A Z-score is a statistical measurement that indicates the number of standard deviations a particular data point is from the mean of a dataset. In the context of artificial intelligence and machine learning, Z-scores are often used to standardize data, allowing for better comparison across different datasets or features. By transforming raw data into Z-scores, practitioners can identify outliers and anomalies, as values with high absolute Z-scores may indicate unusual observations. This normalization process is crucial in algorithms sensitive to the scale of input data, such as clustering or neural networks, ensuring that all features contribute equally to model training and performance.