent nature of the evidence base, underscoring the need for empirical research in South Africa and comparable emerging-economy contexts to validate and operationalise the proposed framework.
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Copyright (c) 2026 Mushimbei M. Mwansa, Gardner Mwansa, Matipa Ricky Ngandu (Author)
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