From complexity to clarity: Generative AI in data analytics

Choppa, Narendra Kumar Reddy and Sajja, John Wesley and Komarina, Govindaraja Babu (2025) From complexity to clarity: Generative AI in data analytics. World Journal of Advanced Research and Reviews, 26 (3). pp. 349-361. ISSN 2581-9615

The rapid pace of digital transformation has established data analytics as a critical driver of organizational success, yet traditional methods often face challenges in complexity, scalability, and accessibility. This article explores how Generative AI, augmented by AI agents, transforms data analytics by streamlining workflows, enhancing decision-making, and delivering personalized analytics experiences across the analytical lifecycle. Leveraging cloud-native architectures, edge computing, and integration with enterprise platforms like SAP S/4HANA, Microsoft Fabric, Power BI, and Azure AI Foundry, Generative AI and AI agents automate data preparation, enable natural language querying, generate predictive and prescriptive insights, and enhance visualization and narrative storytelling. AI agents drive autonomous tasks, such as real-time anomaly detection and workflow orchestration, amplifying analytical agility. Empirical evidence demonstrates significant quantitative benefits—reduced time-to-insight by 63% and increased analytics adoption by 210% alongside qualitative gains in decision quality and cross-functional collaboration. The article highlights transformative outcomes, including cost efficiency, organizational agility, and democratized data strategies, while addressing challenges like data governance, ethical AI frameworks, and performance optimization. Open-source GenAI contributions further enrich innovation. Looking forward, it proposes research into real-time analytics, multimodal AI, agent-driven domain adaptations, personalized analytics, and standardized governance, providing a roadmap for next-generation analytics that balances innovation with ethical and organizational imperatives.