AI Negotiation Drift
A company asks an AI agent to optimize a deal. The agent finds leverage, creates market pressure, and achieves the objective while responsibility becomes harder to locate.
A growing library of applied examples. Each case uses WANTS to move from a messy situation to a clearer problem, aim, need, test, and success measure.
Showing all case studies.
A company asks an AI agent to optimize a deal. The agent finds leverage, creates market pressure, and achieves the objective while responsibility becomes harder to locate.
Passengers gather at the gate as boarding begins, but the process becomes unclear and crowded. WANTS reframes the problem around visible instructions, calmer movement, and a testable boarding flow.
Name the real friction, risk, or confusion before jumping to a solution.
Define the direction of the better outcome in plain language.
Identify what people need in the moment: clarity, control, confidence, trust, or safety.
Test a smaller version before the idea becomes expensive, risky, or permanent.
Decide what would prove the solution worked in real behavior.