Despite the long history of studying instant messaging usage, we know very little about how today's people participate in group chat channels and interact with others inside a real-world organization. In this short paper, we aim to update the existing knowledge on how group chat is used in the context of today's organizations. The knowledge is particularly important for the new norm of remote works under the COVID-19 pandemic. We have the privilege of collecting two valuable datasets: a total of 4,300 group chat channels in Slack from an R&D department in a multinational IT company; and a total of 117 groups' performance data. Through qualitative coding of 100 randomly sampled group channels from the 4,300 channels dataset, we identified and reported 9 categories such as Project channels, IT-Support channels, and Event channels. We further defined a feature metric with 21 meta features (and their derived features) without looking at the message content to depict the group communication style for these group chat channels, with which we successfully trained a machine learning model that can automatically classify a given group channel into one of the 9 categories. In addition to the descriptive data analysis, we illustrated how these communication metrics can be used to analyze team performance. We cross-referenced 117 project teams and their team-based Slack channels and identified 57 teams that appeared in both datasets, then we built a regression model to reveal the relationship between these group communication styles and the project team performance. This work contributes an updated empirical understanding of human-human communication practices within the enterprise setting, and suggests design opportunities for the future of human-AI communication experience.