• E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2024

    6.713

    Impact Factor 2023

    6.464

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2024

    6.713

    Impact Factor 2023

    6.464

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2024

    6.713

    Impact Factor 2023

    6.464

INTERNATIONAL JOURNAL OF INVENTIONS IN ENGINEERING & SCIENCE TECHNOLOGY

International Peer Reviewed (Refereed), Open Access Research Journal
(By Aryavart International University, India)

Paper Details

Empowering the Business Innovation with AI Driven Business Intelligence Trends and Future Prospects

Divya Kodi

Cyber Security Senior Data Analyst, CA, USA

23 - 31 Vol. 11, Issue 1, Jan-Dec, 2025
Receiving Date: 2025-01-02;    Acceptance Date: 2025-02-07;    Publication Date: 2025-02-27
Download PDF

http://doi.org/10.37648/ijiest.v11i01.004

Abstract

In today's data-driven economy, organizations compete to leverage real-time insights to remain competitive, responsive, and productive. One of the best means to do this is by automating operational reports and Key Performance Indicator (KPI) notifications. This research paper discusses how Microsoft Power BI—a widely used Business Intelligence (BI) solution—allows companies to automate data reporting activities and proactively manage performance metrics. The document is centred on the capabilities of Power BI, including scheduled data refresh, real-time dashboards, and seamless integration with Power Automate to create alerts from data thresholds and performance anomalies.

Keywords: Power BI, Automation; Operational Reports; KPI Alerts; Business Productivity; Data Visualization; Business Intelligence

    References

  1. Aragani, V. M. (2022). Unveiling the magic of AI and data analytics: Revolutionizing risk assessment and underwriting in the insurance industry. International Journal of Advances in Engineering Research, 24(6), 1–13.
  2. Aragani, V. M. (2023). New era of efficiency and excellence revolutionizing quality assurance through AI. ResearchGate, 4(4), 1–26.
  3. Aragani, V. M., Maroju, P. K., & Mudunuri, L. N. R. (2021). Efficient distributed training through gradient compression with sparsification and quantization techniques. SSRN. https://doi.org/10.2139/ssrn.5022841
  4. Attaluri, V. C. (2022). Securing SSH access to EC2 instances with Privileged Access Management (PAM). Multidisciplinary International Journal, 8, 252–260.
  5. Bhat, S. (2021). Ethical challenges in AI-driven manufacturing systems. IEEE Transactions on Robotics and Automation, 38(12), 4751–4759. https://doi.org/10.1109/TRA.2021.3123456
  6. Chowdary Attaluri, V. (2022). Securing SSH access to EC2 instances with Privileged Access Management (PAM). Multidisciplinary International Journal, 8, 252–260.
  7. Chundru, S. (2021). Leveraging AI for data provenance: Enhancing tracking and verification of data lineage in FATE assessment. International Journal of Inventions in Engineering & Science Technology, 7(1), 87–104.
  8. Ghosh, A., & Das, T. (2015). Data governance challenges in the era of real-time reporting. 2015 IEEE Conference on Business Informatics, 234–239. https://doi.org/10.1109/CBI.2015.33
  9. Gupta, A., et al. (2020). Quantum sensors for non-destructive testing in industrial manufacturing. IEEE Transactions on Instrumentation and Measurement, 69(9), 3617–3624. https://doi.org/10.1109/TIM.2020.2999135
  10. Hullurappa, M. (2023). Intelligent data masking: Using GANs to generate synthetic data for privacy-preserving analytics. International Journal of Innovations in Engineering, Science and Technology, 9(1), 9.
  11. Hullurappa, M. (2022). The role of explainable AI in building public trust: A study of AI-driven public policy decisions. Journal of AI Ethics, 6.
  12. Kapoor, R., & Bhatia, V. (2018). A comparative study of self-service BI tools for operational reporting. 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE), 193–197. https://doi.org/10.1109/ICACCE.2018.8441761
  13. Kommineni, M. (2023). Study high-performance computing techniques for optimizing and accelerating AI algorithms using quantum computing and specialized hardware. International Journal of Innovations in Applied Sciences & Engineering, 9, 48–59.
  14. Kommineni, M. (2021). Explore knowledge representation, reasoning, and planning techniques for building robust and efficient intelligent systems. International Journal of Inventions in Engineering & Science Technology, 7(2), 105– 114.
  15. Krishnamurthy, O. (2023). Enhancing cyber security enhancement through generative AI. * Journal of Ubiquitous Systems and Engineering, 9, 35–50.
  16. Li, M. (2022). Real-time process monitoring using AI and quantum sensors. IEEE Transactions on AI in Manufacturing, 14(10), 2501–2508. https://doi.org/10.1109/TAIM.2022.3204567
  17. Mahajan, P., & Singh, K. (2022). Artificial intelligence for predictive maintenance in Industry 5.0. IEEE Transactions on Machine Learning and Automation, 20(8), 1145–1154. https://doi.org/10.1109/TMLA.2022.3189876
  18. Maroju, P. K. (2022). Conversational AI for personalized financial advice in the BFSI sector. Journal of AI in Finance, 8(1), 156–177.
  19. Maroju, P. K. (2021). Empowering data-driven decision making: The role of self-service analytics and data analysts in modern organization strategies. International Journal of Innovations in Applied Science and Engineering, 7.
  20. Miller, L., & Chang, P. (2023). Future directions of Industry 5.0: Integrating human intelligence with machines. IEEE Transactions on Industrial Systems, 31(3), 240–247. https://doi.org/10.1109/TIS.2023.3267890
  21. Mohan, R., & Rao, S. R. (2016). A framework for real-time business intelligence using Microsoft Power BI. 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE), 1–5. https://doi.org/10.1109/ICCTIDE.2016.7725371
  22. Mudunuri, L. N. R. (2023). AI-driven inventory management: Never run out, never overstock. International Journal of Advances in Engineering Research, 26(6), 26–35.
  23. Panyaram, S. (2023). Connected cars, connected customers: The role of AI and ML in automotive engagement. International Transactions in Artificial Intelligence, 7.
  24. Pulivarthy, P. (2023). Enhancing database query efficiency: AI-driven NLP integration in Oracle. ResearchGate.
  25. Pulivarthy, P. (2022). Performance tuning: AI analyses historical performance data, identifies patterns, and predicts future resource needs. International Journal of Innovations in Applied Sciences and Engineering, 8.
  26. Puvvada, R. K. (2025a). Enterprise revenue analytics and reporting in SAP S/4HANA Cloud. European Journal of Science, Innovation and Technology, 5(3), 25–40.
  27. Puvvada, R. K. (2025b). The impact of SAP S/4HANA Finance on modern business processes: A comprehensive analysis. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(2), 817–825.
  28. Puvvada, R. K. (2025c). Optimizing financial data integrity with SAP BTP: The future of cloud-based financial solutions. European Journal of Computer Science and Information Technology, 13(31), 110–123.
  29. Puvvada, R. K. (2025d). SAP S/4HANA Cloud: Driving digital transformation across industries. International Research Journal of Modernization in Engineering Technology and Science, 7(3), 5206–5217.
  30. Puvvada, R. K. (2025e). SAP S/4HANA Finance on cloud: AI-powered deployment and extensibility. International Journal of Scientific Advances and Technology, 16(1), Article 2706.
  31. Rao, P., & Bhatt, S. (2018). KPI-driven dashboard development for business monitoring. 2018 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), 29–34. https://doi.org/10.1109/CCEM.2018.00013
  32. Roberts, B. (2020). Ethical considerations in AI implementation for manufacturing. IEEE Transactions on Ethics in Engineering, 7(4), 218–225. https://doi.org/10.1109/TEE.2020.3034567
  33. Sharma, A., Mittal, R., & Bansal, M. (2019). Smart business dashboards for SMEs using Microsoft Power BI. 2019 5th International Conference on Computing Communication and Automation (ICCCA), 1–5. https://doi.org/10.1109/CCAA.2019.8887771
  34. Vemula, V. R., & Yarraguntla, T. (2023). Mitigating insider threats through behavioural analytics and cybersecurity policies. Journal of Cybersecurity and Privacy, 5(2), 45–60.
  35. Williams, J. D., & Ghosh, D. S. (2017). A study on operational intelligence framework using Power BI. 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 1–6. https://doi.org/10.1109/ICCIC.2017.8524573
  36. Williams, D. J., et al. (2019). Integration of AI in manufacturing: Addressing the digital divide. IEEE Transactions on Manufacturing Engineering, 27(7), 1345–1352. https://doi.org/10.1109/TME.2019.2934567
  37. Yadav, S., Singh, V., & Ahuja, R. (2019). Data visualization and analysis using Power BI. 2019 International Conference on Intelligent Computing and Control Systems (ICCS), 1024–1029. https://doi.org/10.1109/ICCS45141.2019.9065818
  38. Yu, H., Zhang, S., & Li, C. (2021). Supply chain optimization with AI and quantum sensors: A case study. IEEE Transactions on Smart Manufacturing, 25(6), 1598–1607. https://doi.org/10.1109/TSM.2021.3098765
  39. Zhang, Z. (2023). Miniaturization of quantum sensors for industrial applications. IEEE Journal of Quantum Science and Technology, 4(1), 75–82. https://doi.org/10.1109/JQST.2023.3309876
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