🤖 AI-augmented negotiation
AI & decision support
AI-assisted negotiation: research on negotiating agents (N. Brown et al., CICERO, Science 2022), decision support and bias detection; preparation and debriefing with large language models.
Full detail in the “Origin & history” section below.
Author of the library
Negotiation lecturer at KEDGE Business School
A rare threefold background serving negotiation: teaching, commercial justice and audit, backed by experience as a company director in Bordeaux.
This technique consists in enlisting a generative AI to produce, before and during a negotiation, a range of possible scenarios, equivalent alternative offers (MESO) and decision trees that map out the sequences of move and counter-move. It turns preparation, often linear and intuitive, into a systematic and rapid exploration of the space of possible agreements. The negotiator no longer settles for a plan A and a plan B: they have a portfolio of costed, ranked and ready-to-use trajectories. Here the AI acts as a strategic simulator and a decision-analysis partner, without replacing human judgment on values, the relationship and ethics.
At a glance
Vigilance: moderate (4.0/10) · Preparation required: 8/10
Indicative profile: it situates the “Négociation augmentée par l'IA” family as the AI-augmented negotiation school practises it, not this technique taken in isolation. Techniques from the same family and school therefore share the same profile. NEGOCOACH editorial rating out of 10, non-experimental · the higher the “relational risk” value, the more costly the technique is to the relationship.
NEGOCOACH assessment
Tactical potential 5.7/10 (effectiveness, impact, discretion) and vigilance moderate (relational and ethical risk): two distinct readings, deliberately never merged into a single score that would reward risk. NEGOCOACH editorial rating calibrated from the “Négociation augmentée par l'IA” family and the “AI-augmented negotiation” school. Each criterion is rated out of 10; click to understand what it measures.
How far the technique can carry the negotiation in the intended direction when it is well executed.
Strength of the effect produced on the counterpart's perceptions, emotions and decisions.
How hard it is for the other party to notice the technique is being used. A high value = very discreet.
The information, analysis and rehearsal required upfront to use it effectively.
Potential cost to the relationship and to trust if the technique is spotted, refused or fails. A high value = riskier.
Moral acceptability: fairness, transparency and respect for the counterpart's autonomy. A high value = more defensible.
Documented school
The school this technique stems from is documented by recognised work and established practice, without experimental consensus. This indicator qualifies the school, not this technique taken in isolation.
Indicative NEGOCOACH editorial rating, for teaching purposes. For “Relational risk”, a high value signals a cost to the relationship, not a quality.
The technique combines three legacies. Decision analysis applied to negotiation (decision trees, option trees, scoring) was formalised by Howard Raiffa in "The Art and Science of Negotiation" (Harvard University Press, 1982) then extended in "Negotiation Analysis" (2002). Multiple equivalent simultaneous offers (MESO) were theorised by Victoria Husted Medvec and Adam D. Galinsky (Northwestern University) in the 2000s, with empirical validation by Leonardelli, Gu, McRuer, Medvec and Galinsky (2019). Scenario planning comes from strategic foresight (Herman Kahn at RAND, Pierre Wack at Shell, 1960s-1970s). The novelty, from 2023 onwards, lies in using large language models (LLMs) to generate and iterate these scenarios, MESOs and trees at high speed.
An AI-assisted preparation and steering process in which the negotiator formulates the context to a generative model (stakes, the parties' interests, constraints, BATNA/MESORE, possible agreement zones), then asks it to produce: (1) several scenarios of how things unfold (favourable, median, degraded, breakdown); (2) a set of alternative offers of equivalent value for the sender but differently weighted for the other party (MESO); (3) decision trees spelling out the branches "if the other responds X, then I propose Y", with estimated probabilities and gains. The negotiator critiques, corrects and selects these outputs; the AI serves as an option generator and a "red team", never as a decision-maker.
Application by context
Before a key-account meeting, the salesperson asks the AI for three equivalent offers (firm price / price + services / multi-year subscription) and a tree anticipating objections on price, lead time and warranty, so as to arrive with a prepared response for each fork.
The buyer has several basket scenarios generated (volume vs. lead time vs. exclusivity) and a supplier-by-supplier decision tree, to identify the mix that maximises their value while remaining acceptable on the seller's side.
In a pay or company-agreement negotiation, the HR director and the elected representatives have package scenarios simulated (pay rise / working time / bonuses / remote work) at an equivalent total cost, then a tree of possible responses in the event of deadlock or a strike notice.
In a crisis cell (ransomware, commercial hostage-taking), the team has a quick "demand / stall / concede" decision tree produced with probabilised branches, so as not to improvise under stress and to keep several exits open.
For a coalition or a bill, the AI generates compromise scenarios (tradeable amendments) and maps who shifts with each concession, helping to build alternative offers capable of rallying a majority.
Seller and buyer prepare MESOs combining price, vacancy date, conditions precedent and furniture, then a tree anticipating counter-offers and withdrawal, so as never to get stuck on the price slider alone.
Before a negotiation with a foreign counterpart, the AI generates scenarios sensitive to cultural codes (pace, place of the relationship, hierarchy) and adapted offer variants, to be validated afterwards with a local expert to avoid misreadings.
In an inheritance or a divorce, the parties (or the mediator) have several division scenarios produced that are equivalent in value but different in nature (asset kept vs. cash vs. instalment plan), opening acceptable outcomes where a single split would crystallise the conflict.
Counter-techniques
Negotiation is also played on defence. Here is how to recognise this technique when it is used against you, and turn it around.
The signals that give it away
The counters that defuse it
Turn it into an advantage
Name the manoeuvre: said out loud, a technique loses most of its power.
Reacting emotionally instead of coming back to the facts.
Strengths: broad and rapid exploration of the space of agreements; reduction of fixation and anchoring biases on one's own plan; near-instant production of MESOs and trees that would take hours to build by hand; a "red team" effect allowing you to test your hypotheses; skill-building for less experienced negotiators. Weaknesses: the AI can hallucinate figures, probabilities or facts (false precision); it does not know the other party's real interests or red lines, which it infers; a risk of overconfidence in "clean" scenarios disconnected from the field; dependence on the quality of the prompt and the data provided; confidentiality issues if sensitive information is entered into an uncontrolled tool; value judgment, ethics and the relationship remain beyond the model's reach.
Particularly useful upstream, in the preparation phase, when the negotiation is complex and multi-issue (several tradeable variables), when the other side's scenarios are uncertain, when there is not enough time to build MESOs and trees by hand, or when you want to challenge your own strategy. Also relevant between two sessions to recalculate the branches in light of the information obtained. Less suited to very simple single-issue negotiations, to highly confidential contexts without a secure tool, or to situations where the input data is too thin to produce anything but generalities.
Business · Preparing a multi-issue supplier agreement with generated MESOs, A head of procurement prepares the renewal of a logistics contract. Rather than arriving with a single demand for a price cut, she describes to an AI assistant her interests (cost, lead times, volume flexibility, CSR commitment) and her budget constraints, then asks for three offers of equivalent value for her but weighted differently for the supplier: a steep price cut against guaranteed volume; a stable price against shortened lead times; a slightly higher price against a flexibility clause. The AI also produces a tree anticipating the supplier's likely responses. In session, presenting the three options simultaneously lets her observe which one the supplier prefers, revealing its true priorities, and converge towards an agreement both parties judge more flexible and cooperative. This sequence illustrates the documented contribution of MESOs (Leonardelli et al., 2019) amplified by the speed of AI generation.
Diplomatic · Mapping the branches of a multilateral negotiation, A team preparing a multilateral negotiation (several delegations with partly opposing interests) uses an AI to generate an extended decision tree: for each conceivable concession on a file, the model proposes which delegations are likely to shift and which counter-concessions they might demand. The human team corrects the erroneous hypotheses thanks to its field knowledge, prunes the unrealistic branches and retains two robust proposal sequences. A representative scenario, not attributed to an identified real negotiation, illustrating the use of Raiffa's decision tree at the scale of a multi-actor game.
Faced with a visibly over-prepared opponent rolling out perfectly calibrated multiple offers, keep in mind that the structure of MESOs is designed to make you reveal your preferences: compare the options against YOUR interests and your MESORE, not just against one another, and do not hesitate to answer "none as they stand, here is what matters to me". Ask how the figures and probabilities were established: a precision that does not withstand the question betrays an unvalidated automatic generation. Slow the pace, introduce a variable their decision tree did not anticipate, and test the real solidity of the mandate behind the scenarios presented.
Limits: quality is entirely dependent on the input data and subject to hallucinations; the AI is unaware of the other side's real interests, the unspoken and their red lines; it carries no value judgment or ethical sense. Ethics: transparency does not require revealing that you used an AI, but forbids invoking false "objective" data generated by the machine to manipulate; the confidentiality of third parties (personal data, trade secrets) must be protected and GDPR-compliant (RGPD); in mediation or a sensitive context, the tool must remain a thinking aid and not a means of unbalancing a vulnerable party that has no access to it. Responsibility for the decision remains entirely human.
Related techniques: Multiple Equivalent Simultaneous Offers (MESO), whose production this technique automates; Raiffa's decision analysis and decision trees; scenario planning (strategic foresight); war-gaming / negotiation simulation (role-play, red team); AI-assisted preparation of the "Prepare my negotiation" kind (interest mapping, BATNA/MESORE); the use of AI as a training partner (conversational simulator) or as a transcript analyst in post-negotiation.
Quick exercise
Answer in your head, then reveal the solution. Memory is built through active recall.
Frequently asked questions
This technique consists in enlisting a generative AI to produce, before and during a negotiation, a range of possible scenarios, equivalent alternative offers (MESO) and decision trees that map out the sequences of move and counter-move. It turns preparation, often linear and intuitive, into a systematic and rapid exploration of the space of possible agreements. The negotiator no longer settles for a plan A and a plan B: they have a portfolio of costed, ranked and ready-to-use trajectories. Here the AI acts as a strategic simulator and a decision-analysis partner, without replacing human judgment on values, the relationship and ethics.
It sits on the line: effective, but it can tip into manipulation if it exploits an information asymmetry. Use it with measure and without deliberate deceit.
Reacting emotionally instead of coming back to the facts. The right reflex: slow down and reformulate.
NEGOCOACH does not assess the experimental validation of this technique in isolation. What we document is the grounding of its source school (AI-augmented negotiation): documented school. Full detail is in the "At a glance" section of this page.
Practise with AI
Copy, paste into your assistant, replace the [brackets]. Works with ChatGPT, Claude, Gemini, Mistral, Perplexity.
Build your plan before the meeting
You are an expert negotiation coach. Help me prepare to use the "Scenario and Strategy Generation" technique in the following situation: [describe your situation]. Give me: the conditions for success, a 3-step script, my counterpart's likely objections and how to answer them.
Rehearse against an AI counterpart
Play the role of my counterpart in a negotiation. I am going to test the "Scenario and Strategy Generation" technique. React realistically and with resistance, do not give in too quickly, then at the end analyse my performance and suggest 3 concrete improvements.
Analyse a past negotiation
Here is how my negotiation went: [paste the exchanges]. Analyse whether the "Scenario and Strategy Generation" technique was used well, what worked, the mistakes made, and spell out precisely what I could have done better.
References
Founding works of the 🤖 AI-augmented negotiation school this technique belongs to.
Human-level play in the game of Diplomacy (CICERO), Science
ArticleFAIR (Meta) - N. Brown et al. · 2022
Noise: A Flaw in Human Judgment
BookD. Kahneman, O. Sibony & C. Sunstein · 2021
Co-Intelligence: Living and Working with AI
BookE. Mollick · 2024
AI-assisted negotiation: research on negotiating agents (N. Brown et al., CICERO, Science 2022), decision support and bias detection; preparation and debriefing with large language models.
On video
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Technique map
Every technique sits within a network: what it draws on, what it combines with, where it applies, and how to defend against it.
Spot its signals, neutralise it and turn it around with the defensive playbook on this page.
See the counter-techniquesThis technique consists in enlisting a generative AI to produce, before and during a negotiation, a range of possible scenarios, equivalent alternative offers (MESO) and decision trees that map out the sequences of move and counter-move. It turns preparation, often linear and intuitive, into a systematic and rapid exploration of the space of possible agreements. The negotiator no longer settles for a plan A and a plan B: they have a portfolio of costed, ranked and ready-to-use trajectories. Here the AI acts as a strategic simulator and a decision-analysis partner, without replacing human judgment on values, the relationship and ethics.
Name the manoeuvre: said out loud, a technique loses most of its power.
Reacting emotionally instead of coming back to the facts.
Our programmes turn theory into a concrete advantage.