The collaboration paradox

Dhar, S. (2025). The collaboration paradox: Why generative AI requires both strategic intelligence and operational stability in supply chain management. arXiv preprint arXiv:2508.13942. https://doi.org/10.48550/arXiv.2508.13942

• Year and geographical scope
  2025

• Conceptual and simulation-based study without a defined geographical scope; it models supply chains in abstract form.

Generative AI technology, specifications, concrete applications

• Technology: multi-agent systems powered by large language models (LLMs).

• Specifications: agents integrate retrieval-augmented generation (RAG) for knowledge querying and generative strategic planning.

• Applications: inventory and replenishment management, coordination across multi-tier supply chains, policy generation under disruption, mitigation of the bullwhip effect.

Stage of research. Laboratory/simulation. Not yet industrial pilot or real-world deployment.

Implementation insights

• Levels and barriers

  • Technological: ensuring stability of multi-agent interactions; balancing generative strategy with operational execution; mitigating emergent unintended behaviours.

  • Organisational: redesign of internal processes to integrate autonomous agents; governance of hybrid human–machine decisions.

  • Environmental/external: uncertainty and variability in supply chain environments, vulnerability to disruption.

  • Key barriers: “collaboration paradox”, i.e. naïvely collaborative agents may hoard inventory and destabilise operations; coordination challenges across hierarchical levels; trust in generative outputs.

• Organisational conclusions

  • Generative AI cannot be deployed in isolation; it requires co-existence with robust operational rules that maintain material flows.

  • A two-layer architecture is essential: a strategic layer (generative intelligence) and an operational layer (collaborative control).

  • Adoption requires monitoring mechanisms, continuous oversight, and cultural adaptation to hybrid decision-making.

  • Training managers to interpret and supervise generative policies is critical.

• Economic/financial conclusions

  • The study does not report empirical ROI or cost data; however, it stresses that poorly designed agent interaction may degrade performance relative to conventional approaches.

  • Economic benefits emerge only when hybrid governance mitigates the collaboration paradox.

  • Properly designed architectures can outperform naïve generative collaboration, suggesting that investment in robust control frameworks yields significant economic value.