The road to integration: mapping barriers and challenges in AI implementation processes within KMSs

Purpose Integrating artificial intelligence (AI) technologies into knowledge management systems (KMSs) offers transformative potential for organizations by enhancing knowledge-intensive business processes. However, this integration may face barriers and challenges. This study investigates the organizational implications for effective change management and structured implementation processes required for a beneficial integration of AI into KMSs. Design/methodology/approach A quantitative research design was employed using an online survey developed for this study and completed by 378 professionals across diverse roles, organizational sizes, and sectors. The instrument assessed perceptions of four barrier categories – human, technological, financial, and ethical-regulatory – through validated multi-item scales. Statistical analyses, including repeated-measures ANOVA, MANOVA, and t-tests, were conducted to identify differences across demographic and organizational variables. Findings Technological and ethical-regulatory barriers are perceived as more significant than financial ones. Managers identified human-centered hurdles, such as resistance to change and skills gaps, as more immediate threats than financial constraints. Knowledge managers expressed significant concerns regarding technical integration with existing KMSs. Smaller organizations reported higher levels of human and technological complications compared to medium and large firms. Graduates demonstrated greater sensitivity to ethical-regulatory issues, whereas the high-tech sector perceived fewer obstacles overall. No significant differences emerged across gender, age, or seniority. Originality/value By connecting established models of technology adoption with empirical evidence from knowledge-intensive contexts, this study strengthens understanding of AI integration within KMSs as a socio-technical process shaped by organizational contingencies and governance imperatives. It contributes to the literature on business process management by framing AI integration as a process-oriented transformation rather than a purely technological upgrade.