The relationship between knowledge management and artificial intelligence: A thematic analysis from Scopus

Abstract

Objective. This study examined the scientific literature addressing the relationship between artificial intelligence (AI) and knowledge management (KM) to identify the main issues around this binomial. Design/Methodology/Approach. We used co-word analysis as our bibliometric technique. We only worked with each article's keyword and keyword plus variable. Each cluster within the map was assigned a generic name according to the theme it represented. We also conducted some analysis based on the degree of centrality of keywords per cluster. We also performed qualitative analyses of each cluster's terms and word relationships. Results/Discussion. The co-occurrence map of terms revealed nine clusters related to the relationship between KM and AI: (1) main and central themes, (2) innovation and system design, (3) knowledge representation and learning, (4) theoretical models and information management, (5) collaborative networks and dynamics, (6) natural language processing, (7) ethics and governance, (8) visualization and knowledge representation, and (9) emerging and specialized areas. Conclusions. This study contributes to closing a gap in the literature by demonstrating that integrating AI and KM is a key alliance to meet the challenges of the knowledge society. AI strengthens conventional KM processes and opens new opportunities to create organizational and societal value. However, implementing AI requires a balanced approach that combines technological innovation with ethical and human considerations.