Find AI UX Risks With This Claude Skill
Evaluate how your AI product behaves with this Claude skill. Surface where it could break trust, confuse people, or cause harm. Find the gaps before users do.
A guide to finding the UX risks in your AI product before users do. Covers what makes AI risks different from traditional digital products, why they're easy to miss, and the AI UX Risk Audit: a framework and Claude skill that evaluates your AI product across 6 lenses. The skill is available to paid subscribers at the end of the article.
The Risk Layer
AI solutions carry a layer of risk that traditional digital experiences don't. These risks appear in moments of interaction with AI where the system responds in ways you can't fully predict. Ways that can harm people and the business.
I’m not talking about evaluating the AI model, that’s a different topic.
Here's what I mean by AI UX risks:
The AI oversteps, does something it was never supposed to do
People can’t tell why the AI made a decision
People can’t check if what they’re seeing is accurate
People can’t stop, undo, or override what the AI did
People assume the AI is more reliable than it is
Someone gets harmed and nobody saw it coming
Imagine you’re working on an AI-powered project management tool. You already know what each feature does, you might even have a robust product requirements document, a prototype of the flow, and you know how the AI features should function.
But you don't know where the experience could go wrong.
Unlike a traditional product, the system isn't deterministic. The same input won't always produce the same output. You can't map every path because new ones emerge with every interaction. And somewhere in those paths are the moments where people lose trust, lose control, or can't recover from something the AI got wrong.
I built a framework to help me surface AI UX risks for digital products in my client work. Then I transformed in a Claude skill
And I'm sharing it with you.
What are Claude skills?
Skills are folders of instructions, scripts, and resources that Claude loads dynamically to improve performance on specialized tasks. Skills teach Claude how to complete specific tasks in a repeatable way. More About Skills
Why This Matters
When you build an AI solution, it can feel straightforward: you connect an API, define some rules, wrap it in UI, and the system works and returns outputs.
The result AI produces looks structured, complete, and confident, so people act on it.
A score influences a judgment call.
A ranking reshapes someone’s priorities for the week.
People stop verifying because the product never told them they should.
Your product is shaping decisions, and the people using it may have no idea how much they’re relying on it.
Because of complexity and the unpredictable nature of AI, these risks are easy to overlook: a missing explanation, hidden numbers, a recommendation that can’t be questioned.
The consequences? Users lose trust and leave. Wrong decisions get made at scale. In sensitive domains, you’re looking at legal and compliance exposure.
A study analyzing 202 real-world AI incidents from 2023 to 2024 found that many of the most serious harms were tied not just to model errors, but to how organizations designed, deployed, and governed AI systems, including missing safeguards, weak transparency, and poor implementation choices. It also showed that most incidents were surfaced externally, often only after harm had already occurred. Read the study here
This kind of evaluation should happen early, while you still have room to shape how the AI behaves.
The AI UX Risk Claude Skill
What is it
A structured evaluation system for AI product behavior, built as a Claude skill.
This is useful for designers, product people, and builders who work on AI experiences and want to evaluate the risks and uncover gaps. It also serves as a perfect groundwork for team discussions or workshops.
It evaluates how your AI solution behaves across 6 lenses:
Boundaries. What should this AI never do?
Expectations. What will people wrongly assume?
Trust & Clarity. Can people understand and verify what the AI produces?
Control & Autonomy. Can people steer, pause, or override it?
Safety & Misuse. What could harm real people or data?
Failure & Recovery. What happens when the AI is wrong?
