Techniques are described for adaptive and augmented decision making by an artificial intelligence (AI) engine, such as an engine that employs machine learning techniques. A decision-making process may be executed to make a decision regarding operations of the organization, and the AI engine may be employed to analyze the various aspects of a decision and determine a risk level associated with the decision. The risk level may be a combination of the probability of a negative outcome and a magnitude of loss that may occur due to a negative outcome. The automated process may also determine a confidence level that indicates a degree of confidence in the determined risk level. Risk and confidence may be independent values. Implementations may enable risk mitigation by providing a risk estimate with higher confidence than traditional methods.