Predictive analytics in observability aims to foresee potential system failures, performance bottlenecks, or resource constraints before they happen, enabling teams to act preemptively. AI holds the promise of making this possible by analyzing metrics, logs, and traces in real time to detect trends and anomalies that could lead to potential issues. However, current AI systems often struggle with accurate predictive analytics due to data complexity and the complexity of modern cloud architectures. To overcome these challenges, comprehensive, high-quality data is needed, as well as context-aware AI models. Despite these hurdles, AI is already making an impact in observability through practical use cases such as automated root cause analysis, anomaly detection with real-time correlation, and event-based metrics with automated insights. Lumigo is uniquely positioned to lead this evolution by integrating high-quality observability data with AI-powered tools, offering the foundation required for predictive analytics.