Why it matters
Before teams define goals or tactics, they need a shared understanding of the situation. This part of the model clarifies the external environment, the likely process, the relevant constraints, and the counterparty’s decision logic.
Purpose
The purpose is to avoid premature strategy. Teams learn to distinguish facts from assumptions, identify blind spots, and understand what may shape the negotiation before they decide what to do.
Typical questions
- What do we know?
- What are we assuming?
- What could surprise us?
- What limits our room to maneuver?
- Who are we really dealing with?
Outcome
A clearer, more reliable view of the negotiation context.
Why it matters
Once the context is clearer, the team defines what value means in this negotiation. This includes the real issues, interests, trade-offs, acceptable boundaries, success levels, stakeholder expectations, and decision criteria.
Purpose
The purpose is to improve decision quality. Teams learn to avoid price-only thinking, vague ambition, unclear thresholds, and internal misalignment. They create a shared view of what matters, what is possible, what can move, and what must be protected.
Typical questions
- What are the real issues?
- What matters most, and to whom?
- What is possible?
- What is our minimum, target, and ambition?
- Who must approve or support the outcome?
- How do we compare different options?
Outcome
A structured decision logic for better deals and stronger internal alignment.
Why it matters
After the team understands the situation and defines value, the focus shifts to how the negotiation should be conducted. This includes strategy, value creation, value claiming, argumentation, anchoring, table setup, roles, process design, and rehearsal.
Purpose
The purpose is to translate internal logic into external action. Teams learn how to make preparation usable in the room: what to address first, how to argue, how to claim without destroying the deal, who should speak, how the process should run, and what should be tested before the real meeting.
Typical questions
- What is our strategic approach?
- Where do we create value, and where do we claim value?
- How should we argue and anchor?
- Who should be in the room?
- What sequence supports our objectives?
- What should we rehearse before execution?
Outcome
A coherent engagement design that connects preparation with negotiation behavior.
Why it matters
Even strong preparation can break down when the room becomes tense, fast, political, or ambiguous. This part of the model focuses on maintaining alignment during execution and after the agreement.
Purpose
The purpose is to reduce value leakage. Teams learn to detect when they drift from goals, mandate, strategy, or roles; manage escalation and deviation; stabilize communication; and capture lessons for future negotiations.
Typical questions
- Are we still aligned with our goals and mandate?
- Are we reacting or deciding?
- Is the negotiation deviating from the intended path?
- Do we need to pause, reset, escalate, or continue?
- How do we capture the agreement and learn from the case?
Outcome
More consistent execution, fewer reactive concessions, and better organizational learning.
Situation — Understand the context
Why it matters
Teams often enter negotiations with a narrow internal view and miss external forces that shape what is realistic, credible, or expected.
Participants learn to
- map market, regulatory, legal, and actor context
- separate facts, assumptions, and unknowns
- identify blind spots before strategy starts
- improve preparation credibility
AI acceleration
Use AI to structure external information, compare assumptions, and generate targeted clarification questions.
Output
A negotiation environment map with facts, assumptions, unknowns, and key gaps.
Situation — Understand the context
Why it matters
Negotiations fail when teams prepare for the wrong kind of process: one-shot vs. multi-round, formal vs. informal, decision-ready vs. exploratory.
Participants learn to
- identify the likely process type
- anticipate decision and approval flow
- clarify likely outcome form
- spot process uncertainties early
AI acceleration
Use AI to generate alternative process scenarios and pressure-test assumptions about timing, protocol, and decision flow.
Output
A process hypothesis with likely path, approval logic, and open uncertainties.
Situation — Understand the context
Why it matters
Teams often treat internal assumptions, habits, or preferences as fixed constraints. This reduces room to maneuver before the negotiation even starts.
Participants learn to
- distinguish hard constraints from soft constraints
- map dependencies, approvals, timing, and scope boundaries
- identify realistic alternatives
- test where flexibility may exist
AI acceleration
Use AI to challenge assumed constraints, identify missing dependencies, and develop scenario questions.
Output
A constraint and dependency map showing real limits, assumed limits, and flexibility zones.
Situation — Understand the context
Why it matters
Counterparty analysis often becomes guesswork. Formal titles, visible behavior, or stereotypes are mistaken for actual authority, incentives, and decision logic.
Participants learn to
- map the counterparty organization, team, and decision path
- distinguish role, authority, mandate, and influence
- identify likely incentives and internal pressures
- treat behavior hypotheses as hypotheses, not facts
AI acceleration
Use AI to structure counterparty hypotheses, reveal missing information, and generate validation questions.
Output
A counterparty intelligence map with decision logic, actors, incentives, and validation needs.
Value — Define value, limits, and priorities
Why it matters
Many negotiations become price-driven because teams have not structured the full issue space.
Participants learn to
- identify negotiable issues beyond price
- distinguish positions from underlying interests
- map priorities for both sides
- spot value asymmetries before designing trades
AI acceleration
Use AI to expand the issue inventory, challenge fixed-pie assumptions, and identify missing interests.
Output
An issue architecture with interests, priorities, asymmetries, and open questions.
Value — Define value, limits, and priorities
Why it matters
Teams often move too quickly from positions to offers. Better agreements require option design before bargaining begins.
Participants learn to
- create multiple deal structures
- combine issues into coherent packages
- design contingent mechanisms for uncertainty
- assess feasibility and implementation risk
AI acceleration
Use AI to generate alternative value structures, compare package logic, and identify implementation risks.
Output
A value design map with options, packages, contingencies, and feasibility risks.
Value — Define value, limits, and priorities
Why it matters
Ambition is useful only when the feasible agreement space is understood. Otherwise teams confuse targets with limits.
Participants learn to
- assess alternatives and BATNA realism
- define reservation points, tolerances, and deal-breakers
- build ZOPA hypotheses
- identify what could shift feasibility
AI acceleration
Use AI to test agreement-space assumptions, identify hidden flexibility, and generate clarification questions.
Output
An agreement-space map with limits, tolerances, ZOPA hypotheses, and sensitivity drivers.
Value — Define value, limits, and priorities
Why it matters
Teams often agree on a headline goal but remain misaligned on priorities, trade-offs, timing, and acceptable movement.
Participants learn to
- define success across financial, operational, risk, strategic, and relationship dimensions
- structure best-case, target, and minimum levels
- map counterparty goal hypotheses
- define goal evolution across negotiation rounds
AI acceleration
Use AI to test internal consistency, expose goal conflicts, and generate counterparty goal hypotheses.
Output
A goal architecture with priorities, aspiration levels, tensions, and counterparty assumptions.
Value — Define value, limits, and priorities
Why it matters
Good negotiation plans fail when internal stakeholders are misaligned or the negotiator’s mandate is unclear.
Participants learn to
- map who matters and who decides
- distinguish decision-makers, influencers, and blockers
- define mandate boundaries and escalation triggers
- identify alignment risks before the meeting
AI acceleration
Use AI to structure stakeholder maps, test mandate assumptions, and surface hidden approval risks.
Output
A stakeholder and mandate map with decision paths, flexibility zones, and alignment risks.
Value — Define value, limits, and priorities
Why it matters
Negotiation options are often compared through intuition, politics, or isolated metrics. This creates inconsistent decisions.
Participants learn to
- define value dimensions beyond price
- translate financial and non-financial factors into comparable logic
- use scorecards, thresholds, and sensitivity views
- expose false precision and weak assumptions
AI acceleration
Use AI to structure comparison models, stress-test assumptions, and generate scenario-based evaluations.
Output
A value comparison model with criteria, scoring/monetization logic, and sensitivity risks.
Interaction — Design the engagement
Why it matters
Without a clear strategic logic, teams mix cooperative and competitive moves reactively.
Participants learn to
- define strategic posture
- decide where to create value and where to claim value
- structure sequencing and concession logic
- define switch points and deadlock paths
AI acceleration
Use AI to test strategy coherence, simulate alternative approaches, and identify fragile assumptions.
Output
A negotiation strategy map with approach, sequencing, concession logic, and adaptation triggers.
Interaction — Design the engagement
Why it matters
Value creation does not happen automatically. It must be prepared, tested, and adapted during the interaction.
Participants learn to
- identify trade-off opportunities
- structure joint fact-finding
- use contingent mechanisms
- avoid unrealistic or over-complex value-creation ideas
AI acceleration
Use AI to generate trade-off hypotheses, test contingent structures, and identify implementation barriers.
Output
A value-creation strategy with trade opportunities, joint fact-finding paths, and practical guardrails.
Interaction — Design the engagement
Why it matters
Strong claiming can improve outcomes, but over-claiming can destroy feasibility, trust, or ratification.
Participants learn to
- design credible anchor logic
- structure concessions and conditional movement
- manage information exposure
- balance pressure with agreement feasibility
AI acceleration
Use AI to test anchor credibility, identify over-claiming risks, and simulate counter-pressure.
Output
A value-claiming strategy with anchor options, concession logic, information discipline, and risk guardrails.
Interaction — Design the engagement
Why it matters
Persuasion fails when arguments, anchors, and tactical responses are prepared separately or inconsistently.
Participants learn to
- build 3–4 core arguments
- align arguments to stakeholder decision logic
- justify anchors credibly
- prepare structural responses to objections and counter-anchors
AI acceleration
Use AI to test argument strength, generate likely objections, and improve response logic.
Output
An argumentation and tactical framework with core arguments, anchor rationale, and if–then response logic.
Interaction — Design the engagement
Why it matters
A strong strategy can fail because the wrong people are in the room, roles are unclear, or the agenda creates pressure too early.
Participants learn to
- define team size and composition
- assign roles and speaking boundaries
- design agenda, pacing, and information flow
- prepare fallback paths for disruptions
AI acceleration
Use AI to test process risks, role ambiguity, agenda fragility, and possible counterparty setups.
Output
A process and table design with roles, agenda, information flow, and fallback paths.
Interaction — Design the engagement
Why it matters
A plan that looks complete on paper may fail under objections, time pressure, role confusion, or escalation.
Participants learn to
- identify what must be stress-tested
- design realistic counterparty scenarios
- test openings, arguments, roles, and pauses
- revise the plan based on rehearsal findings
AI acceleration
Use AI to generate pressure scenarios, simulate counterparty reactions, and structure rehearsal debriefs.
Output
A rehearsal plan with scenarios, pressure tests, observation focus, and improvement actions.
Control — Stay aligned under pressure
Why it matters
Pressure pulls teams away from goals, mandate, strategy, and roles. Misalignment often happens before anyone notices.
Participants learn to
- detect alignment drift early
- identify which anchor is at risk: goals, mandate, strategy, or roles
- use micro-alignment tools such as pause, summarize, caucus, or re-anchor
- move from reaction to deliberate action
AI acceleration
Use AI to generate training snippets and rehearse diagnosis of alignment risks.
Output
A live alignment checklist with pressure signals, team reminders, and micro-alignment tools.
Control — Stay aligned under pressure
Why it matters
Negotiations rarely follow the plan. Teams need a way to detect, classify, and respond when the room shifts.
Participants learn to
- distinguish self, team, counterparty, and strategic deviations
- recognize hardball, urgency, agenda drift, and authority plays
- decide whether to intervene, pause, reset, or escalate
- avoid reactive escalation
AI acceleration
Use AI to create deviation scenarios, rehearse classification, and test intervention choices.
Output
A deviation-control playbook with triggers, classifications, and response options.
Control — Stay aligned under pressure
Why it matters
What is said and how it is experienced are not the same. Communication can stabilize or destabilize the negotiation.
Participants learn to
- improve questioning, listening, and summarizing
- read task, relationship, and process signals
- align verbal and non-verbal behavior
- use stabilizing language under pressure
AI acceleration
Use AI to generate dialogue snippets, identify communication risks, and practice stronger response alternatives.
Output
A behavioral stabilizer toolkit with questions, summaries, reset language, and signal checks.
Control — Stay aligned under pressure
Why it matters
Negotiation does not end when the meeting ends. Poor capture, unclear ownership, and weak learning create downstream value leakage.
Participants learn to
- capture agreement content clearly
- identify interpretation and implementation risks
- define monitoring and escalation needs
- convert lessons into reusable organizational knowledge
AI acceleration
Use AI to review agreement robustness, surface ambiguity, and structure post-negotiation learning.
Output
An agreement management and learning map with risks, owners, monitoring points, and lessons learned.
