Author Description

Organizations worldwide grapple with the complexities of incorporating machine learning into their business models while ensuring sustainability. Decision-makers, data scientists, and business executives face the challenge of navigating this terrain to drive innovation and maintain a competitive edge. Building Business Models with Machine Learning provides a comprehensive solution, offering practical insights and strategies for integrating machine learning into organizational plans. By bridging the gap between theory and practice, we empower readers to leverage machine learning effectively, enabling them to develop resilient and flexible business models. The book serves as a vital resource for those seeking to understand the nuances of sustainable management in a volatile, uncertain, complex, and ambiguous (VUCA) world. It addresses key challenges such as irrational decision-making and the need for adaptive systems in modern business environments. Through a combination of theoretical frameworks and empirical research findings, our book equips readers with the knowledge and tools needed to navigate these challenges successfully. Whether you are a seasoned professional, a postgraduate MBA program, or a managerial sciences student, this book offers invaluable insights that will significantly enhance your understanding and application of machine learning in business models. Building Business Models with Machine Learning focuses on problem-solving and practical application. It provides a roadmap for incorporating machine learning into organizational strategies and covers a wide range of topics, including sustainability in business models, machine learning in healthcare, and online fraud detection. By offering a thorough manual for integrating learning into organizational plans, this book enables readers to develop data-driven, adaptable business models essential for success in today's dynamic business landscape.