AI-Driven Business Process Optimization: A Design-Science Framework for Enhancing Operational Efficiency in U.S. Enterprises

Artificial intelligence (AI) has demonstrated strong potential to support complex decision processes in enterprise environments; however, its adoption in large U.S. organizations often faces persistent barriers including lack of transparency, fragmented data interpretation, governance concerns, and limited integration into operational workflows. This paper addresses these challenges by proposing a structured, design-science–based framework that explains how AI techniques can be methodically embedded into enterprise business processes while maintaining interpretability and managerial oversight. Unlike empirical studies that rely on datasets, performance metrics, and training validation, this research focuses on conceptual development, operational feasibility, and governance alignment, making it directly relevant to enterprise deployment settings. Grounded in established literature across predictive monitoring, clustering logic, and explainable AI (XAI), the proposed framework integrates analytical reasoning with process transparency, providing both functional and governance benefits. The methodology uses a structured four-stage approach, involving problem diagnosis, conceptual development, integration architecture specification, and scenario-based validation. The results are evaluated through realistic enterprise-inspired process scenarios rather than numerical benchmarking, demonstrating that the framework enhances process visibility, reduces decision uncertainty, supports balanced workload allocation, and increases managerial trust in AI-assisted decisions. The findings confirm that AI can serve as a traceable decision-support mechanism rather than a black-box automation tool. The framework offers clear guidance on how AI outputs may be interpreted, audited, and justified—an essential requirement for organizational compliance and transparency. This work contributes theoretically by consolidating predictive reasoning and interpretable modeling within a unified structure, and it provides practical insights for enterprise-level AI adoption. Future research may extend this framework using empirical workflow datasets and longitudinal assessments to examine implementation maturity over time.