Author
Abstract
This paper explores the transformative impact of AI- powered Business Intelligence (BI) dashboards across multiple sectors, including healthcare, retail and e-commerce, customer service, and IT technology. We analyze how these advanced tools leverage machine learning, natural language processing, and real- time analytics to provide actionable insights, improve decision- making processes, and enhance operational efficiency. In healthcare, we examine applications ranging from predictive patient care to medical imaging diagnostics, highlighting how AI-powered dashboards are improving patient outcomes and resource allocation. The retail and e-commerce section discusses the use of AI in personalization, inventory management, and fraud detection, demonstrating significant improvements in sales and customer satisfaction. Our analysis of the customer service sector reveals how AI is revolutionizing sentiment analysis and automated response systems, leading to enhanced customer experiences. In the IT sector, we explore the role of AI in system monitoring, cybersecurity, and performance optimization. The paper also addresses the challenges associated with im- plementing AI-powered BI dashboards, including data privacy concerns, integration with existing systems, and ethical consid- erations. We conclude by discussing future trends, such as the integration of AI with emerging technologies like 5G and Internet of Things (IoT), and the potential for these dashboards to address global challenges across various industries. Our comprehensive analysis provides valuable insights for organizations considering the implementation of AI-powered BI dashboards and highlights areas for future research in this rapidly evolving field.
Suggested Citation
Aditi M Jain, 2023.
"AI-Powered Business Intelligence Dashboards : A Cross-Sector Analysis of Transformative Impact and Future Directions,"
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 9(4), pages 524-536, July.
Handle:
RePEc:jbh:ijsrcs:v9:y2023:i4:id:hcseit23564514
DOI: 10.32628/CSEIT23564514
Note: Article URL: https://ijsrcseit.com/CSEIT23564514
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