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Abstract
The advent of artificial intelligence (AI) has significantly revolutionized autonomous systems, enabling them to execute complex decision-making processes with increased precision, efficiency, and adaptability. This paper investigates the role of AI in enhancing decision-making across various autonomous domains—ranging from autonomous vehicles to robotics and industrial automation. The objective is to dissect how AI algorithms such as deep learning, reinforcement learning, and probabilistic models have contributed to real-time decision frameworks. A comprehensive literature review highlights seminal contributions and identifies gaps in scalability, generalization, and interpretability. Using a conceptual methodology based on comparative analysis, this research synthesizes data and frameworks to evaluate AI’s influence on decision architectures. Key findings suggest that while AI has drastically improved autonomy and reduced human intervention, challenges remain in unpredictable environments. The significance of this work lies in its historical reflection, offering a foundation for current advancements and shaping the direction for AI development in autonomous systems
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