This research provides a systematic review of AI and ML applied to the BI context from 2014 to 2024. By characterizing the article and citation distribution and by tracing the topics of publications over time, this study outlines major developments, landmark contributions, and the future outlook of the research discipline at hand. However, despite rapid advancements, existing research remains fragmented. It lacks a consolidated understanding of how AI and ML are shaping modern BI systems, creating an urgent need for an integrated review. Prior studies often examine isolated techniques or industry-specific implementations, but very few provide a comprehensive synthesis that maps long-term trends, methodological patterns, and unresolved challenges in AI/ML-enabled BI. The study reviews more than 200 research articles obtained from top academic databases and finds a rise in the integration of AI/ML with conventional BI systems. Our results show the emergence of real-time, predictive, and automotive analytics as much-needed and valuable to consumers. Artificial Intelligence strengthens the capabilities for visualizing data, so complex information becomes easier to access by those who need it. Research literature shows an increase in publications about AI and ML applications in BI systems because these technologies have gained substantial practical importance. The field of study investigates the ways AI and ML improve BI systems by paying special attention to predictive analytics as well as decision-making processes. The main aspects of interest unite advanced AI implementations with user-centric tools that serve multiple industries. Future directions for researchers are AI ethics in BI, as well as creating simple AI tools for non-programmers and investigating the influence of AI-based BI systems on different industries in the long run. This study also presents future research suggestions.
Mandaliya et al. (Wed,) studied this question.