The trucking industry generates large volumes of operational data through fuel transactions, GPS and telematics systems, electronic logging devices, maintenance records, dispatch systems, safety platforms, and financial tools. Yet these data are frequently fragmented across functional departments, limiting managers' ability to identify cross-system anomalies and prioritize emerging operational risks. This paper develops a practice-informed conceptual framework for an artificial-intelligence-enabled 'intelligence layer' in trucking operations. The framework is grounded in operational observations documented during the early development of Dieselly.ai and is analytically connected to recent literature on machine learning in transportation, fuel-consumption modeling, predictive maintenance, anomaly detection, explainable artificial intelligence, and human-AI decision support.
A structured conceptual analysis was used to organize recurring operational problems into four design requirements: cross-functional data integration, anomaly-based attention prioritization, human-in-the-loop decision support, and context-aware explanation. The analysis suggests that the primary value of AI in fleet operations may lie less in replacing existing transportation-management systems than in connecting their outputs, detecting inconsistencies, and directing managerial attention toward the vehicles, drivers, transactions, or maintenance conditions most likely to require investigation. Fuel efficiency, suspicious fuel transactions, maintenance risk, and safety monitoring are discussed as illustrative use cases.
The paper proposes a layered architecture that separates data integration, contextual inference, anomaly detection, explanation, and human decision authority. Because the study is conceptual and does not report a controlled field trial, no causal claims about operational performance are made. The framework is intended to guide future product development and empirical validation of AI-supported decision systems in trucking.