Accelerated digital transformation and the growing use of artificial intelligence technologies in Russian businesses in a competitive environment have exacerbated the lack of holistic management approaches to integrating machine learning models into business process management. The purpose of this study is to analyze the current state of such integration in a competitive environment, identify key barriers, and propose a methodology for synchronizing the BPM and ML lifecycles. In accordance with this goal, the following tasks are addressed: synchronizing the stages of the enterprise's business process lifecycle with the stages of the machine learning model lifecycle, identifying business problems and analyzing the situation, developing a machine learning model, aligning the goals and scope of ML application between business strategy and BPM objectives, and comparing approaches to integrating ML into BPM internationally and in Russia. The study developed a proprietary model for synchronizing the BPM and ML lifecycles, introduced metrics for evaluating integration effectiveness (forecast accuracy, productivity gains, implementation speed, etc.), conducted a systematic comparative analysis of international and Russian approaches (presented in a table), and proposed a structured step-by-step roadmap for implementing AI in business processes, specifying stages, participants, deadlines, and resources. This study confirmed that ML integration can increase productivity, reduce costs, and improve the customer experience of enterprises in a competitive environment. However, its adoption is hampered by a number of barriers (poor data quality, skills shortages, resistance to change, security risks, etc.).