This conference proceeding presents a methodological framework for designing an AI-driven interoperable market intelligence system to support predictive labor analytics and sector-level economic resilience modeling in the United States. The framework integrates heterogeneous data sources, interoperability mechanisms, artificial intelligence techniques, predictive analytics, and decision-support capabilities to enhance workforce forecasting and strategic planning. The proposed architecture consists of five interconnected layers: Data Sources, Interoperability, AI and Predictive Analytics, Economic Resilience Modeling, and Decision Support Systems. By leveraging machine learning, labor market intelligence, and cross-sector data integration, the framework enables stakeholders to identify emerging workforce trends, anticipate labor shortages, and evaluate sector-specific resilience under changing economic conditions. An illustrative healthcare workforce disruption scenario demonstrates the practical application of the proposed framework. The study contributes a conceptual and methodological foundation for future research and implementation of AI-enabled market intelligence systems that support evidence-based policy development, workforce competitiveness, and sustainable economic growth in the United States. Presented at the ACCSA Global Conference on April 25, 2026.
Olivia Oluchi Ebepu (Sat,) studied this question.
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