The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed: Avoiding the Anti-Patterns of AI | RAND - As ever, communicating the requirements and the problem to be solved to IT is difficult and often results in solving the wrong problem, at least in the right way: “In failed projects, either the business leadership does not make them- selves available to discuss whether the choices made by the technical team align with their intent, or they do not realize that the metrics measuring the success of the AI model do not truly represent the metrics of success for its intended purpose. For example, business leaders may say that they need an ML algorithm that tells them the price to set for a product–but what they actually need is the price that gives them the greatest profit margin instead of the price that sells the most items. The data science team lacks this business context and therefore might make the wrong assumptions. These kinds of errors often become obvious only after the data science team delivers a completed AI model and attempts to integrate it into day-to-day business operations.”