Model Drift
AI OperationsWhat It Means
Model drift occurs when an AI system's accuracy and reliability decline over time because the real-world data it encounters differs from the data it was originally trained on. Think of it like a GPS system that becomes less accurate as new roads are built and traffic patterns change - the underlying world has shifted, making the original training obsolete.
Why Chief AI Officers Care
Model drift directly threatens business value by causing AI systems to make increasingly poor decisions, potentially leading to revenue loss, compliance violations, or damaged customer relationships. As Chief AI Officer, you need robust monitoring systems and retraining processes to detect drift early and maintain the ROI on your AI investments while ensuring regulatory compliance.
Real-World Example
A credit scoring model trained on pre-pandemic data begins approving risky loans and rejecting good customers because economic behaviors fundamentally changed during COVID-19, requiring the bank to retrain the model on recent data to restore accurate risk assessment.
Common Confusion
Many executives assume model drift only happens with old models, but it can occur within weeks or months of deployment if market conditions, customer behavior, or business processes change rapidly.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare, model drift occurs when AI diagnostic or treatment recommendation systems become less accurate as patient...
Finance: In finance, model drift commonly affects credit scoring models, fraud detection systems, and algorithmic trading strateg...
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- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
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