Artificial intelligence and machine learning are related but not synonymous, and understanding the difference significantly impacts hardware selection, software architecture, and system delivery for embedded engineers. For years, embedded systems were almost entirely deterministic, executing predefined sequences without deviation, as industrial automation demanded. Machine learning changed this by training models on historical or representative data to capture statistical relationships and perform inferences on unseen data. Once deployed, the embedded processor executes the trained model rather than learning. For example, a traditional monitoring system might compare vibration levels against predefined thresholds, while a machine learning model evaluates hundreds of features simultaneously to recognize combinations correlating with bearing wear or shaft imbalance. AI takes this further by combining ML inference with deterministic software, sensor fusion, communications, cybersecurity, scheduling, optimization algorithms, and contextual awareness. This enables systems to make increasingly autonomous decisions, such as detecting a bearing problem, evaluating production schedules, communicating with neighboring equipment, notifying maintenance personnel, updating a digital twin, and adjusting operating parameters. The machine learning model remains one component of a larger decision-making framework.