How knowledge management systems trap dynamic organisations into outdated practices: a systematic review

• KMS can unintentionally embed outdated knowledge in dynamic settings. • Competency traps are reinforced through over-reliance on internalized routines. • Study challenges assumptions about KMS as inherently adaptive tools. • Offers new insights on digital transformation risks in volatile environments. • Suggests adaptive KMS design to avoid organizational rigidity traps. Knowledge Management Systems (KMS) are widely used to capture institutional knowledge and support organizational decision-making. Paradoxically, the very systems designed to foster learning, and adaptation can entrench outdated practices, creating competency traps . For example, healthcare organization may continue relying on pre-pandemic protocols because its KMS prioritizes them, or technology firm may repeatedly surface obsolete coding practices from its knowledge base. Understanding how such traps arise and how they can be prevented represents a key gap in the literature. To address this, the present review analysed 65 peer-reviewed studies published between 2019 and 2025 to investigate the paradoxical effects of KMS. The findings integrate mechanisms, contextual factors, and interventions into a unified framework and identify seven recurring mechanisms that reinforce competency traps: codification bias, algorithmic reinforcement, rigid governance, performance-driven adherence, narrow classification, cultural legitimation, and failure to remove outdated knowledge. Environmental dynamism (the speed and unpredictability of change) tends to worsen these problems, while diverse knowledge sources and deliberate unlearning can mitigate them. From this evidence, the study proposes four intervention strategies: introducing time-based review features, increasing diversity in search and recommendation algorithms, adopting flexible governance structures, and implementing regular “challenge” processes to test prevailing knowledge. The framework connects causes, contexts, and solutions, offering clear guidance for designing KMS that remain relevant in dynamic environments. While such systems can enhance learning in stable settings, they must be deliberately managed to stay adaptive. Organizations are therefore advised to complement KMS with mechanisms that encourage questioning, experimentation, and renewal to avoid stagnation.