Artificial intelligence terminology is frequently misused in business and casual conversation, blurring important technical distinctions and leading to false expectations and poor software choices. The article clarifies 11 commonly confused concepts. First, AI is the broad umbrella concept of machines performing tasks requiring human reasoning, while Machine Learning (ML) is a specific subset that analyzes datasets to identify patterns; all ML is AI, but not all AI uses ML. Second, deep learning is a specialized evolution of ML using artificial neural networks with many layers, processing unstructured data like images and raw audio without manual feature engineering. Third, Predictive AI analyzes historical data to forecast outcomes or calculate risk, whereas Generative AI creates entirely new original content, such as text, images, or code. Fourth, current conversational chat models are Large Language Models (LLMs), not sentient or all-knowing artificial general intelligence. The article explains these and other distinctions to help teams evaluate software, discuss security risks, and navigate digital tools accurately.