The Human-Machine Knowledge Spiral

2026-06-28

AI Summary

This study addresses the challenge posed by machine tacit knowledge to organizational innovation in the age of artificial intelligence. Extending Nonaka’s SECI knowledge spiral model, it introduces the novel concept of “machine tacit knowledge” and proposes a triadic human–machine–organization framework for knowledge creation. By integrating knowledge management theory, organizational learning mechanisms, and AI system interaction analysis, the research demonstrates that organizations must cultivate shared contexts to enable dynamic conversions among human explicit knowledge, human tacit knowledge, and machine tacit knowledge. This framework not only enriches classical theories of knowledge creation but also provides a theoretical foundation and practical pathways for sustaining innovation in AI-driven, knowledge-intensive enterprises.

Abstract

Nonaka emphasized that innovation is the result of a continuous back-and-forth between tacit and explicit knowledge. Artificial intelligence introduces a fundamentally new object into this process -- tacit machine knowledge -- but Nonaka's ideas are more relevant than ever. The central role of the knowledge-creating company remains the same: to create the shared context in which different kinds of knowledge can feed off each other, become organizational knowledge, and set off further cycles of innovation.

Problem

• tacit knowledge
• explicit knowledge
• artificial intelligence
• knowledge spiral
• organizational innovation

Innovation

• tacit machine knowledge
• knowledge spiral
• human-machine collaboration
• organizational knowledge creation
• AI and innovation