Knowledge Documentation Practices in AI Initiatives: A Systematic Literature Review
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Artificial intelligence (AI) has become an essential component of organizational strategy; however, the knowledge generated during AI projects, including design decisions, model behavior, development processes, and practitioner insights, is often insufficiently documented. This study aimed to examine how knowledge documentation is currently practiced in organizations implementing AI. Using a systematic literature review approach, this study analyzed twenty peer-reviewed studies published between 2020 and 2026. The articles were collected from five major academic databases, namely Scopus, ScienceDirect, ACM Digital Library, IEEE Xplore, and Emerald Insight, and were selected following the PRISMA 2020 guidelines. Based on five research questions structured using the PICOC framework, several key findings emerged. First, knowledge management in AI contexts has shifted from static repositories toward more dynamic and AI-supported systems, although this transition introduces challenges such as model drift, inconsistent documentation practices, and difficulties in capturing tacit knowledge. Second, the literature presents various knowledge documentation methods and frameworks, indicating that the field remains in development without a universally accepted standard. Third, structured documentation has been shown to positively influence organizational learning, knowledge reuse, and the overall effectiveness of AI initiatives. Fourth, despite these benefits, significant gaps remain, particularly regarding the absence of standardized AI/machine learning (ML) documentation practices and the limited integration of documentation throughout the AI lifecycle. In response to these challenges, this study proposed the Knowledge Documentation Framework for AI Initiatives (KDF-AI), consisting of twelve components organized into five phases and supported by different maturity levels. Overall, this review highlighted the increasing importance of knowledge documentation as a core capability in AI-driven organizations and provided a foundation for future research and practical implementation.
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Copyright (c) 2026 Finannisa Zhafira, Fitria Handayani , Dana Indra Sensuse , Sofian Lusa
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