AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
In plain terms
Model drift is the gradual decline in an AI system's accuracy as the real world moves away from the data it learned from. An AI feature is not done at launch. Without monitoring, performance decays silently, which is why running AI is an ongoing operating cost, not a project that ends.
Model drift is what happens when you train a weather forecasting model in summer and try to use it in winter. The world changed; the model did not.
A demand forecasting model trained before a major market shift underperforms after it. The team that monitors drift catches it in weeks. The team that does not discovers it from a stockout.
Monitoring for drift is part of every production AI deployment. Budget for it at design time, not as a reactive fix after quality has already declined in production.
Do not assume a model that was accurate at launch will remain accurate without monitoring. All production AI systems require ongoing evaluation as the world they predict keeps changing.
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