Aug 2026· European Conference on Knowledge Management· 0 citations· 21 references
TL;DR
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.
Abstract
The rapid advancement of artificial intelligence is fundamentally reshaping the architecture and logic of Knowledge Management Systems (KMS). Traditionally, KMS have been designed around repositories, taxonomies, and retrieval mechanisms for storing and redistributing explicit knowledge. However, in the AI era – particularly with the emergence of generative models – the role of KMS extends beyond storage and retrieval, towards active participation in knowledge processing and knowledge creation. This paper examines the evolution of KMS in the AI era through the lens of the SECI model (Socialisation, Externalisation, Combination, Internalization). It argues that AI tools can introduce a new operational dynamic within each SECI phase if properly addressed. In the externalisation process, AI systems can facilitate the conversion of tacit and loosely articulated insights into structured representations. In the combination phase, machine learning models may enable pattern discovery and synthesis across heterogeneous knowledge sources. During internalisation, AI-powered assistants can support experiential learning by contextualising and personalising information. Most importantly, socialisation can be augmented through collaborative AI-mediated environments that enhance collective intelligence, reshaping how shared meaning is constructed. The study critically explores how AI-enhanced KMS can transform from passive infrastructures to evolve into adaptive cognitive systems supporting KM. While AI may increase speed, scalability, and pattern recognition, it also introduces epistemological risks – such as bias propagation, over-automation, and erosion of human judgment. The paper discusses how organisations can mitigate these risks while developing resilient and adaptive KM practices. Adopting a conceptual and integrative approach, the research analyses current technological capabilities and conceptual KM frameworks. This research proposes an updated perspective on KMS as a hybrid socio-technical ecosystem. In such systems, AI tools do not replace human knowledge actors but extend their cognitive and organisational capacities. 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.
Generative AI is changing the role of knowledge in organisations. Traditional knowledge management (KM) systems have primarily supported storage, access and retrieval, assuming that knowledge is interpreted and applied by human users. In AI-enabled environments, however, organisational knowledge increasingly becomes a direct input into execution, shaping generated proposals, analyses, summaries, recommendations and other workflow outputs. This shift exposes a limitation of retrieval-oriented KM: fragmented, outdated or weakly governed knowledge can be amplified through AI-generated outputs, reducing consistency, reliability and trust. This paper introduces executable knowledge systems as a conceptual model for structuring organisational knowledge to support reliable human and AI-assisted execution. The term executable is used in a socio-technical sense. Knowledge does not necessarily become code, but is curated, validated and embedded into workflows so that it can guide outputs, decisions and actions. The paper distinguishes this concept from prior work on executable knowledge graphs and executable knowledge bases, which primarily focus on deterministic execution through rules, scripts or formalised representations. The paper further develops a framework of decay and compounding loops to explain how AI-enabled knowledge systems evolve over time. In decay loops, AI-generated outputs re-enter the knowledge environment without sufficient validation, allowing inconsistency and low-quality knowledge to accumulate. In compounding loops, curated knowledge assets are refined through governed feedback, domain ownership and controlled reuse, enabling improvements in reliability over time. The framework is informed by an exploratory case study within a global professional services organisation, where a curated knowledge environment was introduced to support AI-assisted workflows in the Retail, Consumer Products, Travel and Transportation domain. The evaluation compared outputs generated from a controlled, subject matter expert (SME)-validated knowledge dataset with outputs generated from an unconstrained organisational knowledge base. Findings indicate improved retrieval relevance and output quality when AI systems operate on validated knowledge assets. The paper contributes to KM research by reframing KM as a system design challenge for AI-enabled execution and by positioning governance, validation and feedback control as central mechanisms for reliable organisational knowledge use.
Sara Michelazzo, Parmeet Kaur, Saurabh Saxena· European Conference on Knowl...· 0 citations
The architecture of AI-KMS is examined, focusing on components like knowledge acquisition modules, inference engines, and user interfaces, along with the integration of deep learning and ontologies for improved knowledge representation, which shows improved accuracy in knowledge retrieval and decision-making efficiency.
Z. Yusuf, Vinoj M· International Journal of Art...· 0 citations
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.
Lindokuhle Vuyisile Bridget Mkhize, M. Subban· European Conference on Knowl...· 0 citations
The rapid evolution of Generative AI (GenAI) has transformed the ways in which knowledge is created, shared, interpreted, and applied in organization and educational contexts. While earlier studies have often focused on the technical capabilities of GenAI or on the detection of synthetic content, less attention has been given to how these tools influence knowledge management processes at the individual and group levels. This study addresses that gap by examining how GenAI affects the production, transfer, validation, and use of knowledge among individuals and within collaborative settings. The aim of this study is to explore the current state of 'AI vs AI' by generating a dataset comprising texts, images, audio and video, utilizing a set of freely available AI tools. This pilot illustrative study was based on a two-stage strategy: in the first stage synthetic media was generated with different GenAI tools to mimic human creativity. A second stage, which involved a rigorous evaluation of AI-detection tools for each modality separately. Texts were created in three different GenAI tools and Images were prompted to mimic Renaissance paintings by Michelangelo and Raphael. Audio was generated with the purpose of mimicking interviews with the Renaissance painters. Videos, finally, were created to be deceptive deepfakes, placing persons in environments and situations where they never have been in real life. Findings provide a comparative analysis of tools' accuracy and false-positive rates that could act as valuable guidelines in the increasing flood of AI-generated content. This research contributes to the growing discussion on how organizations and institutions can manage the opportunities and risks associated with GenAI in knowledge-intensive environment. The study highlights the need for critical AI literacy, transparent knowledge practices, and governance mechanisms that ensure the responsible integration of GenAI into individual and collaborative knowledge work.
Anastasiia Iufereva, Peter Mozelius· European Conference on Knowl...· 0 citations
The use of Generative Artificial Intelligence (GenAI) is deeply impacting all Knowledge Management (KM) processes. In particular, due to its ability to generate new content in different forms, the new technology is deemed capable of deeply transforming the knowledge creation process, which is considered the highest and most impactful stage of KM processes. Despite this, a comprehensive understanding of how companies can leverage GenAI to create organizational knowledge is lacking, both empirically and theoretically. Regarding the latter, scholars have recently underlined that prior research has yet to focus on the transformation of the SECI model and Ba theory, the most widely used conceptual frameworks for interpreting the organizational knowledge creation process, in the era of human-intelligence interaction. However, in the last two years, some studies have examined whether the SECI model needs to be revised in light of GenAI. Based on a review of the 19 systematically identified articles on Scopus, the present paper identifies, discusses, and compares the three different conceptual approaches adopted by scholars in dealing with the topic in question: a) applying the SECI model in its original version; b) adapting the original SECI model with small adjustments; c) developing a new SECI-based model. The paper compares the three approaches, highlighting how they assume different notions of the role of GenAI in the knowledge creation process and the types of knowledge involved. The academic and practical implications that arise from the study are discussed in the conclusions.
E. Scarso, K. Kirchner· European Conference on Knowl...· 0 citations
The agentic era has arrived, marking a shift from passive generative artificial intelligence (GenAI) systems to autonomous AI systems which possess the ability to act on behalf of the user. The increased autonomy, rapid decision-making, and the growing presence of intelligent and self-directed systems, that is characteristic of the agentic era, has implications on how various functions within an organisation operate. One of these functions is knowledge management (KM). As knowledge work becomes more distributed, dynamic, and augmented by agentic technologies, traditional knowledge management skill sets need to be adapted. However, the specific knowledge management skills required for knowledge management practitioners to operate effectively in this agentic landscape remain underdefined. In a bid to address this gap, this study explores the perceptions of experienced knowledge management academics, towards knowledge management in the agentic era and the skill set required to operate effectively in the agentic landscape. This study adopted an exploratory qualitative design with asynchronous interviews selected as the data collection instrument. Asynchronous interviews were chosen as they allow for reflective engagement with the abstract and emerging notion of agency in the practice of knowledge management. The study employed a purposive sample of academics with recognised expertise in knowledge management. The interview data was analysed using thematic analysis to identify key skill domains and perspectives regarding knowledge management in the agentic era. The overall contribution of this study is to the emerging body of knowledge management literature on the agentic era by identifying the skills and competencies that knowledge management practitioners require to remain effective in increasingly AI-enabled organisational environments.
P. Lefika· European Conference on Knowl...· 0 citations
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