Antonius Felix, Julius Sutrisno, Selly Swandari, Devi Yurisca Bernanda, Agung Stefanus Kembau, Vincentius Leonardo
The exponential growth of video consumption has outpaced traditional, labor-intensive production workflows, creating a need for scalable solutions. This research introduces a Python-based framework integrating multi-modal AI including LLM-powered scripting, text-to-video synthesis, and voice cloning to automate end-to-end production. Systematic deployment across e-commerce, education, and marketing sectors reveals transformative results. Empirical data shows a 73% reduction in production time (from 120 to 32 minutes) and an 85% decrease in unit costs (from Rp70,000 to <Rp10,000). Furthermore, the framework achieved a 267% increase in throughput and a 19% improvement in quality consistency. Beyond operational metrics, the study demonstrates that AI-driven automation democratizes high-volume content strategies previously reserved for resource-rich competitors. However, the findings emphasize that technical deployment must be supported by comprehensive change management and workforce upskilling to succeed. Ultimately, as AI capabilities advance, video production automation is transitioning from a competitive differentiator to a strategic necessity. Organizations failing to adopt these frameworks risk significant capability gaps in an increasingly automated digital landscape. © 2026 IEEE.
Bunda Mulia University, Department of Digital Business, Jakarta, Indonesia; Brawijaya University, Department of Management, Jakarta, Indonesia; Mulawarman University, Department of Economic Development, Samarinda, Indonesia; Bunda Mulia University, Department of Informatics, Jakarta, Indonesia
