Have you modernized your Problem Management process to include machine learning/AI capabilities in identifying problems and potential solutions? Taken IT Problem Management beyond IT into the business processes? Merged SRE practices into ITIL Problem Managment?

Yes, by incorporating machine learning/AI capabilities to identify issues and solutions. Additionally extended these practices beyond IT to address broader business processes, while also integrating Site Reliability Engineering (SRE) principles into ITIL Problem Management for more comprehensive and effective solutions.

A few ways our analytics team has started using AI/ML

Automated Anomaly Detection in data >> 
ML models analyze system behavior and detect anomalies that could indicate potential problems. This helps in proactive identification and resolution of issues before they impact services.

Predictive Analytics for identifyiing future trends>> 
ML models predict potential problems based on historical data, helping in proactive problem management and prevention.

Pattern Recognition for user behaviour
ML algorithms recognize patterns in incident data, aiding in the identification of recurring issues or common causes of problems.

Chatbots for Issue Resolution:
AI-powered chatbots can handle routine incidents and provide solutions based on knowledge bases and previous interactions.

Natural Language Processing (NLP):
NLP can be used to analyze incident descriptions, classify issues, and suggest relevant solutions.