Audio segmentation is a crucial element in turning ideas into reality by leveraging artificial intelligence to process different sound types. It enables AI to interpret between various audio components such as speech, music, and environmental sounds. The core concept behind audio segmentation is to split audio recordings into distinct, homogeneous segments, which can then be analyzed for specific tasks like speaker diarization, environmental sound event detection, music structure analysis, and speech segmentation. However, audio segmentation presents several challenges including overlapping sounds, poor audio quality, and the need for carefully annotated datasets. Advanced tools like Encord are being used to overcome these challenges by providing features such as precision labeling, layered annotations, temporal classification, and AI-assisted annotation, which enhance audio data quality and streamline datasets for deep learning. Audio segmentation is driving significant advancements in the audio AI industry, fuelling the demand for several audio AI solutions, and its applications cover various sectors including speech technology, security and surveillance, media and entertainment, healthcare, education, and more.