Secure Closed-Loop AI Decision Intelligence for Enterprise Transportation and Regulatory Systems with EV Infrastructure

Abstract

Closed-loop and causal artificial intelligence (AI) systems have emerged as transformative technologies for supporting real-time decision intelligence across complex enterprise transportation networks and regulatory environments. Traditional AI approaches primarily rely on correlation and batch learning, often failing to adapt swiftly to dynamic conditions or provide robust explanations for decisions, which is critically important in safety-critical and compliance-regulated industries. Closed-loop AI refers to systems that continuously monitor outcomes, update models, and adjust actions autonomously, thereby enabling self-correcting behavior. Causal AI enhances interpretability and decision quality by modeling causal structures rather than mere statistical associations. This paper explores how integrating closed-loop learning with causal reasoning generates decision intelligence capabilities that drive adaptive operational performance in enterprise transportation systems, ranging from logistics and fleet management to real-time regulatory compliance monitoring. Through an extensive literature review, we situate current research developments and gaps. We propose a research methodology including data sources, simulation environments, and evaluation metrics tailored to causal closed-loop systems. Our results indicate significant improvements in decision accuracy, responsiveness to regulatory changes, and operational resilience. We conclude by outlining future research trajectories aimed at wider adoption of these AI paradigms in transportation and regulatory ecosystems.