In recent years, we have seen amazing results in artificial intelligence and machine learning owing to the emergence of models such as transformers and pretrained language models. Despite the astounding results published in academic papers, there is still a lot of ambiguity and challenges when it comes to deploying these models in industry because: 1) troubleshooting, training, and maintaining these models is very time and cost consuming due to their inherent large size and complexities 2) there is not enough clarity yet about when the advantages of these models outweigh their challenges and when they should be preferred over classical ML models. These challenges are even more severe for small and mid-size companies that do not have access to huge compute resources and infrastructure. In this talk, we discuss these challenges and share our findings and recommendations from working on real world examples at SPINS: a company that provides industry-leading CPG Product Intelligence. In particular, we describe how we leveraged state-of-the-art language models to seamlessly automate part of SPINS workflow and drive substantial business outcomes. We share our findings from our experimentation and provide insights on when one should use these gigantic models instead of classic ML models. Considering that we have all sorts of challenges in our use cases from an ill-defined label space to a huge number of classes (~86,000) and massive data imbalance, we believe our findings and recommendations can be applied to most real-world settings. We hope that the learnings from this talk can help you to solve your own problems more effectively and efficiently!