About the Author

Nikhil Krishnan, Ph.D., is the Group Vice President of Products at C3 AI. At C3 AI, he is responsible for product management, product marketing, and AI/machine learning.

Over nearly a decade at C3 AI, Dr. Krishnan has developed deep experience in designing, developing, and implementing complex, large-scale enterprise AI and ML products and solutions to capture economic value. This book offers practical advice and insights for managers gathered over years of managing enterprise AI/ML products and projects.

Dr. Krishnan has extensive experience in unlocking business value from the application of enterprise AI across industry verticals, including financial services, manufacturing, oil and gas, healthcare, utilities, and government. He has been involved in large-scale enterprise AI transformations at many of the world’s largest, most complex, and iconic organizations, including Bank of America, Baker Hughes, Shell, Koch Industries, and the United States Air Force.

Prior to C3 AI, Dr. Krishnan was an associate principal at McKinsey & Company, where he was a leader in McKinsey’s Advanced Industrials and Energy Practices.

Dr. Krishnan was formerly an assistant professor at Columbia University in earth and environmental engineering. He also worked as a research engineer at Applied Materials, Inc.

Dr. Krishnan earned a bachelor’s degree from the Indian Institute of Technology, Madras, and holds a master’s and Ph.D. in mechanical engineering from the University of California, Berkeley.

Enterprise AI and Machine Learning for Managers has been eight years in the making. This guide synthesizes many of the lessons we have learned at C3 AI – often the hard way – in designing, developing, and implementing some of the largest and most complex global enterprise AI/ML applications.

It is the result of endless intense discussions and debates with talented colleagues and customers, with hard-won lessons emerging from trying different solutions to challenging problems. The most important lessons we have learned in data science have often emerged from genuine confoundment about why specific, often “textbook,” solutions do not work in the real world. This guide seeks to capture the management lessons we have learned from years of practical experience.

I would like to acknowledge the significant support and contributions of many current and former colleagues who have been instrumental in making this work possible, including Alex Capretta, Turker Coskun, Satprit Duggal, Eric Marti, Lila Fridley, Zico Kolter, Henrik Ohlsson, Louis Poirier, Adrian Rami, and Alisha Roeder. I would also like to thank Tom Siebel, Ed Abbo, and Houman Behzadi at C3 AI for their support and encouragement of this work, and for their input and feedback.