In this article, we cumulate previous research findings indicating that shopping by use of artificial intelligence assistants can shape consumer behavior. We contribute to the literature on cognitive decision-making algorithms in data-driven retail intelligence by showing that machine learning algorithms can optimize the performance of system processing data. Throughout January 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “data-driven retail intelligence” + “cognitive decision-making algorithm,” “consumer sentiment,” “consumer choice,” and “shopping behavior.” As we inspected research published in 2022, only 138 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 21, generally empirical, sources. Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, MMAT, and ROBIS.