Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. Despite its promising advances, artificial intelligence has yet to cause a transformational change in many industries. And in many cases, the problem is not necessarily with the technology but with the way we perceive it. Power and Prediction, a new book by Ajay Agrawal, Joshua Gans, and Avi Goldfarb, explores the fundamental challenges standing in the way of AI adoption in different industries. A sequel to their acclaimed Prediction Machines, the new book discusses what needs to change before organizations can benefit from the full potential of advances in artificial intelligence.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. From top artificial intelligence (AI) scientists warning that deep learning will push radiologists out of employment, to healthcare professionals heralding that AI will redefine the doctor-patient relationship, to tech executives promising that fully self-driving cars are just around the corner, AI has been marked with plenty of failed predictions in recent years. Despite the remarkable advances in AI, it has yet to play its transformational role in many industries. However, when compared to other technological milestones such as the steam engine, electricity and the internal combustion engine, it is no surprise that AI adoption is slow. Ajay Agrawal, Joshua Gans and Avi Goldfarb, professors at Toronto University and authors of the new book Power and Prediction, believe that we are at a stage where the power of AI is evident but its widespread adoption has yet to come. And to better deal with the challenges that stand in the way of leveraging the power of AI, we must understand not only the applications where it is used but also the systems in which it operates.

Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare and Professor of Marketing at the Rotman School of Management, University of Toronto. Avi is also Chief Data Scientist at the Creative Destruction Lab, Senior Editor at Marketing Science, and a Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. This work has been discussed in White House reports, Congressional testimony, European Commission documents, the Economist, the Globe and Mail, National Public Radio, the Atlantic, the New York Times, the Financial Times, the Wall Street Journal, and elsewhere. He holds a PhD in economics of Northwestern University.

CURT NICKISCH: Welcome to the HBR IdeaCast from Harvard Business Review. A shiny new piece of technology is not good enough on its own. It needs to be implemented at the right time, used in the right context, and accepted in the right culture, applied in the right way. In short, it needs to be part of the right system. AI can help individuals and teams make better predictions, combine that with judgment and you get better decisions. But those decisions have ripple effects on other parts of the system, ripple effects that can undermine the very prediction that was made. Our guest today says, "If organizations want to take artificial intelligence to the next level, they need to get better at coordinating optimal decisions over a wider network."