The increasing importance of knowledge management systems (KMS) as a source of competitive advantage and the recent advances in artificial intelligence (AI) makes the question of appropriate integration of AI and human intelligence (HI) in KMS very pertinent. This paper aims to develop a framework that helps identify appropriate AI-HI integration in KMS for different structure, content and context-related parameters.
The paper uses an inductive approach that leverages Shneiderman (2020) framework to develop a hierarchical framework demonstrating the level of sophistication of AI and identifies an appropriate level for a firm given its structure, content and context-related characteristics.
The absent, assistive, augmented and automated (4A) model of AI integration has been developed, and the relevant structure, content and context-related variables of KMS have been identified. Using the 4A model, the appropriate AI level for an organization, considering multiple structure, content and context-related parameters is identified. Based on the model, efficiency and control are identified as the two principal objectives of KMS and the trade-off between them is elucidated.
To the best of the authors’ knowledge, this paper develops a new model to determine the appropriate level of AI and human interaction in KMS and demonstrates its applicability in multiple situations. The 4A model is applicable across other domains, besides KMS.
