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.

Experiment with 120 senior executives. Side with asymmetric LLMs achieved buyers 48.2% better deals and sellers 40.6% better outcomes. Symmetric LLM yielded 84.4% higher joint gains.

Traditional negotiation theory views trust and sequential information sharing as prerequisites for value creation. But the symmetric LLM conditioned showed negative correlation between information sharing and joint gains. Technology symmetry substitutes traditional trust-building process.

LLMs were implemented as a preparation-phase assistant tool:

1. Training: participants attend 2.5-hr training on strategic application of LLM capabilities and negotiation-specific prompting strategies
2. Preparation: 60-minute prep period, LLM was instructed to apply the learned prompting strategies to analyze their negotiation positions and develop potential solutions.
   1. Anticipating counterparts’ underlying interests throughs scenario analysis
   2. Engaging in virtual role-playing to test approaches and refine strategies
   3. Identifying potential objections and developing counterarguments in advance
3. Negotiation lasted 35 minutes. Participants in LLM condition were permitted to consult their AI tools during one three-minute timeout.