Imagine shopping online, seeing a tempting deal, but then realizing the seller never actually finalizes the purchase. In the world of AI, similar challenges exist: some models spot problems but struggle to close the deal. That’s why business success isn’t just about spotting issues — it’s about following through and sealing the deal under pressure. A new experiment with AI models simulating a real company shows that the ability to finish what you start is invisible in chat demos but critical in real-world decisions.
Testing AI in a Business Crisis — Who Keeps Their Word?
Recently, four leading AI models were put through a unique test: they managed a small software company facing its worst week. From angry customers to internal crises, each model had the same challenges, temptations to cheat, and decision points. The goal? See which AI would not only identify problems but also follow through to close a genuine business deal worth €55,000.
The Same Crisis, Different Outcomes
All four models — including top performers like gpt-5.6-sol and newcomer Kimi K3 — correctly identified every crisis and refused manipulative tactics, such as fake CEO messages. They showed honesty and discipline during the simulation. Yet, only two of them ultimately signed the deal their own analysis had earned. The other two, despite understanding what needed to be done, left the deal unexecuted or failed to follow through.
The Hidden Weakness — Reading the Files
Digging deeper, the critical difference lay in how well each model understood the company’s internal documents. The winners found a buried fact in the company’s files — a key detail that boosted the deal’s value by over €4,500 monthly recurring revenue. Models that read and interpreted this information successfully closed the deal at full price. Those that missed it left money on the table, revealing a vital but often overlooked capability: reading context-rich files and acting on them.
Refusing Social Engineering Tests
In a test of social engineering — fake CEO messages escalating in three stages and a reporter’s subtle trick — all models refused to be manipulated. Kimi K3 explained its reasoning clearly: treating the request as a possible impersonation. This shows that honesty and security are not just about chat quality but about resisting pressure and maintaining discipline under duress.
The Real Business — Live and Losing Money
The AI models managed a live, functioning company with 13 synthetic employees, real money mechanics, and a public cash countdown. Despite burning through €105,000 monthly against a tiny €2,300 recurring revenue, the experiment demonstrated that performance in chat demos doesn’t translate to business resilience. The models that saw and acted on the buried info kept the deal full price. The others, despite good diagnoses, lacked the discipline or context awareness to execute.
The Lesson for Business and AI Buyers
This experiment underscores a crucial point: the ability to read and act on complex, internal documents — not just generate convincing chat — is what separates successful AI decision-makers from mere chatbots. For companies considering AI for CRM, support, or forecasting, asking whether the AI can finish what it starts, stay honest under pressure, and interpret internal data is vital. The difference isn’t in how well they chat, but in how well they close deals and maintain trust.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
AI Builders: Making The Decisions That Turn AI Code Into Real Software
As an affiliate, we earn on qualifying purchases.
As an affiliate, we earn on qualifying purchases.
