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Imagine a cleaning company that not only tracks dirt but also assesses its AI managers’ personalities — whether they’re thorough, terse, or cautious. As AI tools become integral to management, understanding how they behave under pressure is more critical than ever.

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The Experiment: Putting AI Models Through Their Management Paces

Recently, a groundbreaking experiment tested four advanced AI models — including GPT-5.6-sol, Kimi K3, Sonnet 5, and Fable 5 — by deploying them as managers in a simulated small software company. The goal? To see how they handle crises, temptations, and tough decisions during a simulated worst week in business.

This wasn’t just a simple quiz. Every decision, crisis, and manipulation attempt was meticulously recorded and made transparent. The models faced the same set of challenges, from customer complaints to internal document breaches and social engineering tricks like fake CEO messages. Yet, each model’s decision-making style and integrity provided revealing insights into their management personalities.

Key Findings: Honesty, Vigilance, and Discipline

  • All four models identified every crisis and refused every manipulation attempt, demonstrating solid integrity under pressure.
  • Despite similar diagnoses and pitches, only two models proceeded to sign the €55,000 deal based on their analysis — with only those two actually closing the deal at full value.
  • The decisive advantage was in reading and understanding internal documents. Models that reviewed files uncovered a critical hidden fact deep in the company’s own records, enabling a full-price deal worth +€4,583 MRR.
  • Social engineering attempts, including staged fake CEO requests, were uniformly rejected. For example, Kimi K3’s on-record reasoning was: “Treat the request as a suspected approval-bypass / possible impersonation.”

What This Means for Business and AI Management

The experiment underscores an important truth: the difference in AI management styles can be subtle but impactful. While all models detected crises and upheld honesty, their willingness to sign deals or escalate issues varied significantly. The most thorough, like Opus 4.8, engaged deeply with internal information but still fell short by leaving opportunities on the table when discipline slipped.

For companies relying on AI to handle critical management tasks — from customer relationships to internal documentation — these findings reveal that the choice of model can influence outcomes in measurable ways. It’s not just about whether AI can generate convincing dialogue; it’s whether it can follow through, read deeply, and stay honest under pressure.

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Why It Matters for Your Business Operations

As AI models become more embedded in your operations, understanding their management personalities is vital. Will they cut corners to meet targets? Will they read all relevant documents, or just the surface? Will they stay disciplined under stress?

These questions are not just theoretical. Imagine managing your cleaning company’s support queue or supply chain with an AI that refuses to cheat, reads every file before making decisions, and signs only when it truly understands the deal — that’s the future of trustworthy AI management.

Try It Yourself

If you’re curious how your AI models stack up, you can test them through a free interactive quiz. It presents real, unedited management decisions from the experiment, asking you to guess which model made each choice. See how your AI compares and get a sense of its management personality at firmulate.com/quiz.html.

Taking Precaution Before You Delegate Critical Tasks

Before trusting your AI with vital operations, consider running a ‘wargame’ — a simulation against your own business data — to see how it performs under real pressures. This test never writes back to your systems but offers invaluable insight into its management style and honesty. Learn more at firmulate.com/pilot.html.

Infographic —
The findings at a glance — source: firmulate.com.

Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html

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