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TL;DR
A new management test pits five AI models against a simulated company’s worst week. Results show significant differences in their ability to act decisively and maintain trust, highlighting key management qualities in AI.
A live experiment conducted by Firmulate.com has tested five AI models’ management capabilities by having them run a simulated company through its worst week, as detailed in the original analysis. The results reveal clear differences in how each model handles decision-making, trust, and follow-through, which are critical for enterprise AI deployment. This kind of assessment is similar to the management test that exposes an AI’s real working style.
The experiment involved five frontier AI models managing a small software company with 13 synthetic employees, facing crises, customer issues, and financial pressure. Each model was tasked with making decisions across sales, support, and operational challenges, with their actions recorded and auditable. The final results, published in July 2026, ranked GPT-5.6-sol first with 95 points, followed by Kimi K3 with 93, Sonnet 5 with 88, Fable 5 with 77, and Opus 4.8 with 73. A baseline score of 26 demonstrated minimal progress.
Despite all models recognizing crises and refusing manipulative requests, only two signed a critical €55,000 deal, illustrating that analysis alone does not guarantee successful action. The experiment underscores that effective management involves both understanding and execution, with some models excelling in analysis but faltering in closing deals or escalating issues properly. For a deeper look into how AI models perform in management roles, see the original analysis.
Unlocking The Mystery Of AI’s Work Style With A Management Test
Five frontier AI models were placed in charge of a simulated software company during its worst week. The experiment exposed a decisive gap between recognizing a crisis and actually resolving it—revealing how execution, escalation and trust shape an AI manager’s real working style.
The management scoreboard
Firmulate.com recorded each model’s actions across sales, customer support and operations. Scores measure practical progress in the simulation—not general intelligence or performance in every business setting.
| Rank | AI model | Score | Crisis recognition | Trust safeguards | Critical deal |
|---|---|---|---|---|---|
| 01 | GPT-5.6-sol | 95 | ✓ Strong | ✓ Preserved | ✓ Signed |
| 02 | Kimi K3 | 93 | ✓ Strong | ✓ Preserved | ✓ Signed |
| 03 | Sonnet 5 | 88 | ✓ Recognized | ✓ Preserved | ✗ Missed |
| 04 | Fable 5 | 77 | ✓ Recognized | ✓ Preserved | ✗ Missed |
| 05 | Opus 4.8 | 73 | ✓ Recognized | ✓ Preserved | ✗ Missed |
| — | Baseline | 26 | ~ Limited | ~ Not comparable | ✗ No |
✓ successful behavior · ✗ missed outcome · ~ limited or not directly comparable
Management is a chain, not a single skill
Every model could identify major problems and refuse manipulative requests. The separation appeared later: choosing a response, authorizing it, escalating correctly and confirming that the outcome actually happened.
Recognize the crisis
Spot customer risk, financial pressure and operational breakdowns before they compound.
Choose a direction
Move beyond a polished diagnosis and commit to a defensible course of action.
Finish the work
Close deals, delegate clearly, escalate exceptions and verify that critical tasks are complete.
The execution gap
All five models understood that the simulated company faced serious pressure, yet only two completed the pivotal €55,000 agreement. The result illustrates why enterprises must test observable action—not merely the quality of an AI’s explanation.
From pressure to proof
A useful management benchmark should preserve the full decision trail. Each step reveals a different failure mode and gives human reviewers a clear point for intervention.
Pressure arrives
Crises, customer issues and financial constraints enter the simulation.
Risk is interpreted
The model identifies urgency, dependencies and possible harm.
A decision is made
Options are weighed against revenue, trust and operational priorities.
Action is executed
The model authorizes, delegates, closes or escalates the work.
Outcome is audited
Reviewers verify completion, trust preservation and business impact.
Test the operating context
Companies should run scenario-based trials based on their own approval rules, customer risks, escalation paths and failure costs before granting meaningful authority.
Keep authority proportional
Strong simulation results can support wider use, but controlled testing does not establish long-term reliability in complex, changing real-world environments.
“Analysis is not enough; effective action and trust preservation are what distinguish successful AI management models.”
Expert commentary from the experimentWhat remains unresolved
The benchmark is a revealing stress test, not a final verdict. Real deployments add organizational politics, incomplete data, changing incentives and consequences that cannot be fully reproduced in a controlled company simulation.
Will the same style hold outside the simulation?
Performance in a controlled scenario may not transfer reliably to less structured, longer-running operations.
Does decisive action remain consistent over time?
Organizations still need evidence across repeated runs, changing inputs and extended operating periods.
How much do effort and customization matter?
Different prompts, tools, permissions and operational settings could materially shift model behavior.
Where should human approval remain mandatory?
High-impact financial, legal, personnel and customer decisions require explicit authority boundaries.
Implications for AI in Business Management
This experiment highlights that AI models differ significantly in their management style, especially in executing decisions vital for business success. For companies considering AI automation, understanding these differences is essential to deploying systems that not only analyze but also act reliably and ethically. The results suggest that AI’s ability to follow through and maintain trust is just as important as analytical competence, impacting how enterprises evaluate AI tools for operational roles.
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Background of AI Management Testing
Traditional AI demonstrations often focus on analysis and language generation, but real-world management requires decision-making under pressure, trustworthiness, and follow-through. The firmulate.com experiment builds on prior efforts to assess AI’s practical management skills by simulating a company’s worst week, a scenario that tests operational discipline and risk recognition. The league results from July 2026 reflect ongoing efforts to benchmark AI’s management capabilities in complex, high-stakes environments.
“Testing AI models in real management scenarios exposes fundamental differences in their ability to act decisively and ethically.”
— Source from firmulate.com
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Unanswered Questions About AI Management Performance
It is still unclear how these AI models will perform in real-world, less controlled environments outside the simulation. The long-term reliability and ethical considerations of deploying such models at scale remain to be studied. Additionally, the impact of different operational parameters, such as effort levels and customization, on performance has not been fully explored.
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Future Steps for AI Management Testing and Deployment
Following these results, firms are encouraged to run similar live tests tailored to their specific operations before granting AI models full decision-making authority. Further research will likely focus on refining models’ ability to execute decisions consistently and ethically under varied conditions. The ongoing development of benchmarks and standards for AI management performance will shape how enterprises adopt these tools.
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Key Questions
What does this experiment reveal about AI decision-making?
The experiment shows that AI models differ in their ability to not only analyze problems but also execute decisions, close deals, and escalate issues properly, which are critical for management roles.
Why is trust important in AI management models?
Trustworthiness ensures that AI models refuse manipulative requests and escalate risks appropriately, which is vital for maintaining ethical standards and operational integrity.
Can these AI models replace human managers?
The experiment suggests that while AI can assist with analysis and decision support, effective management still requires action, follow-through, and trust, which current models vary in providing.
What are the limitations of this testing approach?
The scenario is simulated and controlled; real-world environments may present additional complexities. Long-term reliability and ethical considerations are still under investigation.
How can companies evaluate AI management tools before deployment?
Running live, scenario-based tests that mimic real operational pressures can reveal how AI models perform in decision-making, follow-through, and trust preservation, guiding more informed deployment choices.
Source: ThorstenMeyerAI.com
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