AI Tools for Data Performance Enhancement: A Comprehensive Review

Abstract

Artificial intelligence tools are revolutionizing data analytics by enhancing performance across diverse organizational contexts. The evolution from basic rule-based systems to sophisticated machine learning frameworks has enabled unprecedented capabilities in data visualization, automated model building, and comprehensive analytics. Traditional visualization platforms now incorporate AI-driven pattern recognition and natural language query processing, democratizing access to complex analytical capabilities. Automated machine learning solutions have transformed predictive modeling by simplifying feature engineering and model deployment while maintaining competitive performance metrics. Comprehensive analytics platforms consolidate previously fragmented processes into unified environments, addressing fundamental challenges in data preparation and workflow automation. Large language models represent the newest frontier, bridging gaps between technical and business stakeholders through intuitive interfaces, code optimization, and multimodal processing capabilities. Despite impressive advancements, successful implementation requires attention to organizational context, data governance, and ethical considerations to realize the full potential of AI-enhanced data performance.