The intersection of artificial intelligence and business intelligence : a systematic mapping study

Abstract

The research fields artificial intelligence and business intelligence have been growing in popularity in recent years. In the modern digital landscape, the data is continuously increasing. Traditional BI tools are not designed to handle large volumes of data. The integration of AI into BI can mitigate this problem. By extracting value from unstructured data with AI and BI, organisations can be more productive, make better decisions, understand market conditions. The combination of AI and BI can be highly beneficial for companies. However, the combination of the research fields is relatively new and mapping studies have been absent so far. This thesis contributes to this research gap. In this thesis a systematic mapping study has been conducted on the two research themes artificial intelligence and business intelligence. The goal of the study was to provide a comprehensive understanding/overview of the existing research landscape, research trends, and identification of potential areas of future exploration in the domain of AI-BI. The study collected 121 accepted articles from numerous journals and conferences, revealing a fragmented yet evolving research landscape with no dominant methodologies. The findings highlight key research gaps, particularly in validation research and AI ethics within the AI-BI domain. The study also emphasizes the significant academic implications of AI and BI integration, including the need for interdisciplinary research approaches and standardized methodologies. Industry implications point towards leveraging AI for enhanced predictive analytics and decision-making in diverse sectors such as retail, e-commerce, healthcare, and finance. These insights are critical for informing future research directions, shaping industry practices, and guiding educational strategies in the rapidly advancing field of AI and BI.