The ability to oversee and assess the performance of artificial intelligence systems deployed within a business setting is becoming increasingly vital. This includes tracking key performance indicators, detecting anomalies, and ensuring models function as intended throughout their lifecycle. An example includes observing a fraud detection model’s accuracy over time to identify potential data drift or bias that might compromise its effectiveness.
Effective oversight of AI systems delivers several key benefits. It helps maintain regulatory compliance, enhances model accuracy, and mitigates risks associated with biased or underperforming models. Historically, reliance on initial model performance without continuous monitoring has led to significant financial and reputational consequences for organizations.
