This paper presents a literature review examining how organizations adopt and integrate artificial intelligence (AI) into managerial decision-making processes. Drawing on 24 peer-reviewed sources published primarily between 2013 and 2023, the review synthesizes findings across three themes: the theoretical foundations of technology adoption as applied to AI, the organizational and human factors that shape AI integration, and the documented impacts of AI on decision quality and speed. The review employs a systematic search and thematic synthesis approach informed by established methodological guidance. Findings indicate convergence around the view that AI adoption is less a discrete technology-implementation event than an ongoing sociotechnical process shaped by managerial trust, data infrastructure, and organizational readiness. The review identifies three persistent gaps: a shortage of longitudinal studies tracking decision outcomes after adoption, limited attention to small and mid-sized firms relative to large enterprises, and inconsistent operationalization of "AI adoption" as a construct across studies. The paper concludes by proposing a research agenda addressing these gaps and outlining implications for management practice.