AI-Powered Knowledge Management Systems Across Industries: A Systematic Review of Applications, Implementation Barriers, and Ethical Challenges

Abstract

This systematic literature review (SLR) evaluates the existing literature on the benefits, implementation challenges, and ethical concerns associated with Artificial Intelligence (AI)-driven Knowledge Management Systems (KMS) across industries. The SLR followed PRISMA guidelines to identify studies from Scopus, Web of Science, JSTOR, and Google Scholar, using inclusion and exclusion criteria. Critical Appraisal Skills Programme (CASP) checklists were used to assess methodological quality and risk of bias in the included studies, and a structured narrative synthesis was employed to synthesize the findings. The review of 21 articles reveals benefits like improved knowledge capture and creation, storage, retrieval, personalization, and efficient dissemination, which lead to effective decision-making and performance improvements. The implementation barriers are categorized as organizational, technological, ethical, and financial, which generate a lack of trust, inability to manage, lack of interoperability, and monetary constraints. These barriers can be overcome by adopting Kotters’ Eight Stage Change Model, developing interoperability frameworks, evolving ethics benchmarks and standard guidelines for governance, and using viability analyses that incorporate both financial and non-financial considerations. In addition to bridging the gap between AI and KMS theories, the paper also provides practical and actionable insights about managing implementation and governance challenges.

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