Aug 2026· European Conference on Knowledge Management· 0 citations· 12 references
TL;DR
The results demonstrate that AI enhances knowledge creation, sharing, and decision-making when embedded within human-centred and learning-oriented organisational environments and highlight the need for organisations to balance technological innovation with human capability development and ethical governance to ensure effective and responsible AI adoption.
Abstract
The increasing integration of artificial intelligence (AI) into organisational environments is transforming knowledge management (KM) practices, yet scholarly understanding of how these developments support organisational learning and strategic decision-making remains fragmented. While existing research highlights the role of AI in enhancing knowledge retrieval, analytics, and decision-support capabilities, it often adopts a techno-centric perspective that underplays the influence of human, organisational, and contextual factors. This fragmentation limits the development of a coherent understanding of AI-enabled knowledge management as a unified construct. This study addresses this gap by conducting a systematic literature review (SLR) guided by PRISMA 2020 guidelines to synthesise and critically evaluate research on AI-enabled knowledge management. Peer-reviewed journal articles and conference papers published between 2010 and 2025 were retrieved from Scopus, Web of Science, EBSCOhost, and ProQuest. Following a rigorous screening and selection process, the final corpus was analysed using descriptive and thematic synthesis techniques to identify patterns, relationships, and conceptual gaps within the literature. The findings reveal five interrelated themes, namely, AI as an enabler of knowledge management processes; human-AI collaboration and the reconfiguration of knowledge work; AI-enabled organisational learning; AI-enabled knowledge management as a strategic capability; governance, ethics, and contextual contingencies. The results demonstrate that AI enhances knowledge creation, sharing, and decision-making when embedded within human-centred and learning-oriented organisational environments (Kitsios and Kamariotou, 2021; Jarrahi et al., 2023). Uncritical reliance on AI may ostensibly lead to automation bias, reduced critical engagement, and weakened knowledge quality (Storey, 2025). The study advances theory by reconceptualising AI-enabled knowledge management as a socio-technical capability mediated by human-AI interaction and shaped by governance and contextual conditions (Paschen et al., 2020; Rezaei, 2025). An integrative conceptual framework is proposed to capture these relationships and to provide a foundation for future empirical research. Practically, the findings highlight the need for organisations to balance technological innovation with human capability development and ethical governance to ensure effective and responsible AI adoption.
Artificial intelligence (AI) is transforming digital knowledge systems (DKS), reshaping how organizations generate, interpret, and apply knowledge in support of organizational learning and strategic decision-making. While AI-enabled systems enhance analytical capability, predictive insight, and information processing speed, their implications for learning quality and epistemic judgment remain insufficiently understood. This study addresses this gap through a systematic literature review that synthesises research across knowledge management, information systems, and organizational learning. Adopting a concept-centric approach, the review analyses peer-reviewed studies published between 2000 and 2026, focusing on how AI-enabled digital knowledge systems are conceptualised and how they influence knowledge processes, organizational learning, and strategic decision-making. The findings indicate a shift from viewing digital knowledge systems as passive repositories toward conceptualising them as mediating infrastructures that augment, generate, and orchestrate knowledge. These systems expand knowledge creation through data-driven insights, enhance knowledge sharing through algorithmic mediation, and increasingly shape how knowledge is applied in decision contexts. However, the analysis also reveals a tension between informational efficiency and authentic learning. While AI-enabled systems support sensemaking and improve decision support under conditions of uncertainty, they may encourage overreliance on algorithmically generated outputs, reduce opportunities for critical reflection, and narrow interpretive perspectives. These dynamics highlight the importance of governance conditions, including epistemic transparency, trust calibration, and learning-oriented organizational practices. The study develops an integrative framework that positions AI-enabled digital knowledge systems as mediating infrastructures linking knowledge processes to organizational learning and strategic decision-making under specific governance and learning conditions. The findings contribute to knowledge management and information systems research by providing a coherent conceptualisation of how AI-enabled systems reshape the epistemic foundations of learning and decision-making in contemporary organizations.
Peter L. Mkhize· European Conference on Knowl...· 0 citations
Technology adoption plays a central role in the success of organisations, where organisational maturity can only be achieved based on learning organisation principles and agile teams for cross-functional collaboration. These constructs are seldom examined in tandem, and are more often approached as separate aspects in the complex adaptive systems in today’s organisations. Recent AI developments necessitate a re-evaluation of how learning organisations are optimised in digitally transformed environments. Knowledge management (KM) has evolved into a strategic enabler of organisational learning and adaptive capacity. This paper reports on findings of a scoping review that explored trends in organisational maturity, conceptualised as the extent to which organisations institutionalise continuous learning, reflective practice, and knowledge integration across systems, culture, and leadership. Databases Google Scholar, Scopus, Web of Science and ProQuest Central were searched and sources published from 2009 to 2025 were selected for thematic analysis. Using the PRISMA-ScR reporting guidelines and Covidence for data extraction, 37 articles were selected and analysed to identify trends and gaps in research. Findings indicate that sustainable competitive advantage emerges when organisational maturity, AI integration, and continuous learning are aligned to support anticipatory, evidence-based decision-making in complex environments.
Brenda van Wyk· European Conference on Knowl...· 0 citations
This paper presents a critical integrative review of how artificial intelligence-driven knowledge management may support decision-making quality in medium-sized technology companies. It argues that generative AI shifts the central knowledge management challenge from retrieval to trustworthiness. While generative AI improves access to dispersed organisational knowledge, its outputs may lack traceable sources, contain confident errors, or blur the boundary between reliable knowledge and probabilistic text. Drawing on classical knowledge management theory, recent AI and generative AI research, decision-making literature and regional implementation evidence, the paper develops a conceptual framework in which knowledge management practices mediate the relationship between AI-driven knowledge management and decision-making quality. Perceived challenges such as poor data quality, weak governance, limited skills and overreliance on AI may weaken this relationship. The paper identifies provenance, validation, governance and human review as core practices for trustworthy AI-supported decision-making.
Abstract: The aim of this paper is to develop an interpretation of the temporal development of Knowledge Management (KM) through the lens of variation-selection-retention (VSR) logic. More specifically, it examines how KM arrangements emerge, gain support, become stabilised, and subsequently follow different post-retention trajectories over time. Although KM research has generated rich insights into knowledge creation, transfer, innovation, and technological enablement, it has been less explicit in theorising what happens after KM arrangements are introduced and retained within organisations. This paper addresses that gap by proposing an evolutionary reading of KM that extends beyond implementation and performance. The study adopts a PRISMA-informed, theory-driven integrative review of Scopus-indexed journal articles in the Business, Management and Accounting subject area published between 2000 and 2026, complemented by a focused VOSviewer term co-occurrence analysis. The final corpus of 25 studies was analysed through interpretive synthesis, with VSR used as a sensitising framework and bibliometric mapping employed to support the identification of recurring thematic configurations. The analysis identifies four connected configurations of KM scholarship: KM as routinised capability, ecologically embedded knowledge creation, sharing-driven innovation, and technology-mediated infrastructure. These configurations show that KM literature already contains important temporal and evolutionary insights, but that these remain dispersed across adjacent streams. Building on them, the paper develops an evolutionary conceptual model in which retention is not treated as an endpoint. Instead, retained KM arrangements may follow four post-retention trajectories: renewal, dormancy, replacement, or obsolescence. These trajectories are shaped by the interaction of functional relevance, organisational support, social legitimacy, and adaptive renewability. The paper contributes in three ways. First, it offers a temporally oriented reinterpretation of KM as an evolving organisational phenomenon. Second, it extends existing debate beyond implementation and performance by foregrounding differentiated post-retention trajectories. Third, it proposes a conditions-based framework that can guide future empirical research on the long-run viability, adaptation, and decline of KM practices and systems across different organisational settings.
Alessandra Vitale, Marco Valeri· European Conference on Knowl...· 0 citations
The paper concludes that the future of KMS lies not in more sophisticated repositories, but in intelligent systems capable of dynamic codification, contextual reasoning, and continuous organisational learning, redefining the balance between human and machine agency in organisational knowledge processes.
A. Antonova, Dilyan Georgiev, Anikó Csepregi· European Conference on Knowl...· 0 citations
Generative artificial intelligence (Gen AI) and large language models (LLMs) offer substantial potential to improve how organisations capture, organise, retrieve and reuse knowledge. Existing knowledge management (KM) frameworks, however, seldom integrate Gen AI/LLM-specific processes, data governance, and ethical requirements in highly regulated public-sector settings, and few have been empirically evaluated. This study assesses the feasibility of adopting GenAI and LLMs for organisational KM and develops a corresponding Knowledge Management and Artificial Intelligence (KMAI) framework for a national communications regulator. Guided by Diffusion of Innovation (DOI) theory, the study employed a multi-method qualitative design comprising a literature review, semi-structured interviews with six KM representatives, a focus group discussion involving fourteen participants using the LEIQ™ model for SWOT analysis, and a content-validity evaluation by three experts. Interview data were analysed thematically, SWOT findings were transformed into strategies through TOWS analysis, and framework relevance was assessed using item- and scale-level content validity indices. The findings indicate that adoption is feasible in this setting: participants perceived clear relative advantage, compatibility, trialability and observability, while complexity was manageable when supported by adequate skills, data classification, secure infrastructure and governance. The principal risks concerned data quality, privacy, security, misinformation, over-reliance on AI, and organisational resistance. The proposed KMAI framework achieved acceptable content validity across all clusters, with S-CVI/Ave values ranging from 0.89 to 1.00, and was refined into four strategic thrusts: People, Process, Technology and Data. Because the evidence derives from one organisation and a three-member expert panel, the framework is presented as an empirically grounded and internally validated proposition whose extension to other agencies is analytic rather than statistical; the boundary conditions governing such extension are stated explicitly, and confirmatory validation with a larger expert panel and independent organisational sites is identified as the necessary next step. The study extends DOI-based adoption analysis by showing that data governance and ethics condition Gen AI/LLM adoption in organisational KM and provides a practical, staged framework for regulated public-sector organisations.
Surya Sumarni Hussein, Nur Azaliah Abu Bakar, S. Hamidi et al.· International Journal of Adv...· 0 citations
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