Abstract
Artificial intelligence (AI) is revolutionising human resource management through automation, data-driven decisions, personalised learning, and improved skills-gap detection. Nevertheless, AI adoption in training and development (T&D) programs in the Nigerian banking sector remains low. Therefore, this situation has become a barrier to the development of human capital and to digital transformation. This systematic literature review aims to identify the factors influencing AI adoption in T&D within Nigerian banks and then to analyse the interrelationship among the constructs. This review also examines the mediating role of T&D-AI Fit between influencing factors and AI adoption in T&D. The study uses the Task–Technology Fit (TTF) model as the theoretical framework. Using the PRISMA 2020 guidelines, 46 peer-reviewed studies published between 2018 and 2025 were identified from multiple databases and analysed using thematic synthesis. The review Key enablers include training needs assessment, training design quality, AI system attributes (usability, reliability, personalisation), and individual factors (employee attitudes and digital competency). The major barriers are inadequate infrastructure, resistance to organisational change, fears of job displacement, and regulatory uncertainty. In addition, the T&D-AI fit was shown to mediate the relationship between these factors and successful AI adoption. Achieving strong task–technology alignment (T&D–AI fit) is critical for overcoming contextual barriers and maximising AI benefits in resource-constrained banking environments. This review highlights practical implications for digital skill-building, infrastructure improvement, and ethical AI governance while extending the TTF model to emerging economies.