Innovation Practices Using AI technology: Exploring Human-Machine Knowledge Conversion

Abstract

The use of AI in innovation practices creates a conundrum for knowledge creation theory. Current AI technology possesses the ability to engage with data in ways that differ from the original design due to their response to contextual input data. AI may be said to embody tacit knowledge as it evolves and learns about the human collaborator and the organisational and technological context. We explore knowledge conversion involving human and AI to understand the process of digital innovation starting from the mutually constituted innovation agency. We articulate a process model of knowledge creation extending Nonaka’s seminal SECI model starting with a sociomaterial lens on knowledge conversion and a recursive relationship between the outcome of knowledge creation and the material interactions of human and AI. Our analysis links characteristics of digital innovation with the potential of AI to perform skilful work or, in other words, appear to act on tacit knowledge. This insight contributes to the literature on digital innovation a missing link for seeing material agency as intermingled with human agency and to organizational knowledge creation theory the possibility of the machine becoming part of a team. We discuss theoretical and practical implications of our study for the economics of digital innovation and suggest avenues for future research.