This paper presents a comparative analysis of three leading AI speech-to-text (STT) systems: Descript.com, Google Vertex AI Studio (Chirp), and OpenAI Whisper. The objective of the study is to evaluate the accuracy, functionality, and potential applications of these technologies, with a particular focus on their integration into artillery command and fire control systems. The analysis outlines the evolution of speech recognition technologies, from traditional methods based on Hidden Markov Models (HMMs) to modern deep neural networks, including Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Transformer-based architectures. Practical testing was conducted on a dataset of English and Czech recordings with varying audio quality. The results indicate that Google Chirp achieves the highest accuracy in English transcriptions, while OpenAI Whisper demonstrates superior performance for the Czech language. Additionally, the paper explores the optimization of STT systems for combat environments, including the use of Ant Colony Optimization (ACO) algorithms to minimize errors and enhance the relevance of transcriptions. The study also highlights security risks associated with deploying cloud-based STT services in military applications and emphasizes the advantages of on-premise solutions to ensure data protection. Finally, the paper discusses strategies for modernizing defense capabilities through AI technologies. It advocates for increased investment in automated command and control systems, fire control, and situational awareness, emphasizing their crucial role in improving response times and the accuracy of artillery fire support.