In the realm of enterprise speech-to-text (STT), the focus is not on finding the perfect model but rather on selecting and adapting an STT model tailored to specific applications and domains. Fine-tuning is essential when adaptation falls short, allowing a pre-trained model to better suit unique audio data with only minimal labeled audio, substantially improving accuracy. Despite its benefits, fine-tuning traditionally required significant computational resources, but methods like Low-Rank Adaptation (LoRA) have made it more accessible by reducing trainable parameters and GPU needs. Fine-tuning offers notable improvements in domain and accent adaptation, yet it isn't a one-time task due to model drift, necessitating ongoing updates and user feedback integration to maintain performance. To maximize STT success, start with baseline metrics and adaptations like keyword boosting, progressing to fine-tuning if needed, as even minor accuracy improvements can yield significant time savings and unlock new use cases.