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Abstract
The research focuses on five AI-driven systems: AI-based business intelligence, machine learning dashboard systems, cognitive data visualization platforms, smart analytics interfaces, and neural-enhanced business dashboards. The evaluation framework is based on four main criteria: contextual intelligence platforms, adaptive analytics ecosystems, autonomous intelligence generation, and immersive intelligence interfaces. Each criterion was given equal weighting (0.25) in the WSM process to maintain a balanced evaluation of system performance. The methodology involved normalizing data, assigning weights, and calculating preference scores to rank the systems. indings show that machine learning dashboard systems performed best, scoring 0.83926, due to their strong capabilities in immersive interfaces and well-rounded performance across other areas. AI-powered business intelligence systems followed closely with a score of 0.81411, excelling especially in contextual and autonomous intelligence. Smart analytics interfaces placed third (0.74329), with neural-enhanced business dashboards fourth (0.74246), leading in adaptive analytics but weaker in intelligence generation. Cognitive data visualization platforms scored lowest (0.73029), showing moderate performance across all criteria. The results suggest that machine learning dashboards provide a more comprehensive solution for organizations seeking integrated big data analytics tools. This study highlights the distinct advantages of different AI visualization systems and offers valuable guidance for decision-makers choosing suitable AI-powered platforms. Additionally, it demonstrates the utility of the WSM methodology in evaluating complex multi-criteria technology systems, contributing to the expanding knowledge on AI-enhanced business intelligence and data visualization.
