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The Recursive Theory of Knowledge Augmentation: Integrating human intuition and knowledge in Artificial Intelligence to augment organizational knowledge

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Abstract

Artificial intelligence (AI) has increased the ability of organizations to accumulate tacit and explicit knowledge to inform management decision-making. Despite the hype and popularity of AI, there is a noticeable scarcity of research focusing on AI's potential role in enriching and augmenting organizational knowledge. This paper develops a recursive theory of knowledge augmentation in organizations (the KAM model) based on a synthesis of extant literature and a four-year revised canonical action research project. The project aimed to design and implement a human-centric AI (called Project) to solve the lack of integration of tacit and explicit knowledge in a scientific research center (SRC). To explore the patterns of knowledge augmentation in organizations, this study extends Nonaka's SECI (socialization, externalization, combination, and internalization) model by incorporating the human-in-the-loop Informed Artificial Intelligence (IAI) approach. The proposed design offers the possibility to integrate experts' intuition and domain knowledge in AI in an explainable way. The findings show that organizational knowledge can be augmented through a recursive process enabled by the design and implementation of human-in-the-loop IAI. The study has important implications for research and practice.

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Notes

  1. The Project made possible the study of the behavior of prehistoric man who lived in a cave in southern France between 600,000 and 90,000 years ago.

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Authors and Affiliations

  1. Paris Nanterre University, 200 Av. de La République, 92000, Nanterre, France

    Antoine Harfouche & Bernard Quinio

  2. Business School Lausanne, Rte de La Maladière 21, 1022, Chavannes-Près-Renens, Switzerland

    Mario Saba

  3. Research Center, Léonard de Vinci Pôle Universitaire, 92 916, Paris La Défense, France

    Peter Bou Saba

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  1. Antoine Harfouche
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  2. Bernard Quinio
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Correspondence to Antoine Harfouche.

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Harfouche, A., Quinio, B., Saba, M. et al. The Recursive Theory of Knowledge Augmentation: Integrating human intuition and knowledge in Artificial Intelligence to augment organizational knowledge. Inf Syst Front 25, 55–70 (2023). https://doi.org/10.1007/s10796-022-10352-8

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