Abstract :

The Increasing Volume And Complexity Of Enterprise Data Demand Advanced Analytical Systems Capable Of Delivering Timely And Actionable Insights. Traditional Business Intelligence (BI) Tools Primarily Focus On Descriptive Reporting And Require Significant Manual Effort For Analysis. This Paper Presents An AI-augmented Business Intelligence Framework Implemented Using Microsoft Power BI, Integrating Automated Insight Generation, Anomaly Detection, And Natural Language–driven Analytics. The Proposed Framework Leverages Built-in AI Capabilities Such As Automated DAX Query Generation, Anomaly Detection In Time-series Data, And AI-assisted Narrative Explanations Through Copilot. These Features Enable Users To Interact With Data Using Natural Language Queries, Automatically Identify Patterns And Outliers, And Generate Meaningful Business Insights Without Requiring Deep Technical Expertise. Additionally, The System Incorporates Augmented Analytics Techniques, Including Trend Analysis, Root-cause Exploration, And Contextual Explanations, Enhancing Decision-making Processes. The Integration Of AI Transforms Power BI Dashboards From Static Visualization Tools Into Intelligent, Interactive Systems Capable Of Proactive Insight Delivery. Experimental Evaluation Using Real-world Datasets Demonstrates Improvements In Analysis Efficiency, Insight Discovery Speed, And User Accessibility Compared To Conventional BI Approaches. The Results Highlight The Effectiveness Of AI-driven Augmentation In Reducing Manual Workload While Improving The Accuracy And Interpretability Of Business Insights. This Work Contributes To The Advancement Of Intelligent Decision Support Systems By Providing A Practical And Scalable Framework For Integrating AI Capabilities Within Modern BI Platforms. Keywords: Artificial Intelligence, Business Intelligence, Power BI, Augmented Analytics, Anomaly Detection, Natural Language Query, Decision Support Systems

Published:

30-3-2026

Issue:

Vol. 26 No. 3 (2026)

Page Nos:

934-949

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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