Vicky, Product Designer at Sprint
AI is a powerful tool that can enhance your design process. It won’t design your product for you or solve user problems instantly. That said, when used correctly, it can help you progress faster and identify issues sooner. It may also surprise you with new ideas. Here’s a look at practical ways AI can support various aspects of design work, that we've been using at Sprint Innovations
AI is especially useful for spotting trends and patterns in large datasets. Such as survey responses, user feedback, or customer support tickets. It can help to group information into common themes. This makes it easier to extract useful insights from large amounts of data.
Example tools: ChatGPT, Gemini, or DeepSeek
Method: Begin with a spreadsheet where each row represents a single data point. This could be a user comment, survey response, or support ticket. Using your chosen LLM, you can prompt it to:
Limitations: AI can mislabel data. You’ll need to check its output and refine prompts as needed. While the AI can speed up analysis, it doesn’t remove the need for human judgment.
An underrated use of Large Language Models or LLMs is to simulate customer interviews. They can help you ask better questions and get up to speed on industry-specific knowledge.
Example tools: ChatGPT, Gemini, or DeepSeek
How it works:
Limitations: AI is not a replacement for real customer research. Real customers will surprise you in ways AI can’t fully simulate. It can, however, help you arrive prepared and empowered so you get the most out of (often time-constrained) customer interviews.
Heat maps are visual representations of user attention, showing where people are most likely to focus on a page. In traditional usability testing, they are generated by tracking real users’ eye movements. Predictive heat maps use AI to show where users focus based on design patterns. This helps you assess your layout before testing it with real users.
There are various tools available to analyse landing or marketing pages before spending on user testing. Many of them report over 90% accuracy compared to real (non predictive) heat maps.
Example Tools:
Best for:
Not good for:
Limitations: These tools use visual pattern recognition to predict where users are likely to focus. They don’t account for authentic user intent or content clarity. Think of them as a “visual hygiene” check—ideal for catching flaws before moving to real testing.
Finding the right stock photo can take longer than expected. They can also be pricey, generic, or not quite fit your brand. AI-generated imagery gives you options. There are many AI image generation tools available, two of which have made it into my workflow.
Limitations: AI-generated photographs don’t always look right. Hands and faces often look strange, and you can't reliably recreate the same person. In high stake marketing campaigns or UX, AI photos can feel “off.” Real photos or edited stock images are often better for creating trust and relatability.
There are too many new AI wireframing and prototyping tools to list them all. The practical impact (so far) is extremely mixed. The tools worth noting, integrate with Figma or are inbuilt and can provide responsive design ideas.
Magic Patterns (Application and Chrome extension)
Figma AI Beta
If used with intention, purpose and sufficient preparation AI tools can help you in a number of ways. They can help you to prepare for interviews. Test designs early, find patterns in feedback, and create cool visuals. Ultimately, having this help at hand can speed up your creative process.