In the evolving landscape of machine learning (ML), the explainability of complex models remains a pivotal challenge, especially in bridging the understanding between technical experts and frontline staff. This paper explores the role of Generative AI (GenAI) in demystifying the intricacies of ML explainability methods, such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). Our investigation delves into the trust gap that often arises due to the technical nature of these methods and how GenAI can serve as a mediator in this scenario. By leveraging GenAI’s capabilities in synthesizing comprehensible visualizations and intuitive explanations, we propose a framework that enhances the interpretability of ML results for non-technical audiences. This framework not only facilitates better understanding but also fosters trust and collaborative decision-making among diverse stakeholders. Our findings indicate that GenAI can significantly contribute to the democratization of ML knowledge, thereby empowering frontline staff to engage more effectively with ML outputs. This research underscores the potential of GenAI as a transformative tool in making ML more accessible and trustworthy across various sectors.