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.
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AI Counterpart Analysis
Négociation augmentée par l'IATechnique 355 / 360
Author of the library
Alexandre Baumberger
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.
Teaching, KEDGE Business SchoolNegotiation lecturer since 2014 (12 years).
Commercial justice, Commercial CourtJudge from 2018 to 2026: litigation, then insolvency proceedings.
Audit & advisory, over 20 yearsTax, employment and financial audit in major firms, for large groups.
AI-Based Counterparty Analysis consists in using artificial-intelligence tools to collect, sort and synthesise the available public information (OSINT) about the other party before and during a negotiation. It turns a heterogeneous mass of open data (press, professional networks, registers, press releases, publications) into a usable map of the counterpart's interests, constraints, style and likely BATNA. Well conducted, it reduces information asymmetry and saves considerable preparation time. But it has value only under strict human control, within an explicit ethical and GDPR (RGPD) framework, because the AI amplifies relevance as much as error and bias.
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
How to read this rating
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.
Effectiveness7/10 · High
How far the technique can carry the negotiation in the intended direction when it is well executed.
Psychological impact5/10 · Moderate
Strength of the effect produced on the counterpart's perceptions, emotions and decisions.
Discretion5/10 · Moderate
How hard it is for the other party to notice the technique is being used. A high value = very discreet.
Preparation8/10 · Very high
The information, analysis and rehearsal required upfront to use it effectively.
Relational risk3/10 · Low
Potential cost to the relationship and to trust if the technique is spotted, refused or fails. A high value = riskier.
Ethics6/10 · High
Moral acceptability: fairness, transparency and respect for the counterpart's autonomy. A high value = more defensible.
Level of evidence
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.
Summary: AI-Based Counterparty Analysis
Origin & history
The technique has no single attributable inventor: it is the convergence of three traditions. On one hand, open-source intelligence (OSINT), formalised by the intelligence communities then popularised for civilian use, notably by Michael Bazzell ("Open Source Intelligence Techniques", successive editions since 2012). On the other, the negotiation-preparation doctrine grounded in information and the BATNA (Fisher, Ury & Patton, "Getting to Yes", 1981/1991, Harvard Negotiation Project). Finally, the spread of large language models (2022-2023), which made automated corpus synthesis accessible. The label "AI-based counterparty analysis" is a recent usage descriptor (sales-enablement and business-negotiation practitioners, 2023-2025) and not a doctrinal brand; any attribution to a single author would be false.
Definition and principle
A structured approach consisting in formulating a precise intelligence question about a counterparty (who decides, under what constraints, with what alternatives, what style), having AI tools collect and cross-reference only the legally accessible open-source information, then producing a human-verified synthesis that serves as working hypotheses for preparing and conducting the negotiation. This is neither espionage nor automated decision-making profiling: the AI prepares, the human decides.
Objectives of the technique
Reduce information asymmetry by reconstructing the other party's likely interests, constraints and priorities
Estimate the counterparty's BATNA and pressure points to calibrate one's own anchors and concessions
Identify the real decision-makers, the decision circuit and the influential stakeholders
Anticipate the likely style, arguments and objections to prepare responses and scenarios
Save preparation time by synthesising a vast, heterogeneous corpus into actionable, verifiable briefs
Concrete examples of application
Application by context
The same technique, across every negotiation settings
Before a key-account meeting, the AI aggregates annual reports, press releases, job postings and the prospect's LinkedIn posts to reveal their current budget priorities and decision cycle, steering the value pitch.
Context 2 / 8
Procurement negotiation
A buyer has the AI synthesise a supplier's financial health, order book and customer dependence to estimate its real room for manoeuvre and pressure to close before discussing prices.
Context 3 / 8
Labour negotiation
In preparation for a negotiation with employee representatives, management maps via open sources the public positions of the unions and precedents from sector-wide agreements to anticipate demands and red lines.
Context 4 / 8
Crisis management
In crisis management (product recall, hostage-taking, media conflict), the AI compiles in near real time the counterpart's public statements and history to assess their credibility, their consistent demands and their signals of openness.
Context 5 / 8
Political negotiation
A campaign or coalition team has a negotiating partner's votes, speeches and public commitments analysed to identify exploitable points of convergence and non-negotiable subjects.
Context 6 / 8
Real-estate negotiation
Before an offer, the buyer has the AI cross-reference the listing history, how long the ad has been up, area transactions and signals of the seller's motivation (relocation, inheritance) to adjust their price anchor.
Context 7 / 8
Cross-cultural negotiation
Facing a foreign counterpart, the AI synthesises communication codes, the local decision hierarchy and the company's precedents to avoid missteps and adapt the pace and register of the negotiation.
Context 8 / 8
Family negotiation
During the division of an estate or a family mediation, the approach transposes soberly by listing public or shared facts and documents (deeds, asset values) to objectify the positions, without ever surveilling relatives.
Counter-techniques
Spot and neutralise this technique
Negotiation is also played on defence. Here is how to recognise this technique when it is used against you, and turn it around.
Detect
The signals that give it away
A sudden imbalance in the exchange
Pressure to decide quickly
An argument you cannot verify
Neutralise
The counters that defuse it
Slow down and reformulate
Ask for facts and sources
Concede nothing without a counterpart
Turn around
Turn it into an advantage
Name the manoeuvre: said out loud, a technique loses most of its power.
The trap to avoid
Reacting emotionally instead of coming back to the facts.
Strengths and weaknesses
Strengths: a massive reduction in preparation time; coverage of a corpus a human could not read; detection of weak signals and non-obvious connections; upskilling of junior negotiators; the structuring of testable hypotheses. Weaknesses: models' hallucinations and false attributions; outdated or biased data; the illusion of certainty ("the AI said so") leading to erroneous anchors; a risk of intrusive over-profiling and of ethical/GDPR (RGPD) drift; a mirror effect (the other party does the same); dependence on public sources that are sometimes misleading or manipulated.
When to use this technique?
Particularly useful ahead of important, documented stakes: B2B key-account negotiations, strategic purchasing, M&A operations, partnerships, disputes with a high volume of public information, and any context where the counterpart leaves a rich digital footprint. Of little relevance, even inadvisable, when public information is scarce, when the stake is intimate, or when the relationship of trust takes precedence over an informational advantage.
Famous cases
Business · Preparing a key-account SaaS contract renewal, Representative scenario (not attributed to a named company). A sales team prepares the renewal of a multi-year contract with a large client. Before the meeting, it asks an AI assistant to synthesise the recent press releases, the latest annual report, the open job postings and the public statements of the sponsor on the client side. The synthesis, re-read and corrected by the negotiator, reveals a SaaS cost-reduction plan and the arrival of a new head of procurement. The team recalibrates its offer around licence consolidation and prepares responses to the anticipated price objections, rather than rolling out its standard pitch. The gain is not magic: it comes from better-targeted hypotheses, verified with the client in the meeting.
Diplomatic · Open-source intelligence as the foundation of preparation, A case documented to illustrate the principle, not a specific AI tool. The intelligence communities officially recognise OSINT as a discipline: the 2024-2026 OSINT strategy of the US Office of the Director of National Intelligence formalises the collection and analysis of public information to inform decision-makers, including in support of sensitive negotiations (border, diplomatic). This institutional precedent shows that value lies not in raw collection but in analysis driven by a precise question, a logic the AI is now industrialising for business negotiation, with the same verification requirements.
Common mistakes
Taking the AI's output as an established fact rather than as a hypothesis to be verified (risk of hallucination and false attribution)
Crossing the ethical/GDPR (RGPD) line: intrusive profiling, sensitive personal data, non-public or illegally obtained sources
Anchoring your strategy on outdated data or on a single, uncorroborated viewpoint
Forgetting that the other party can analyse you in the same way and detect an excess of information you have revealed
Overloading yourself with unprioritised information instead of answering a precise negotiation question
How to recognise and counter this technique
To recognise that you are being "analysed" yourself: a counterpart who cites precise, recent details about your internal priorities, your calendar or your constraints betrays advanced OSINT preparation. To defend yourself: control your digital footprint and your team's (what is public is public for everyone); do not mechanically confirm "facts" put forward by the other side that could be erroneous AI extrapolations serving as anchors; ask control questions to test the real solidity of their information; deliberately introduce new information in session that the prior analysis could not contain, to regain the initiative.
Limits and ethics
Technical limits: models hallucinate, go out of date, and reflect the biases of their sources; a synthesis can appear reliable while being false. Legal limits: the GDPR (RGPD) strictly governs the processing of personal data (legal basis, minimisation, purpose, right to object, article 22 on automated profiling); the EU AI Act adds obligations for certain uses; the line with invasion of privacy and unlawful economic intelligence is thin. Ethical limits: stay on the side of fair preparation, never surveillance, manipulation or the exploitation of unduly obtained data. Golden rule: public sources only, a preparation purpose, human control and decision, traceability, and abstention as soon as a doubt over legality or fairness arises.
Variants and related techniques
Related techniques: BATNA/MESORE preparation (Fisher-Ury), which it feeds; stakeholder mapping and analysis of the decision circuit; negotiation-style profiling (used with caution); AI negotiation simulation (a generated adversarial role-play); competitive monitoring and economic intelligence; the "in-session AI copilot" that suggests responses in real time. It is distinct from espionage and social engineering, which fall outside the legal and ethical framework.
Going further
Michael Bazzell, "Open Source Intelligence Techniques" (the reference manual on legal open-source collection)
Roger Fisher, William Ury & Bruce Patton, "Getting to Yes" (preparation through interests and the BATNA)
Regulation (EU) 2016/679 (GDPR), notably article 22 on profiling, and the CNIL guidelines on AI
IC OSINT Strategy 2024-2026 (ODNI), an institutional illustration of open-source analysis
Scientific foundations
Roger Fisher, William Ury & Bruce Patton (1991) Getting to Yes: Negotiating Agreement Without Giving In (2nd ed.) Penguin Books
European Parliament and Council of the European Union (2016) Regulation (EU) 2016/679 (General Data Protection Regulation), art. 22 (automated decision-making and profiling) Official Journal of the European Union, L 119
Michael Bazzell (2021) Open Source Intelligence Techniques: Resources for Searching and Analyzing Online Information (8th ed.) Independently published
Quick exercise
Test yourself before answering
Answer in your head, then reveal the solution. Memory is built through active recall.
1Quels signaux doivent vous alerter ?
A sudden imbalance in the exchange
Pressure to decide quickly
An argument you cannot verify
2Quelles parades appliquer ?
Slow down and reformulate
Ask for facts and sources
Concede nothing without a counterpart
Frequently asked questions
The questions we get most
What is the "AI Counterpart Analysis" technique?
AI-Based Counterparty Analysis consists in using artificial-intelligence tools to collect, sort and synthesise the available public information (OSINT) about the other party before and during a negotiation. It turns a heterogeneous mass of open data (press, professional networks, registers, press releases, publications) into a usable map of the counterpart's interests, constraints, style and likely BATNA. Well conducted, it reduces information asymmetry and saves considerable preparation time. But it has value only under strict human control, within an explicit ethical and GDPR (RGPD) framework, because the AI amplifies relevance as much as error and bias.
Is the "AI Counterpart Analysis" technique ethical?
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.
How do you defend against "AI Counterpart Analysis"?
Reacting emotionally instead of coming back to the facts. The right reflex: slow down and reformulate.
What is the "AI Counterpart Analysis" technique based on?
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
Three ready-to-use prompts
Copy, paste into your assistant, replace the [brackets]. Works with ChatGPT, Claude, Gemini, Mistral, Perplexity.
Prepare
Build your plan before the meeting
You are an expert negotiation coach. Help me prepare to use the "AI Counterpart Analysis" 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.
Play the role of my counterpart in a negotiation. I am going to test the "AI Counterpart Analysis" technique. React realistically and with resistance, do not give in too quickly, then at the end analyse my performance and suggest 3 concrete improvements.
Here is how my negotiation went: [paste the exchanges]. Analyse whether the "AI Counterpart Analysis" technique was used well, what worked, the mistakes made, and spell out precisely what I could have done better.
Founding works of the 🤖 AI-augmented negotiation school this technique belongs to.
Human-level play in the game of Diplomacy (CICERO), Science
Article
FAIR (Meta) - N. Brown et al. · 2022
Noise: A Flaw in Human Judgment
Book
D. Kahneman, O. Sibony & C. Sunstein · 2021
Co-Intelligence: Living and Working with AI
Book
E. 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.
The author
Author
On video
See the technique in action
Videos to picture AI Counterpart Analysis and anchor it through examples.
AI-Based Counterparty Analysis consists in using artificial-intelligence tools to collect, sort and synthesise the available public information (OSINT) about the other party before and during a negotiation. It turns a heterogeneous mass of open data (press, professional networks, registers, press releases, publications) into a usable map of the counterpart's interests, constraints, style and likely BATNA. Well conducted, it reduces information asymmetry and saves considerable preparation time. But it has value only under strict human control, within an explicit ethical and GDPR (RGPD) framework, because the AI amplifies relevance as much as error and bias.
The right reflex
Name the manoeuvre: said out loud, a technique loses most of its power.
Never do this
Reacting emotionally instead of coming back to the facts.
5.7/10 tactical potential Moderate vigilance Documented school
Master this technique in real situations?
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