Limitations of Generative AI in Real-Time Decision-Making

Chand, Chaitenya (2025) Limitations of Generative AI in Real-Time Decision-Making. International Journal of Innovative Science and Research Technology, 10 (6): 25jun935. pp. 1351-1354. ISSN 2456-2165

Abstract

Generative AI has emerged as a groundbreaking technology, offering transformative capabilities in domains like natural language processing and image generation. Despite its successes, the application of generative AI in real-time decision-making systems remains a challenge due to issues such as computational latency, output reliability, and lack of interpretability. This study investigates these limitations through a detailed literature review and experimental analysis. We adopted a hybrid methodology involving lightweight model architectures and rule-based constraints to mitigate these challenges. Results show that our approach reduces latency by 20% and enhances reliability by 15% compared to traditional generative models. The findings underscore the importance of optimizing generative AI for time-sensitive applications and highlight future directions for research.

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