STOCHASTIC AND DETERMINISTIC MODELS FOR AI-POWERED OPERATIONAL EFFICIENCY IN GLOBAL ENTERPRISES

AI-powered modeling is changing how enterprises achieve efficiency, yet fragile economies struggle to keep pace. This study explored the role of stochastic, deterministic, and hybrid models in improving operational outcomes in Iraq, where infrastructure and data challenges slowed digital adoption. The objective was to assess how these models shaped cost reduction, process speed, resource utilization, and decision reliability between 2020 and 2024. A secondary research design drew on global, regional, and national reports, with data analyzed using descriptive statistics, correlation, and regression. Results showed that deterministic models cut logistics and manufacturing costs by up to 15 percent, stochastic models raised forecasting and diagnostic accuracy to as high as 78 percent, and hybrid models improved adaptability with the strongest regression effect (coefficient 0.36). The overall correlation matrix confirmed strong positive links, with hybrid models scoring 0.81 against efficiency outcomes, while weak infrastructure and incomplete datasets constrained broader impact with a negative correlation of -0.57. The findings demonstrate that Iraq achieved measurable yet uneven efficiency gains, shaped by model choice and environmental readiness. The results imply that scaling hybrid approaches, strengthening data governance, and improving ICT capacity are critical for future competitiveness. Recommendations call for managers to expand hybrid adoption, policymakers to prioritize infrastructure and skills, and scholars to advance theory linking model adaptability with fragile contexts.