Meitu • Summer 2026
Making AI Creativity More Accessible and Engaging
TIMELINE
June – Sept 2026
ROLE
Product Management
UX Research & Design
Product Operations
FOCUS
AI Creative Tools
Feature Discovery
User Engagement
TEAM
1 Product Intern
2 Product Manager
3 Product Designers
5 Dev
TOOLS
Feishu
Jira
Miro
Figma
01 / OVERVIEW
Making powerful AI tools easier to discover, understand, and use.
At Meitu, I worked across AI-powered creative products to uncover user needs, identify product opportunities, and translate insights into actionable improvements. Through competitive research, user interviews, and cross-functional collaboration, I helped shape two product initiatives: Workspace Templates and Image Remix.
02 / CONTEXT
Powerful creative tools, but opportunities for better discovery.
AI-powered editing tools are rapidly expanding what users can create. Yet introducing more capabilities doesn’t necessarily make a product easier to use.
Why it mattered
As DesignKit evolved, we needed to understand how users discovered creative features, what prevented them from engaging, and how we could make the experience more intuitive.
Before: DesignKit’s template discovery experience
PROBLEM
How might we help users discover and use AI creative tools with less friction?
MY APPROACH
Connect user needs with product opportunities through three stages:
1. Discover 2. Define 3. Improve
03 / PROCESS
How the work unfolded
01 DISCOVER
Understanding users and the competitive landscape.
I analyzed six competing AI editing tools, including Canva, CapCut, and Adobe Express, to evaluate feature discovery, template organization, and creative workflows. Alongside this research, I reached out to long-term users and conducted 30 interviews to understand their creative habits, frustrations, and unmet needs.
Competitive benchmarking matrix + user interview insights
02 DEFINE
Turning research into actionable product opportunities.
I synthesized competitive patterns and user feedback to identify opportunities for improving template discovery and expanding AI-powered editing capabilities. These insights informed product requirements and feature discussions with product managers, designers, and developers.
Research synthesis / opportunity mapping
03 IMPROVE
Translating insights into product experiences.
I contributed to PRDs and iteration meetings for two initiatives:
Workspace Templates: improving template discovery through AI capability-based categorization and a more structured browsing experience.
Image Remix: expanding creative workflows by allowing users to recreate and remix visual content through AI-powered editing.
PRD excerpts and annotated feature iterations
04 / KEY DECISIONS
Four decisions that shaped the experience
01 Prioritize discoverability
Make existing creative capabilities easier to find before introducing unnecessary complexity.
02 Organize around user intent
Structure template categories around what users want to create and the AI capabilities they need.
03 Reduce the creative learning curve
Make advanced AI editing workflows more approachable through clear entry points and examples.
04 Measure engagement, not just exposure
Evaluate whether product improvements encourage users to meaningfully interact with creative features.
05 / SOLUTION
Making creative exploration more intuitive.
Two product initiatives emerged from the research, each addressing a different part of the creative journey.
Workspace Templates — Helping users find the right starting point.
Organizing templates around AI capabilities to make browsing more intuitive and help users move from inspiration to creation.
Workspace Templates interface mockup
Image Remix — Making AI-powered creation more approachable.
Extending remix functionality into image editing, giving users more ways to experiment with and recreate visual content.
Image Remix before-and-after workflow
06 / IMPACT
The results, measured
+46%
Template usage
+22%
Feature engagement
2
Product initiatives contributed to
These reported improvements reflect stronger adoption and engagement with the creative experiences I helped develop.
07 / REFLECTION
What I learned along the way
Great features need great discoverability.
Even powerful AI capabilities can go underused when users struggle to find or understand them. Improving how users discover a feature can be just as valuable as building something new.
Research is most valuable when it informs decisions.
Competitive analysis and user interviews helped me move beyond assumptions and connect user frustrations with concrete product opportunities.
Product thinking extends beyond the interface.
Working with product, design, and engineering teams taught me to balance user needs, business goals, and implementation constraints.
What’s next?
This experience strengthened my interest in building accessible, human-centered products that make emerging technology feel intuitive rather than intimidating.
Have an idea or a question about this project?