The rapid integration of artificial intelligence (AI) into organisational training offers unparalleled opportunities to enhance employee skills and foster innovation. This study aims to develop and validate a comprehensive framework that examines how AI-driven training systems—combining adaptive content, SECI-based knowledge cycles, and human–AI feedback loops—can deliver measurable improvements in workforce capability, innovation outcomes, and well-being, within ethically governed environments. The paper synthesises evidence from academic and practitioner sources to build a multi-layered conceptual model. The model highlights five critical stages: Contextual Readiness (assessed via TOE and TAM), Training Design and Implementation (adaptive AI and Reciprocal Human–Machine Learning), Governance and Ethical Oversight (bias auditing, XAI, human-in-the-loop, privacy), Mechanisms (trust-driven uptake, skill acquisition, innovation, well-being), and Organisational Outcomes (productivity, retention, psychological safety). Feedback loops are explicitly embedded to ensure continuous refinement. Theoretically, the framework bridges organisational learning and responsible AI theories, while practically, it offers a roadmap for practitioners to deploy AI training thoughtfully, emphasising strategic readiness, inclusive design, oversight, and cultural alignment. Future research directions include empirical validation across diverse industries and quantitative testing of causal pathways using longitudinal designs.