Artificial Intelligence for Business Optimization
Research and Applications
- Available for pre-order. Item will ship after July 22, 2021
Artificial Intelligence for Business Optimization: Research and Applications is primarily a business book that discusses the research and associated practical application of Artificial Intelligence (AI) and Machine Learning (ML) in order to achieve Business Optimization (BO). AI comprises wide range of technologies, databases algorithms and devices. This book aims for a holistic approach to AI by focusing on developing business strategies that will not only automate but also optimize business functions, processes and people-behavior.
This book explores AI and ML from a business viewpoint with the key purpose of enhancing customer value. This book applies research methods and fundamentals from a practitioner's viewpoint and incorporates discussions around risks and changes associated with utilization of AI in business. Furthermore, governance risks, privacy and security are also addressed in this book to ensure compliance of AI/ML applications. Readers should find direct and practical applications of the discussions in this book quite useful in their work environment. Researchers will find many ideas to explore further in the applications to AI to business.
Table of Contents
Chapter 1: Artificial Intelligence and Machine Learning: Opportunities for Digital Business
Chapter 2: Data to Decisions: Evolving Interrelationships
Chapter 3: Digital Leadership: Business Strategies for AI adoption
Chapter 4: Statistical Understanding of Machine Learning Types: AI & ML in the Business Context
Chapter 5: Dynamicity in Learning: Smart Selection of Learning Techniques
Chapter 6: Intelligent Business Processes with Embedded Analytics
Chapter 7: Adopting Data-driven Culture: Leadership and Change Management for Business Optimization
Chapter 8: Quality and Risks: Assurance and Control in BO
Chapter 9: Cybersecurity in BO: Significance and challenges for Digital Business
Chapter 10: Natural Intelligence and Social aspects of AI-based Decisions
Chapter 11: Case Study: Investing in the Future Technology of Self-Driving Vehicles
Dr Bhuvan Unhelkar (BE, MDBA, MSc, PhD, FACS) has extensive strategic and hands- on professional experience in the Information and Communication Technologies (ICT) industry. He is a full Professor and lead faculty of IT at the University of South Florida Sarasota-Manatee (USFSM), and is the founder and Consultant at MethodScience and PlatiFi. He is also an adjunct Professor at Western Sydney University, Australia and an honorary Professor at Amity University, India . His current industrial research interests include AI and ML in Business Optimization, Big Data and business value and Business Analysis in the context of Agile. Dr. Unhelkar holds a Certificate-IV in TAA and TAE, Professional Scrum Master - I, SAFe (Scaled Agile Framework for Enterprise) Leader and is a Certified Business Analysis Professional® (CBAP of the IIBA). Tad Gonsalves is full Professor in the Department of Information & Communication Sciences, Sophia University, Tokyo, Japan. Dr. Gonsalves' research areas include Bio-inspired Optimization techniques and application of Deep Learning techniques to diverse problems like autonomous driving, drones, digital art and computational linguistics. He holds a BS in theoretical Physics and MS in Astrophysics and earned his PhD in Information Systems from Sophia University, Tokyo, Japan. His research lab in Tokyo specializes in multi-GPU computing. Dr. Gonsalves is the author of Introduction to AI: A Non-Technical Introduction, (Sophia Univ. Press, 2017) which serves as a standard AI textbook for the university curriculum.