When AI Joins the Table: How Large Language Models Transform Negotiations 

Abstract

This study investigates how Large Language Models (LLMs) transform business negotiations. Through an experiment with 120 senior executives, we examined negotiations with symmetric and asymmetric AI assistance. When only one side had access to LLMs, they gained substantial advantages-buyers achieved 48.2% better deals and sellers 40.6% better outcomes compared to their counterparts. However, symmetric LLM access yielded even more striking results, with 84.4% higher joint gains compared to non-assisted negotiations. This improvement came with increased information sharing (+28.7%), creative solution development (+58.5%), and value creation (+45.3%). Notably, when both sides used LLMs, they relied less on traditional trustbuilding approaches while maintaining fairness, with minimal gain differences between parties (2.2%). Based on these findings, we introduce 'technological equilibrium' to explain how equal AI access transforms negotiation dynamics. While early adopters showed clear advantages, our results suggest that symmetric access ultimately promotes both value creation and procedural fairness through technological parity, enabling integrative outcomes even when trust is limited.

Keywords: business negotiations, Large Language Models, negotiation strategies, fairness in negotiations, trust in negotiations, power in negotiation, AI-driven negotiation strategies