INTEGRATION OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE MANAGEMENT AND STRATEGIC DRIVER PLACEMENT WITHIN MODERN TRANSPORTATION LOGISTICS

Abstract

The global commercial transportation sector faces unprecedented operational volatility, driven primarily by systemic commercial driver shortages and highly inefficient, manual talent acquisition frameworks. This comprehensive study evaluates the structural transition from legacy human resource (HR) screening methodologies to automated, Artificial Intelligence (AI) driven evaluation models within high-volume logistics networks. By analyzing a real-world ecosystem of over 3,800 commercial transport professionals, this paper demonstrates how deploying AI algorithms in the applicant screening process yields a 40% reduction in administrative processing times. Furthermore, the integration of Federal Motor Carrier Safety Administration (FMCSA) and Department of Transportation (DOT) compliance checks into the AI architecture successfully mitigates high-risk operational liabilities. The findings indicate that the survival and scaling of modern logistics enterprises depend directly on transitioning to data-centric, automated Talent Acquisition infrastructures.