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This paper investigates the impact of large language model (LLM) diffusion on supply chain management, focusing on productivity, managerial perspective, and institutional power. Through a comparative qualitative analysis of five leading LLM ecosystems (Claude, ChatGPT, Gemini, LLaMA, and Mistral), the study reveals that diffusion outcomes depend on the socio-technical alignment between AI systems, human workflows, and governance structures. The research highlights the trade-offs between proprietary platforms that accelerate productivity and open-weight ecosystems that foster localized innovation, emphasizing the importance of interpretability and equitable governance for sustainable AI adoption.
LLM adoption in supply chains hinges on socio-technical alignment and governance, not just model performance: proprietary platforms boost productivity but create dependency risks, while open-weight ecosystems foster localized innovation.
Background: The rapid diffusion of large language models (LLMs) such as Claude, ChatGPT, Gemini, LLaMA, and Mistral is reshaping logistics and supply chain management by embedding generative intelligence into planning, coordination, and governance processes. While prior studies emphasize algorithmic capability, far less is known about how differences in diffusion pathways shape productivity outcomes, managerial cognition, and institutional control. Methods: This study develops and applies an integrative analytical framework—the AI Diffusion Triad—comprising Productivity, Perspective, and Power. Using comparative qualitative analysis of five leading LLM ecosystems, the study examines how technical architecture, access models, and governance structures influence adoption patterns and operational integration in logistics contexts. Results: The analysis shows that diffusion outcomes depend not only on model performance but on socio-technical alignment between AI systems, human workflows, and institutional governance. Proprietary platforms accelerate productivity through centralized integration but create dependency risks, whereas open-weight ecosystems support localized innovation and broader participation. Differences in interpretability and access significantly shape managerial trust, learning, and decision autonomy across supply chain tiers. Conclusions: Sustainable and inclusive AI adoption in logistics requires balancing operational efficiency with interpretability and equitable governance. The study offers design and policy principles for aligning technological deployment with workforce adaptation and ecosystem resilience and proposes a research agenda focused on diffusion governance rather than algorithmic advancement alone.
