Which advanced data modeling approaches (e.g., anomaly detection, digital twins, neural networks) have proven most effective in supporting equipment reliability in your operations?
How do you approach the integration of AI tools with existing CMMS, ERP, and MES systems while maintaining data integrity and operational continuity?
What are the most pressing limitations of current AI models in pharma maintenance, and where do you see opportunity for innovation?
How are you addressing the governance, validation, and regulatory scrutiny of AI-driven decisions within GMP environments?
How can organizations institutionalize AI insights to create a culture of predictive reliability, rather than isolated pilot success stories?