Knowledge Management Meets Artificial Intelligence: A Systematic Review and Future Research Agenda Maayan Nakash 1,2 and Ettore Bolisani 1 1 Department of Management and Engineering, University of Padova, Vicenza, Italy 2 Department of Management, Bar-Ilan University, Israel Maayan.Nakash@biu.ac.il Ettore.Bolisani@unipd.it Abstract: In the complex mosaic of the digital age, the tactical incorporation of artificial intelligence (AI) within knowledge management (KM) is revealed as a central business component of technology management. The current study aims to clarify the intersection between KM and AI in organizational contexts. Specifically, this paper represents a preliminary step to investigate the potential impacts of AI on KM research and practice. Building on a database we created from Scopus, we shine a spotlight on trends in pertinent peer-reviewed scientific articles published in the last decade (2013-2023) on the KM- AI nexus. In addition, the paper presents an extended systematic analysis of literature, which synthesizes theoretical and empirical works conducted to date on this topic. Through a review of the available studies, we strive to shed light on effective KM frameworks and strategies in the era of AI. As extant research in the literature is largely theoretical, we propose to conduct empirical research on AI technologies in core KM processes such as acquisition, documentation, sharing, and application of knowledge. In addition, we recognize that the challenges and barriers to implementing AI in KM systems are not in focus and deserve to ignite further research. The anticipated contributions from such inquiries promise not only to augment the corpus of knowledge within the discipline, but also to furnish KM practitioners with the insights necessary for the crafting of efficacious systems. This research marks the advent of a transformative scholarly epoch, wherein the harmonious integration of KM and AI emerges as the bedrock of organizational ingenuity and strategic acumen. It distinguishes itself from prior works by pinpointing knowledge gaps in the synergy between disciplines and underscores the imperative for future research to bridge these lacunae. Keywords: Artificial Intelligence, Cognitive Computing, Machine Learning, Knowledge Management, Knowledge-Driven Organizations 1. Introduction Knowledge management (KM) has been recognized for decades as a key component of business strategies for organizational success (Al Mansoori et al. 2020; Bolisani and Bratianu, 2018; Jallow et al. 2020). It is identified as “the process of creating value from an organization’s intangible assets” (Liebowitz 2004, p.4). Sometimes it is associated with a formal approach to governing the creation, transfer, retention, and use of a firm's explicit and tacit knowledge resources (O'Leary 1998) while other times it is simply an informal or “emergent” approach (Bolisani et al. 2016). KM is now a pervasive attitude that integrates concepts of information technology, computer science, organizational behavior, human resource management, strategic management, and more (Bolisani et al. 2023; Edwards and Lönnqvist 2023; Liebowitz 2001). Technology is often seen as a key element of KM since it enables better knowledge flow facilitation (Nakash and Bouhnik 2022). For this reason, the rapid upsurge of widespread artificial intelligence (AI) applications can have a tremendous impact on how we see KM and KM processes (KMPs). AI leverages computational techniques for machine learning (ML) and has been widely adopted across various domains (Sanzogni et al. 2017). Simply put, AI refers to instilling intelligence into autonomous machines, aiming to enable the performance of tasks requiring human cognition. That is, AI attempts to mimic functions like human learning, decision-making, and problem-solving (Jallow et al. 2020; Sanzogni et al. 2017; Vadari and Desik 2021). AI is considered one of today’s most disruptive technologies. Its benefits have been assessed in terms of improved outputs, amplified innovation, and greater profitability (Yigitcanlar et al. 2020). Moreover, AI is a powerful competitive tool (Jackson 2019) with significant business impacts (Malik et al. 2023). It can optimize diverse processes, reducing labor requirements and increasing efficiency (Mishra and Pani 2021). Consequently, AI can disrupt today’s KM landscape by providing an integration of the two extreme views of KM: one that sees KM as a substantial technological challenge, where the new systems can process structured explicit knowledge in a highly efficient way; and the other that describe the core KM challenge as that where humans need to create, handle, and effectively share their tacit knowledge components. Indeed, even in the history of companies that intensively use KM (Bolisani et al. 2016), there has often been a struggle to combine these opposite views that represent two sides of the same coin. Today, AI presents the opportunity to ultimately and conclusively integrate the technological and human viewpoints within the field of KM. 544 Proceedings of the 25th European Conference on Knowledge Management, ECKM 2024