Critical Success Factors for Machine Learning Implementation in Organizations: An Analysis of Readiness, Data Governance, and Organizational Capabilities Through a Systematic Literature Review from 2020 to 2026 - Fastabiqul Khairat (Institute of Computer Science (IOCS), 2026)
Advances in digital transformation and Artificial Intelligence (AI) have driven organizations to adopt Machine Learning (ML) to improve operational efficiency, decision-making quality, and competitiveness. However, the success of ML implementation is still influenced by various organizational, data, and technological factors. This study aims to identify and synthesize the critical success factors for Machine Learning implementation in organizations through a Systematic Literature Review (SLR) approach. The study was conducted in accordance with the PRISMA 2020 guidelines, which cover the stages of identification, screening, eligibility assessment, and article selection. A total of 78 articles that met the inclusion criteria during the 2020–2026 period were analyzed using descriptive, thematic, and narrative approaches. The study identified five key factors influencing the successful implementation of machine learning: organizational readiness, data governance, organizational capability, technological capability, and environmental factors. Among these factors, organizational readiness, data governance, and organizational capability were the most dominant. This study produced a conceptual model that explains the relationships among the critical success factors for Machine Learning implementation and provides theoretical and practical contributions to organizations in designing effective and sustainable ML implementation strategies.