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What is a Model Drift?
Model drift describes a situation where an AI system becomes less accurate over time because the real world it is analyzing has changed. When a system is first built, it learns patterns from historical data. If the environment shifts, those old patterns no longer match current reality. The AI continues to apply its original logic to new situations, which leads to outdated or incorrect results. Think of it like a weather forecaster who relies on climate data from fifty years ago to predict today's storms. Because the climate has changed, their old models are no longer reliable. Businesses must regularly update these systems to ensure they reflect the current state of the market.
Why this matters to you
If you rely on AI for business decisions, you need to know if the tool is still relevant. When models drift, they can lead to poor financial projections, bad customer service recommendations, or incorrect inventory levels. Monitoring for drift helps you catch these errors before they impact your bottom line or your reputation with clients.
How you might hear this
Our sales forecasting tool is showing model drift because it was trained on data from before the recent supply chain changes.
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