When evaluating AI, the metrics that matter most depend on the specific tasks and goals of the model. At AEHEA, we emphasize metrics that clearly reflect real-world performance and the business value delivered. Choosing the right metrics is crucial because it guides development, informs stakeholders, and ultimately determines if the AI system is successful.

For classification tasks, accuracy, precision, recall, and the F1-score are typically key. Accuracy measures how often the model makes the correct prediction overall. Precision shows how often positive predictions were correct, while recall measures the ability to catch actual positives. The F1-score balances precision and recall, making it especially useful when dealing with imbalanced data. These metrics provide a nuanced view of model performance beyond simple correctness.

In predictive or regression tasks, we commonly use mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). These metrics measure how closely predictions match actual outcomes. Lower scores indicate better performance. For models providing recommendations or ranking results, metrics like click-through rate (CTR), conversion rate, or average revenue per user (ARPU) are often more meaningful. They directly link AI performance to business outcomes.

At AEHEA, we also prioritize metrics around fairness, explainability, and operational performance. This includes monitoring model response times, error rates, and resource usage. Additionally, we track user satisfaction and adoption rates, as AI success is ultimately measured by real-world impact. By choosing metrics thoughtfully, we ensure AI aligns with strategic goals, provides actionable insights, and continuously delivers value over time.