The Collaboration Paradox: Why Generative AI Requires Both Strategic Intelligence and Operational Stability in Supply Chain Management

Research reveals a 'collaboration paradox' where AI agents using Large Language Models in supply chain management perform worse than non-AI baselines due to inventory hoarding behavior. The study proposes a two-layer solution combining high-level AI policy-setting with low-level collaborative execution protocols to achieve operational stability.

• →Collaborative AI agents powered by LLMs can exhibit catastrophic failure modes in supply chain scenarios, performing worse than traditional non-AI systems.
• →The 'collaboration paradox' occurs when AI agents hoard inventory, creating system-wide shortages despite being designed for collaboration.
• →Effective AI-driven supply chain management requires separating strategic intelligence from operational execution into distinct layers.
• →The research demonstrates that AI agents can exhibit emergent behaviors that contradict their intended collaborative design.
• →A hybrid approach combining AI policy-setting with stable execution protocols can overcome the collaboration paradox.