Word Error Rate (WER) is a crucial metric for evaluating the performance of Automatic Speech Recognition (ASR) systems, determining the accuracy of speech-to-text conversions by measuring discrepancies between ASR-generated transcripts and error-free reference transcripts. While a lower WER signals better performance, challenges arise as benchmarks might not reflect real-world conditions, leading to suboptimal ASR performance in enterprise settings. Factors influencing WER include training datasets, acoustic and speaker variability, and language complexity, with common datasets like Fleurs, LibriSpeech, and Common Voice often used for training. However, limitations such as ground truth variations, limited dataset representation, and lack of standardization render benchmarks sometimes ineffective in capturing ASR performance in practical scenarios. Real-world examples, like the Whisper model's tendency to hallucinate or Microsoft's findings on ASR's struggle with conversational nuances, underscore these challenges. Consequently, improving ASR evaluation involves using relevant datasets, incorporating real-world conditions, and considering niche-specific needs, as relying solely on WER might overlook models that, with customization, could excel in particular applications.