AI model evaluation: bridging technical metrics and business impact

Evaluating AI models goes beyond simplistic performance metrics; it is a nuanced strategic journey that requires everyone who is involved in AI development; data scientists, stakeholders, project leaders and subject matter experts all need to understand that a single accuracy score can be misleading, and that quantifiable error measures is just the first step of the model evaluation process. A comprehensive approach is essential to assess real-world implications, identify potential biases, and appreciate the complex interplay between technical capabilities and business impact.