In "Prediction Machines," economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explore the advancement and growing use of artificial intelligence (AI). The key to AI is not actually intelligence but prediction. This text looks at the value of prediction and data, the importance of trade-offs, and the impact of AI in the workplace. Beneficial to business leaders, financial analysts, policy makers, and students, "Prediction Machines" offers insights, tools, and strategies on how to adapt businesses to the world's ever-growing use of AI. Part 2, covering chapters 7 through 11, focuses on decision making. The components of decision making are outlined, including judgment. The value and complexity of judgment and predicting judgment are thoroughly discussed. Levels of automation in decision making are also explored, from minor machine involvement to fully automated decision making. Chapter 11 explores the idea of full automation in decision making. Implementing AI to a task doesn't necessarily mean full automation of that task because it can't always provide judgments. However, if the machine is able to predict, make judgments, and perform the action, the task is considered fully automated, and humans are removed from the process. Tasks that transition to full automation first are typically the ones for which full automation would deliver the highest returns. The issues of liability with full automation and policy development concerning liability are also discussed.