Artificial Intelligence-Driven Business Intelligence for Strategic Energy and ESG Management: A Systematic Review of Economic and Policy Implications

This literature review examines how artificial intelligence (AI)-powered business intelligence (BI) platforms are being leveraged to advance energy management and sustainability (ESG) goals in corporations. A systematic search of recent studies from Scopus, IEEE, Web of Science, and other databases yielded ~65 relevant peer-reviewed sources. We synthesized findings into five thematic areas: (1) AI applications in ESG reporting and automation, (2) BI systems for energy data visualization and monitoring, (3) predictive analytics for carbon and utility forecasting, (4) real-time dashboards for corporate sustainability decision-making, and (5) risks, biases, and ethical considerations of ESG technology. The review finds that AI-driven BI tools are streamlining sustainability reporting and assurance, enabling real-time energy monitoring and analytics, and improving forecasting of carbon footprints and energy consumption. These technologies have helped organizations identify efficiency opportunities and inform strategic sustainability decisions, with reported energy savings and emissions reductions in various cases. However, challenges persist, including data integration issues, algorithmic biases, and the need for ethical frameworks to govern AI in ESG. We identify critical research gaps such as (under-studied sectors and the social and governance dimensions of ESG tech) and propose directions for future investigation.