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Full Description
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.
Contents
Part 1. Introduction
Chapter 1. Unveiling the AI-Driven Knowledge Asset Landscape: Foundations, Innovations, and Integration Strategies; Miltiadis 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 Lytras
