Deciding Under Uncertainty: A Systematic Review and Integrated Theories–Contexts–Methods and Antecedents– Decisions–Outcomes Framework of Artificial Intelligence-Driven Decision-Making in Small and Medium-Sized Enterprises

Abstract

significant knowledge gaps remain regarding how small and medium-sized enterprises (SMEs) leverage AI under volatile business conditions. This study systematically reviews AI-driven decision-making in SMEs operating under environmental uncertainty. Following the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol, we analyze 72 peer-reviewed articles (2018–2025) and develop an integrated theories–contexts–methods and antecedents–decisions–outcomes (TCM–ADO) framework. The findings show that manufacturing is the dominant research context and that Asia, especially China, accounts for 55% of studies, with quantitative cross-sectional surveys the prevailing methodology. AI adoption is shaped by technological, organizational, environmental, and leadership antecedents that influence strategic, operational, financial, and human–AI decision processes; the resulting outcomes span business performance, innovation, sustainability, and resilience. Notably, environmental uncertainty amplifies rather than diminishes AI benefits, positioning AI as an adaptive mechanism during turbulence rather than a barrier. The review contributes an integrated framework that connects how the phenomenon is studied with what is substantively known about it, and it offers a structured agenda for future work. For practitioners, the findings underscore the value of treating AI strategically, building complementary capabilities, and maintaining flexible organizational structures.