A REVIEW OF AI-POWERED DATA VISUALIZATION IN ENTERPRISE REPORTING: DASHBOARD DESIGN AND INTERACTIVE ANALYTICS

The increasing complexity and volume of data within modern enterprises have significantly elevated the importance of advanced, intuitive, and adaptive visualization tools for reporting and analytics. This study systematically reviews and conducts a meta-analysis of existing research on artificial intelligence (AI)-powered data visualization dashboards, with a specific emphasis on their design, interactivity, and analytical effectiveness within enterprise contexts. It critically evaluates the role of AI technologies—including predictive analytics, anomaly detection, and natural language processing—in enhancing dashboard usability, interpretability, scalability, and responsiveness. By integrating findings from diverse industry sectors, including healthcare, finance, public administration, and humanitarian organizations, the analysis demonstrates consistent positive impacts of AI-enhanced dashboards on user satisfaction, decision-making efficiency, and cognitive load reduction. The synthesis highlights best practices in adaptive interface design, emphasizing personalized visualization strategies that dynamically respond to user roles and organizational functions. Furthermore, systematic benchmarking and rigorous comparative analysis illustrate dashboards' substantial value in maintaining competitive advantage and facilitating organizational agility. Critical challenges identified include ensuring algorithmic transparency, maintaining high data quality, and addressing infrastructure constraints for scalability. Overall, the findings offer robust evidence that strategic, user-centered implementation of AI-driven dashboards significantly enhances organizational decision-making capabilities, operational efficiency, and user engagement. This extended review contributes valuable insights for practitioners and researchers aiming to leverage AI technologies effectively in enterprise analytics, emphasizing continuous improvement through benchmarking, transparent algorithmic practices, and targeted user-oriented design methodologies.