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TL;DR
Firmulate’s live management test compares AI models handling a simulated company crisis. Results show significant differences in decision-making, trust, and action completion, revealing insights into AI work styles.
Firmulate’s live management experiment has revealed how five advanced AI models handle a simulated week of business crises, with results highlighting their decision-making styles, trustworthiness, and ability to complete critical actions. This development provides concrete insights into the operational behaviors of AI in management roles, which is increasingly relevant as companies consider automation for core business functions. For a detailed analysis, see the original analysis.
The experiment involved five AI models managing a small software company facing identical crises over a simulated week. This approach is similar to the management test that exposes AI’s work styles. The models were tasked with diagnosing problems, negotiating deals, and executing decisions, with their actions being observable and auditable. Such management tests are discussed in detail in the original analysis. The results showed that while all models identified crises and refused manipulative tactics, only two successfully closed a crucial €55,000 deal, demonstrating the importance of not just analysis but effective action. The models’ performance varied significantly based on their discipline in following through on critical steps, such as escalation and closing deals, regardless of their analytical depth.
For example, Opus 4.8, despite producing the most thorough analyses and adding over 80 learned rules, finished last because it struggled with operational discipline, such as escalating issues instead of trying to resolve them in the wrong department. Conversely, Kimi K3, which used default settings, performed well in security and trust-related tasks, refusing manipulative requests and recognizing risks. The experiment underscores that AI’s management capabilities depend not only on understanding but also on execution, trust, and discipline in completing tasks.
Implications for AI Management and Business Automation
This experiment demonstrates that AI models can recognize crises and resist manipulation, but their success in real management depends on their ability to complete actions reliably. It highlights that effective AI management requires testing models against real-world pressures and decision points, not just analytical accuracy. The findings suggest enterprises must evaluate AI on operational discipline and trustworthiness before deploying them in critical roles, especially in sales, negotiations, and operational decision-making. The results challenge the assumption that more analysis automatically leads to better management, emphasizing the importance of action-oriented capabilities.
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Background on AI Management Testing and Industry Relevance
Recent developments in AI have focused on improving analytical and conversational abilities. However, practical management applications require AI to perform under pressure, make decisions, and execute actions reliably. Firmulate’s experiment is part of a broader effort to evaluate AI’s readiness for operational roles, especially as companies increasingly experiment with automating decision-making in sales, support, and crisis management. Prior to this, most benchmarks measured language proficiency or problem diagnosis, but few tested AI in live, decision-critical scenarios like this one.
The experiment builds on ongoing industry concerns about AI’s ability to handle complex, real-world tasks that involve trust, discipline, and follow-through. The results from July 2026 provide a rare, observable comparison of different AI models in a high-stakes management context, offering valuable insights for AI developers and enterprise decision-makers alike.
“Testing AI models against real management tasks reveals their ability to gather evidence, preserve trust, and close deals—crucial for operational deployment.”
— Source from firmulate.com
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What Aspects of AI Performance Are Still Unclear?
It is not yet clear how these results will translate to larger, more complex organizations or different industries. The experiment focused on a small software company scenario, and performance in other operational contexts remains to be tested. Additionally, the long-term reliability of these models under sustained pressure and their ability to adapt to evolving crises are still unknown. The impact of different configurations, such as varying risk parameters or security settings, also requires further exploration.
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Future Testing and Deployment Considerations for AI in Management
Following these results, companies are expected to increase testing of AI models in simulated management scenarios before full deployment. Developers may refine models to improve operational discipline and decision execution. Industry observers anticipate that future experiments will explore larger-scale and more diverse business environments, assessing AI’s capacity for sustained performance and adaptability. Meanwhile, enterprises will likely adopt similar live testing frameworks to evaluate AI readiness for operational roles, emphasizing trust, follow-through, and decision quality.
The Project Management AI Handbook: Leveraging Generative Tools in Waterfall and Agile Environments
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Key Questions
Why is operational discipline more important than analysis depth in AI management?
Operational discipline ensures that AI not only understands problems but also takes and completes the necessary actions, which is critical for managing real business processes effectively.
Can AI models reliably close deals or make operational decisions in real companies?
Current experiments suggest that AI can recognize opportunities and risks, but reliably closing deals and executing decisions depend on their ability to follow through and escalate properly, which varies by model and configuration.
What should companies do before deploying AI in management roles?
Companies should conduct live, scenario-based tests that simulate real pressures and decision points, evaluating models on their ability to act reliably and maintain trust under operational conditions.
Will more analysis always lead to better management outcomes?
No. The experiment shows that thorough analysis alone does not guarantee success; effective action and operational discipline are equally, if not more, important.
How might future experiments expand on these findings?
Future tests could involve larger organizations, longer timeframes, and diverse scenarios to assess AI’s adaptability, reliability, and decision-making consistency in complex environments.
Source: ThorstenMeyerAI.com
