Currently, increasing attention is devoted to the digitization of operations and business processes in companies to provide more value to their customers. Organizations have started to adopt technological advancements, such as Robotic Process Automation (RPA), to optimize their operations and reduce the burden of repetitive, low-value tasks. RPA improves productivity by automating structured processes. To harness these benefits, organizations face the challenge of identifying process activities that are viable automation candidates. The goal of this work is to propose a richer RPA lifecycle by integrating Multi-Criteria Decision Analysis (MCDA) and Artificial Intelligence (AI). MCDA, and more specifically the fuzzy analytical hierarchy process (Fuzzy AHP), offers a structured method for prioritizing automation candidates while handling uncertainty in expert evaluations. AI further extends RPA’s capabilities to more complex and unstructured tasks, enabling intelligent, adaptive automation. By combining these three approaches, we propose a smarter, data-informed framework to guide automation strategies and maximize the impact of RPA within organizations.