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The integration of Artificial Intelligence (AI) and Machine Learning (ML) with traditional Business Intelligence (BI) systems is revolutionizing how organizations derive actionable insights from data. This paper explores sustainable approaches to enhancing BI through AI-driven data analytics, focusing on methods that balance computational efficiency, environmental impact, and long-term operational viability. By bridging conventional BI frameworks with sustainable ML techniques, organizations can achieve more accurate predictions, automated decision-making, and adaptive analytics while minimizing resource consumption. Case studies demonstrate the effectiveness of sustainable AI models in improving data insight quality and operational scalability. The findings emphasize the critical role of sustainable AI practices in driving innovation and responsible data management in modern enterprises.

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AI-Driven Data Insights: Bridging Traditional BI with Sustainable Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • Dhaval Patolia

摘要

The integration of Artificial Intelligence (AI) and Machine Learning (ML) with traditional Business Intelligence (BI) systems is revolutionizing how organizations derive actionable insights from data. This paper explores sustainable approaches to enhancing BI through AI-driven data analytics, focusing on methods that balance computational efficiency, environmental impact, and long-term operational viability. By bridging conventional BI frameworks with sustainable ML techniques, organizations can achieve more accurate predictions, automated decision-making, and adaptive analytics while minimizing resource consumption. Case studies demonstrate the effectiveness of sustainable AI models in improving data insight quality and operational scalability. The findings emphasize the critical role of sustainable AI practices in driving innovation and responsible data management in modern enterprises.