Recent research indicates that despite extensive investments in artificial intelligence, many organizations have yet to fully leverage its potential in strategic decision-making. This study integrates fragmented evidence on the role of artificial intelligence across the stages of strategic decision-making and develops a conceptual framework based on a systems approach encompassing input, process, and output stages. Using a structured literature review, 55 articles published between 2019 and 2025 were retrieved from Scopus, Web of Science, and Google Scholar and analyzed through Braun and Clarke's (2006) six-phase thematic analysis, supported by MAXQDA 2024. The findings indicate that, at the input stage, artificial intelligence enhances the quality of decision-making inputs through predictive analysis, data processing, strategic intelligence enhancement, and key stakeholder analysis. At the process stage, automation, risk identification, scenario modeling, enhanced decision rationality, and resource optimization improve decision-making efficiency and agility. At the output stage, improved business performance, adaptive and innovative strategy execution, strategic performance evaluation, and data-driven competitive advantage were identified. In addition, organizational, human, ethical, legal, data-related, privacy, security, and transparency-related challenges emerged as cross-cutting conditions that may influence all three levels of the decision-making cycle. The study's contribution lies in developing an integrated systems framework that links inputs, processes, and outputs while accounting for feedback among these levels.