The Meaningfulness of Knowledge for the Design of Sustainable AI

Abstract

A growing number of business processes can be characterized as knowledge-intensive. The ability to speed up the necessary transfer of knowledge between individuals in business processes can lead to a huge competitive advantage, for instance, if intelligent information systems are enabled to analyze complex patterns of knowledge flows and adapt to the individual knowledge transfer situation. This paper introduces a time-dependent knowledge transfer model incl. an experimentation tool with the intention of predicting and furthermore accelerating the speed of knowledge transfer among people and information systems. While intelligent information systems start to function as management instances on the basis of this model, experiment results show the validity of considering intervening information systems as strategic tools for improving resource allocation in processes and clarifying the meaningfulness of knowledge for the creation of sustainable AI.