The goal is to shift OpenShift must-gather from just collecting data to intelligently analyzing, predicting, and even suggesting resolutions for cluster issues. This pivot is essential because current must-gather outputs can be overwhelmingly large and complex, leading to slow, reactive troubleshooting and requiring deep expertise. By integrating AI, we can significantly reduce the Mean Time To Resolution (MTTR), proactively identify problems, and enhance the overall customer experience by making diagnostics faster and more accessible.

AI focus on the following areas would be critical to improve the customer experience and reduce the burden on the CEE organization.  This will help to make the OpenShift operations smarter, more efficient, and far less reliant on manual intervention, directly improving system reliability and customer satisfaction. The must gather area is also being earmarked as a selected area to leverage AI to improve customer experience. 

1. Automated Log Analysis and Anomaly Detection: Using AI to quickly find critical patterns and unusual events within the vast amounts of log data, eliminating the need for manual sifting.
2. Intelligent Event Correlation and Root Cause Analysis (RCA): Leveraging AI to connect dots between various events, metrics, and logs across OpenShift components to pinpoint the exact root cause of an issue, not just the symptoms.
3. Predictive Analytics for Proactive Health Monitoring: Employing AI to analyze historical data and forecast potential issues like resource exhaustion or performance bottlenecks before they impact services, enabling proactive intervention.
4. Intelligent Search and Recommendation for Knowledge Base: Improving how support and users find answers by using AI for semantic search and context-aware recommendations from Red Hat's extensive documentation and knowledge base.