Strategic decision-making (SDM) is inherently complex, requiring decision-makers to balance short-term constraints with long-term objectives while navigating vast, often overwhelming, volumes of data. These challenges are further exacerbated by the cognitive limitations of human decision-makers. In this study, we investigate the integration of Retrieval-Augmented Generation-enhanced Decision-Support Systems (RAG-DSS) – a technology combining Generative AI capabilities with external knowledge retrieval – into SDM processes to mitigate perceived complexity and support strategic decision tasks through natural language interaction. Employing a case study methodology, we investigate a business management simulation seminar, where 12 participants engaged with a context-specific RAG-DSS. Data collection included analysis of participants’ interactions with the system (prompts) and semi-structured interviews conducted before and after its introduction. The findings demonstrate that the RAG-DSS reduces perceived SDM complexity by facilitating faster and more intuitive access to relevant, structured information, generating context-specific strategic suggestions, and enabling predictive analyses and simulations using natural language queries. Participants reported increased confidence and a smoother decision-making process, with the RAG-DSS serving as a tool for reassurance and validation. However, challenges emerged, including risks of iterative user-chatbot loops and concerns about over-reliance on the system’s outputs. This research highlights the potential of RAG-enhanced DSSs to address SDM complexity, offering actionable insights for their integration into organizational decision processes while identifying areas for further research to optimize their implementation and mitigate unintended consequences.