In this post, I’ll share a few ways I’ve been using AI as part of my design workflow. From exploring visuals with Lovart and Loveable, to conducting and analyzing user interviews with Claude and Gemini NotebookLM, generating ideas with Figma Make, and bridging design and development with Cursor—I’ve been experimenting with how these tools can support creativity, speed up iteration, and spark further directions.
My goal isn’t to replace design thinking, but to explore how AI can become a true collaborator with designers.
Lovart — Let’s Play with Some Logos!
After several user interviews, our team realized that DirectBooker’s current logo wasn’t fully capturing the story we wanted to tell. While recognizable, the existing mark leaned heavily on a paper-airplane/send icon, which some participants felt was more literal than inspiring. This sparked a discussion within the team about exploring a more abstract, simplified direction—something that could reflect the brand’s energy and forward movement without being tied to a single object.
I began with a competitive analysis of well-known travel brands to understand the visual patterns shaping the industry. Three themes stood out across competitor logos:
Prominent wordmarks – clean, legible type treatments that ensure brand recognition.
Distinctive icons or symbols – often travel-related or abstracted forms that create memorability.
Strong, saturated colors – bold palettes that convey energy and visibility in crowded marketplaces.
AI-Assisted Exploration
With the competitive analysis in mind, I translated those findings into prompts and fed them into several AI design tools—including Looka, Loveable, Lovart, and Gemini (Nano Banana). Each platform had a slightly different strength: some leaned more literal, while others produced unexpected abstractions. Among them, Lovart stood out by generating outcomes that felt closest to the direction we envisioned—simpler, more abstract marks that moved away from the “send” or “paper airplane” feel of the current logo.
Rather than treating these outputs as finished designs, I used them as rapid sketches—sparks of inspiration to filter, refine, and eventually evolve into more thoughtful logo directions.
From Exploration to Refinement
AI helped surface a wide range of possibilities and played a valuable role in keeping the creative conversation moving. However, the real refinement came from the design team—applying judgment, brand alignment, and craft to shape the concepts.
In the end, AI gave us breadth and inspiration, while I, as a designer, made sure the work carried the focus, consistency, and polish it needed.
Gemini NotebookLM — Your User Interview Copilot
User interviews and testings are one of the most important parts of my design process, but they can also be time-consuming to synthesize. For this testing, I experimented with Gemini NotebookLM as a “copilot” to help me analyze interview transcripts more efficiently. By feeding in raw conversations, I was able to quickly surface key themes, recurring pain points, and notable quotes.
Instead of replacing my own analysis, NotebookLM acted as a partner—highlighting potential insights and patterns that I could then validate, refine, and connect back to the design goals.
Filling the Analysis Gap in Moderated Testing
I’ve found AI to be especially useful during moderated testing.
In unmoderated testing, many platforms like UserTesting.com or UserInterviews.com already provide built-in analysis—such as score charts, sentiment breakdowns, and positive vs. negative highlights. But in moderated testing, insights often rely heavily on participants’ spoken responses, which are harder to process systematically.
This is where NotebookLM really fills the gap. By uploading Gemini-recorded transcripts, I can quickly generate a user-feedback mind map and even pull direct quotes when I need to reference a specific comment on a feature. It saves hours of manual scanning and makes moderated sessions just as actionable as unmoderated ones.
User testing analysis workshop in NotebookLM
User mind map based on interview transcripts
And, this is just the beginning—I plan to keep experimenting with how AI can power the testing phase and uncover even deeper insights from user research🌟.
Cursor — Bridging Design and Development
One of the challenges in product design is ensuring smooth handoff between design and engineering. Specs can be detailed, but questions always come up: how should this component behave, what’s the best way to implement this interaction, or how do we make the design scalable?
I began experimenting with Cursor and Figma Make as a way to close this gap. By combining natural language prompts with code context, Cursor helps me quickly test front-end ideas, validate design feasibility, and even generate starter code for interactions.
[This section is still evolving — more to come soon✨]