The Adoption of AI in Business Intelligence Initiatives for Refining Business Operations: A Systematic Review

Abstract

Organizational methods for data-driven decision-making and operational improvement have changed because of the incorporation of Artificial Intelligence (AI) into Business Intelligence (BI). To investigate how AI methods-such as machine learning, natural language processing, deep learning, and robotic process automation-improve the analytical capabilities of BI systems, this review synthesizes recent contributions from academia and industry. Research shows that AI-driven BI gives businesses a greater competitive edge by enhancing risk management, customer experience, operational effectiveness, and forecasting accuracy. The assessment does, however, also point out enduring difficulties, such as exorbitant implementation costs, problems with data quality, a lack of skilled workers, and rising ethical and governance concerns. To categorize recent developments, adoption obstacles, and new trends, including edge analytics, explainable AI, and regulatory frameworks, a methodical literature review technique was used. The results indicate that the best-positioned companies to achieve long-term value are those that can strategically match AI-enabled BI with business goals while resolving socio-technical and ethical issues. By compiling knowledge and outlining potential paths for the adoption of AI in BI, this work contributes.