Business Process Architecture (BPA) provides a holistic, portfolio-centric view of organizational processes; however, a key challenge in its development particularly within the Riva methodology lies in the manual identification of Units of Work (UOW) from Essential Business Entities (EBEs). This task is typically performed through expert workshops and subjective interpretation of Riva filters, making it time-consuming, difficult to scale, and prone to inconsistency across practitioners. To address this limitation, this study investigates the use of Generative Artificial Intelligence (GAI) to support and partially automate UOW identification and architectural modeling. The paper focuses on Riva step three (classification of EBEs into UOWs) and step four (derivation of UOW relationships and diagrams), evaluating two Large Language Models (LLMs) ChatGPT and DeepSeek against expert-validated results from a real-world banking deposits case study. Quantitative analysis shows strong alignment with expert classifications, where DeepSeek achieved perfect precision, recall, and F1-score, while ChatGPT demonstrated near-perfect performance. Beyond classification accuracy, both models successfully generated UOW diagrams, exhibiting complementary strengths in lifecycle rigor and functional reasoning. As a primary contribution, this work introduces GAIRivaClassifier, a semi-automated algorithm that operationalizes Riva filters, lifecycle detection, and heuristic reasoning to support systematic UOW identification and diagram generation. The findings provide empirical evidence that GAI can effectively augment design-time BPA activities, enabling hybrid human-Artificial Intelligent (AI) collaboration for more consistent, scalable, and adaptive Riva-oriented Process Architecture (PA) development.

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