The contribution of GenAI to business analytics

Ángel Salazar & Martin Kunc

Journal of Business Analytics2025https://doi.org/10.1080/2573234x.2024.2435835article
AJG 1ABDC C
Weight
0.58

What the paper says

This paper explores the integration of Business Analytics (BA) with Artificial Intelligence (AI) by considering evidence from existing literature through an augmented research process supported by Generative AI (GenAI). GenAI can streamline the process of literature review and synthesis while enriching researchers’ analytical capabilities, e.g. screening literature, finding themes across multiple papers, creating conceptual categories. Therefore, the study seeks to understand the issues and benefits of incorporating GenAI tools into research processes, such as academic writing, based on the authors’ first-hand experiences. The research process involves automatic retrieval of relevant papers from online repositories based on predefined keywords, followed by summarisation of the retrieved documents using language models. The summarised insights are then used to draft the article. The integration of AI with BA requires a gradual approach, leveraging existing analytical capabilities and expertise as a foundation. The study relies on the authors’ experiences and examples to draw conclusions. More comprehensive empirical studies can validate the findings. The paper provides insights for researchers and practitioners interested in leveraging GenAI tools to enhance their work and highlights the potential and challenges of applying these tools in business analytics applications.

12 citations

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https://doi.org/https://doi.org/10.1080/2573234x.2024.2435835

Or copy a formatted citation

@article{ángel2025,
  title        = {{The contribution of GenAI to business analytics}},
  author       = {Ángel Salazar & Martin Kunc},
  journal      = {Journal of Business Analytics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1080/2573234x.2024.2435835},
}

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Evidence weight

0.58

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.58 × 0.4 = 0.23
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.