How to Actually Evaluate AI Design Tools (A Framework Most People Skip)

Everyone recommends AI design tools. Almost nobody evaluates them properly. Here's a six-dimension framework for making real choices.

Everyone is recommending AI design tools right now. Listicles, rankings, comparison charts. But almost nobody is asking the question that actually matters: does this tool help me evaluate AI design tools properly, or am I just chasing features?

In my thesis research, I used a framework called brukskvalitetsprismat, the usability quality prism, developed by Mattias Arvola. It analyzes products across six dimensions. I originally applied it to academic literature. But I’ve realized it works just as well as a practical tool for choosing and evaluating the AI design tools flooding the market right now.

The six dimensions to evaluate AI design tools

The framework looks at any tool or system through six lenses: technical, practical, communicative, organizational, aesthetic, and ethical. Each one reveals something different about whether a tool is genuinely useful or just impressive in a demo.

Let me walk through each dimension and apply it to the current generation of AI design tools.

1. Technical: does it actually work?

This is the baseline. How reliable is the output? Does the tool produce consistent results or do you get wildly different quality depending on how you phrase a prompt? Technical quality also includes performance. A tool that takes 45 seconds to generate a screen is a different proposition than one that responds in five.

In practice: Figma Make scores high here because it integrates with your existing design system. The AI understands your components, tokens, and spacing. Google Stitch offers two generation modes, one fast and one higher quality, which gives you control over that tradeoff. Newer tools like Flowstep generate complete user journeys, not just isolated screens, which raises the technical bar for what counts as useful output.

2. Practical: does it fit how I actually work?

A tool can be technically impressive and still be useless if it doesn’t fit your workflow. The practical dimension asks: can I integrate this into my existing process? Does it save time on real tasks, or does it create a new task (learning the tool, cleaning up its output, exporting to my actual design environment)?

This is where many AI tools fall short. They generate beautiful screens in isolation, but getting those screens into your Figma file with proper layers, naming conventions, and component structure takes longer than designing from scratch. The practical question isn’t ”can this tool make a screen?” It’s ”does this tool make my Tuesday afternoon faster?”

3. Communicative: does it help me understand and communicate?

Design tools aren’t just production tools. They’re thinking tools and communication tools. The communicative dimension asks: does this tool help me explain design decisions to clients? Does it help my team align on direction? Does it produce artifacts that facilitate conversation?

AI tools that generate multiple variations quickly can be powerful for client presentations. Instead of presenting one direction, you can show three and have a real conversation about preference. But the output needs to be clear enough that non-designers can react to it meaningfully. A low-fidelity concept that communicates intent is more useful than a polished screen that locks the conversation into visual details too early.

4. Organizational: does it work for my team and context?

Who on the team can use this tool? Does it require design expertise to operate, or can a product manager generate a first draft? How does it handle collaboration? What happens to the outputs, do they live in a shared system or in someone’s personal tool?

The organizational dimension matters more than most designers realize. A tool that only the designer can use is a personal productivity boost. A tool that the whole team can use changes how the team works together. Figma Make’s strength here is obvious: it lives inside Figma, where the whole team already works. Standalone tools like Moonchild AI or Stitch require an extra step to bring outputs into the shared environment.

5. Aesthetic: does the output meet a professional standard?

AI-generated designs have a look. You’ve probably noticed it. Clean, generic, competent but soulless. The aesthetic dimension asks whether a tool’s output can stand on its own, or whether it always needs significant visual refinement.

This varies enormously between tools. Some generate output that looks like a wireframe wearing a suit. Others, especially those that integrate with your design system, can produce output that’s genuinely close to your existing visual language. The question isn’t whether the AI has good taste. It’s whether its output gives you a head start or a cleanup job.

6. Ethical: what am I trading for this convenience?

The ethical dimension was the one most people skipped in 2023. It’s harder to ignore now. Where does this tool’s training data come from? What happens to the designs I feed into it? If I’m using it for client work, does the client’s proprietary visual language end up in someone else’s training set?

This also extends to the ethics of the output. AI tools can bake in accessibility problems, cultural assumptions, and interaction patterns that work for some users but not others. The tool won’t flag these issues. That’s still the designer’s job. As Nielsen Norman Group has noted, AI tools can improve efficiency but don’t automatically improve the quality of design thinking.

Putting the framework to use

Next time you’re evaluating an AI design tool, skip the feature comparison. Instead, run it through these six questions:

Does it work reliably? Does it fit my actual workflow? Does it help me communicate and think? Does it work for my team? Does the output meet my standards? And what am I giving up to use it?

No tool scores perfectly across all six. The point is to make an informed choice about which tradeoffs you’re comfortable with, rather than chasing whatever got the most upvotes on Product Hunt this week.

In the previous post, I wrote about how the AI design landscape has changed since 2023. In the next and final post in this series, I’ll look at what AI still can’t do, and why that’s where designers matter most.