Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented Analytics Article Swipe
Darash Desai
,
Avani Desai
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.33889/ijmems.2025.10.3.036
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.33889/ijmems.2025.10.3.036
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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- Type
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- Language
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- OA Status
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- References
- 31
- Related Works
- 10
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All OpenAlex metadata
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https://doi.org/10.33889/ijmems.2025.10.3.036Digital Object Identifier
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Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented AnalyticsWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-03-06Full publication date if available
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Darash Desai, Avani DesaiList of authors in order
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https://doi.org/10.33889/ijmems.2025.10.3.036Publisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.33889/ijmems.2025.10.3.036Direct OA link when available
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.employing | 73 |
| abstract_inverted_index.featuring | 78 |
| abstract_inverted_index.framework | 57, 72, 129 |
| abstract_inverted_index.integrate | 59, 131 |
| abstract_inverted_index.practical | 56 |
| abstract_inverted_index.pragmatic | 71 |
| abstract_inverted_index.precursor | 158 |
| abstract_inverted_index.sciences, | 36 |
| abstract_inverted_index.technical | 99 |
| abstract_inverted_index.training, | 120 |
| abstract_inverted_index.workflows | 3 |
| abstract_inverted_index.Artificial | 13 |
| abstract_inverted_index.Generative | 12 |
| abstract_inverted_index.Successful | 104 |
| abstract_inverted_index.decisions. | 146 |
| abstract_inverted_index.excellence | 140 |
| abstract_inverted_index.interviews | 80 |
| abstract_inverted_index.literature | 43 |
| abstract_inverted_index.particular | 48 |
| abstract_inverted_index.practices, | 153 |
| abstract_inverted_index.real-time, | 144 |
| abstract_inverted_index.technology | 38 |
| abstract_inverted_index.workflows. | 169 |
| abstract_inverted_index.data-driven | 145 |
| abstract_inverted_index.interaction | 97 |
| abstract_inverted_index.management, | 118 |
| abstract_inverted_index.operational | 139 |
| abstract_inverted_index.qualitative | 75 |
| abstract_inverted_index.theoretical | 45 |
| abstract_inverted_index.Intelligence | 1, 14 |
| abstract_inverted_index.disciplines. | 85 |
| abstract_inverted_index.integration. | 123 |
| abstract_inverted_index.AI-integrated | 167 |
| abstract_inverted_index.Organizations | 124 |
| abstract_inverted_index.effectiveness | 94, 114 |
| abstract_inverted_index.professionals | 82 |
| abstract_inverted_index.organizational | 112 |
| abstract_inverted_index.decision-making | 27 |
| abstract_inverted_index.democratization | 21 |
| abstract_inverted_index.semi-structured | 79 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.09072763 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |