Artificial Intelligence for Business Optimization

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.

Foreword by Andy Lyman. Preface. Readers. Figures. Acknowledgments. Authors. 1 Artificial intelligence and machine learning: Opportunities for digital business. 2 Data to decisions: Evolving interrelationships. 3 Digital leadership: Strategies for AI adoption. 4 Machine learning types: Statistical understanding in the business context. 5 Dynamicity in learning: Smart selection of learning techniques. 6 Intelligent business processes with embedded analytics. 7 Adopting data-driven culture: Leadership and change management for business optimization. 8 Quality and risks: Assurance and control in BO. 9 Cybersecurity in BO: Significance and challenges for digital business. 10 Natural intelligence and social aspects of AI-based decisions. 11 Investing in the future technology of self-driving vehicles: Case study. Appendix A: Frameworks and libraries for ML. Appendix B: Datasets for ML and predictive analytics. Appendix C: AI and BO research areas. Index.