Adoption rate measures whether intended users take up and continue using a product or feature. In AI products, it is a shared, user-centered outcome rather than a substitute for technical metrics. section 12.1 section 1.5
Use adoption rate to connect product decisions with user value:
- Align data science, engineering, UX, and domain priorities around adoption, user satisfaction, and practical usability instead of isolated measures such as precision, recall, or latency. section 12.1
- Treat adoption as an evolving result: gather user feedback, improve accuracy and usability, and communicate how feedback shapes later updates. section 12.4
- Protect adoption by addressing privacy, bias, fairness, transparency, security, and predictable failure handling; damaged trust can reduce use. section 12.4 section 11.0
AI recommendations in a B2B product
A team evaluating action recommendations should not declare success from model performance alone. It should also ask whether customers use the recommendations, find them practical, understand their limitations, and provide feedback that improves later releases. section 12.1 section 11.0
