Recent advances in AI are best thought of as a drop in the cost of prediction. By prediction, we don’t just mean the future—prediction is about using data that you have to generate data that you don’t have, often by translating large amounts of data into small, manageable amounts. For example, using images divided into parts to detect whether or not the image contains a human face is a classic prediction problem. Economic theory tells us that as the cost of machine prediction falls, machines will do more and more prediction. Prediction is useful because it helps improve decisions. But it isn’t the only input into decision-making; the other key input is judgment. Consider the example of a credit card network deciding whether or not to approve each attempted transaction. They want to allow legitimate transactions and decline fraud. They use AI to predict whether each attempted transaction is fraudulent. If such predictions were perfect, the network’s decision process is easy. Decline if and only if fraud exists. Get the full story at Harvard Business Review