Automated Insights in Business Intelligence: Evaluating Power BI's AI-Driven Analytics Features

Authors: Selvakumar Kalyanasundaram

DOI: https://doi.org/10.37082/IJIRMPS.v14.i1.232907

Short DOI: https://doi.org/hbnbz6

Country: United States

Full-text Research PDF File:   View   |   Download

Abstract: The rapid growth of enterprise data has driven Business Intelligence (BI) platforms to evolve from static reporting tools into intelligent systems capable of delivering automated insights. This paper evaluates Microsoft Power BI’s AI-driven analytics capabilities, examining how embedded artificial intelligence enhances data exploration, insight discovery, and organizational decision-making. Key features analyzed include Quick Insights, Key Influencers, Decomposition Tree, Smart Narratives, AI visuals, and business-integrated generative-AI. The study assesses these capabilities across three dimensions: accessibility for non-technical users, depth and quality of insights, and impact on decision-making speed and effectiveness. Results indicate that Power BI’s AI features significantly reduce manual analytical effort by automating pattern detection, anomaly identification, root cause analysis, and natural-language explanations, thereby enabling effective self-service BI and accelerating insight generation. A brief comparative perspective contextualizes Power BI’s strengths in scalability, cloud integration, and governance relative to other leading BI platforms. Despite these benefits, the analysis identifies limitations related to data quality dependence, model transparency, the complexity of advanced DAX usage, and the need for targeted user training to ensure proper adoption. The paper concludes with practical implications for enterprises seeking to leverage AI-enabled BI to improve insight velocity while maintaining analytical reliability and governance.

Keywords: Business Intelligence, Automated Insights, AI-Driven Analytics, Microsoft Power BI, Quick Insights.

Paper Id: 232907

Published On: 2026-01-28

Published In: Volume 14, Issue 1, January-February 2026

Share this