The study is oriented toward a comprehensive examination of the processes of deep integration of artificial intelligence technologies into the commercial framework of contemporary organizations. The focus is placed on the evolution of practices: from predominantly pilot-based and experimental use of generative models to their systematic incorporation into operational activities and large-scale deployment across business processes. The analysis addresses the key mechanisms through which intelligent assistants transform the performance of commercial units, including changes in core key performance indicators — conversion rate (CR), customer retention level, and aggregate revenue. The empirical foundation relies on a dataset from recent years, formed by materials from leading consulting organizations [McKinsey, Gartner, Deloitte], as well as on the results of relevant academic studies indexed in [Scopus] and [Web of Science]. Based on a comparative analysis of sources, a pronounced asymmetry of effectiveness is identified: AI leaders demonstrate a multiple — up to threefold — return on investment, whereas a substantial share of companies remains within the zone of initial testing and limited implementations, failing to achieve a comparable economic effect. A separate emphasis is placed on the restructuring of the functional profile of sales representatives under conditions of partial automation of up to 20 % of sales tasks and the strengthening of hybrid work arrangements. A qualitatively new architecture of human–machine interaction is formed, in which algorithmic components assume a significant share of analytical, routine, and communication-supporting operations, while human labor shifts toward contextual management, interpretation, and decision-making under conditions of uncertainty. Taken together, these processes establish new parameters of productivity and controllability of commercial processes.