A Cross-Disciplinary Knowledge Management Framework for Generative Artificial Intelligence in Product Management: A Case Study From the Manufacturing Sector

Abstract

This paper presents a cross-disciplinary knowledge management framework designed to enhance the integration of Generative Artificial Intelligence (GenAI) in product management within the manufacturing sector. The framework focuses on designing, monitoring, and optimizing the business value of GenAI solutions by leveraging best practices from both knowledge management and artificial intelligence engineering disciplines. The study highlights the use of LLMOps methodology for continuous monitoring and multi-agent approach for continuous improvement. The research employs a qualitative case study methodology, focusing on a leading large international manufacturing firm that has implemented GenAI solutions in its product management. The study involves interviews with stakeholders and document collection and analysis. This case study contributes to the literature by providing a structured approach to incorporating GenAI into product management in the manufacturing sector, facilitating cross-disciplinary knowledge sharing. The study advances the understanding of using cross-disciplinary knowledge management framework for advanced AI applications in business projects and encourages further research. The framework serves also as a guide for manufacturing firms aiming to implement advanced AI providing actionable insights for designing, monitoring, and optimizing the business value of GenAI solutions. It underscores the potential of academic researchers as catalysts in such projects and proposes a method for continuous knowledge transfer and improvement through a knowledge flywheel.