Automatic speech recognition (ASR) systems have made significant advancements over the past decade, but they often exhibit language biases, particularly struggling with regional accents, dialects, and non-dominant languages. This bias is largely due to ASR models being trained on datasets dominated by standard U.S. English, which results in poorer performance for speakers deviating from this norm, such as those with regional accents or those who code-switch between languages. The consequences of this bias include poor customer experiences, missed global opportunities, reputational risks for companies, and potential cultural and linguistic erosion. Measuring ASR performance using word error rate (WER) highlights the disparities in accuracy across different speech styles. Companies like Gladia are addressing these biases by designing purpose-built ASR models that focus on inclusivity and balanced language coverage, using advanced techniques such as selective denoising to improve accuracy across diverse speech environments. The future of ASR lies in building systems that are not only fast and scalable but also inclusive and fair, capable of supporting a wide range of languages and accents to preserve linguistic diversity and enhance global engagement.