I’m going to start with a Steve Jobs clip. In it, he says “you’ve got to start with the customer experience and work backwards to the technology. You can’t start with the technology and figure out how you’re going to sell it.” You can’t make people want something.
It’s easy to get excited about AI. New models and tools pop up every week, promising smarter, faster, and more impressive capabilities. But just because you can use AI doesn’t always mean you should. If the user’s need isn’t clear, adding AI might not help — it might even get in the way.
To create useful, meaningful AI products, designers need to focus on solving real problems. Let’s talk about how to avoid AI gimmicks and stay purpose-driven.
The “Technology First” Trap
It’s tempting to start with the technology. You see a shiny new demo – an LLM or a voice-recognition model, and think, How can I use this in my product? This leads to features that exist because they’re possible, not because they’re helpful.
A classic example? Chatbots everywhere. If a simple form or FAQ page can solve a user’s problem faster, a chatbot might just slow them down. Another example is AI-driven recommendations that miss the mark, leaving users frustrated or confused.
Adding AI for the sake of it can make your product feel overcomplicated or even gimmicky.
Start with the Problem, Not the Tech
Great design starts with understanding users. What are they trying to do? Where do they get stuck? How can we make their experience smoother or more effective?
If AI can solve that problem in a way other tools can’t, that’s when it makes sense to use it. AI should be a tool in your toolkit, not the driving force behind your design decisions.
Ask Yourself:
- What’s the user need or pain point? What is the user trying/wanting to do?
- Why is X technology the best solution for this?
- Would a simpler solution work better?
If you can’t answer these questions, pause. Do some research. Become a user yourself.
Real-World Examples of Purpose-Driven AI
1. Personalized Learning Platforms
AI can help create customized learning paths for students based on their progress. Here, AI solves a real problem: adapting content to different quiz results to cover gaps. Without AI, this level of personalisation would be much slower and more intensive to achieve.
2. Language Translation Tools
Real-time translation tools meet a clear user need: breaking down language barriers. AI makes these tools faster. In this case, the tech serves the user’s goal.
3. Accessibility Features
AI-driven tools like screen readers or voice recognition software help users with disabilities navigate the digital world. These tools use AI to solve specific challenges and improve access.
Signs You Might Be Adding an AI Gimmick
- It doesn’t solve a clear user problem.
- Users are confused about why it’s there.
- A simpler solution would work just as well.
- The (AI) feature doesn’t add real value.
If any of these apply, reconsider your approach.
Keep It Simple
New technology should make things easier, not harder. If your “AI” feature adds friction or complexity, it might not be the right solution. Simplicity and clarity are always more important than showing off advanced technology.
The goal of design is to make life better for users. AI is powerful, but it’s just one tool of many. Focus on understanding user needs first, and let those needs guide your decisions. If AI genuinely improves the experience, use it. If not, don’t be afraid to keep things simple.
Good design isn’t about what’s possible. It’s about what’s useful.