From data to decisions: A narrative review of business intelligence and predictive analytics framework for enhancing SME competitiveness and economic resilience in the United States

Small and medium-sized (SME) business organizations constitute the structural foundation of the United States economy but are systematically under-served by advanced business intelligence (BI) and predictive analytics infrastructure, which is structurally threatening to inclusive economic growth and resiliency. This narrative review critically summarizes peer-reviewed literature (2020-2025) to understand new trends, frameworks, and uses of BI and predictive analytics to increase U.S. SME competitiveness and economic resilience and define gaps in governance and future research priorities. The data shows that there is a paradigm shift between retrospective reporting to real-time and AI-enhanced analytics, adaptive dashboarding, cloud-based predictive models, agentic supply-chain pipelines, and machine-learning-based scenario planning are changing the operations of the SMEs. There are still critical gaps in data literacy, fair access to AI and bias in algorithms, and governance mechanisms that are tuned to the scale of SME deployment. Empirical claims across the literature vary in methodological rigor and should be viewed with proper caution before the standardized replication. Implementation science, ethical AI governance in line with NIST AI RMF, ISO/IEC 42001, and OECD AI Principles, and SME-specific digital resilience benchmarks should be the priorities of future research to democratize data-driven decision-making in the U.S. SME sector.