Adoption of Artificial Intelligence and Human Resource Upskilling in Emerging Markets: Evidence from Small and Medium Enterprises in Oyo State, Nigeria

This study investigates how the adoption of artificial intelligence (AI) is associated with workforce upskilling within small and medium-sized enterprises (SMEs) in Oyo State, Nigeria. While AI holds considerable transformative promise for human resource development, a critical knowledge gap persists regarding its regional implementation dynamics, particularly within the context of developing economies. Drawing on an integrated five-theory framework – the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), the Technology-Organisation-Environment (TOE) framework, Human Capital Theory, and Sociotechnical Systems Theory – this study develops and tests a conceptual model linking three theoretically grounded AI adoption dimensions (organisational integration, AI training programmes, and data-driven decision-making support) to employee skill enhancement outcomes. Employing a quantitative cross-sectional survey design, the structured questionnaires were administered to 135 respondents comprising HR professionals, SME operators, and employees drawn from approximately 72 SMEs across diverse industry sectors in Ibadan Metropolis. Results indicate that AI integration in HR upskilling practices remains largely nascent (M = 2.116). Nevertheless, Pearson correlation and regression analyses revealed significant positive associations between key AI adoption dimensions and employee skill enhancement (R = 0.750, R2 = 0.562, p < 0.001). These associations are interpreted as correlational rather than causal given the cross-sectional design. Prominent barriers include infrastructural inadequacies, educational deficiencies, policy gaps, and socio-cultural resistance. The study concludes with actionable policy and practice recommendations, and acknowledges methodological limitations including common method bias risk, absence of formal EFA/CFA, and cross-sectional design constraints.