Vol 5 - Special Issue

Open Journal in Information Systems Engineering

List of Articles

Ce numéro spécial, « Apports et limites de l’intelligence artificielle pour la gestion des connaissances tacites en entreprise », reprend une sélection retravaillée de trois des huit contributions ayant été présentées à l’atelier "Connaissances Tacites" (KM-IA).

Digital artificial intelligence (AI) is ubiquitous and constantly interacts with humans, drawing on both their explicit and tacit knowledge. Unlike humans, who possess both formalized knowledge and a wealth of tacit knowledge shaped by experience, AI does not hold any intrinsic knowledge. It generates responses by exploiting algorithmic models and accumulated datasets, but encounters limitations in understanding and reproducing tacit knowledge, which is often unarticulated and highly context-dependent. However, AI could play a key role in the articulation and transmission of such knowledge. By interacting with humans, it may assist in structuring informal knowledge, identifying recurring patterns in decision-making, and facilitating the exchange of expertise within organizations. Inspired by the concept of "Ba" defined by Nonaka, which describes a shared space that fosters knowledge creation, AI could act as a catalyst for formalizing certain aspects of tacit knowledge, while simultaneously raising major epistemological challenges, such as bias and the opacity of AI models. In this article, we analyze the capabilities and limitations of AI in addressing informal knowledge. We explore the mechanisms by which it could contribute to the emergence of a hybrid intelligence, combining human reasoning with algorithmic assistance, and discuss the practical, ethical, and equity-related implications of this interaction, particularly in domains where intuition and experience are essential, such as medicine, education, and strategic decision-making.

This study explores the capture and valorization of tacit knowledge within scientific organizations, using BRGM as a case study. Facing the strategic challenge of knowledge management, we examine the transformation of indi-vidual knowledge into a collective asset via a three-pronged methodological approach: theoretical framework, use of AI tools to identify and transcribe this knowledge, and proposal of a CBR architecture with an AI agent ("beregem") to solve problems in geosciences. This research contributes to the management of scientific tacit knowledge through AI-based solutions.

This paper explores the perpetuation of practitioners’ tacit knowledge in the context of projects aimed at designing Artificial Intelligence (AI) uses in organizations. By comparing an interdisciplinary review of the state of the art on tacit knowledge with an observational field study of 7 application cases in France and Switzerland, this article sheds light on the dynamics of capturing practitioners’ tacit knowledge during the design and operation of AI models and highlights three areas for consideration: (1) the emergence of new devices for translating practitioners’ know-how into data models and capturing tacit knowledge through the maieutic carried out in the design phase, (2) the difficulty of taking unconscious tacit knowledge into account when judging AI in use, revealing issues of interpretability, cognitive bias and trust, and (3) the capture of knowledge, including tacit knowledge, as the primary goal of Data Science projects. But this capture may not be desired by the practitioners or even introduce an intermediation that prevents the development of further tacit knowledge derived from real-life experience in favour of that linked to the use of AI. These considerations lead to the improvement of tacit knowledge perpetuation devices, as long as their legitimacy is justified, and the risks are mitigated.