Previous studies have highlighted the numerous benefits of artificial intelligence (AI) models in enhancing the management of organizational knowledge assets. However, the adoption of AI in organizational settings often encounters significant barriers that hinder its optimal implementation. This paper presents preliminary findings from a timely study that uniquely focuses on the perceived obstacles at the intersection of AI and knowledge management (KM) in organizations. Our objective was to understand the perspectives of employees and managers regarding four dimensions of barriers and challenges in integrating AI technologies into knowledge management systems (KMSs) in business: human, technological, financial, and ethical-regulatory. A voluntary and anonymous online questionnaire was completed by 378 respondents from various industries. The results reveal that financial barriers were reported to be the least significant by both regular employees and managers. Instead, nearly half of the participants expressed concerns about technical barriers, particularly the inadequacy of their organizations’ technological infrastructure to support AI applications effectively. A significant percentage of 82.28% of the sample mentioned organizational barriers, specifically noting that employees lack the necessary skills to leverage AI for enhancing organizational KM. Furthermore, nine out of ten respondents indicated that a substantial cultural shift is essential for facilitating AI adoption within their organizations. Concerns about the potential leakage of sensitive information due to AI usage were significant, with approximately two-thirds of respondents highlighting this issue. Additional ethical barriers were prominent, with three out of four participants reporting a lack of clear organizational procedures to ensure information security and privacy in AI applications. These findings have significant theoretical and practical implications for the discipline of KM in general, and for KMSs in particular. These findings lay a fertile ground for future empirical investigations into the relationship between AI and KM.