Dieter Rams wrote his good design principles back when design meant physical objects. Don Norman helped us make sense of digital interfaces through affordances and feedback loops. Hick's Law, Fitts's Law, the Gestalt principles: all of them came from watching how people interact with the world, and all of them still hold up.

But the design experience is shifting. AI doesn't only change what a product does. It changes how the interaction itself works. The system isn't simply responding anymore. It's guessing at what you want, taking actions for you, and occasionally doing things you never fully asked for.

These 8 principles draw on research from Google's PAIR, Microsoft's HAX guidelines, and the emerging field of agentic AI design, written in plain language any designer can pick up and use. They apply whether you're working on a recommendation engine, a generative tool, an AI assistant, or anything in between.

1. Start with a real problem, not a cool feature

Don't add AI because you can. Add it because it genuinely makes something easier or better. Users don't care whether something is "powered by AI." They care whether it works for them. If you stripped the AI label off, would the feature still be worth building? If not, go back to the drawing board.

Questions to ask:

  • Does this solve a problem users actually have, or one we assumed they have?
  • Would users notice or care if the AI wasn't there?
  • Have we talked to real users about whether this helps them?

2. Keep the human in charge

AI should feel like a very smart assistant, not a boss. People need to feel like they're the ones making decisions and the AI is just helping them get there faster. The moment users feel something is happening to them rather than for them, you've lost them.

Questions to ask:

  • Can the user override, ignore, or undo what the AI does at any point?
  • Does the interface make it obvious that the human has the final say?
  • Are there moments where the AI acts without giving the user a chance to review?

3. Set honest expectations from the start

AI isn't magic, even when it feels like it, and users will work that out quickly if you oversell it. Help people understand what the AI is good at and where it's likely to fail, before they discover it themselves at the worst possible moment. Trust builds slowly and breaks fast.

Questions to ask:

  • Does our onboarding explain what the AI can and can't do in plain language?
  • Are we showing confidence levels when the AI is unsure, instead of always sounding certain?
  • What happens when a user trusts the AI too much and it gets something wrong?

4. Make intent explicit: ask before acting

Don't let the AI guess, especially when something important is at stake. Start with suggestions before actions. For bigger tasks, be upfront about what the AI is going to do, what it won't touch, and what needs approval before anything happens.

Questions to ask:

  • Does the AI ask clarifying questions before taking on complex or important tasks?
  • Do users know exactly what the AI is about to do before it does it?
  • Are there clear limits on what the AI can act on without explicit permission?

5. Be transparent where it changes what users do

Background AI can be invisible, and that's completely fine. A recommendation algorithm quietly doing its job doesn't need a label. But when AI shapes a decision the user cares about, they should be able to understand why it happened and opt out if they want.

Questions to ask:

  • Do users know when AI is influencing what they see or what happens next?
  • Is there a clear, accessible way to find out why the AI made a specific suggestion?
  • Can users opt out of AI features they don't want?

6. Give users real control over high-stakes decisions

Some decisions are too important to happen automatically. When an action is irreversible or consequential, like deleting data, sending a message, or making a financial decision, design for proper human review. Make it visible, make it clear, and make sure the user is genuinely the one deciding.

This is not the same as adding a confirmation dialog. It's about making sure users actually understand what they're confirming, and that they have everything they need to make the call themselves.

Questions to ask:

  • Have we identified which actions in our product are high-stakes or irreversible?
  • Are we giving users a real preview of what will happen, not just "are you sure?"
  • Does the user have enough information at this moment to make a genuinely informed decision?

7. Make it easy to fix mistakes

AI will get things wrong. That's not a bug, it's just reality. What matters is how easily those mistakes can be caught and corrected. If fixing an error feels like more work than doing the task manually, users will stop trusting the feature altogether.

Graceful failure is a design skill. The best AI experiences don't hide errors or pretend they didn't happen. They surface them clearly and make correction feel effortless.

Questions to ask:

  • How many steps does it take to undo or correct what the AI did?
  • Is it obvious when the AI is uncertain or might be wrong?
  • Does every AI action have a clear fallback or recovery path if it fails?

8. Create feedback loops

Every AI output needs a way for users to say "this was wrong" or "this helped." That loop is what rebuilds trust when things go badly, makes users feel heard, and improves the product over time. If users can't talk back to the AI, they'll just walk away from it.

Feedback mechanisms don't have to be complicated. A thumbs up, a simple edit, a "why did this happen?" link: small signals add up to a much stronger product over time, and they tell users that someone is actually listening.

Questions to ask:

  • Is there a simple way for users to rate, correct, or flag any AI output?
  • Do we have a way for users to understand why the AI did what it did?
  • Are we actually using that feedback to improve things, or just collecting it?

These principles aren't final. As AI changes, some of this will need updating. What matters today might matter less tomorrow, and we'll run into new problems nobody has thought of yet. But they work for building AI products right now.

The design field is learning as it goes. We're all figuring out what works together, in real time. One thing stays the same though: it's still about the person using your product. Keep the human in the loop, yes, but more than that, keep them at the centre. In the end it comes down to trust. Earn it and you have something people actually use. Skip it and you have another feature nobody cares about.