The text discusses the challenges and solutions associated with implementing data provenance in modern AI workflows, emphasizing that traditional provenance tools are inadequate for managing the complexities of machine learning processes. It suggests integrating MLOps with existing systems to enhance visibility into AI development, automate provenance tracking, and create a return on investment (ROI) tracking system to highlight the benefits of provenance. Best practices include designing systems with traceability, standardizing metadata capture, treating provenance as a trust layer, and aligning it with governance policies. Organizations are encouraged to monitor provenance like system uptime and scale it with AI workflows, ensuring accountability without excessive bureaucracy. By effectively integrating provenance into AI operations, businesses can improve reliability, trustworthiness, and compliance, making these processes an invisible yet essential part of their infrastructure.