Effective change management is crucial to maintain the integrity and performance of AI/ML and Generative AI (GenAI) models in rapidly evolving landscapes. Recent high-profile failures have highlighted the importance of robust governance in managing model changes to mitigate risks such as data inconsistencies, biased outcomes, and operational failures. The best practices include establishing a model governance committee, conducting detailed impact assessments, documenting changes, and implementing regular review and validation processes. GenAI models require additional considerations such as extensive testing and real-time monitoring of user feedback. Continuous monitoring post-change is essential to validate effectiveness and ensure ongoing alignment with organizational objectives and regulatory standards. Lessons from recent failures and regulatory guidance emphasize the need for proactive planning, stakeholder collaboration, and transparent communication throughout the process of change. Model owners play a central role in change management by distinguishing between material and non-material changes, notifying stakeholders, and maintaining comprehensive logs. By adhering to these best practices, organizations can navigate the complexities of model updates, ensure compliance, and maximize the potential of their AI capabilities, while reinforcing stakeholder trust and positioning themselves as leaders in responsible AI innovation.