Beyond accuracy: holistic success metrics

AI success involves more than technical accuracy metrics. While F1 scores measure how well a model performs in terms of precision and recall, they don't capture the full picture of user experience with AI features.

Consider measuring the following metrics:

• Adoption rates and continued engagement, which show whether users find value in the AI functionality over time
• Trust indicators through user behavior patterns, such as how often people accept or override AI suggestions
• Efficiency improvements in user workflows, comparing time-to-completion or error rates before and after adding AI
• Satisfaction through both direct feedback and indirect signals, like how frequently features are used

For augmentation systems, measure complementary performance, evaluating how the human-AI team performs compared to working alone. Context-specific metrics matter too: an AI writing assistant might track accepted suggestions, while a recommendation system might measure discovery diversity. Good metrics align with specific user goals and business objectives rather than focusing only on model performance.