Search
I was the sole product designer on the team. I collaborated with my product manager, Bill to come up with half a year's worth of work for our team.
⚠️ Sadly, the team was disbanded during active development, so while improvements to basic search are currently being shipped, the redesigned search results experience has yet to launch, hopefully in the near future.
Background
Search at Asana hadn’t seen meaningful updates in years. Both the user experience and backend performance had fallen behind. It felt slow, outdated, and frustrating to use.
Near the end of the half, I was asked to spend a few weeks exploring a new direction for both our basic search (modal) and full-page search results. Around the same time, Asana’s AI efforts were gaining momentum, and designers were encouraged to explore how AI could enhance the areas we owned.
Proposal for basic search
Basic search received interface updates to feel more modern centered on the screen and more responsive to user input. We explored ways for users to narrow their experience through typing. For example, as shown below, users can trigger a task filter by typing and then tabbing into the suggested filter.
I also worked with engineering to understand how we could enable predictive search.
AI in basic search
My PM and I explored ways to thoughtfully integrate AI into the search experience. While we didn’t see search as the primary entry point for AI chat, we did want to surface intelligent, contextual responses where they made sense.
In the concepts below, you’ll see examples like: profile cards for people, AI-generated answers summarized from internal knowledge bases, chat handoff, and trending searches based on user input.
Each of these ideas aimed to help users spend less time searching and more time actually doing their work.
New search experience in action
Thankfully, an AI team had bandwidth to take this work on and build it out. It proved to more viable to build the new experience from scratch rather than overhaul the existing search dropdown.
Proposal for search results
The search results experience had several core issues. First, it reused our project list view, which made it feel visually overwhelming and poorly suited for search. Second, it often returned different results than basic search. This inconsistency stemmed from how it searched the full contents of tasks without clearly explaining why certain results appeared.
In the concept below, I addressed this by introducing clearer result formatting and term highlighting to give users better insight into why a result was shown. Beyond that, I incorporated an AI-generated summary to help users quickly understand key insights from their search. The overall visual treatment was also refined to improve clarity, reduce cognitive load, and create a more focused, usable experience.
Search results as a capability for other surfaces
As part of this work, I aimed to extend the patterns I was defining in Search to other surfaces, namely Inbox and Projects. Both had their own filtering logic and were lacking search experiences.
This proposal distilled some of the core ideas from the Search Results exploration and applied them across these areas to help teams and leads envision what a unified, scalable search experience could look like.
Conclusion
I’m really proud of this work. While it was disappointing to see the team and initiative deprioritized in favor of other company goals, I’m glad we were able to ship the basic search improvements with support from another team. It was a reminder that good ideas can outlast org changes and can find a home when the time is right. I’m still hopeful the full Search Results vision will have its moment.