Two years in the past, I noticed one thing that made me uncomfortable: each time I examined a public AI instrument on threat administration questions, it gave me horrible recommendation. Not simply unhelpful. Actively unhealthy.
I’d ask ChatGPT about threat matrices, and it might enthusiastically clarify their advantages. Claude would stroll me by implementing enterprise threat administration frameworks. Gemini would assist me construct threat urge for food statements. Copilot would advocate colourful warmth maps for visualization. All of them have been spectacularly unsuitable. The issue wasn’t that they didn’t know sufficient. The issue was that they knew an excessive amount of of the unsuitable issues.
What “Most Possible” Truly Means
Giant Language Fashions work on a easy precept: they predict probably the most possible subsequent phrase based mostly on patterns of their coaching information. However “most possible” doesn’t imply “most correct.” It means “most frequent.” And what’s most frequent on the web in terms of threat administration? 1000’s of pages explaining the best way to construct threat registers. A whole lot of consulting agency articles about threat urge for food statements. Countless templates for warmth maps and compliance frameworks. The fashions are trapped in an echo chamber of widespread however deeply flawed practices.
I examined this repeatedly. After I requested about threat matrices, the AIs would initially defend them. Solely once I pushed again with particular tutorial citations would they reluctantly admit the plain: threat matrices embed harmful biases and mathematical errors that may result in horrible selections. However right here’s the factor – most individuals received’t push again. They’ll take the primary reply, assume the AI is aware of what it’s speaking about, and implement recommendation that feels subtle however is basically damaged.
Ask RAW@AI about this put up or simply discuss threat administration
Two Worlds, Accelerating Aside
This completely captures the cut up in our career: RM1 versus RM2. RM1 is the world of artifacts. Insurance policies, registers, urge for food statements, warmth maps. They fulfill auditors and regulators. They appear spectacular in board shows. However they not often have an effect on how capital truly will get allotted or how methods get formed. RM2 integrates quantitative strategies into actual enterprise selections. As an alternative of manufacturing standalone threat stories, it makes planning, budgets, and investments risk-aware. It doesn’t ask “What’s our threat urge for food?” It asks “How do uncertainties change the selection we’re about to make?”
AI is accelerating the divergence between these two worlds. Common-purpose LLMs supercharge RM1. They generate threat registers sooner than any human might. They produce polished urge for food statements in seconds. They automate compliance stories with ease. However all this paperwork leaves precise selections untouched.
That’s why I constructed RAW@AI. Not as one other chatbot, however as a specialised instrument skilled on RM2 ideas, grounded in the appropriate sources, and constructed with guardrails that forestall it from falling into the popular-but-wrong entice. For 2 years now, my staff has used it for precise threat administration work – the type of evaluation and resolution help that threat groups must ship.
The distinction isn’t delicate. It’s the distinction between astrology and astronomy.
Right here’s what worries me: if AI can already produce registers and insurance policies sooner than any human, what’s left for threat managers to do? The reply is interpretation. Turning probabilistic fashions into enterprise perception. Embedding uncertainty into strategic conversations. Making threat evaluation a driver of choices, not only a compliance train. The danger supervisor of the longer term isn’t a custodian of paperwork. They’re an architect of choices. However you may’t get there by asking ChatGPT the best way to handle threat. You’ll simply get a sooner solution to do what doesn’t work.
I printed a benchmark in August 2025 testing main LLMs on threat administration questions. The outcomes have been clear: none of them have been match for goal. Though considering fashions are getting higher.
That ought to fear each threat skilled who’s enthusiastic about utilizing AI of their work. Generic AI doesn’t simply give poor threat recommendation – it amplifies the worst practices in our discipline whereas giving customers the phantasm of sophistication. It makes mediocrity really feel trendy. And in threat administration, mediocrity isn’t innocent. It prices cash. It misallocates capital. It builds overconfidence in selections that must be questioned. The selection isn’t whether or not to make use of AI. The selection is whether or not you accept instruments that reinforce what’s widespread, or insist on instruments that ship what’s right. As a result of there’s a distinction. And in our career, that distinction is measured in tens of millions.
Discover the outcomes of the Danger Benchmark: https://benchmark.riskacademy.ai
Meet RAW@AI, specialised AI for threat administration: https://riskacademy.ai
See how AI is remodeling RM2 at Danger Consciousness Week 2025, 13–17 October: https://2025.riskawarenessweek.com
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