Summary

Alessandro Cappelli discusses the challenges of bringing generative AI models to production, highlighting that 95% of AI pilots fail to reach this stage. He argues that reinforcement learning (RL) is crucial for systematically improving AI models, and that current approaches using proprietary or fine-tuned open-source models lack a scientific method for addressing defects and monitoring performance. The key takeaway is that getting an MVP is just the first mile, and the real challenge lies in developing a robust, adaptable approach to deploying and continuously improving AI models in enterprise environments.