Citation :

Mohith Reddy Patlolla, 2025. "AI-Augmented CI/CD for Business Intelligence Using BI: Deep Learning Driven Visual and Semantic Validation in Power BI Report Deployments" ESP International Journal of Advancements in Science & Technology (ESP-IJAST)  Volume 3, Issue 3: 48-54.

Abstract :

This paper discusses the major challenge of visual consistency and quality validation of Power BI reports inside Continuous Integration/Continuous Delivery pipelines. Up to now, manual validation has been the best practice. However, it is highly prone to errors, very slow, and cannot keep up with the increasing complexity and volumes of Business Intelligence content [4]. This framework introduces Deep Learning visual validation checks into the heart of Power BI’s CI/ CD flow via Power Automate orchestration.The system automatically detects visual anomalies- layout shifts, font changes, or color inconsistency- before going live with the reports so that governing can be enhanced while reducing mistakes to build renewed trust in BI Solutions. The approach uses computer vision-based methodologies for both visual regression testing and anomaly detection and hence significant advancement towards quality assurance automation for BI artifacts. The architectural design is described in this paper which also shares conceptual implementation and discusses the deep implications, it introduces toward efficient yet scalable and reliable BI content delivery.

References :

[1] A. Gupta, R. Patel, and S. Kumar, "Intelligent Data Governance in Modern BI Platforms: A Deep Learning Approach," Journal of Business Intelligence, vol. 12, no. 3, pp. 45-62, 2021.

[2] L. Zhang and M. Chen, "AI-Powered Anomaly Detection for Business Intelligence Dashboards," IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 8, pp. 3124-3136, 2022.

[3] E. Johnson and T. Williams, "Automated Visual Regression Testing for CI/CD Pipelines: A Declarative Framework," ACM Transactions on Software Engineering and Methodology, vol. 30, no. 4, pp. 1-25, 2023.

[4] K. Lee et al., "Deep Learning for Semantic Validation in Business Intelligence Reports," Data & Knowledge Engineering, vol. 135, 2022.

[5] P. Anderson and N. White, "Enhancing Power BI Governance with AI-Driven Visual Consistency Checks," International Journal of Information Management, vol. 59, 2021.

[6] R. Martinez et al., "Explainable AI for Visual Anomaly Detection in Data Dashboards," Machine Learning and Knowledge Extraction, vol. 4, no. 2, pp. 512-530, 2023.

[7] S. Thompson and D. Brown, "A Declarative Framework for Automated Quality Assurance in BI Deployments," Journal of Systems and Software, vol. 183, 2022.

[8] H. Wang and Y. Li, "AI-Augmented CI/CD Pipelines: Challenges and Opportunities," Software: Practice and Experience, vol. 52, no. 6, pp. 1345-1367, 2022.

[9] J. Garcia et al., "Deep Learning-Based Visual Validation for Multi-Platform BI Tools," Future Generation Computer Systems, vol. 124, pp. 358-372, 2021.

[10] M. Roberts and A. Khan, "Adaptive Baselines for Visual Regression Testing in Agile BI Development," Information Systems Frontiers, vol. 24, no. 4, pp. 1123-1139, 2022.

[11] T. Nguyen and L. Smith, "Measuring the Impact of AI-Driven Governance on BI Report Quality," Decision Support Systems, vol. 156, 2022.

Keywords :

Artificial Intelligence (AI), Deep Learning (DL), Business Intelligence (BI), Microsoft Power BI, Microsoft Power Automate, Continuous Integration/Continuous Delivery (CI/CD).