Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented Analytics

Business Intelligence (BI) workflows benefit from the improved access to insights that Generative Artificial Intelligence (GenAI) can bring, allowing for swifter democratization of data access and improved decision-making across various domains such as finance, retail, life sciences, education technology (EdTech), etc. Although existing literature discusses theoretical models or particular case studies, it does not provide a practical framework to integrate GenAI into BI. This study fills the gap by devising a pragmatic framework employing the qualitative research method featuring semi-structured interviews with professionals in varied disciplines. The results show that GenAI can improve the effectiveness of the interaction between technical experts and business users. Successful adoption, however, hinges on clarity of the organizational goals, effectiveness of the data management, user training, and system integration. Organizations can apply the proposed framework to integrate GenAI into BI systems to focus on operational excellence and support for real-time, data-driven decisions. These insights serve to advance BI practices, and act as a precursor to the future research in the domain of AI-integrated BI workflows.

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Publication Details

Journal
International Journal of Mathematical Engineering and Management Sciences
Published
2025-03-06
DOI
https://doi.org/10.33889/ijmems.2025.10.3.036
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
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article

Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented Analytics

Avani Desai, Darash Desai
1 links
International Journal of Mathematical Engineering and Management Sciences
Big Data and Business Intelligence
article

Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented Analytics

Avani Desai, Darash Desai
article en

Abstract

Business Intelligence (BI) workflows benefit from the improved access to insights that Generative Artificial Intelligence (GenAI) can bring, allowing for swifter democratization of data access and improved decision-making across various domains such as finance, retail, life sciences, education technology (EdTech), etc. Although existing literature discusses theoretical models or particular case studies, it does not provide a practical framework to integrate GenAI into BI. This study fills the gap by devising a pragmatic framework employing the qualitative research method featuring semi-structured interviews with professionals in varied disciplines. The results show that GenAI can improve the effectiveness of the interaction between technical experts and business users. Successful adoption, however, hinges on clarity of the organizational goals, effectiveness of the data management, user training, and system integration. Organizations can apply the proposed framework to integrate GenAI into BI systems to focus on operational excellence and support for real-time, data-driven decisions. These insights serve to advance BI practices, and act as a precursor to the future research in the domain of AI-integrated BI workflows.

International Journal of Mathematical Engineering and Management SciencesVol. 10(3)
Bristol-Myers Squibb (Sweden) (SE), Columbia University (US)
Openalex Percentile: Top 94%
Big Data and Business Intelligence
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Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented Analytics — Avani Desai, Darash Desai · International Journal of Mathematical Engineering and Management Sciences (2025) | TGRS Research Map | TGRS