As industries evolve, AI models must adapt to shifting data landscapes but what happens when those shifts impact accuracy and reliability? This session will cover the challenges of model drift, exploring strategies for real-time monitoring, automated retraining, and adaptive feedback loops to maintain model performance. Understand the basics to sustain AI performance in evolving enterprise landscapes by addressing data distribution shifts, minimizing degradation, and implementing proactive drift mitigation techniques from a technical and strategic perspective.