| dc.description.abstract | This study examines how Artificial Intelligence (AI)–driven insight recommendation
systems can enhance Business Intelligence (BI) and organizational decision-making
within Microsoft Power BI environments. Traditional BI dashboards are largely
descriptive and struggle to deliver proactive, actionable insights as enterprise data
becomes increasingly complex and heterogeneous. To address this limitation, the study
explores the integration of AI techniques specifically machine learning (ML), natural
language processing (NLP), and predictive analytics into BI platforms. A systematic
literature review was conducted following PRISMA-guided systematic review principles,
analyzing 84 peer-reviewed articles retrieved from major academic databases using
keywords such as AI-driven analytics, Power BI recommendation systems, intelligent
dashboards, and predictive business intelligence. The selection focused on studies
addressing AI-enabled BI tools, automated insight generation, dashboard intelligence,
and enterprise decision-support systems (DSS). The analysis reveals several key trends:
AI-enhanced BI systems improve decision accuracy, reduce user cognitive load, and
support real-time operational intelligence. NLP facilitates conversational analytics,
ML techniques uncover latent data patterns, and predictive analytics enables forward looking recommendations. Despite these benefits, challenges remain in terms of system
integration, data quality, model interpretability, and user trust. The study concludes
that AI-driven insight recommendation systems represent a significant evolution of BI
in Power BI, transforming dashboards from descriptive reporting tools into prescriptive
decision-support platforms. The primary contribution of this research is the synthesis of
existing evidence into an integrated conceptual framework for designing AI-enhanced
BI systems, highlighting both their transformational potential and key implementation
challenges.
Keywords: p | en_US |
