“The real world is messy and unpredictable, and AI systems must learn to navigate it.” - Fei-Fei Li, AI scientist and educator
LLMs put to work
LLMs are glamourous inventions. One expects them to do almost anything now, including business negotiations! But detailed research on how large language models behave in structured negotiation scenarios finds that strong benchmark scores do not always translate into effective real world performance. A study (by Mingyu Jeon et al.) studied six major models (LLMs) and observed wide variation in how they reacted to real negotiation scenarios, and pressure, incentives and social cues.
World is not a Lab
A lab is a guided and controlled environment, but the real world is not. Benchmark suites such as MMLU, HumanEval and GPQA measure reasoning and coding ability, but companies need LLMs that show real operational dependability. The study found high scoring systems, including Claude 3.5 Sonnet, may not do well in unpredictable negotiation settings. On average, buyers won only 41 percent of matches and sellers 43 percent, with competitive and cunning personas delivering the most inconsistent results.
Human negotiations are complex
Models were tested in a buyer-seller environment with differing personas such as altruistic, cooperative and self interested. Outcomes shifted sharply depending on persona choice, often diverging from expected accuracy levels. Some models show a stronger sensitivity to pressure, while others tend to crack.
Procurement function affected most
As companies begin deploying agentic AI into procurement and service operations, behavioural stability is very crucial. Governance moves beyond accuracy checks toward understanding how a model interprets incentives or responds emotionally in sensitive workflows. Poor behavioural reliability can lead to flawed customer interactions or strategic missteps. The study's authors argue that organisations need robust behavioural evaluation alongside standard benchmarks. Scenario based testing, multi agent simulations and social reasoning assessments may better reveal how models handle uncertainty, compliance and persuasion under stress.
Summary
The study shows that benchmark excellence does not guarantee real world negotiation strength. AI systems behave differently when incentives, personas and pressure enter the picture, highlighting the need for behavioural audits in enterprise deployments.
Food for thought
If an AI system can reason well but behaves unpredictably under pressure, should it be trusted in high stakes business decisions?
AI concept to learn: Agentic AI
Agentic AI refers to systems that can take actions, pursue objectives and respond adaptively to changing situations. These models go beyond answering questions and begin operating within workflows. A beginner should understand that agency introduces both powerful opportunities and important behavioural risks.
[The Billion Hopes Research Team shares the latest AI updates for learning and awareness. Various sources are used. All copyrights acknowledged. This is not a professional, financial, personal or medical advice. Please consult domain experts before making decisions. Feedback welcome!]
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