The role of artificial intelligence in knowledge management

Explainable Ontology-Based Intelligent Decision Support System for Business Model Design and Sustainability

Background: Case-Based Reasoning (CBR) is a problem-solving paradigm that uses knowledge of relevant past experiences (cases) to interpret or solve new problems. CBR systems allow generating explanations easily, as they typically organize and represent knowledge in a way that makes it possible to reason about and thereby generate explanations. An improvement of this paradigm is ontology-based CBR, an approach that combines, in the form of formal ontologies, case-specific knowledge with domain one in order to improve the effectiveness and explanation capability of the system. Intelligent systems make daily activities more easily, efficiently, and represent a real support for sustainable economic development. On the one hand, they improve efficiency, productivity, and quality, and, on the other hand, can reduce costs and cut waste. In this way, intelligent systems facilitate sustainable development, economic growth, societal progress, and improve efficiency. Aim: In this vision, the purpose of this paper is to propose a new generation of intelligent decision support systems for Business Model having the ability to provide explanations to increase confidence in proposed solutions. Findings/result: The performance results obtained show the benefits of the proposed solution with different requirements of an explanatory decision support system. Consequently, applying this paradigm for software tools of business model development will make a great promise for supporting business model design, sustainability, and innovation.

AI-enabled knowledge sharing and learning: redesigning roles and processes

Purpose

This paper aims to investigate the role of AI in facilitating knowledge sharing and learning in organizations and the redesign of AI-enabled knowledge workers’ roles and processes.

Design/methodology/approach

This paper develops a framework for analyzing AI’s role in different knowledge management activities, explores the impact of AI in transforming knowledge workers’ roles and processes in knowledge sharing and learning and presents recommendations for tailored AI-enabled knowledge management systems for modern knowledge worker environments.

Findings

The authors synthesize the elements from different parts of the relevant literature and develop a unified framework consisting of three dimensions of AI systems, three knowledge management (KM) activities and two types of AI–human interactions. Based on this framework, the authors summarize the primary use cases supported by AI-enabled knowledge management systems (KMS) and compare them with the traditional KMS use cases. The authors find that a single type of AI system is insufficient to support the increasingly complex nature of knowledge workers’ activities, manifested in three dimensions – process, engagement and content; a tailored AI system should be developed to support knowledge workers in their unique roles and processes.

Originality/value

With the growing interest in AI and its applications to KM, this research provides managerial insights for practitioners to effectively adopt AI in managing knowledge assets in organizations.