AI-DRIVEN DATA SCIENCE MODELS FOR REAL-TIME TRANSCRIPTION AND PRODUCTIVITY ENHANCEMENT IN U.S. REMOTE WORK ENVIRONMENTS

Abstract

The rapid expansion of remote and hybrid work environments in the United States has intensified the need for reliable communication technologies capable of enhancing productivity, accessibility, and compliance. Among these, artificial intelligence (AI)-driven transcription systems have emerged as critical tools for supporting real-time documentation and collaboration. This study systematically reviewed existing scholarship to evaluate the role of AI-based data science models for transcription in workplace productivity, focusing specifically on U.S. remote work contexts. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search was conducted across major academic databases and grey literature sources. From an initial pool of 1,236 records, 97 studies met all inclusion criteria and were synthesized for analysis. The findings highlight five significant domains. First, technological advances, particularly in deep neural networks, transformer-based architectures, and self-supervised learning, have substantially improved transcription accuracy and adaptability, though gaps remain in handling noise, multi-speaker dialogue, and accent variation. Second, transcription consistently enhanced productivity by reducing cognitive load, minimizing redundancy, and strengthening organizational memory, with 28 studies explicitly linking these systems to measurable efficiency gains. Third, organizational integration was found to be uneven, with adoption most prevalent in highly regulated sectors such as healthcare, finance, and law, while smaller enterprises faced resource and cultural barriers. Fourth, accessibility emerged as a central contribution, with 19 studies showing transcription’s role in supporting workers with hearing impairments, cognitive challenges, and language barriers, though access was unevenly distributed. This synthesis demonstrates that AI-driven transcription has matured into a core component of digital workplace infrastructures.