AI-Enabled Predictive Analytics for U.S. Small-Business Resilience: A Policy-Neutral, Data-Driven Assessment

Abstract

This research investigates the role of AI-enabled predictive analytics in strengthening the resilience of small businesses within the United States through a policy-neutral, evidence-driven lens. The study identifies the core dimensions of small-business vulnerability, examines the capacities and constraints of contemporary predictive models, and assesses how machine-learning techniques can support anticipatory decision-making without embedding normative or ideological assumptions. By synthesizing multi-source economic indicators, firm-level operational data, and market-volatility metrics, the paper constructs an integrated analytical framework for forecasting financial distress, operational disruptions, and adaptive recovery potential. Findings demonstrate that AI-driven forecasting methods significantly enhance early-warning accuracy, reduce information asymmetry, and improve managerial responsiveness when compared with traditional heuristic or intuition-based approaches. However, the effectiveness of these tools ultimately depends on data representativeness, interpretability safeguards, and alignment with small-business resource environments. The study concludes by outlining a neutral, scalable adoption model that supports resilience building while avoiding prescriptive policy judgments.