### Q1: What are the basic functionalities of TensorFlow that make it suitable for predictive analytics in product management?  
### Q2: How does IBM Watson facilitate automated reporting processes for product managers with minimal technical skills?  
### Q3: What are the key differences between TensorFlow and IBM Watson that may affect a product manager's decision on which tool to use for AI implementation?  
### Q4: How would you approach implementing TensorFlow using a low-code method to create a predictive analytics model?  
### Q5: What moderate-level coding skills are necessary to fully utilize the features of TensorFlow for product management tasks?  
### Q6: In what complex ways could IBM Watson's natural language processing capabilities be integrated into existing business systems to analyze customer sentiment?  
### Q7: How can effective communication of AI tool capabilities enhance collaboration between technical and non-technical teams in product management?  
### Q8: What strategies can product managers use to communicate the benefits of AI tools like TensorFlow and IBM Watson to stakeholders?  
### Q9: How important is it for product managers to develop communication skills when discussing technical implementations of AI tools with their teams?