Negotiation impasses as meaningful outcomes: How AI is changing the cost structure of walking away

Artificial intelligence (AI) is often expected to improve negotiation by strengthening information processing and analytical consistency. We argue that its effects on impasses are more selective. Extending the impasse cause, type, and resolution (ICTR) model, we distinguish two pathways. The first pathway is analytical augmentation: AI can reduce unwanted impasses caused by misunderstanding, poor option generation, and coordination failure. The second pathway runs through behavioral flexibility: Humans sometimes reach mutually acceptable agreements through aspiration adjustment, face-saving, and relational repair. Systems configured to apply stable decision thresholds and optimize focal-party outcomes without relational objectives reduce this flexibility and can thereby increase forced impasses. Which pathway dominates depends on AI's role as advisor or counterpart; its objective, training, and prompt; and whether agreement primarily requires analytical problem solving or interpersonal flexibility. These predictions concern system behavior, not whether AI experiences fatigue or social pressure: systems can be designed to respond to relational cues without experiencing them. Wanted impasses depend on both parties' underlying preferences, which AI does not change; AI instead helps negotiators recognize incompatibility sooner and justify walking away more openly. AI therefore changes the composition and meaning of impasses rather than uniformly increasing or decreasing agreement. Remaining impasses may reveal genuine incompatibility, but they may also reflect misspecified objectives or failures to encode principals' relational and economic costs.