Start Date
10-12-2017 12:00 AM
Description
Recent advances in Artificial Intelligence (AI) have increased the ability of organizations to analyze data to support decisions. However, there is little focus to date, on the potential role of AI in organizational knowledge creation. This paper develops a framework of organizational artificial knowledge creation based on a synthesis of the literature, and the implementation of a multi-agent AI in an organization. We identify five stages for developing organizational artificial knowledge: 1) Extracting and Collecting, 2) Curating, 3) Ingesting, 4) Training and Testing, 5) Analyzing and Predicting. We also identified two main practices triggered by the development of the AI multi-agent that distinguish them from traditional IS: the ability to initiate a dialogue between the different actors which can lead to the consolidation and aggregation of the organizational knowledge, and the ability to establish recursive and reflexive relation between individual knowledge and the organizational artificial knowledge.
Recommended Citation
HARFOUCHE, Antoine; Quinio, Bernard; Skandrani, Sana; and Marciniak, Rolande, "A Framework for Artificial Knowledge Creation in Organizations" (2017). ICIS 2017 Proceedings. 15.
https://aisel.aisnet.org/icis2017/General/Presentations/15
A Framework for Artificial Knowledge Creation in Organizations
Recent advances in Artificial Intelligence (AI) have increased the ability of organizations to analyze data to support decisions. However, there is little focus to date, on the potential role of AI in organizational knowledge creation. This paper develops a framework of organizational artificial knowledge creation based on a synthesis of the literature, and the implementation of a multi-agent AI in an organization. We identify five stages for developing organizational artificial knowledge: 1) Extracting and Collecting, 2) Curating, 3) Ingesting, 4) Training and Testing, 5) Analyzing and Predicting. We also identified two main practices triggered by the development of the AI multi-agent that distinguish them from traditional IS: the ability to initiate a dialogue between the different actors which can lead to the consolidation and aggregation of the organizational knowledge, and the ability to establish recursive and reflexive relation between individual knowledge and the organizational artificial knowledge.