The increasing integration of Artificial Intelligence (AI) within Lean Six Sigma management practices raises critical questions about its impact on employee learning and collaboration. This study investigates whether AI disrupts traditional, experience-based, socially mediated learning or if it functions as a complementary tool that enhances continuous improvement. Three objectives guide the research: 1) to evaluate key adult learning theories – Kolb’s experiential cycle, Mezirow’s transformative learning, and Bandura’s social learning – in the context of Lean Six Sigma initiatives; 2) to analyse AI learning mechanisms, including deep learning, backpropagation, and reinforcement learning from human feedback (RLHF), comparing them to human social learning processes; and 3) to determine the potential for a symbiotic relationship between human and AI driven learning. A mixed method approach combines a systematic literature review via ResearchRabbit with the author’s two decades of Lean Six Sigma experience and a comparative analysis framework. The conceptual analysis suggests that AI has the potential to support reflective learning, simulate expert behaviour patterns, and facilitate knowledge consolidation. Importantly, these enhancements may occur without disrupting the critical reflection or collaboration essential to human social learning. The proposed conceptual framework for hybrid human – AI learning environments demonstrates that AI integration preserves essential social learning stages while offering data-driven insights. These results provide practitioners with evidence-based guidance for designing AI-augmented Lean Six Sigma programmes and suggest avenues for longitudinal field studies on hybrid learning outcomes.