Digital knowledge twin to extract, share, and utilize Gen-Ba knowledge

Abstract

This article focuses on the importance and challenges of extracting, sharing, and utilizing explicit, latent, and tacit field knowledge (called “Gen-Ba knowledge”) in industries such as manufacturing, healthcare, and agriculture. While generative AI has improved access to explicit knowledge, handling tacit and latent knowledge remains challenging. To address this, we have proposed the concept of a “Digital Knowledge Twin,” in which Gen-Ba knowledge is extracted as knowledge fragments including voices, images, and sensor data and it is shared among members through internalization workshops. This article discusses this concept and key technologies needed for its implementation.