Data drift refers to gradual changes in CRM data patterns over time, which can impact the accuracy of analytics, AI models, and automated decision-making. Drift occurs due to shifting customer behaviors, market changes, or outdated data collection methods. For example, a CRM?s lead scoring model trained on past data may become less effective as customer preferences evolve. Businesses monitor data drift using machine learning algorithms that detect shifts and adjust models accordingly. Proactively managing data drift helps organizations maintain data accuracy, predict trends, and optimize AI-driven CRM processes.