International Journal of Scientific Research and Engineering Development

Artificial intelligence (AI) has emerged as a transformative technology in organizational decision-making processes, fundamentally altering how businesses analyze data, predict outcomes, and allocate resources. This systematic review synthesizes empirical evidence from 2020-2025 regarding AI's impact on business decisionmaking across multiple sectors. Following the PICO framework and PRISMA guidelines, we searched five major databases (PubMed, Web of Science, Scopus, IEEE Xplore, and Business Source Complete) identifying 47 peer-reviewed studies meeting inclusion criteria. Our findings demonstrate that AI-driven decision systems enhance decision speed by 15-20%, improve accuracy by 10-18%, and optimize operational efficiency across financial services, retail, manufacturing, and healthcare sectors. Machine learning algorithms, predictive nalytics, and generative AI systems show significant impact in fraud detection, customer behavior analysis, and demand forecasting. However, challenges including data quality concerns, algorithmic bias, implementation costs, and organizational readiness remain critical barriers to widespread adoption. This review concludes that while empirical evidence strongly supports AI's effectiveness in structured decision contexts, successful implementation requires strategic alignment, workforce training, and comprehensive governance frameworks. Future research should address long-term sustainability, ethical considerations, and industry-specific applications.