Artificial Intelligence in Project and Business Operations Management in US: A Systematic Review of Decision-Support Models

Abstract

This study examines the role of artificial intelligence in project and business operations management in the United States, with particular emphasis on AI-driven decision-support models that improve managerial effectiveness across cloud-enabled and enterprise case contexts. The problem addressed is the fragmented understanding of how AI supports interconnected project and operational decisions in real organizational environments, despite rising adoption across digitally intensive sectors. Accordingly, the purpose of the study is to synthesize how AI capabilities, predictive models, and intelligent decision-support systems influence planning, scheduling, forecasting, workflow coordination, risk control, and organizational performance. The research adopted a quantitative cross-sectional, case-based review design grounded in 50 selected studies representing cloud and enterprise-oriented cases from sectors such as construction, logistics, manufacturing, finance, information technology, and general management. Key variables included AI capability, decision-support model effectiveness, project management function improvement, business operations improvement, managerial decision quality, and organizational or implementation barriers. Data were organized through a structured extraction matrix and analyzed using descriptive frequency, percentage distribution, cross-case comparison, and literature-based 5-point Likert aggregation. The findings show strong overall support for the study framework, with an overall literature support score of 4.32 out of 5. AI capability recorded 4.37, decision-support effectiveness 4.34, managerial function improvement 4.36, project management improvement 4.28, business operations improvement 4.41, and decision quality and risk control 4.35, while implementation barriers also remained substantial at 4.09. Approximately 72% of the reviewed studies reported strong or very strong positive contributions of AI, 18% reported moderate or conditional support, and 10% emphasized limitations more than benefits. Cross-case results further showed the strongest outcomes in logistics and supply chain with a mean of 4.45 and manufacturing with 4.42. The study concludes that AI has become a significant decision-support infrastructure in US management practice, but its benefits depend on governance, data quality, explainability, readiness, and strategic alignment for sustainable organizational value.