A Modular AI Framework for Electric Truck Fleet Transition: Addressing Multi-Dimensional Complexity Through Organizational Readiness

The transition from diesel to electric trucks faces a critical adoption gap despite technological maturity and favorable economics. This study identifies multi-dimensional planning complexity, spanning technical, economic, operational, and organizational dimensions, as a primary barrier that existing decision support tools fail to address. Through systematic literature review and analysis of Danish transport sector data, we develop the AI-Readiness Framework for Fleet Electrification (ARFFE), a modular decision support system adapted to different organizational readiness levels. Our secondary data analysis illustrates that two frequently overlooked factors, the CO2-differentiated road tax savings of 430,000–465,000 DKK over five years and charging strategy decisions creating cost differences of 930,000 DKK, have greater economic impact than traditionally emphasized factors. The framework comprises five progressive modules mapped across four readiness stages and four planning dimensions, creating an integrated decision support system for evaluating an estimated 50,000+ scenarios. This research contributes theoretically by proposing AI as a “mediating technology” in socio-technical transitions and practically by providing an actionable framework illustrated through Danish transport sector analysis.