The recent advancements in Large Language Models (LLMs) have opened new possibilities for the automation of software and process modelling. This thesis investigates the capability of AI systems to generate and interpret Business Process Model and Notation (BPMN) diagrams, with the aim of assessing their sufficiency, syntactic correctness and structural coherence. Although BPMN offers a standardized visual language for describing business workflows, creating and evaluating these diagrams manually is still a demanding and error-prone activity. To tackle this problem, four different AI models (ChatGPT, Copilot, Gemini and DeepSeek) were evaluated through a structured framework inspired by the COPE (Context, Objective, Prompt, Evaluation) methodology.