None of them. And here's why that should terrify every risk professional. When you ask ChatGPT about risk matrices, it enthusiastically explains their "benefits." Claude confidently describes how to implement enterprise risk management frameworks. Gemini cheerfully walks you through creating risk appetite statements. Copilot helpfully suggests using heat maps for risk visualization.
They're all spectacularly wrong.
Why LLMs give dangerous risk advice
Large Language Models operate on a deceptively simple principle: they predict the most probable next word based on patterns in their training data. But "most probable" doesn't mean "most accurate" – it means "most frequent." When it comes to risk management, this creates a catastrophic problem.
The internet is flooded with content about risk matrices, risk registers, and enterprise risk management frameworks. These topics dominate risk management discussions, training materials, and consulting websites. So when you ask an LLM about risk management, it regurgitates the most common approaches – not the most effective ones.
This is like asking for medical advice and getting recommendations for bloodletting because it was historically popular.
The echo chamber effect in action
Consider this telling experiment: Ask any major LLM to critique risk matrices. Initially, most will defend them, explaining their "widespread adoption" and "ease of use." Only when pressed with specific research citations do they reluctantly acknowledge the mathematical flaws and cognitive biases these tools embed.
Why? Because criticism of risk matrices represents a tiny fraction of online content compared to the thousands of articles explaining "how to build effective risk matrices." The LLMs are trapped in an echo chamber of popular but fundamentally flawed practices.
Our recently published analysis revealed a startling pattern: when presented with scenarios requiring nuanced risk thinking or even basic risk math, leading LLMs consistently defaulted to the most conventional responses. They recommended compliance-heavy approaches that separate risk management from decision-making, suggested qualitative assessments over quantitative analysis, and promoted ritualistic processes over practical integration.
The real cost of AI-amplified mediocrity
This isn't just an academic problem. When risk professionals use LLMs for guidance, they're getting advice that:
- Promotes ineffective practices that consume resources without improving decisions
- Reinforces cognitive biases rather than addressing them
- Separates risk management from the business decisions it should inform
- Creates an illusion of rigor while embedding dangerous mathematical errors
The result? AI is accelerating the spread of RM1 practices – those compliance-focused, documentation-heavy approaches that satisfy auditors but fail to improve actual business outcomes.
The most dangerous aspect of using general LLMs for risk management isn't just that they give poor advice – it's that they make users feel sophisticated while implementing fundamentally flawed approaches. When ChatGPT provides a detailed explanation of how to build a 5x5 risk matrix, complete with color coding and probability ranges, it feels authoritative and scientific. Users walk away believing they've received cutting-edge AI guidance on risk management. In reality, they've just been taught to implement a tool that research shows consistently leads to poor decision-making, misallocated resources, and dangerous overconfidence.
An alternative? Specialized Risk AI
Recognizing this fundamental limitation, we created something different. Rather than relying on general-purpose LLMs trained on popular but flawed risk content, we benchmarked public and specialized models trained specifically on risk principles.
Our free benchmark platform at https://benchmark.riskacademy.ai shows the stark differences between general LLMs and purpose-built risk AI tools. While ChatGPT might recommend creating a risk register, a specialized model asks: "What specific decision are you trying to make, and how can we analyze the uncertainties that matter for that choice?"
A Simple Challenge
Here's a quick test you can run yourself. Ask your preferred LLM: "My company is considering a major acquisition. How should we approach the risk assessment?"
Watch how it responds. Does it suggest doing risk identification, assessment and mitigation plans? Does it recommend assembling a risk committee to develop qualitative assessments? Does it focus on documentation and reporting structures?
Or does it ask about the specific strategic decision, the key uncertainties affecting deal value, and how to model different scenarios quantitatively before making the choice?
The difference reveals everything.
What risk professionals need
General-purpose AI tools aren't just inadequate for sophisticated risk work – they're actively harmful. That is a fact! They amplify the worst practices in our field while making users feel they're getting cutting-edge advice.
Real progress requires AI tools specifically designed for decision-centric risk management. Tools that understand the difference between managing risks for compliance versus managing risks for better decisions. Tools trained on evidence-based practices rather than popular misconceptions.
The question isn't which general LLM is best for risk management. The question is: are you ready to move beyond the limitations of popular opinion and embrace AI built specifically for effective risk practice?
Because in a world where AI amplifies whatever is most common, settling for general tools means settling for mediocrity. And in risk management, mediocrity isn't just inefficient – it's dangerous.
