Retrieval-Augmented Generation (RAG) has become one of the most visible mechanisms for grounding large language model outputs in organizational knowledge sources. While the technical literature on RAG architectures is substantial, the evidence on how RAG is deployed inside organizations, and on the organizational consequences of those deployments, has not been systematically synthesized. This paper addresses that gap by treating RAG as a hybrid knowledge capability pairing a generative model with curated organizational knowledge and human oversight. The study reports a systematic literature review of peer-reviewed work on RAG in business and organizational contexts published between 2023 and 23 February 2026, covering Scopus and Web of Science in line with the PRISMA 2020 reporting framework. From 727 records after database-side filtering, 513 were screened and 41 retained. The studies were synthesized through the empirical-to-conceptual taxonomy development method of Nickerson, Varshney and Muntermann (2013) into a three-dimensional framework linking business application contexts, RAG architectures, and reported organizational consequences. Three findings stand out. First, adoption concentrates in knowledge-intensive support functions: enterprise knowledge retrieval, document intelligence, and decision support together account for around seventy percent of the corpus. Second, Classic RAG remains the most frequent single configuration, while Advanced and Agentic variants together appear in 18 studies and concentrate in deployments with more demanding task structures. Third, the most consistent finding is not a benefit but a limitation: residual output quality issues appear in 26 of 41 studies and recur across every architectural category and every application context, while data-quality dependence is the most frequent implementation challenge. The paper contributes a business-oriented taxonomy empirically distinct from architecture-first surveys, positioning RAG as a socio-technical capability whose organizational value depends on the surrounding data, governance, and oversight infrastructure. For practitioners, it supports deliberate decisions about where RAG delivers value, which architecture fits which task, and which conditions enable successful deployment.