This study explores the integration of ChatGPT-4 as a decision-making tool and evaluates its effectiveness in comparison with traditional Multi-Criteria Decision-Making (MCDM) methods. In particular, it examines ChatGPT-4’s potential to generate weighting, ranking, and clustering outcomes that align with MCDM methods’ assessments through the use of chain-of-thought and few-shot prompting techniques. by employing a structured methodology and prompt-engineering strategies, this research investigates how AI-assisted decision-making can enhance transparency, efficiency, and accuracy in multi-criteria selections. Building on existing literature regarding AI’s role in MCDM, the study specifically focuses on grouping EU member and candidate countries based on their logistics performance. The findings highlight few-shot prompting as a strong alternative to conventional MCDM methods, particularly in scenarios that demand speed and flexibility. Results further indicate that the few-shot prompting technique outperforms the chain-of-thought technique. Moreover, the study suggests that AI-powered tools can serve as a practical substitute for traditional MCDM approaches, especially in fast and adaptive decision-making processes. The incorporation of AI into established decision-making frameworks has the potential to substantially enhance outcomes, particularly in data-driven and rapidly evolving environments.