With Artificial Intelligence (AI) reshaping how businesses operate, the integration of intelligent technologies into Knowledge Management (KM) processes offers new opportunities for optimizing data-driven decision-making and enhancing organizational performance.
AI-Driven Knowledge Management Assets, Volumes 1 and 2 explore KM as a critical element of business strategy and managerial practice, especially in an era of rapid AI adoption. Authors examine KM’s foundational and advanced aspects through a managerial lens, highlighting how AI is reshaping contemporary KM practices, and delve into traditional KM strategies and cutting-edge AI applications. Each chapter is enriched with case studies and empirical research that showcase the real-world benefits and challenges of integrating AI into KM. From uncovering the theoretical underpinnings of KM to examining AI-driven innovations that create competitive advantages, this work offers actionable insights and perspectives on future developments. Authors address ethical, sustainability and managerial issues, equipping readers with the tools to navigate the complexities of AI-infused KM practices.
Providing a valuable resource for business leaders, academics, and students, these volumes support those looking to integrate AI into KM to drive strategic decision-making and operational efficiency. Merging traditional knowledge management practices with the latest AI advancements, they prepare readers to harness technology for innovative solutions, positioning their organizations for success in the modern business landscape.
AI-Driven Knowledge Management Assets, Volumes 1 and 2 explore Knowledge Management as a critical element of business strategy and managerial practice, especially in an era of rapid Artificial Intelligence adoption.
Part
1. Introduction
Chapter
1. Unveiling the AI-Driven Knowledge Asset Landscape: Foundations,
Innovations, and Integration Strategies; Miltiadis D. Lytras and Meir Russ
Part
2. AI-Driven Decision Support Systems in Business
Chapter
2. How Artificial Intelligence Affects the Decision-Making Process in
Business: AI-Driven Decision Support Systems; Cem Ufuk Baytar
Chapter
3. AI-Driven Decision Support Systems for Transforming Employee
Engagement and Training in Business; Jill Courtney
Part
3. Case Studies on AI-Driven Knowledge Management Systems
Chapter
4. Implementing Artificial Intelligence for Knowledge Management in
Small and Medium Enterprises; Kabiru Ishola Genty, Godwin Kaisara, Sulaiman
Olusegun Atiku, and Hylton James Villet
Chapter
5. Technology Using Artificial Intelligence (AI) to Enhance
Productivity and Sustainability in Atlantic Salmon Production and Challenges
from Knowledge Management; Per Harald Rødvei, Knut Ingar Westeren, and Martin
Munkeby
Chapter
6. Transforming Knowledge Management through Synergistic AI-Human
Collaboration; Viraj Dawarka and Geshwaree Huzooree
Chapter
7. Architectural AI Design Patterns for Knowledge Management
Processes; Giovanna Di Marzo Serugendo and Lamia Friha
Chapter
8. Cooperation Between Artificial Intelligence and Lateral
Transshipment: Qualitative study; Elleuch Fadoi
Part
4. The Role of AI and KM in Enhancing Employee Relationship and Talent
Management
Chapter
9. How AI influences Employees' Organisational Behaviour in
Workplaces; Mandy Mok Kim Man, Wong Wan Ting, Lebene Soga, and Maria
Fernandez-Muiños
Chapter
10. AI in Action: Decoding the Employee Experience Connection to
Boost Engagement; Puneet Kumar and Nayantara Padhi
Chapter
11. Reimagining Talent Management through the AI-Knowledge Nexus;
Unnar Theodorsson
Part
5. The Future of AI in Knowledge Management: Challenges and
Opportunities
Chapter
12. The Prospective Developments of Artificial Intelligence in the
Domain of Knowledge Management: Challenges and Opportunities; Viraj Dawarka,
Alisha Hingun Goolam Gukan, and Aisha Bibi Idoo
Chapter
13. AI-Driven Knowledge Management for Development in the Global
South: Bridging Digital Divides through Localized Innovation; Nanette Y.
Saes, Bruce W. Watson, and Liam R. Watson
Chapter
14. Artificial Intelligence and Knowledge Management Systems:
Transforming the Future of Business Operations in the Fourth Industrial
Revolution; Rashmi Kumari, Sujata Priyambada Dash, and Rajeshwari Chatterjee
Part
6. Conclusions
Chapter
15. Safeguarding the Future: Addressing Fraud, Misuse, and Ethical
Vulnerabilities in AI-Driven Knowledge Management; Meir Russ and Miltiadis D.
Lytras
Miltiadis D. Lytras is a Professor (Full) at The American College of Greece and a Visiting Research Professor at Effat University in Saudi Arabia. Dr. Lytras is a world-class expert in the fields of digital transformation and technology-enabled learning. He is an expert in advanced computer science and management, editor, lecturer, and research consultant, with extensive experience in academia and the business sector in Europe, the Middle East, and Asia.
Meir Russ is a Professor Emeritus in Management at the Austin E. Cofrin School of Business at the University of Wisconsin-Green Bay, USA, a Research Fellow at the Stellenbosch University in South Africa and a Visiting Professor at the University of ód, Poland. He is presently the Editorin-Chief of The Online Journal of Applied Knowledge Management (OJAKM).
