The findings indicate that Generative AI, machine learning, fuzzy logic, fuzzy logic, natural language processing, recommender systems, support vector machines, semantic AI, and random forests are the principal artificial intelligence techniques.
Abstract
Aim/Purpose
To systematically review the literature on artificial intelligence (AI) and knowledge sharing in order to identify AI that support organizational knowledge sharing, examine organizational factors that influence their effectiveness, and assess the resulting organizational performance outcomes.
Background
Organizations increasingly rely on AI to enhance knowledge sharing processes. While prior research has examined AI, knowledge sharing, and organizational performance as separate constructs, there is limited synthesis regarding how AI techniques, organizational conditions, and knowledge sharing practices collectively affect organizational performance.
Methodology
The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines were followed in this study. Articles published between 2015 and 2025 were retrieved from the Scopus and IEEE Xplore databases using predefined search criteria. After screening titles, abstracts, and full texts, 32 peer-reviewed studies that met the inclusion criteria were analyzed.
Contribution
This study integrates four dimensions of AI-enabled knowledge sharing into a single conceptual framework by synthesizing AI techniques, organizational moderating factors, AI–knowledge-sharing relationship types, and organizational performance outcomes. The study provides a comprehensive overview of current research while identifying important gaps for future investigation.
Findings
The findings indicate that Generative AI, machine learning, fuzzy logic, natural language processing, recommender systems, support vector machines, semantic AI, and random forests as the principal artificial intelligence techniques. Trust, leadership support, employee engagement, and role clarity are the most frequently reported organizational moderating factors. AI-enabled knowledge sharing is primarily associated with transformational organizational change and is linked to enhanced decision-making, innovation, and productivity.
Recommendations for Practitioners
This study offers specific and practical insights into the impact of AI-based knowledge sharing systems on business performance. Practitioners and organizational leaders are encouraged to evaluate the integration of AI into knowledge-sharing processes to enhance business outcomes.
Recommendations for Researchers
Subsequent research should validate and expand upon the relationships identified in this review by employing longitudinal and empirical research designs across a range of industries and organizational contexts.
Impact on Society
As organizations increasingly integrate AI into knowledge management, understanding how AI enhances knowledge sharing can improve organizational learning, innovation, and decision-making.
Future Research
Future research should investigate industry-specific AI-enabled knowledge-sharing practices, emerging generative AI applications, governance mechanisms, and longitudinal organizational outcomes associated with AI adoption.
Background: Business analytics knowledge is a tacit resource that serves as a driving force in aiding data-driven decisions for organisational competitive advantage and efficient and effective service delivery. It is vital to explore the factors which impact knowledge sharing within data analytics teams.
Objectives: The study aimed to identify the prominent information systems capabilities and knowledge sharing factors which are vital for improved business analytics within the public enterprise organisations.
Method: Through a systematic literature review, this study examines the mechanisms by which knowledge retention can be achieved. Data were collected from AIS eLibrary, AJIS, Web of Science, Scopus and ScienceDirect databases. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed.
Results: The knowledge possessed by subject matter experts (SMEs) is business domain knowledge, which cannot be easily accessed unless it is retained within various knowledge repositories within the organisation. Such a situation results in public organisations losing out on their intellectual capital. The study findings reveal that social factors such as knowledge sharing, knowledge retention, scarcity in sourcing the right skills, analytics competence and analytics culture, trust, play significant roles in improving business analytics.
Conclusion: To reap the benefits, both social and technical factors should be considered as part of a social activity system, rather than being applied in isolation. This article discusses the best practices that public enterprise organisations should consider from a social perspective for improved business analytics.
Contribution: The study contributes to the body of knowledge through addressing the existing gap experienced by public enterprise organisations through investigating prominent factors influencing information systems capabilities and knowledge sharing for improved business analytics.
Shingai Javani, John Mangundu· South African Journal of Inf...· 0 citations
Human-artificial intelligence (AI) collaboration has become an important topic in organizational management, as AI technologies are increasingly used in decision-making and everyday work activities. Although interest in this topic has grown in recent years, the literature remains fragmented and does not clearly explain how human-AI integration supports performance beyond basic efficiency improvements. This paper explores how this interaction can support organizational hyper-performance. The study is based on a systematic literature review of peer-reviewed business and management research. The analysis incorporates 80 studies, indexed in Scopus and Web of Science from 2019 to 2026. The searches were updated on 16 February 2026. Conceptual, qualitative, and quantitative studies are examined using thematic analysis. The findings show that hyper-performance does not result automatically from AI adoption. Instead, it depends on how organizations design decision-making processes and assign roles and tasks between humans and AI systems. Clear human-AI complementarity and well-defined decision-making structures are key enabling factors. The study concludes that organizational hyper-performance through human-AI integration is possible only under certain conditions and that clearer concepts and practical guidelines are needed for both research and management practice.
Jolanta Vīcupe, Agnis Stibe, Tatjana Tambovceva· International Scientific Con...· 1 citation
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
The Knowledge Documentation Framework for AI Initiatives (KDF-AI), consisting of twelve components organized into five phases and supported by different maturity levels, is proposed, highlighting the increasing importance of knowledge documentation as a core capability in AI-driven organizations and provided a foundation for future research and practical implementation.
Finannisa Zhafira, Fitria Handayani, Dana Indra Sensuse et al.· Jurnal Impresi Indonesia· 0 citations
The integration of artificial intelligence (AI) into organizations is reshaping management practice, competitive strategy, and the experience of work, yet scholarship remains fragmented across disciplines and is predominantly Western-centric. This systematic literature review synthesizes interdisciplinary evidence on how AI transforms leadership and management functions, drives business model innovation, and alters workplace dynamics, with particular attention to Southeast Asian and Philippine contexts. Peer-reviewed, English-language journal articles published between 2016 and 2025 addressing the organizational, strategic, or workforce implications of AI were included; purely technical studies and non-peer-reviewed materials were excluded from the corpus, with institutional reports used only for contextualization. Scopus, Web of Science, ABI/INFORM, Business Source Complete, and the ACM Digital Library were searched. Screening followed the PRISMA 2020 guidelines: 1,245 records were identified, and 128 studies met all inclusion criteria and were synthesized thematically. First, AI integration facilitates an augmented leadership model in which effective managers combine human judgment with algorithmic capability. Second, AI operates as a strategic capability that enables adaptive, data-centric business models. Third, workplace dynamics exhibit a bifurcation effect whereby AI augments some roles while displacing or deskilling others. The review contributes an integrative tripartite framework connecting leadership, strategy, and workforce transformation, and identifies priorities for research and policy in emerging economies. No protocol was registered.
Teodulfo Mahilum, Michelle Go, Rosemarie Magno et al.· JPAIR Multidisciplinary Rese...· 0 citations
The literature on organizational learning and knowledge management theories highlights the importance of integrating the elements that comprise them to develop strategies for effective learning, which enhances the capabilities of both individuals and organizations. It also emphasizes the significance of individual learning and experience in the creation and transmission of knowledge, underscoring the need for effective and contextualized management to boost performance and innovation. This research aims to characterize Being, Knowing, Doing, Know‐How, and Context by identifying patterns and trends that allow for their efficient integration to propose strategies that improve organizational processes within Ibero‐American SMEs. A mixed methodology was employed, combining a systematic literature review, conceptual analysis, and in‐depth interviews with eleven experts on the subject. The study identified 121 characteristics related to the chosen components in the literature. Through analysis and component reduction by homogenizing criteria, a total of 21 characteristics were distilled, including Adaptation, Leadership, Teamwork, Knowledge, Competencies, Practice, Innovation, Creativity, Technology, and Environment. This research contributes to existing knowledge by proposing a conceptual model that integrates the relationships and characteristics identified during the analysis process.
Jackeline Guerra Gómez, Elkin Olaguer Pérez Sánchez, Norely Margarita Soto Builes· Knowledge and Process Manage...· 0 citations
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