AI retraining: complete definition in AI for SMEs

AI retraining is the process of updating an AI model with more recent data to maintain its performance. A model is not static: markets change, vocabularies evolve, usages shift — without retraining, a model's accuracy gradually drifts (model drift). Retraining can be complete (all data) or incremental (only new data).

What it changes for an SME

Retraining mainly concerns custom models and classification systems:

• a document classification model that must absorb the new product categories each semester;
• a forecasting model that must integrate last quarter's real history;
• a RAG system whose document base must be refreshed when procedures change (in that case, it is more re-indexing than retraining).

Best practice

Define a retraining frequency per model (quarterly, monthly, triggered by a degradation threshold) and document each cycle. The trap: retraining too often with too-recent data can introduce seasonality bias. In fractional AI leadership, we monitor model performance to decide the right retraining moment.