Integrating Artificial Intelligence into Human Resource Knowledge Systems for Intelligent Management

Abstract

Decision support systems (DSS) are increasingly vital to the daily operations of modern organizations. Data
warehousing provides the infrastructure to extract, cleanse, and store large volumes of data, enabling knowledge
workers to make fact‑based decisions. However, much of a firm’s intellectual capital resides in the minds of
employees rather than in digital systems. To address this gap, a new generation of knowledge‑enabled systems is
required, capable of capturing, organizing, leveraging, and disseminating both data and tacit knowledge. This
paper proposes an extension of the traditional data warehouse model through a knowledge warehouse (KW)
architecture. The KW framework facilitates the coding, retrieval, and sharing of organizational knowledge,
thereby enhancing decision quality across the enterprise. This approach signals a new direction for DSS,
emphasizing knowledge improvement as a central purpose. Future DSS effectiveness will be measured not only
by data processing capacity but also by how well systems promote knowledge, strengthen decision makers’ mental
models, and improve organizational decision making.