Human-Centered Design of Generative AI Systems for Personalized Media and Enterprise Products

Abstract

Generative artificial intelligence is increasingly embedded in personalized media platforms and enterprise products, yet many systems remain optimized for technical performance rather than human experience. This study examines how human-centered design principles can be systematically integrated into generative AI systems to enhance personalization, usability, trust, and ethical alignment. Using a mixed-methods research framework, the study evaluates generative AI prototypes across media and enterprise contexts by combining user interaction metrics, experiential assessments, and governance-oriented indicators. The results demonstrate that deeper personalization and context-aware generation significantly improve perceived usefulness, interpretability, and user trust while reducing cognitive workload. Visual and statistical analyses further reveal a synergistic interaction between personalization depth and contextual awareness, highlighting their combined influence on sustained engagement and acceptance. Ethical transparency and explainability emerge as critical enablers of adoption, reinforcing the importance of embedding governance mechanisms directly into system design. Overall, the findings establish a human-centered design framework that supports the development of generative AI systems that are not only intelligent but also trustworthy, adaptive, and aligned with diverse user needs in media and enterprise environments.